Harmonizing FORIN for climate change adaptation \u0026 disaster risk management to develop...

$: Harmonizing FORIN for climate change adaptation \u0026 disaster risk management to develop multi-sectoral narratives for Metro Manila$

Harmonizing FORIN for climate change adaptation & disaster risk management to develop

multi-‐‑sectoral narratives for Metro Manila

IRDR International Centre of Excellence – Taipei Technical Report No. 3

Charlotte Kendra Gotangco1,2

Volume 1 Authors: C. Kendra Gotangco, Jairus Josol, Michael Padilla, John Paolo Dalupang, Justin See, and Raiza Elumba

Volume 2 Authors: I. Introduction: C. Kendra Gotangco, II. Physical Sector: Gemma Narisma, Faye Cruz, Emilio Gozo, May Celine Vicente, Patricia Sanchez, III. Health Sector: John Wong, Norman Dennis Marquez, IV. Economic Sector: Ramon Clarete, Joey Sescon, Philip Arnold Tuaño, V. Social Sector: Emma Porio, John Paolo Dalupang, Emily Roque, Justin Charles See, VI. Summary: C. Kendra Gotangco 1Manila Observatory 2 Dept. of Environmental Science, Ateneo de Manila University


Acknowledgements

This document was produced under the project entitled “Harmonizing FORIN for Climate Change Adaptation and Disaster Risk Management to Develop Multi-‐‑sectoral Narratives for Metro Manila.” This project was implemented by the Manila Observatory, with the collaboration of the Sociology and Anthropology Department, Economics department, Environmental Science department, Physics department and Health Sciences program of the Ateneo de Manila University, and the University of the Philippines School of Economics. This project was supported by a grant to conduct follow-‐‑on research from the 2012 Advanced Institute on Forensic Investigations of Disasters (FORIN). Funding for participant follow-‐‑on research was provided by the IRDR International Centre of Excellence (ICoE) in Taipei through ICSU and the US National Science Foundation (Grant Number GEO-‐‑0627839). The 2012 Advanced Institute on FORIN was organized by START and the IRDR International Centre of Excellence (ICoE) in Taipei, together with IRDR International, ICSU, and Taiwan'ʹs National Science and Technology Center for Disaster Reduction (NCDR). Funding for the Institute was provided by the IRDR International Centre of Excellence (ICoE) in Taipei through ICSU.

Disclaimer

This technical report has been prepared as an output from follow-‐‑on research from the 2012 Advanced Institute on Forensic Investigations of Disasters (FORIN). Any opinions stated herein are those of the author(s) and do not necessarily reflect the policies or opinions of ICoE and its partners.

Creative Commons License This Report is licensed under a Creative Commons Attribution NonCommercial-‐‑NoDervis 3.0 Unported License. This publication may be freely quoted and reproduced provided the source is acknowledged. No use of this publication may be made for resale or other commercial purposes.


TABLE OF CONTENTS

VOLUME 1 1

Rationale 2

Hypotheses and Essential Elements 5

Objectives 7

Research Methodology and Templates 9

Conceptual Framework 12

Research Questions 14 Non-‐‑Sector-‐‑Specific Core Questions 14 Non-‐‑Sector Specific Generic Questions 15 Sector-‐‑Specific Questions 16

Physical Sector 16 Health Sector 19 Economic Sector 21 Social Sector 24

Indicators 27 Risk Assessment Indicators 28

What is Risk? 28 What is Hazard? 28 What is Exposure? 29 What is Vulnerability? 29 What is Adaptive Capacity? 29

Action M&E Indicators 30

References 32

Appendix 34

VOLUME 2 54

Introduction 55 References 58

Chapter 1: Increasing risk to disasters due to the effects of climate change and urban development 59 Framework and Approach to CCA-‐‑DRM FORIN in the Physical Sector 59

Climate change, variability and extremes and Urbanization 60


Interfaces with Other Sectors 66 Application to Metro Manila 66

Contextualization 67 Evolution of Risk in Metro Manila 69 Climate Change and Extremes in Metro Manila 70 Current and Projected Risk to Flooding 75

Conclusions and Recommendations 78 References 79

Chapter 2: Tracking the Health Impact of Climate Change in Metro Manila: Understanding the Risks on the Health System 82

Framework and approach to CCA-‐‑DRM FORIN in the Health Sector 83 Framework 83

Application to Metro Manila 95 Cities at Risk 95 A Vulnerable Population 96 Adaptability and Sustainability of Health Systems 98 Beyond the Borders of Metro Manila and Climate Change 98


Chapter 3: Valuing economic damages due to natural calamities in the Philippines 105 Introduction 106 The Philippine economic performance and natural disasters caused by severe weather disturbances 107

Overview of Philippine economic performance 107 Natural calamities and the economy 109 Severe weather disturbances and their economic effects in the national capital region 110 Severe weather disturbances and their economic effects in Philippines 114 Effect of Weather Disturbances on Prices in Metro Manila 116

Review of Literature 120 Tools to Measure Impact 121 Macroeconomic Impacts of Disasters 124 Impacts of Climate Change and Relationship with Natural Disasters 129 Summary and Causal Loop Diagram 131


Chapter 3: Social Sector: Narratives on Flooding and Climate Change in Metro Manila 139 Frameworks 140 Methodology/Data Sources 140 Risk 141 Social Vulnerability 141 Social Factors that Determine Risk to Communities 142 Transitions in History that Changed the Distribution of Impacts in Metro Manila. 144

Post-‐‑WWII to the 1960s: Emergent Social and Environmental Trends 144


1970s-‐‑1980s: Urbanization Intensifies, Increasing Environmental Degradation and Social Services Deficit 145 1990s-‐‑2000s: Intensification of Impacts of Urbanization and Environmental Degradation 145 DRRM and Climate Change 146

Pre-‐‑Disaster Social Trends Continues 147 Government Laws, Measures, and Policies: DRRM and Climate Change 148

National Climate Change Action Plan 149 Disaster Risk Mitigation at the City Level: Marikina City 150

Social Impacts of Flooding in Metro Manila 150 Impacts on Real Property 150 Impact on the delivery of basic services 151 Impact on Lost Work or School Days 152 Impact on Loss of Livelihood 153 Impact on Loss of Personal and Household Items 153 Impact on Health 153 Impact on Personal Finances 154 Impact on Gender and Household Resource Management 154 Perceived Positive Impacts by Community Residents 155 DRRM and Climate Change Perceptions Among Affected Residents 155 Impacts due to Climate Change 157

Flood Impacts: Intersections of Social, Ecological-‐‑Environmental and Health Vulnerabilities 158 Community Preparation Efforts 159

Community Preparation for Hazards Associated with Extreme Events 159 Community Preparation for Hazards Associated with Changing Climate 161 Access to Information on Relevant Hazards 161 Social and Cultural Barriers to Adaptation and Resilience 162

Community Perceptions of Hazard, Risk and Vulnerability 162 Flooding as Normal 162 Increasing Awareness of Risk 163 Climate Change and Extreme Events 163 Education Programs and Trainings in the Community 164

Response and Recovery 164 Insights from the Study of Antecedent Social Conditions to Risks and Disasters 165 References 167 Appendices 172

Appendix V.1: 172 Appendix V.2: Accounts of Popular Narratives on the Causes, Impacts and Solutions to the Various Typhoons in Metro Manila 180

Summary 190 References 192


IRDR ICoE – Taipei, Technical Report No. 3

Harmonizing FORIN for climate change adaptation and disaster risk management 1

Volume 1 Authors: C. Kendra Gotangco, Jairus Josol, Michael Padilla, John Paolo Dalupang, Justin See, and Raiza Elumba

This documented is an Addendum to the FORIN Project Report (suggested citation: Integrated Research on Disaster Risk. (2011). Forensic investigations of disasters: The FORIN Project (IRDR FORIN Publication No. 1).Beijing: Integrated Research on Disaster Risk.), and was intended to accompany and potentially be integrated with the original. The objective of this Addendum is to explore how the original report can be enhanced to reflect an integrated Climate Change Adaptation -‐‑ Disaster Risk Management (CCA-‐‑DRM) perspective. The Addendum was patterned after the original FORIN Project Report, and in the process of reworking it from a CCA-‐‑DRM perspective, it drew from and quoted from the original document. However, the authors contributing to this Addendum fully acknowledge and recognize the content coming from the original report and do not claim such as their own.




Rationale

The Forensic Investigations of Disasters (FORIN) Project evolved in response to the question of why we continue to suffer losses due to disasters despite advances in science and technology. “Forensic” in this case is meant to signify “systematic, probing and dispassionate investigations,” (IRDR 2011) that are broad-‐‑based, comprehensive and interdisciplinary in their approach to identifying and addressing drivers of risk. However, in parallel with the growing recognition of the need to be proactive in addressing disasters, there is also a growing realization that climate change represents another stressor on development and is also a source of risks. The trend in policy and research today is to integrate disaster risk management (DRM) with climate change action planning, specifically, adaptation (CCA) in developing countries.

However, CCA and DRM have evolved as two distinct and largely independent disciplines, heretofore lacking in meaningful collaboration. Thus, although both fields have been actively engaged in reducing vulnerability and risk over the past several decades, communities still continue to suffer the impacts of hazards (Thomalla et al., 2006). Over the past 20 years, disasters losses triggered by climate or weather hazards in particular have increased (Solecki, Leichenko, O’Brien, 2011; Birkmann & Teichman, 2010). Furthermore, this process of addressing risks must be cognizant not only of present conditions but also how these conditions

may evolve into the future in specific contexts to ensure sustainable development.

The connections between CCA and DRM are articulated in Figure 1. CCA and DRM share the twin goals of reducing risks and keeping development on-‐‑track (Brooks et al. 2011). A direct connection is of course the category of extreme weather events, the frequency of severity of which will be affected by climate change. However, the slow onset aspects of climate change can also be connected to DRM through changes in the integrity of ecological and geophysical systems. The current practice on DRM focuses only on rapid onset hazards (discrete events) and short-‐‑term measures (disaster warning/response/relief) (Thomalla et. al, 2006). It has not taken into consideration long-‐‑term stressors and/or non-‐‑extreme events, in particular, the gradually changing climate “normal” (Alan Lavell, personal communication, 2012). But because natural and anthropogenic climate change modifies the nature of hazards (IPCC, 2012) and the context where these hazards occur, current understandings of hazards may no longer work for assessing and projecting risk (Helmer, 2006; Solecki et al. 2011).

Thus, present disaster risk reduction may no longer be effective without considering climate change. DRM must incorporate evolving risks associated with climate change to ensure the sustainability of its strategies in the long-‐‑run (Mitchell & Van Aalst, 2008). Conversely, CCA would also benefit from the integration as DRM is an




essential first step to adaptation (Mitchell & Van Aalst, 2008). Communities that are well-‐‑equipped to deal with current climate variability are in a better position to cope with future variability. Hence, the benefit of the integration of CCA and DRM is mutual or dynamic. It allows their respective methodologies to be integrated (CCA-‐‑DRM), reducing risk both the short-‐‑ and long-‐‑run time frame under a single initiative.

There are substantial gaps, however, that hinder a truly integrative approach to CCA and DRM:

• Disasters have widely been experienced.

There is an established community of practitioners and clearer government actions. However, the focus for planners has been on preparedness and response. In this sense, the FORIN approach to a

holistic construction of risk will be not only helpful, but arguably necessary. This gap may be the least difficult one to overcome, especially through the collaboration among researchers and planners.

• Climate change, on the other hand, is not an easy concept for many stakeholders to grasp for many reasons, not the least of which are the time lags, the subtlety of slow onset impacts and the lack of proximate causes and effects that can be readily experienced in everyday life. It is not clear what sort of actions or policies qualify as adaptation, and neither are there established methods for evaluating adaptation options.

Figure 1. Preliminary framework for connecting CCA and DRM (Gotangco 2012, revised from Gotangco Castillo 2007 with input from IRDR FORIN Faculty, Dr. Alan Lavell)

Climate Change Adaptation:

Reduce risk to:

Disaster Risk Management

Reduce risk to:

Gradual changes in climatic parameters

Extreme weather events with increased

frequency and severity

Changes in mean

temperature

Changes in precipitation

patterns

Sea level rise

Climate- and weather-related

events

Geophysical events

Ecological events

Direct connection Other events (e.g. technological,

terrorism)

Hazards that are associated with changing

climate “normals”

Hazards that are associated with extreme

events




• Projects that do attempt to integrate CCA and DRRM are better able to do so from the perspective of changes in the frequency and severity of extreme events. This is a limited view of CCA. How to deal with risks associated with the gradual changes in climate is less straightforward.

• While the FORIN Project report (IRDR 2011) provides a comprehensive framework for investigating and addressing risks, it is mainly oriented towards disaster risk reduction and management. There is a need, therefore, to have a clear and holistic framework and approach to CCA in the same way as we have the FORIN approach for DRM. A starting point for the integration is to recognize and define two types of risks: (a) Risks to hazards associated with the “non-‐‑routine” or extreme events, and (b) Risks to hazards associated with gradually changing “normals” (e.g. sea level rise, aggravation of urban stressors, lessening agricultural productivity and other ecosystem changes, decreasing viability of tourist spots, increased

probability of landslides due to dry soils, increased incidence of adverse health impacts, etc.).

• Such an integrated framework would allow analysis of common variables that affect risk and resilience to both climate change and disaster impacts. It can help identify factors related to disaster risk that can influence vulnerabilities to long-‐‑term climate change; and, conversely, it can help identify factors related to climate change that influence vulnerabilities to rapid-‐‑onset extreme weather events. Ideally, a CCA-‐‑DRM FORIN approach would facilitate the CCA-‐‑DRM research and planning process by broadening the investigation and targeting of risk drivers.

• Using the connections illustrated in Figure 1, this document therefore identifies the entry points of CCA into the elements described in the original FORIN document. It describes potential ways of enhancing the current FORIN framework into a CCA-‐‑DRM FORIN approach, particularly in the different sectors involved in CCA-‐‑DRM work.




Hypotheses and Essential Elements

The FORIN approach was designed to address four basic ideas, in the form of hypotheses, concerning risk. These are the risk reduction hypothesis, the integration hypothesis, the responsibility hypothesis and the communication hypothesis (IRDR 2011). These are intended as useful starting points for FORIN case studies. But although these were originally conceptualized specifically with disaster risk in mind, they are easily adaptable for both the risks associated with extreme events (the realm of disaster risk management) and the risks associated with gradually changing conditions (the realm of climate change adaptation:

1. The Risk Reduction Hypothesis states that “new and more probing research and understanding of the reasons for growth in public vulnerability and wider exposure would enable and stimulate improved DRR.” (IRDR 2011) The same applies to CCA. More specifically, new and more probing research can help us define the connections between DRR/DRM and CCA. This research should then be channeled towards to enabling more efficient use of resources and more responsive decision-‐‑making in addressing the different forms of risk over various spatio-‐‑temporal scales.

2. The Integration Hypothesis states that “new and more integrated and participatory research is required to yield more useful and effective results.” (IRDR 2011) This is meant to address

how research has mainly been fragmented along disciplinary lines, “in their own relative professional isolation,” (IRDR 2011) without meaningful engagement of stakeholders beyond their being information providers or recipients of research output. The need not just for an interdisciplinary approach but more so, a transdisciplinary approach involving practitioners and stakeholders on the ground is especially true for CCA, which requires long-‐‑term adjustments and transformational change, as well as DRM. Firstly, CCA-‐‑DRM strategies need to be coherent across sectors (e.g. social, economic, governance) and need to be evidence-‐‑based to be effective. Secondly, communities need to have a sense of ownership of these strategies for them to be sustainable over the long-‐‑term.

3. The Responsibility Hypothesis posits that “responsibility for continued growth in vulnerability and exposure is locally specific and diffuse over individuals, organizations, jurisdiction, and over time.” (IRDR 2011) Therefore, research can trace the continued growth in vulnerability and exposure through historical developments and feedbacks among the different sectors and stakeholder groups in a community. In the process of identifying the structure of responsibilities, these can be channeled towards taking action on both CCA and DRM.




4. The Communication Hypothesis posits that the intended recipients of knowledge concerning risk are “unaware of the insights or alternatively resistant to the knowledge and information and may feel threatened by it.” (IRDR 2011) Therefore, “new and better ways of communicating scientific understanding…are required.” (IRDR 2011) This becomes especially true when attempting to integrate CCA and DRM because of the existing conceptual confusion about climate change, how it manifests physically, and how it is connected to or different from the experience of disasters which is often more concrete for stakeholders. Without proper information, we risk maladaptation.

These hypotheses are not meant to be a complete or exhaustive set of ideas, however, and the IRDR FORIN Report (IRDR 2011) acknowledges that these may be further refined or new hypotheses may be defined, especially now that we are attempting to integrated long-‐‑term climate risks with disaster risks. In any case, the essential elements of a forensic approach that should be embodied in any study can likewise be adapted for CCA-‐‑DRM. These are (IRDR 2011):

1. Investigation of circumstances, causes and consequences of losses due to hazards associated with extreme events as well as hazards associated by changing “normals” or baselines; and identification of conditions that have limited or prevented loss, built resilience and adaptive capacity.

2. Operationalization and testing of a series of hypotheses of feedbacks and causality (including the cascading of hazards, the role of land use and the built environment in conditioning risk, the interactions of development pathways and risk, the dynamics of perceptions, values, communication and accountability).

3. Identification especially of key factors in the expanding numbers of losses during extreme or non-‐‑routine events, or accumulated over the past few decades due to changing climatic conditions; and the demonstration of how these factors enter into the determination of risks and disasters.

4. Investigation of the use of existing interdisciplinary knowledge and research, drawing from the physical and social sciences, in assessment and management of existing and projected risks.




Objectives

The objectives of FORIN investigations are categorized into policy, management, science, development and disaster risk reduction objectives, and are sufficiently diverse and broad so as to allow for a seamless integration of climate change adaptation. The following are proposed revisions to the objectives (as revised and reworked from the original text) to explicitly include and address both the risks associated with non-‐‑routine, discrete events, and the risks associated with changing climatic conditions.

Policy Objectives:

• To experiment with transdisciplinary and multi-‐‑stakeholder inputs in multi-‐‑level planning processes to address adaptation gaps to current climate variability and other extreme events, and to address long-‐‑term transformational change.

• To encourage participation by decision-‐‑makers as they develop public policy.

• To guide policy across and involving all key disciplines.

• To guide public and private investments that reduce the different forms of risk over short-‐‑, medium-‐‑ and long-‐‑term time scales.

Management Objectives:

• To focus attention on the link between research findings and improved policy application.

• To develop a reference bank of quality case studies to be placed on the IRDR

website for wide availability to interested parties, in close coordination with relevant databases and networks in this field.

• To effectively communicate: • the interconnections between disaster

risk management and climate change adaptation; o the common underlying factors

contributing to risk to discrete events and/or risk to long-‐‑term changing normal.

o the root causes of specific past disasters and how these have affected adaptive capacity and future development.

Scientific Research Objectives:

• To advance methodological diversity in addressing the different forms and dimensions of risk.

• To test existing theories and concepts, and to implement evidence-‐‑based results (drawing on multiple lines of evidence, including indigenous or traditional knowledge and experiences).

• To build strong, interdisciplinary, “in-‐‑country” capacity of young researchers for policy-‐‑oriented research.

Development Objectives:

• To substantiate that generic causes and contributors to risk have local manifestations: “one size solutions do not work everywhere,” so disaster risk management and climate change




adaptation strategies must be contextualized.

• To promote a “learning culture” amongst all stakeholders.

• To advance understanding of how the causal factors of disasters can be major impediments to long-‐‑term climate change adaptation and overall sustainable development.

• To identify situations where development initiatives and trends can also become causal factors in disasters and contribute to overall long-‐‑term vulnerability (including instances of “maladaptation”).

• To guide recovery and reconstruction efforts so as to build resilience and adaptive capacity (i.e. to “build back better” rather than simply “building back”).

• To communicate to the public, mainly through media and community-‐‑based organizations (CBOs), key messages to shape values, perceptions and behaviors that are required for a paradigm shift in this area of study, and for overall transformational change in development trends.

Integrated CCA-‐‑DRRM Objectives:

• To promote sustainable risk management and risk reduction policy-‐‑making (addressing both forms of risk) through science-‐‑based research and findings.

• To guide the implementation of the Hyogo Framework and to present key case studies by the 2015 HFA target date to guide post-‐‑HFA needs.

• To give a priority focus on a holistic ecosystems-‐‑based approach for reducing human consequences (e.g. social and economic vulnerability), and building resilience and adaptive capacity.

• To change paradigms, by shifting responsibility from nature, the physical environment and distributing it accordingly to real circumstances and conditions involving all sectors of society including the individual and the collective. This entails increasing responsibility for all stakeholders in managing risk (in particular, governments but also individuals, households, communities, local government, etc.)

• To develop case studies that illustrate “risk-‐‑drivers”, again, both for risks associated with non-‐‑routine discrete events and risks associated with changing climatic conditions.




Research Methodology and Templates

The FORIN report (IRDR 2011) describes three ways (below) in which the FORIN approach to disaster research would be different from most of the previous disaster case studies. These strengths of the FORIN approach to DRR/DRM are likewise strengths of an integrated CCA-‐‑DRM FORIN approach:

1. “First, the investigations will penetrate more deeply into the fundamental causes of disasters in a broad multidisciplinary, and comprehensive manner, and they will engage specialists from any and all relevant fields.” (IRDR 2011) In the context of CCA-‐‑DRM, such investigations will delve comprehensively into the fundamental roots or drivers of risk and vulnerability to facilitate evidence-‐‑based actions. These actions will not only address the adaptation gap to current risks but also to projected risks.

2. “Second, while investigations will preferably be carried out independently of governments, they will also require authority, support, and promotion from expert and professional institutions, and civil society. In order to be truly investigative and forensic in spirit, the studies must be empowered to pursue the evidence wherever it leads in order to be able to report fully on the train and ensemble of events, responsibilities and actions that account for the losses.” (IRDR 2011) CCA-‐‑DRM investigations must likewise necessarily be broad-‐‑based and objective, to the extent

possible, to better trace and understand the drivers of both current and future risk. Thus, there must be active engagement of and support from all relevant sectors of society to foster acceptance of responsibilities and ownership of actions to reduce potential losses. In this way can sustainable strategies be put in place, and fragmentation due to blame-‐‑seeking and defensiveness be avoided.

3. “Third…the intended outcomes will not concentrate on a precise identification of any specific locus of responsibility, but rather will help bring about a paradigm or cultural shift in the ways in which disasters are understood and risks and managed.” (IRDR 2011) A CCA-‐‑DRM FORIN approach also requires a shift towards a more comprehensive understanding of risk – firstly, that risk is a confluence of hazard, exposure and vulnerability; and secondly, that risk is not confined to that associated with extreme events but also to risk associated with changing baseline or climatic conditions. Going forward, resilience-‐‑building to current risks may not be sufficient to sustain development; rather, transformational change in lifestyles, industries, institutions, systems and processes may be required.

There are five templates provided by the FORIN report (IRDR 2011) for pursuing this class of investigations – the Critical Cause Analysis, the Meta-‐‑Analysis, the Longitudinal Analysis, the Scenarios of




Disaster, and the FORIN Narratives. All of these approaches are adaptable for a CCA-‐‑DRM FORIN research by placing them within the broader context of long-‐‑term development trends vis-‐‑à-‐‑vis gradually changing climatic means in addition to changing trends in extreme events.

1. Critical Cause Analysis: This method was originally intended to identify and address the root causes of disaster events (IRDR 2011). It may be placed within the broader CCA-‐‑DRM perspective by using it to address the root drivers of risks, and the “critical thresholds” beyond which the community is unable to cope with the hazards and losses accumulate. These thresholds may likewise be defined for climate means as well as extreme events. The tasks of identifying proactive strategies, establishing monitoring methods and formulating corrective actions are still included under this template.

2. Meta-‐‑Analysis: According to the FORIN report, “meta-‐‑analyses are systematic reviews of available literature carried out to identify and assess consistent findings across diverse studies.” (IRDR 2011) In the context of disaster research, this method was meant to be used in conjunction with statistical analyses to “look for causal linkages, the strength of relationships among factors…and the effectiveness of interventions.” (IRDR 2011) A meta-‐‑analysis is also a popularly and similarly used method for climate change analysis to find multiple, robust evidences of change

and good practices for adaptation and mitigation. Thus, this method is also appropriate for a broad CCA-‐‑DRM investigation.

3. Longitudinal Analysis: In the context of disaster research, a “longitudinal reconstruction allows for repeated observations of the same events…These are detailed place-‐‑based re-‐‑analyses… and are used to more fully understand the contexts and processes that expose people and their assets to risk.” (IRDR 2011) This remains applicable to a CCA-‐‑DRM FORIN study. A longitudinal analysis could compare development trends against disaster and climate trends to determine which factors give the most insight as to the drivers of risk. To address the climate change dimension, an additional component of the analysis would be to study the motivations behind actions taken towards development – Are they spontaneous or planned? Is climate change a factor? Are the same or different choices likely to continue of climate change is recognized as a factor, considering both the impacts of climate change on extreme events as well as changing baselines? The value of the longitudinal reconstruction is indeed in providing in-‐‑depth understanding of the evolution of and correlations between development and risk.

4. Scenarios Approach: The “scenarios of disasters” approach combines a reconstruction of a community’s context with a science-‐‑based scenario of a known and possible future hazard to




determine projected impacts (IRDR 2011). This method is also well-‐‑known in the climate change community, since climate projections as based on scenarios of emissions and other decisions communities might make on land use, population growth, industries and technologies. A CCA-‐‑DRM scenario, therefore, would represent a storyline of projected development trends subjected to projected changes in climate, including changes in the frequency and severity of extreme events. The interactions between climate changes and other geophysical and ecological hazards would also be included in this study to arrive at a holistic picture of potential impacts.

5. FORIN Narrative: As explained in the FORIN report, any of the above templates, because of the required comprehensiveness of the investigation, may “entail considerable effort and

space to develop in detail.” (IRDR 2011) The narrative therefore represents a preliminary investigation of the factors, issues and processes in the development of a society which contribute either to worsening risk or building resilience and adaptive capacity. A CCA-‐‑DRM FORIN narrative, similarly to the disaster research narrative, would be longitudinal in time. It would preserve the key elements of a narrative which are (i) that is illustrates the spatial and temporal scales over which risk and its interaction with development should be understood; (ii) that it be multi-‐‑stakeholder and interdisciplinary in perspective; (iii) that it identify roles and responsibilities of different actors in addressing the different dimensions of risk; and (iv) that it be framed in such a way as to “permit the assimilation of the information and create a space for those involved constructive engagement.” (IRDR 2011)




Conceptual Framework

The original conceptual framework for FORIN investigations is reproduced in Figure 2. This is meant as a guide for the key questions of investigations, leading to the “identification and risk drivers and opportunities for risk reduction and resilience enhancement.” (IRDR 2011) As a complement to this, Figure 3 proposes a broader CCA-‐‑DRM FORIN framework that explicitly includes characterization of projected risks and the potential need for transformational change to adapt to hazards in the long-‐‑term. This framework traces iterative process of generating interdisciplinary research to serve as the foundations for evidence-‐‑based strategies for managing risks. This is meant both as a framework that captures the cycle of research and actions, and as a framework for analysis to determine how existing

knowledge is being used to achieve desired outcomes.

The proposed CCA-‐‑DRM FORIN framework preserves the original FORIN principles, such as the need for a comprehensive approach that engages researchers from different fields and stakeholders from different sectors. Risk research or knowledge generation in this framework involves the holistic analysis of hazard, exposure and vulnerability in the past, the present and projected into the future. This is so that investigations can capture both the risks associated with discrete extreme events as well as risks associated with long-‐‑term gradual changes in baseline climatic conditions. The process of risk assessment includes the identification of risk drivers and the

Figure 2. FORIN conceptual framework for key questions (IRDR 2011)




development of options to address these drivers. The contribution of indigenous or traditional knowledge in the analysis of vulnerability and adaptation is explicitly recognized here.

The Capacity-‐‑building element involves the mechanisms already identified in the original FORIN by which we can influence the distribution and magnitude of drivers of risk. These include proper communication and education initiatives to increase understanding and awareness of underlying causes of risk. Also included are the top-‐‑down governance decisions which should be in consonance with bottom-‐‑up community-‐‑initiated actions. The FORIN report specifically identifies governance as a critical element for risk management in a

state. The components of the capacity-‐‑building element represent the process by which the results of research are concretized and implemented.

The effectiveness of such processes is evaluated through an analysis of the Outcomes component. This refers to whether risk was indeed reduced and resilience enhanced. However, more than bridging the adaptation gap to current risks, targets should including building long-‐‑term adaptive capacity (e.g. through climate-‐‑proofing infrastructures or system), and even transformational change in certain industries or sectors if they are shown to really be unsuitable given projected hazards. The ultimate outcome is sustainable development.

Figure 3. CCA-‐‑DRM FORIN Framework

Research/Knowledge • Risk Assessment

o H, E, V o Past, Present, &

Projected • Including

indigenous/traditional knowledge

Outcomes • Risk Reduction • Enhancing Resilience

Sustainable • Building Adaptive Capacity

Development • Transformational Change

Capacity Building • Communication • Understanding &

Awareness • Governance (top-down

laws & policies) • Community-based

strategies (bottom-up actions)

Inter-sectoral,

Multi-Stakeholder, & Transdisciplinary

Approach




Research Questions

The FORIN report (IRDR 2011) provides a series of core questions and generic questions to help organize investigations. These are synthesized and adapted here for a CCA-‐‑DRM FORIN approach. In addition, more in-‐‑depth sector specific questions are also provided are a starting point for developing multi-‐‑sectoral perspectives with a view towards identifying connections and integrating findings.

Non-‐‑Sector-‐‑Specific Core Questions

1. For past disasters, what were understood to be the proximate causes of the disaster or the triggering natural events?

a. Have these events been changing over time (e.g. in terms of frequency, severity, spatial distribution)? What are the historical trends or past records of this particular type of event?

b. What is known or understood about the mechanisms behind these events – have they been forecasted or predicted?

c. Do changes in baseline climatic conditions affect these triggering mechanisms or mediate their impacts in any way?

d. Has the quality or status of knowledge improved?

2. Has the existing knowledge (as described about) widely available, accessible and understandable for relevant stakeholders?

a. Were there any decision-‐‑makers, actors, stakeholders, or peoples at risk who were unaware of the information (or less aware than they should have been)?

b. How long has the knowledge been available, and has the use of this knowledge towards decision-‐‑making improved?

3. What are the factors contributing to the risk associated with a particular event – be it (i) a

discrete, non-‐‑routine event, or (ii) gradual changes in “normal” climatic conditions?

4. How have the risks (described above) been perceived and understood by each and all categories of stakeholders?

5. What strategies, laws policies or measures have been considered to prevent adverse

impacts, reduce existing risk to current hazards and avoid new risk from projected changes? a. Were there options rejected, or targets and standards compromised? Why? b. Is there a national platform for risk reduction to complement context-‐‑specific

localized strategies? c. To what extent have these strategies actually been put into place?




d. Have implemented strategies been effective? How and to what extent? What proportion of needs has it been possible to meet and over what time scale (e.g. immediate post-‐‑disaster needs vs. long-‐‑term development needs)?

6. Provide a detailed description of roles and actions of key personnel and agencies for:

a. Pre-‐‑event disaster risk reduction b. Post-‐‑event emergency response, recovery and rehabilitation c. Long-‐‑term resilience-‐‑building, adaptation and transformational change in relation to

current and future levels of risk

7. Provide a list of impacts across sectors in qualitative and quantitative terms associated with: a. The occurrence of specific disasters b. The record of repeated disasters (e.g. compounding impacts) c. Historical long-‐‑term changes in the mean state of climate

8. What have the development trends been over the past decades?

a. What are the general socio-‐‑economic trends over time? b. How have socio-‐‑economic conditions changed specifically after each disaster event?

How have these changes affected the overall development trends? c. What is the nature of community recovery, resilience-‐‑building and adaptation? d. Have pre-‐‑disaster trends been continued, exacerbated or reversed? What are the

major factors to explain these?

9. Assess the distribution of impacts (both positive and negative) within the community: a. Were there certain groups or individuals who lost more or benefitted more during

extreme events? b. Have there been certain groups or individuals who have lost more or benefitted

more from gradual shifts in climate? c. Has there been any sense of unfairness or discrimination in the community as a

result of the above? d. Has the social and political structure of the community therefore changed, whether

after particular disasters or over time?

10. Is there an overall or prevalent community-‐‑shared view of the current and future risks they face? Alternatively, are they contrasting or conflicting views? Describe and explain.

Non-‐‑Sector Specific Generic Questions

G1. What conditions or factors have limited or prevented losses (i) during extreme events, (ii) as accumulating over time due to gradually changing climatic conditions?




G2. What key factors have affected or caused major damage (i) during extreme events, (ii) as accumulating over time due to gradually changing climatic conditions?

G3. What were the critical developments or transitions in recent history that changed the

severity and/or distribution of impacts?

G4. How have culture and societal norms influenced (i) risks associated with extreme events, (ii) risks associated with gradually changing climatic conditions?

G5. How have economic and political status influenced (i) risks associated with extreme events,

(ii) risks associated with gradually changing climatic conditions? G6. Have there been secondary or tertiary “cascading” hazards or impacts? How are these

manifested over space and time? G7. What were the drivers of resilience (in broad categories)? G8. What were the barriers of risk reduction and management (in broad categories)? G9. Have there been strategies, plans, programmes or institutions set up for risk reduction and

management? Have they been effective in addressing (i) risks associated with extreme events, (ii) risks associated with gradually changing climatic conditions?

G10. Have there been national and international ramifications to (i) localized impacts from

extreme events, (ii) accumulated impacts from gradual changes? G11. Identify the key aspects of local and national risk context evolved over time that

influence the current development level, resilience and adaptive capacity of the community.

Sector-‐‑Specific Questions

These questions adapt and extend the core and generic questions above for specific sectors:

Physical Sector 1. How are risks to physical systems (natural and built environments) evaluated?

a. What is the framework used to assess these risks? b. What are the integral components/factors of this framework? c. How does this framework relate to social, economic, and health risk evaluation

frameworks?




2. How does the physical system relate to and interact with the social, economic, and health sectors?

3. What kinds of hazards are urban areas exposed to?

a. Describe the typology of these hazards (i.e. whether they are geophysical hazards or climate-‐‑and-‐‑weather-‐‑related hazards; or whether they are hazards associated with discrete changes or gradual changes).

b. What is the historical and projected occurrence of these hazards? c. How are these hazards spatially distributed over time?

4. What are the climate-‐‑and-‐‑weather-‐‑related hazards?

a. What are the characteristics of these hazards (e.g. magnitude, frequency, duration, spatial extent)?

b. How have they changed over the years? c. Are they projected to change in the coming years? What is level of uncertainty

associated with these projections?

5. Are there secondary or tertiary cascading hazards? a. What are these and what are its characteristics? b. What events are associated with its occurrence and what are its consequences (e.g.

heavy rainfall events can cause flooding which subsequently brings a whole host of other consequences)?

6. How has the urban landscape developed and what are its characteristics? a. What are the geophysical and environmental features of the urban landscape? b. How have these features changed over the years? What are the drivers of these

changes?

7. What is the interrelationship between exposure to hazards and urban morphology? a. How does urban morphology interact with the hazards, and to what extent do they

mediate or enhance the impacts of the hazards? b. How and to what extent does exposure to hazards alter the urban morphology? c. For hazards associated with discrete events:

i. What is the state of the urban landscape before and after exposure? ii. Did harm persist? Or was it reduced or even reversed? iii. What are the critical determinants of the physical system’s resilience and

recovery? d. For hazards associated with gradual changes:

i. How has the urban landscape changed in relation to exposure to gradually changing hazards?




ii. How is it expected to change in relation continued and future exposure to gradually changing hazards?

iii. How has harm/impact developed in this context?

8. Provide a list of the impacts of the geophysical hazards and climate-‐‑and-‐‑weather-‐‑related hazards on the natural environment on the urban landscape in qualitative and quantitative terms.

9. What is the state of scientific knowledge about urban morphology and the different types of hazards to which it is exposed?

a. Is information available on the historical and projected occurrence of hazards and its spatial distribution?

b. Is information available on historical and future urban development and its characteristics (e.g. land-‐‑use patterns, geophysical and ecological features, environmental quality)?

10. Is the existing scientific knowledge on urban morphology and hazards available and accessible?

a. Where is the knowledge generated? b. How long has the information been available? Has the quality improved? c. Are the knowledge generated conveyed to the relevant stakeholders and

incorporated into their decision-‐‑making process (e.g. Do policy-‐‑makers have access to information and do they use the information in formulating policies? Do people exposed to hazards have access to information and do they use this information in their efforts to reduce risk?)

11. Is indigenous knowledge on the geophysical and environmental processes, the occurrence of hazards, and the practices that reduce harm available?

a. Is there a repository of such indigenous knowledge? b. How does indigenous knowledge relate to existing scientific information? Is it given

comparable importance in research and policy agenda?

12. What types of interventions were proposed to reduce an urban environment’s inherent vulnerability to a specific hazard or across a whole range of hazards (e.g. installation of flood control structures; increasing green spaces to reduce flood risks and sedimentation)?

a. What agencies/stakeholder group proposed these interventions? b. Were any of the interventions rejected, or downscaled? On what grounds were they

modified or rejected? c. For the implemented interventions, are monitoring and evaluation (M&E)

procedures in place? Who/What agencies are responsible for M&E?




i. For interventions with near-‐‑term tangible impacts, were they effective? How and to what extent? Identify the nature and effectiveness of the response measures (e.g. Did the installation of flood control structures effectively prevent flooding?)

ii. For interventions with longer-‐‑term impacts, are processes and relevant components in place that will allow its realization in the future (e.g. Is CCA-‐‑DRM incorporated into the development and implementation of a comprehensive land-‐‑use plan)?

Health Sector 1. How are risks to human health (physical, social, and mental well-‐‑being) evaluated?

a. What is the framework used to assess these risks? b. What are the integral factors/components of this risk framework? c. How do this framework relate to physical, economic, and social risk evaluation

frameworks?

2. How does the health sector relate to and interact with economic, social, and physical sectors?

3. What is health and what are the determinants of health? a. Identify micro-‐‑level (individual) determinants of health (e.g. genetics, lifestyle, age) b. Identify macro-‐‑level (socio-‐‑economic, physical/environmental) determinants of

health (e.g. income, education, health services, clean air and potable water) c. How have these determinants changed over the years?

4. What are the public health consequences (e.g. morbidity, mortality, injury, spread of

infectious and vector-‐‑borne diseases, psychosocial effects) of exposure to: a. Hazards associated with discrete events (e.g. heavy rainfall and flooding)

i. Are these consequences due to direct exposure (e.g. warming temperature and heat stokes) or to indirect exposure to the hazard (e.g. flood contaminates water supply subsequently contributing to the incidence of water-‐‑related diseases, physical stress of the storm exacerbates pre-‐‑existing medical condition)?

ii. What are the factors (macro and micro level determinants) that influence the distribution of these public health consequences? Identify the vulnerable sub-‐‑groups.

iii. What is the profile of public health and its determinants before and after exposure to hazards associated with discrete events?

b. Hazards associated with gradual changes in climate (e.g. warming temperatures, changes in seasonality, rising sea level)?




i. Are the consequences due to direct or indirect continued exposure to the hazard?

ii. What are the factors (macro-‐‑and micro level determinants) that influence the distribution of these public health consequences?

iii. How have the public health consequences and their determinants changed over the years?

iv. How has continued exposure to hazards affected the aggregate health status of the society?

5. How and to what extent do public health consequences feed into future vulnerability (i.e.

influence/affect the socio-‐‑economic and physical determinants)?

6. For infectious and vector-‐‑borne diseases, how do climate and weather-‐‑related changes affect its pathology? How is expected to change relation to projected changes in climate conditions?

7. What is the state of scientific knowledge on the dynamics of public health, the micro and

macro-‐‑level health determinants, and the health impacts of exposure to climate-‐‑and-‐‑weather related hazards?

a. Is data available that would allow a comprehensive assessment of historical and projected health risks in relation to climate and weather-‐‑related hazards?

i. Has there been a change in trends? ii. What were the drivers of these changes?

b. What are the gaps in research on health impacts of hazards associated with discrete events, and of hazards associated with changing climatic conditions?

8. Is the scientific/expert knowledge available and accessible? Has it improved over the years? a. Is there a gap between scientific and common knowledge? What are the efforts

undertaken to bridge it and are they effective? b. Are policy-‐‑makers incorporating the available knowledge in plans and programs

geared towards improving public health?

9. Do cultural and societal norms influence how people manage health risks related to climate-‐‑and-‐‑weather-‐‑related hazards?

a. What is the role of traditional health care providers and how do they relate to the modern health care providers?

b. Are there societal practices that may hinder people from seeking medical help?

10. What interventions/programs were considered to reduce the prevalence of health related consequences due to exposure to climate-‐‑and-‐‑weather-‐‑related risks?

a. What agencies/stakeholder group proposed these interventions?




b. Were any of the interventions rejected, or downscaled? On what grounds were they modified or rejected?

c. For the implemented interventions, are monitoring and evaluation (M&E) procedures in place? Who/What agencies are responsible for M&E?

i. For interventions with near-‐‑term tangible impacts, were they effective? How and to what extent? Identify the nature and effectiveness of the response measures.

ii. For interventions with longer-‐‑term impacts, are processes and relevant components in place that will allow its realization in the future?

11. What interventions were considered to address the environmental and socio-‐‑economic

determinants of health? a. Are these standalone interventions? Or are they related to other

development/environmental programs? b. Who are the agencies responsible for implementing these programs? c. Are there efforts to integrate these interventions?

Economic Sector 1. How are risks to economic systems evaluated?

a. What is the framework used to assess these risks? b. What are the integral components/factors of this framework? c. How does this framework relate to social, health, and physical risk evaluation

frameworks?

2. How do economic systems relate to and interact with the social, health, and physical sectors?

3. What is the status of economic agglomeration in the area? a. What have the critical transitions been in economic development/urbanization trends

over the past several decade that changed the level of economic agglomeration? i. What were the drivers of the economic development/urbanization trends? ii. Were there any decision-‐‑makers, other actors, and stakeholders responsible?

Provide a detailed description of their roles and actions. iii. To what extent have internationally financed infrastructure or development

project activities affect the risk environment of the area? b. How have economic activities (of households and firms) in the area been affected in

the aftermath (short, medium and long term) of disasters? What are the major factors that explain this?

i. What is the nature of economic recovery of households and firms since the disasters occurred (short, medium, and long-‐‑term)? What critical factors and




conditions affected or limited their economic recovery (e.g. availability of skilled workers for reconstruction/repair)? Describe and explain.

ii. Provide a detailed description and list of key personnel and agencies for economic recovery, specifically in relation to economic impacts of: (i) hazards associated with extreme events; (ii) hazards associated with gradually changing climate.

iii. Have pre-‐‑disaster economic development/urbanization trends been continued, exacerbated or reversed? What are the major factors that explain this?

4. In general, what have the economic impacts been of: (i) hazards associated with extreme

events; (ii) hazards associated with gradually changing climate? Provide this as a list in qualitative and quantitative terms.

a. How did these economic impacts change between discrete events and over a longer time period (e.g. past several decades)?

b. What key factors affected or caused the economic impacts from: (i) hazards associated with extreme events; (ii) hazards associated with gradually changing climate?

i. How did each of the key factors influence the risk of different economic sectors?

ii. Were there specific economic sectors that benefitted from the disaster events (e.g. construction sector)?

iii. What conditions or factors limited or prevented economic losses (impacts)? c. What economic strategies, laws, policies, or measures have been considered to

prevent the economic impact (losses) of the events or reduce its consequences (e.g. climate-‐‑proofing/diversification of industries, infrastructures, utilities, and livelihoods) over time?

i. Have any economic options been rejected, or targets and standards reduced? ii. To what extent have those economic strategies, laws, policies or measures

considered been implemented? iii. Were those economic strategies, laws, policies or measures effective, how and

to what extent? Identify the nature and effectiveness of the economic measures in terms of, for example: (a) availability of skilled workers (pace of reconstruction/repairs), insurance, and loans; (b) investments/budgets in CCA-‐‑DRM (e.g. pre-‐‑disaster planning and preparedness and response or in mitigation technologies, protective infrastructures, and warning and response systems); (c) diversification of livelihoods, suppliers, and energy sources; (d) climate-‐‑proofing infrastructures, utilities, livelihoods, and industries.




5. Assess the distribution of economic losses (impacts) of: (i) hazards associated with extreme events; (ii) hazards associated with gradually changing climate, within households.

a. What were the critical transitions in recent history (preconditions) that changed the distribution of economic losses (impacts) within households?

b. How did each of the preconditions influence the risk of households? Describe and explain.

c. What economic mechanisms or types of assistance are available among households to restore their livelihoods?

6. Assess the distribution of economic losses (impacts) of: (i) hazards associated with extreme events; (ii) hazards associated with gradually changing climate, within firms.

a. What were the critical transitions in recent history (preconditions) that changed the distribution of economic losses (impacts) within firms?

b. How did each of the preconditions influence the risk of firms? Describe and explain. c. What economic mechanisms or types of assistance are available among firms to

restore normal business environment?

7. What is the state of awareness of economic actors (households and firms) to the economic impacts of extreme events and changing climate?

a. Was the existing knowledge on economic impacts of extreme events and changing climate widely available and accessible?

b. How long had the knowledge on economic impacts of extreme events and changing climate been available and had it significantly changed or improved in the recent past?

8. What are the coping strategies or risk reduction countermeasures of households and firms to reduce economic losses (impacts) brought by: (i) hazards associated with extreme events; (ii) hazards associated with gradually changing climate?

a. Provide a list of the costs of risk reduction to hazards undertaken by households and firms.

b. Were there economic barriers to the risk reduction of hazards? If yes, what were they (for example, limited financial resources available and limited coverage of insurances)?

9. What is the state of development of the insurance market before and after the disasters occurred and how did it change over time?

a. What factors or conditions contributed or limited the development of the insurance market?

b. How does the development/underdevelopment of the insurance market affect the risk of different economic actors and sectors? Describe and explain.




Social Sector 1. How are risks to social systems evaluated?

a. What is the framework used to assess these risks? b. What are the integral components/factors of this framework? c. How does this framework relate to economic, health, and physical risk evaluation

frameworks?

2. How do social systems relate to and interact with the economic, health, and physical sectors?

3. In general, what are the social impacts of: (i) hazards associated with extreme events; and (ii) hazards associated with gradually changing climate change, on the community? Describe these in quantitative and qualitative terms.

a. Which of these social impacts could be considered as positive, and which can be considered as negative impacts to the community?

b. Has there been any sense of unfairness or discrimination in the community in relation to the distribution of impacts of the hazards, or in relation to actions taken to address these hazards?

4. Assess the distribution of impacts due to disasters within the community related to social factors.

a. What were the critical transitions in social conditions and structures in recent history that changed the distribution of impacts within the community?

b. What are the key social factors or preconditions that determine the risk or resilience of communities? Describe and explain.

c. How have social conditions and structures changed after extreme events? How does the repeated occurrence of extreme events affect the long-‐‑term state of the community does vulnerability accumulate in the long run?

d. Have pre-‐‑disaster social transitions/trends trends been continued, exacerbated or reversed? What are the major factors that explain this

e. What insights can be drawn from the study of antecedent social conditions of pre-‐‑conditions in evaluating impacts of discrete events, of long-‐‑term events, or both?

f. What social mechanisms or types of assistance are available to the community to recover from discrete events, and to be build resilience and adaptive capacity in the long-‐‑run?

5. What is the state of the community’s access to basic services? a. How has access to basic services evolved over the past few decades?

i. What were the drivers of these changes in access to basic services? ii. Who are the decision-‐‑makers, other actors, and stakeholders responsible?

Provide a detailed description of their roles and actions.




b. How has access to basic services in the community affected in the aftermath (short, medium, and long-‐‑term) since the disasters occurred?

i. What critical factors and conditions have affected access to services after disasters? Describe and explain.

ii. List down available resources and networks, both private and government, that will help enable disaster-‐‑stricken areas to recover.

iii. What is the nature of community recovery (e.g. how fast the recovery process was; which parts of the community recovered first and fastest; which members of the community or social groups recovered more quickly and effectively?)

iv. Are communities able to move beyond recovery and “build back better”? c. What is the role of access to basic services in reducing risk or strengthening resilience

before and after an extreme event, and under a gradually changing climate? Describe and explain.

d. How has the community been able to access information of relevant hazards? How has this information been used for social protection? Describe and explain.

6. Describe how the community is preparing and adapting for the impacts of: (i) hazards associated with extreme events; (ii) hazards associated with gradually changing climatic conditions, to the community.

a. Construct a concept map of the various preparations at the individual, community, and institutional levels in response to disasters.

b. Construct a concept map of the various measures at the individual, community, and institutional levels to adapt to climate change.

c. Who usually spearhead disaster preparedness and adaptation activities in the community? Who usually funds these activities?

d. Were there social barriers in the disaster preparations and adaptation efforts of the community? If yes, what were they (e.g. culture, class, religion, ethnicity, and language) and how did they affect the participation of multi-‐‑stakeholder groups in the decision-‐‑making process about response strategies?

e. How do existing social and cultural norms influence the community’s ability to adapt to climate change?

f. How do existing social and cultural norms influence the community’s ability to prepare and respond to disasters?

g. Is there an apparent relationship between poverty in the community and the community’s ability to adapt to climate change? Discuss the extent of this relationship.

h. Is there an apparent relationship between poverty in the community and the community’s ability to prepare for hazards and respond to disasters? Discuss the extent of this relationship.




7. What leadership strategies, government measures, laws, and policies in place determines resilience to disasters and adapt to climate change? List down all related policies if possible.

a. Which of these strategies, measures, laws, and policies are specifically responding to disaster risk reduction and management?

b. Which of these strategies, measures, laws, and policies are specifically responding to climate change adaptation?

c. Which of these strategies, measures, laws, and policies are explicitly responding to both?

d. To what extent have these strategies, measures, laws, and policies have been properly implemented?

e. Provide a detailed description of the roles and actions of key private and government personnel, institutions, and agencies.

8. What is the community’s perception and understanding of risk, disaster risk management and resilience, climate change, and climate change adaptation? How do their perceptions affect their decision-‐‑making process?

a. What meaning does the community attach with the idea of “risk”? b. How much does the community understand about the points of convergence and

points of difference between DRM and CCA? c. How does the community assess their personal risk to hazards brought by extreme

events and climate change? d. Does the community believe that climate changes are related to extreme events? e. Are there any education programs or trainings for CCA and DRM in the community? f. What is the average literacy level of the community? On the average, how many

years of formal education did the community members receive? g. Is there an overall or prevalent community-‐‑shared view of the disaster?

Alternatively, are there contrasting or conflicting views? Describe and explain.

9. What experiences in disasters, disaster risk reduction management, climate change and adaptation, and resilience building does the community have? Relate these experiences as much as possible.

a. What is the community’s history of disasters attributed to extreme events? How often does the community experience these events?

b. How did the people rebuild their community after such extreme events? c. Are there experiences in the community that they attribute to climate change? If

there are, why did they attribute them to climate change? d. Were there any facilities that recorded weather and climatic data in the community

in from the past years to the present? Was the community informed of these findings or was the community given access to this information?




Indicators

A FORIN narrative traces the evolution of hazards, the development trajectory, and the actions undertaken by society to construct a holistic risk framework. This section serves as a guide for writing such narrative by identifying indicators that may be relevant to the analysis of development trends, societal strategies, and biophysical changes in the context of climate change adaptation and disaster risk management. The indicators presented are in no way comprehensive, but are intended as possible starting points for analysis.

Indicators can provide good basis of assessments of risk and assessments of efficiency and effectiveness of risk interventions. In the context of climate change adaptation and disaster risk management, they can help identify what factors are being looked by each discipline in determining risk and specific actions taken to reduce risk. In this study, there are two basic categories of indicators which may be used as possible starting points for analysis/assessment, and which can simplify, quantify, standardize and convey complex and distinct data and information to integrate CCA and DRM (UNFCCC, 2010):

• Risk Assessment Indicators are the variables/factors used to measure the probability of adverse impacts of hazards that are associated with hazards associated with “non-‐‑routine” or extreme events, and with hazards associated with gradually changing climate. Risk assessment is performed to

provide a baseline and assess improvements/progress in improving resilience and adaptive capacity.

• Action Indicators are the variables/factors/milestones used for monitoring and evaluation (M&E) of strategies taken to reduce risk and build adaptive capacity/resilience. Strategies are interventions or activities undertaken in the form of projects, policies, and programs. The purpose of monitoring and evaluation is to keep track of the progress made in implementing CCA-‐‑DRM risk reduction strategies/interventions/activities/procedures and determine its overall effectiveness, efficiency, and utility in building resilience and adaptive capacity (UNFCCC, 2010).

Linking the two indicators: Risk Assessment Indicators describe the status of community under consideration (in terms of hazards, exposure and vulnerability) and thus help determine describe the status of community under consideration (in terms of hazards, exposure, and vulnerability) and thus help determine what actions or strategies should be taken to reduce the risk. Action M&E Indicators are used during the implementation of the chosen strategies to evaluate their effectiveness both in the form of processes that build institutional capacities, and in the form of developmental outcomes achieved. A change in levels of risk, as determined through periodic assessments using the Risk Indicators, is one possible Action M&E Outcome indicator.




Risk Assessment Indicators

R = HEV FRAMEWORK

What is Risk? Crichton (1999) defines risk as the “probability of a loss” resulting from the interaction of three factors: (1) hazard, (2) exposure, and (3) vulnerability. This definition may be expanded to highlight the human dimensions of risk by including the likelihood of harm or injury.

Crichton (1999) further suggests that risk may be visualized as the area of a “risk triangle” whose magnitude is determined by the size of its three side components namely, hazard, exposure, and vulnerability (Figure 4).

Similarly, if any of these components are absent, the remaining components will be unable to form a triangle, which can be construed as the absence of risk (Crichton, 1999). As an illustration, the occurrence of an earthquake of a given magnitude (hazard) alone does not constitute risk; the likelihood of harm or loss also depends on the extent of exposure and the characteristics of those exposed (e.g. whether the buildings can withstand the magnitude of the earthquake that they are exposed to, or whether the people can evacuate to earthquake shelters during an earthquake).

𝑅𝑖𝑠𝑘 = 𝑓(𝐻𝑎𝑧𝑎𝑟𝑑,𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒,𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦)

In a CCA-‐‑DRM analysis, adaptive capacity may also be included as a factor the reduces overall risk:

𝑅𝑖𝑠𝑘 =𝐻𝑎𝑧𝑎𝑟𝑑 𝑥 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑥 𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦

𝐴𝑑𝑎𝑝𝑡𝑖𝑣𝑒 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦

What is Hazard? A hazard is an event or an agent that has the potential to cause harm or losses (UNISDR, 2009). It can come from human activities such as the release of synthesized chemical compounds, and disposal of nuclear waste, or independent of human activities such as earthquakes, tsunamis, or solar flares. Some hazards are hybrids and are thus partly independent of and partly attributable to human activities. Climate-‐‑related hazards belong to this category.

Brooks (2003) classifies climate-‐‑related hazards into three types based on the frequency and the timescales associated with their occurrence:

1. Discrete recurrent hazards are singular climate-‐‑events that occur repeatedly over a couple of months or years (e.g.

Figure 4. The Risk Triangle (Crichton, 1999)




droughts, storms, and extreme rainfall events).

2. Continuous hazards refer to climate-‐‑related conditions that change gradually over a period of time. Its changes are often observed as trends and usually occur on timescales of a decade or more (e.g. increase in mean temperature and mean sea level, changes in mean rainfall).

3. Discrete singular hazards refer to sudden events associated with a new climate regime with potentially catastrophic consequences (e.g. the break-‐‑up of the West Antarctic ice sheet, or shutdown of the thermohaline circulation). As this class of hazards is associated massive changes, its timescales are a century or several centuries long.

What is Exposure? Exposure refers the extent at which populations and elements of human value (e.g. properties, infrastructure) are subjected to a hazard. It is usually expressed as the number of people or properties in an area where a hazard is present, or where a hazard event occurs (UNISDR, 2009).

What is Vulnerability? The notion of vulnerability in literature varies. Brooks (2003) and Adger et al. (2004) classify these diverse definitions into two broad categories according to the factors that they cover and highlight: (1) social vulnerability, and (2) biophysical vulnerability.

In this paper, since the risk framework is used whereby risk is broken down into

hazard, exposure, and vulnerability components, the term vulnerability refers to social vulnerability, which pertains to the properties of a system that affect its ability to cope or deal with the potential impacts of a hazard (Brooks, 2003; Adger et al., 2004). It includes the physical, environmental, and socio-‐‑economic characteristics of a system that mediate or enhance the impacts of a hazard, and ultimately determine the difference between potential and actual harm. For clarity, Brooks (2003) uses the term inherent vulnerability to refer to the physical and environmental components of the system. Socio-‐‑economic variables such as income, quality of housing, and wealth distribution, and physical variables such as land cover and topography are examples of social vulnerability indicators.

The other conception of vulnerability is biophysical vulnerability coined by O’ Brien (2003; in Adger et al., 2004) to refer to the combination of the magnitude and characteristics of a hazard, the extent of a system’s exposure, and a system’s sensitivity to the hazard. Vulnerability framed in this manner is roughly equivalent to risk (Brooks, 2003), and this is roughly the usage of vulnerability in the IPCC assessment reports.

What is Adaptive Capacity? Adaptive capacity is the potential of a system to adjust in the face of a hazard. It is the sum of past conditions, and these conditions vary over time and space depending on socio-‐‑economic, environmental, and physical factors (Brooks, 2003).




Action M&E Indicators

What are process indicators and outcome indicators?

1. Process (or Upstream) Indicators are variables/factors that measure the progress of CCA-‐‑DRM risk reduction interventions in building resilience and institutional capacities through incorporation of CCA-‐‑DRM into development processes, actions, and institutions (Brooks et. al, 2011; Spearman & McGray, 2011). Specifically, these are indicators that can already be used in the short-‐‑ to medium-‐‑term to determine whether concrete actions to implement CCA-‐‑DRM strategies and to help keep development on track in the face of hazards that are taking place. They provide an answer to the question, “What do people need to do to reduce or manage risk and adapt to the gradually changing climate normals?” In Figure 3, process indicators would primarily describe the components of the capacity-‐‑building element. General examples of these indicators include the following (taken and adapted from proposed indicators of Brooks et. al, 2011 and from Spearman & Gray 2011).

a Use of scientific information on hazards in policy and program design; Relevance and quality of informational inputs to CCA-‐‑DRM decisions;

b Proportion of development initiatives that are modified compared to a ‘business-‐‑as-‐‑usual’

case in order to make them more resilient;

c Mechanisms for targeting the vulnerable;

d Institutional framework of regulatory and legal support; Number and quality of laws or policies addressing CCA-‐‑DRM;

e Degree and quality of participant involvement in decision-‐‑making;

f Thoroughness of accounting for risks and vulnerability in decision-‐‑making;

g Whether and how the adaptation process is sustained.

2. Outcome (or Downstream) Indicators are the variables/factors that measure the efficiency, and utility of the implemented CCA-‐‑DRM interventions (Brooks et al., 2011; Spearman & McGray, 2011). Specifically, these indicators point to the evidence of desired impacts of projects, policies, and programs. They provide an answer to the question, “Were the expected targets achieved through the implementation of CCA-‐‑DRM risk management and adaptation projects, policies, and programs?” In Figure 3, the downstream indicators describe the Outcome element which is ultimately sustainable development. For CCA-‐‑DRM FORIN investigations, these indicators can further be categorized into the development outcome indicators and opportunistic outcome indicators (adapted from Brooks et. al, 2011 and from Spearman & Gray 2011).




a Development Outcome Indicators are the variables/factors that refer to the “bottom line” long-‐‑term changes affecting the overall quality of life of the community. Comprehensive and effective CCA-‐‑DRM strategies should be able to contribute to sustainable development in the process of addressing current and future resilience and adaptation gaps. Because these indicators are measured over longer time scales, however, there is a high degree of uncertainty (due to gradually changing conditions), and the shifting baselines need to be considered in the process of measurement and comparison (Brooks, et. al, 2011; Spearman & McGray, 2011). General examples of these indicators include the following (taken and adapted from Brooks et. al, 2011):

• Numbers of beneficiaries and coverage of climate change interventions;

• Numbers of people experiencing reductions in risk based on variety of context specific indicators;

• Value of assets and economic activities protected or made less vulnerable as a result of adaptation interventions;

• Change in degree of exposure to threats;

• Evidence of changed quality of climate-‐‑sensitive natural resource base;

• Evidence of community, sectoral, or institutional understanding and capability to deal with or avoid losses.

b. Opportunistic Outcome Indicators are the variables/factors that allow empirical measurement of the impacts of CCA-‐‑DRM interventions during specific instances, specifically, the occurrence of extreme events (Brooks et. al, 2011). For example, the impacts of two similar heavy rainfall events in the same locality may be compared to determine the effectiveness of any strategic initiatives implemented in the intervening period. General examples of these indicators include the following (taken from Brooks et. al, 2011):

• Construction and use of storm shelters/evacuation centers;

• Installation of, and the lead time provided by, early warning systems.

In summary, process-‐‑based indicators are used to measure the progress of the integration of risk management and adaptation into projects, policies and programs while the outcome-‐‑based indicators are used to measure the effectiveness on the bottom-‐‑line developmental goals.

A master list of potential sectoral Risk Assessment Indicators and Action M&E Indicators is provided as supporting material to this Addendum.




References

Adger, W. N., Brooks, N., Bentham, G., Agnew, M., & Eriksen, S. (2004). New indicators of vulnerability and adaptive capacity. Tyndall Center for Climate Change Research. Norwich: Tyndall Centre for Climate Change Research. Retreived from: http://tyndall.ac.uk/sites/default/files/wp38.pdf

Birkmann, J., & Teichman, K. (2010). Integrating disaster risk reduction and climate change adaptation: key challenges-‐‑-‐‑scales, knowledge, and norms. Sustainability Science, 5(2), 171. doi:10.1007/s11625-‐‑010-‐‑0108-‐‑y.

Brooks, N. (2003). Vulnerability, risk and adaptation: A conceptual framework (Tyndall Center for Climate Research Working Paper No. 38). Retrieved from http://tyndall.ac.uk/sites/default/files/wp38.pdf.

Brooks, N., Anderson, S., Ayers, J., Burton, I., & Tellam, I. (2011). Tracking adaptation and measuring development (IIED Climate Change Working Paper No. 1). Retrieved from http://pubs.iied.org/pdfs/10031IIED.pdf

Crichton, D. (1999). The Risk Triangle. In J. Ingleton (Ed.). Natural disaster management (pp. 102-‐‑103). London, UK: Tudor Rose.

Gero, A., Méheux, K., & Dominey-‐‑Howes, D. (2011). Integrating disaster risk reduction and climate change adaptation in the Pacific. Climate & Development, 3(4), 310-‐‑327. doi:10.1080/17565529.2011.624791.

Integrated Research on Disaster Risk. (2011). Forensic investigations of disasters: The FORIN Project (IRDR FORIN Publication No. 1). Beijing: Integrated Research on Disaster Risk.

IPCC (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-‐‑K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp.

Mitchell, T., & Van Aalst (2008). Convergence of Disaster Risk Reduction and Climate Change Adaptation. Retrieved from http://www.preventionweb.net/files/7853_ConvergenceofDRRandCCA1.pdf.




Spearman, M. and H. McGray. (2011) Making Adaptation Count: Concepts and Options for Monitoring and Evaluation of Climate Change Adaptation. GIZ, BMZ and WRI.

Solecki, W., Leichenko, R., & O’Brien, K. (2011). Climate change adaptation strategies and disaster risk reduction in cities: connections, contentions, and synergies. Current Opinion in Environmental Sustainability, 3(3), 135. doi:10.1016/j.cosust.2011.03.001.

Thomalla, F., Downing, T., Spanger-‐‑Siegfried, E., Han, G., & Rockström, J. (2006). Reducing hazard vulnerability: towards a common approach between disaster risk reduction and climate adaptation. Disasters, 30(1), 39-‐‑48. doi:10.1111/j.1467-‐‑9523.2006.00305.x

UNISDR. (2009). 2009 UNISDR Terminology on Disaster Risk Reduction. Geneva: United Nations. Retreived from: www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf.




Appendix

Table 1. CCA-DRM Risk Indicators List

Indicators for Assessing Risk CCA? DRM? Hazard Exposure Vulnerability Adaptive Capacity

PHYSICAL SECTOR

Geophysical events volcanic eruption

x x

landslides

x x • rainfall-‐induced landslides x

x

earthquakes

x x formation of sink holes

x x

Climate and Weather-‐related indicators historical temperature trends x

x

projected temperature trends x

x historical change in seasonality (onset and duration of dry and wet

season) x

x projected change in seasonality (onset and duration of dry and wet

season) x

x historical changes in sea levels x

x

• historical saltwater intrusion x

x projected changes in mean sea level x

x

• projected saltwater intrusion x

x historical changes in rainfall (amount, intensity, frequency, spatio-‐

temporal variability) x

x • incidences of drought x x x • incidences of heavy rainfall x x x • incidences flooding x x x




projected changes in rainfall (amount, intensity, frequency, spatio-‐temporal variability) x x x

• projected episodes of drought x x x • projected heavy rainfall events x x x • anticipated flooding due to heavy rainfall x x x historical trends in typhoons (intensity, frequency, spatio-‐temporal

variability) x x x • historical incidences of storm surge and other

secondary/concatenated hazards

x x projected trends in typhoons (intensity, frequency, spatio-‐temporal

variability) x x x • probability of storm surge and other secondary/concatenated

hazards

x x Urban morphology/Land-‐use indicators

urban terrain/cover x x

x x • type of land-‐use and spatial distribution/land-‐use patterns x x

x x

• physiographic characteristics x x

x x • vegetative cover x x

x x

• proportion of exposed soil (relevant to flooding: sedimentation, erosion) x x

x x

• impervious surfaces x x

x • urban structure (dimensions of buildings and spaces between

them, street widths and spacing) x x

x hydrology (related to flooding) x x

x x

• peak flow characteristics (determined by: % of impervious area, rate at which water is transmitted across the land to stream channels, characteristics of tributary channels) x x

x x

• total run-‐off (dependent on infiltration characteristics: slope, soil type, vegetative cover) x x

x x

• volume of flood storage (dams, artifical ponds, small reservoirs in stream channels, tanks) x x

x x

• geomorphic features of the channel (bank height, width, bed x x

x x




material) Ecological indicators

forest cover x

x x x coastal wetlands x x

x x x

habitat structure x changes in biodiversity x

x x trophic structure x

x x

water quality x x

x x • industrial wastewater collection and treatment coverage

x x

x

• domestic wastewater treatment coverage

x x

x • type of sewerage systems used

x x

x

• sewage treatment coverage

x x

x • discharge of effluents and raw sewage

x x

x

• physico-‐chemical and biological properties (e.g. turbidity, pH, nitrate level, heavy metal content, coliform)

x x

x

water availability x x

x x • seasonal variation of the hydrologic process x x x

• seasonal variation in water demand x x

x x • historical water use x

x

• rate of infiltration (soil column, soil characteristics: moistrure, soil type, compaction) x x

x

• rate of evapotranspiration x x

x • rate of groundwater extraction and recharge x x

x x

amenity value of water bodies x x

x • stability of the stream channel

x

x x

• accumulation of trash/debris on the water channels and flood plains

x

x x

• changes in the stream biota (eutrophication and algal blooms) x x

x x solid waste generation and collection (related to clogging of waterways/transmission rate, and health and social effects of improper disposal)

x x

x




characteristics of the transport system x x

x x energy mix x x

x x

electricity generation and distribution (source, distribution infrastructure)

x

x x

HEALTH SECTOR

range expansion of vector-‐borne diseases x

x incidence of heat waves x

x

outbreak of water-‐related diseases (e.g. leptospirosis, diarrhea, typhoid) x x x

environmental conditions in evacuation centers

x x • sanitation

x x

• outbreak of respiratory and other skin-‐related diseases

x x access to medical facilities x x

x x

number of fully-‐equipped medical facilities x x

x x medicine price x x

x x

knowledge of diseases that are associated with flooding and other extreme events

x

x x

knowledge on diseases or illness being related to the effects of climate change x

x x

speedy delivery of health services during an epidemic

x

x x speedy delivery of health services durin disasters or extreme events

x

x x

extent of medical insurance coverage x x

x x insurance prices x x

x x

infant and child mortality x x

x x maternal health (as an indicator of vulnerability and resilience) x

x x

SOCIAL SECTOR

Population Size x x

x x Population Density x x

x x

Rate of Urbanization x x

x x




Access to Basic Services x x

x x Level of Urban Migration x x

x

Number of informal settlers x x

x x Location of informal settlements

x

Security of Tenure x x

x x Gender x x

x

Income Level x x

x x Age x x

x

Proximity to rivers (vulnerable populations) x x

x x Education level (literacy) x x

x x

Installment of early warning systems (access and understanding)

x

x x Number of socio-‐civic organizations x x

x x

Access to ICT and use of social media x x

x x Perceptions of risk x x

x x

Family ties, decision making x x

x x Community training x x

x x

Maternal health (as an indicator of vulnerability/resilience) x x

x x x Number of children per family x x

x x

Family size x x

x x ECONOMIC SECTOR

Level of Economic Agglomeration (concentration of economic activities, firms, and wealth in limited areas) x x

x x x

Development of Insurance Market/Credit Facilities (wider take-‐up of insurance/lending services, more funds available post-‐disaster) x x

x x

Household Income Level (low-‐income groups likely to live in cheaper but risky areas; affects recovery/reconstruction phase; availability of financial resources for adaptation options) x x

x x x

Savings x x

x x Remittances x x

x x

Inflation (changes in prices affect reconstruction/recovery phase due x x

x




to demand surge) Coastal Urbanization (concentration of people, infrastructure, assets, and econ. activities in coastal areas relative to sea level rise) x x

x x x

Diversity of Livelihoods (e.g. dependence on imports and narrow range of exports) x x

x x

Diversity of Suppliers (limited number of suppliers can limit production) x x

x x

Availability of Skilled Workers (affects pace of reconstruction)

x

x x Insurance x x

x x

Availability of Loans x x

x x Unemployment x x

x x

Government Funding/Budget Availability for CCA-‐DRM x x

x x GDP Sector Composition (agricultural, industrial, and service) x x

x x

Climate sensitivity of industries x x

x x Weather sensitivity of industries x x

x x




Table 2. CCA-DRM Actions M&E Indicators List

Indicators for Monitoring and Evaluating Actions CCA? DRM? Process Indicator

Dev't/Vulnerability

Outcome Indicator

Opportunistic Outcome Indicator

PHYSICAL SECTOR

increasing basic research ( climate variability, projections, downscaling, assessment of hydrological characteristics,hydrological modeling, etc.) x x x

decreased damage to culturally, economically significant areas or vital infrastructure x x

x x

• climate-proofing infrastructures x x x • revision of building regulations to incorporate CCA-DRM x x x • increasing insurance coverage of climate-sensitive properties x x x water security (in terms of quality and availability for competing

water uses) x x

x x • implementation of a comprehensive rive basin management plan x x x

• increasing the coverage of wastewater/sewage treatment x

x • implementation of policies that regulate the use of water

resources x

x • decrease in human-induced land subsidence x

x

improved water quality in rivers and its tributaries x

x decreased crop vulnerability to droughts x x

x x

• modification of irrigation systems to incorporate the changes in seasonality and precipitation patterns x

x

continous energy supply x x

x x • diversification of energy sources x

x

• climate-proofing energy distribution infrastructures (especially for transport and electricity) x x x

preservation of coastal wetlands x

x




increase in marine productivity and biodiversity x

x decreased incidence of precipitation-induced flooding

x

x

• installation of flood control systems (and other engineering interventions)

x x

• improving urban water channels x x x • implementation of soft interventions to improve surface run-

off (e.g. removing solid waste in river channels, buffer zones and green spaces, increasing pervious areas)

x x

• increasing the coverage and efficiency of solid waste collection

x x

• implementation of a comprehensive solid waste management plan

x x

improved flood, typhoon, and rainfall forecasting

x

x x • increasing the technological and technical capacity of forecasting institutions and other related agencies

x x

• increasing the budget for research and purchase of relevant monitoring equipment

x x

increased access to hydrometeorological forecasts

x

x x decreased property damages due to flooding and other weather-related events

x

x

decreased exposure to flooding and other weather-related events

x

x implementation of a comprehensive land-use plan (incoporates CCA-DRM) x x

x

decrease in the number of properties and infrastructure exposed to storm surge

x

x

• Implementation of a coastal development plan x x x • Installation of sea-walls and other engineering measures

x x

• delineation of coastal buffer zones x x x • decreasing transmission rate (related to peak-flow, run-off and

impervious areas) x x x decrease in erosion and sedimentation rates (see flooding)

x

x x

• increasing vegetative cover x x x • reducing the area of bare/exposed soils x x x




HEALTH SECTOR

increased access to medical faciltiies x x

x more, better health programs on climate and weather related

diseases x x

x improved information on epidemiology after extreme events

x

x

faster delivery of health services during extreme events

x

x policies implemented on sanitation and health x x

x

increasing health budget x x x increasing budget allocation for health-related and epidemiology

research x x x decreased mortality due to climate and weather-related disease

outbreaks x x

x increased involvement of the civil society/NGOs to deliver health services (for profit and voluntary) x x

x x

access to potable water during flooding

x

x better information dissemination on water borne and water related diseases

x

x

barangays are better-equipped to handle disaster-related outbreaks

x

x x healthier population (decreased incidence of disesase, decreased incidence of malnutrition) x x

x

cheaper medicine and health services x x

x increased public awareness on the effects of climate change and

weather-related events on health x x

x

SOCIAL SECTOR

Increased access to basic services (water and food, electricity, medical facilities) x x

x

Increase in per capita or household income x x

x Proper implementation/enforcement of land use planning, zoning

and building codes x x x Presence of disaster risk reduction and management policies

x x

x




Disaster preparedness (community training)

x x Decrease in Mortality Rate

x

x

Increase in number of socio-civic organizations in the community x x x Increase in number of social research related to DRM and CCA x x x Decreasing poverty level and incidence x x

x

Increase in education with regard to CCA and DRM (literacy) x x x Decrease population living in hazard-prone areas [relocation] ->

flooding areas, landslide areas x x

x Increase in stakeholder consultation and participation related to

CCA-DRM policies x x

x Increase access to ICT and social media x x x

Integration of CCA-DRM in school curriculum x x x Improved media communications and portrayal of climate change

and disasters x x x Providing proper housing for informal settlers x x x Decrease loss of social capital (after discrete hazards, or over a

long period of change) x x

x x

ECONOMIC SECTOR

Low Morbidity Rate (e.g. illness due to water contamination from shutdown of treatment plant)

x

x

High and Growing GDP x x

x High and Growing GDP per capita x x

x

High Net Exports (e.g. damages to firms from discrete events or due to climatic shifts could affect imports and exports) x x

x

Decrease Levels of Indebtedness (e.g. gov''t spending for emergency response can increase debt)

x

x

Increase FDI Rates (e.g. foreign companies see a great risk or too much damage = loss in confidence) x x

x

Price Stability/Low Inflation x x

x Decrease Urban Income Inequality (e.g. higher income reduces

natural disaster deaths) x x

x High Employment/Low Unemployment x x

x




Increase Investment in pre-disaster planning and preparedness and response or in mitigation technologies x x x

Increase Investment in protective infrastructure (e.g. sea walls against storm surges, flood protection systems or dams and building elevations) x x x

Increase Investment in improved warning and response x x x Decrease Poverty Level x x

x

Development of long-term urban economic climate scenarios x x x Diversification of livelihoods x

x

Improved employment rate x

x Diversification of suppliers x x x

Implementation of energy saving programs x

x Diversification of energy sources (for power generation and

industrial activities) x

x Development of catastrophe insurance pool and credit facilities. x x x Decreased Loss of Potential Production Output (disruption of

economic activities due to lost power or evacuation of workers) x x

x x Decreased Loss of Potential Production Output (due to changes in productivity brought about by gradual climatic shifts) x

x

Decreased Loss of Expected Income/Earnings (of households, firms, and employees) after extreme events

x

x

Decreased Loss of Expected Income/Earnings (of households, firms, and employees) in climate-sensitive industries due to gradual climatic shifts x

x

Decreased Loss of Homes (and their contents)

x

x Decreased Loss of Productive Capital Assets (machines, firm structures [buildings] and their contents, inventory, vehicles)

x

x

Decreased Loss of Infrastructures or Other Lifelines/Utilities (power, sewage, water, communication, or transportation like roads)

x

x

Decreased Loss of Human Capital/Labor

x

x Decreased Cost of Emergency Response, Relief, & Clean-Up (evacuation and rescue and the clean up costs, such as clearing

x

x




debris from streets) Decreased Cost of Reconstruction/Repairs

x

x

Increased awareness of economic actors of economic impacts of various hazards x x x

Increase pace of reconstruction (to restore production and housing and avoid high losses; e.g. easier and faster building permits)

x x

x

Climate-proofing of infrastructures and utilities (power, water, rail) x x x

Climate-proofing of livelihoods and industries x x x *Climate proofing – a shorthand term for identifying risks to a

development project, or any other specified natural or human asset, as a consequence of climate variability and change, and ensuring that those risks are reduced to acceptable levels through long-lasting and environmentally sound, economically viable, and socially acceptable changes implemented at one or more of the following stages in the project cycle: planning, design, construction, operation, and decommissioning (ADB, 2005). Reference: Asian Development Bank (2005). Climate Proofing: A Risk-based Approach to Adaptation. Retrieved from http://www.adb.org/sites/default/files/pub/2005/climate-proofing.pdf




Table 3. RRL Master List

PHYSICAL SECTOR

AUTHOR YEAR TITLE SOURCE

Abon, C., David, C., & Pellejera, N. 2011 Reconstructing the Tropical Storm Ketsana flood

event in Marikina River, Philippines Hydrology and Earth System Sciences , 15,

1283-1289

Bankoff, G. 2003 Constructing Vulnerability: The Historical, Natural

and Social Generation of Flooding and Social Generation of Flooding in Metropolitan Manila

Disasters , 27 (3), 95-109

Crozier, M. 2010 Deciphering the effect of climate change on landslide activity: A review Geomorphology , 124, 260-267

De Sherbinin, A., Schiller, A., & Pulsipher, A. 2007 The vulnerability of global cities to climate hazards Environment and Urbanization, 19 (1), 39-

64

Delpla, I., Jung, A.-V., Baures, E., Clement, M., & Thomas, O. 2009 Impacts of climate change on surface water quality

in relation to drinking water production Environment International, 35, 1225-1233

Gaillard, J.-C., Liamzon, C., & Maceda, E. 2005 Act of nature or act of man? Tracking the root

causes of increasing disasters in the Philippines Philippine Geographical Journal, 49, 46-65

Gersonius, B., Nasruddin, F., Ashley, R., Jeuken, A., Pathirana,

A., & Zevenbergen, C. 2012

Developing the evidence base for mainstreaming adaptation of stormwater systems to climate

change Water Research , 1-2

Hunt, A., & Watkiss, P. 2011 Climate change impacts and adaptation in cities: a review of literature Climatic Change, 104 (1), 13-49

Jennerjahn, T. 2012 Biogeochemical response of tropical coastal systems to present and past environmental change Earth-Science Reviews, 114, 19-41

Kiss, T., & Blanka, V. 2012 River channel response to climate-and-human-

induced hydrological changes: Case study on the meandering Hernad River, Hungary

Geomorphology, 1-11




Muto, M., Morishita, K., & Syson, L. no date Impacts of Climate Change upon Asian Coastal

Areas: The case of Metro Manila Japan International Cooperation Agency

(JICA)

Rodolfo, K., & Siringan, F. 2006 Global sea-level rise is recognised, but flooding from anthropogenic land subsidence is ignored

around northern Manila Bay, Philippines Disaster , 1, 118-139

Sekovski, I., Newton, A., & Dennison, W. 2012

egacities in the coastal zone: Using a driver-pressure-state-impact-response framework to

address complex environmental problems

Estuarine, Coastal and Shelf Science , 96, 48-59

Suriya, S., & Mudgal, B.V. 2012 Impact of urbanization on flooding: The Thirusoolam sub watershed – A case study Journal of Hydrology, 210-219

Thomalla, F., Larsen, R., Zou, L., & Miller, F. 2008

Building hazard resilent communities in coastal Southeast Asia: Lessons for research, policy &

practice: Stockholm Environment Institute (SEI)

van Aalst, M. 2006 The impacs of climate change on the risk of natural disasters Disasters, 30 (1), 5-18

Virola, R., Lopez-Dee, E., Romaraog, M., & Halcon, L. 2010 Localizing the Measurment of the Impact of

Climate Change. Manila National Statistical Coordination Board

(NSCB) Yasuhara, K., Homine, H.,

Murakami, S., Chen, G., Mitani, Y., & Duc, D. M.

2012 Effects of climate change on geo-disasters in coastal zones and their adaptation Geotextiles and Geomembranes, 30, 24-34

Yumul, G., Cruz, N., Servando, N., & Dimalanta, C. 2011

Extreme weather events and related disasters in the Philippines, 2004-08: a sign of what climate

change will mean? Diasters, 35 (2), 362-382

Zhou, Q., MIkkelsen, P., Halsnaes, K., & Arnbjerg-

Nielsen, K. 2012

Framework for economic pluvial flood risk assessment considering climate change effects and

adaptation benefits Journal of Hydrology , 414-415, 539-549

HEALTH SECTOR





Basara, H.G. & Yuan, M. 2008 Community health assessment using self-

organizing maps and geographic information systems

International Journal of Health Geographics, 7, 67

Chou, Y.-J., Huang, N., Lee, C.-H., Tsai, S.L., Chen, L.S. &

Chang, H.J. 2004 Who is at Risk of Death in an Earthquake? American Journal of Epidemiology, 160, 688-

695

Choudhury, M.A.H.Z., Shahera, B. & Islam, M.A. 2008 Forecasting dengue incidence in Dhaka,

Bangladesh: A time series analysis Dengue Bulletin, 52, 29-37

Costello, A., Abbas, M., Allen, A. Ball, S., Bell, S., Bellamy, R., Friel, S., Groce, N., Johnson, A.,

Kett, M., Lee, M., Levy, C., Maslin, M., McCoy, D.,

McGuire, B., Montgomery, H., Napier, D., Pagel, C., Patel, J.,

de Oliveira, J.A.P., Redclift, N., Rees, H., Rogger, D., Scott, J.,

Stephenson, J., Twigg, J., Wolff, J., Patterson, C.

2009 Managing the health effects of climate change Lancet, 373, 1693-1733

Cruz, E.I., Salazar, F.V., Porras, E., Mercado, R., Orais, V., &

Bunyi, J. 2008

Entomological Survey of Dengue Vectors as Basis for Developing Vector Control Measures in

Barangay Poblacion, Muntinlupa City, Philippines, 2008

Dengue Bulletin, 52, 167-170

DOH 2012 Administrative Order No. 2012-0005: National Policy on Climate Change Adaptation for the

Health Sector Department of Health (DOH), Philippines

Ebi, K.L. 2011 Climate-Associated Changes in Health Outcomes Carneige Institution for Science Epstein, P.R. 2001 Climate change and emerging infectious diseases Microbes and Infection, 3, 747-754

Gharbi, M., Quenel, P., Gustave, J., Cassadou, c., La Ruche, G.,

Girdary, L. & Marrama, L. 2011

Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

BMC Infectious Diseases, 11, 166




Go, J.J. 2008

resentation on Health Impact of Disasters and Climate Change: Challenges to Hospitals and

Public Health Systems for Third Global Congress of Women in Politics and Governance

World Health Organization (WHO), Philippines

Hallegatte, S., Przyluski1, V.& Vogt-Schilb, A. 2011 Building world narratives for climate change

impact, adaptation and vulnerability analyses Nature Climate Change, 1(3), 151-155

Hunt, A & Watkiss, P. 2011 Climate change impacts and adaptation in cities: a review of the literature Climate Change, 104, 13-49

IWGCCH 2009 On Human Health Perspective On Climate Change:

A Report Outlining the Research Needs on the Human Health Effects of Climate Change

Environmental Health Perspectives (North Carolina)

Kovat, S. & Akthar, R. 2008 Climate, Climate change and human health in Asian cities Environment and Urbanization, 20, 165

Lafferty, K.D. 2009 The ecology of climate change and infectious diseases Ecology, 90(4), 888-900

Lau, C.L., Smythe, L.D., Craig, S.B. & Weinstein, P. 2010 Climate change, flooding, urbanisation and

leptospirosis: fuelling the fire? Transactions of the Royal Society of Tropical

Medicine and Hygiene, 104, 631-638

Long, Z.A., Bakar, A.A., Hamdan, A.R. & Sahani, M. 2010 Multiple Attribute Frequent Mining-Based for

Dengue Outbreak L. Cao, J. Zhong, and Y. Feng (Eds.): ADMA

2010, Part I, LNCS 6440, 489–496

Ma, S. Ooi, E.E., & Goh, K.T. 2008 Socioeconomic determinants of Dengue Incidence in Singapore Dengue Bulletin, 52, 17-28

Martens, P. & McMichael, A.J. 2001 Vector-borne diseases, development and climate change: An editorial comment Integrated assessment, 2, 171-172

McGeehin, M.A. & Mirabelli, M. 2001

The Potential Impacts of Climate Variability and Change on Temperature-Related Morbidity and

Mortality in the United States

Environmental Health Perspectives, 109(21), 185-189

McMichael, A.J., Woodruff, R.E. & Hales, S. 2006 Climate change and human health: present and

future risks Lancet, 367, 859-869

Mertz, O., Halsnæs, K., Olesen, J.E. & Rasmussen, K. 2009 Adaptation to Climate Change in Developing

Countries Environmental Management, 43, 743-752




Muto, M., Morishita, K., and Syson, L. no date

Chapter 4: The Intangible Risk analysis (Health) [p. 90, PDF pp.93)] Impacts of Climate Change upon Asian Coastal Areas: The Case of Metro

Manila

Japan International Cooperation Agency (JICA)

Nerlander, L. 2009 Climate Change and Health Commission on Climate Change and Development, Stockholm

Nga, T.T.V. & Fukushi, K. 2012 Infection Risk Assessment with Exposure to

Pathogens in the Flood Water - The case of City of Manila, Philippines

International Conference on Sustainability Science in Asia 2012

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Yoshida, S.

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The American Journal of Tropical Medicine and Hygiene, 82(5), 889-898

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SOCIAL SECTOR


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2008 CITIES AND CLIMATE CHANGE ADAPTATION

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ECONOMIC SECTOR


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Volume 2 Authors: I. Introduction: C. Kendra Gotangco, II. Physical Sector: Gemma Narisma, Faye Cruz, Emilio Gozo, May Celine Vicente, Patricia Sanchez, III. Health Sector: John Wong, Norman Dennis Marquez, IV. Economic Sector: Ramon Clarete, Joey Sescon, Philip Arnold Tuaño, V. Social Sector: Emma Porio, John Paolo Dalupang, Emily Roque, Justin Charles See, VI. Summary: C. Kendra Gotangco




Introduction

The “Forensic Investigations of Disasters” (FORIN) Project Report was published by the Integrated Research on Disaster Risk in 2012 to propose and catalyze a more “demanding and penetrating approach” (IRDR, 2011) to understanding the causes of disasters and the drivers of risk and resilience. This paradigm asserts that the actual causes of disasters are systemic, built into the structure of the community and in the interactions between humans and their environment. Thus, the term “forensic” is used to signify research methods that are systematic and probing.

The FORIN approach seeks to respond to the question, “why, when so much more is known about the science of natural events, including extremes, and when technological capacity is so much stronger, are large-‐‑scale and even small-‐‑ and medium-‐‑scale disasters apparently becoming more frequent and the losses continuing to increase at a rapid rate (IRDR, 2009; White, Kates, & Burton, 2001)?” Disasters triggered by extreme weather events in particular (e.g. typhoons, droughts) are common in the Philippines, and with climate change projected to affect the frequency and severity of extreme weather events, it becomes all the more crucial to be able to identify and address drivers of risk. Climate change represents a stressor on development. Therefore, initiatives in planning and research today are geared towards harmonize disaster risk management (DRM) of extreme weather events with climate change action planning, specifically, adaptation (CCA). This allows

for efficient use of limited resources and more robust, evidence-‐‑based decision-‐‑making over the short-‐‑, medium-‐‑ and long-‐‑terms.

The integration of CCA and DRM can be achieved if we recognize that risk comes in different forms, at the very least: (a) Risks associated with the “non-‐‑routine” or extreme events such as typhoons and the associated episodes of flooding; and (b) Risks associated with gradually changing “normals” (Alan Lavell, personal communication, 2012) such as sea level rise, aggravation of urban stressors, lessening agricultural productivity and other ecosystem changes, decreasing viability of tourist spots, increased probability of landslides due to dry soils, increased incidence of adverse health impacts, etc. CCA and DRM share the goals of reducing risk, but while the latter focuses on risk of the first type, the former addresses both types of risk (i.e. climate change also modifies the nature of extreme weather events as well contributes to changing climate normals). Thus, interventions and strategies that build capacities to cope with current climate variability also contribute towards enhancing long-‐‑term adaptive capacity.

Furthermore, any research on and proposed solution to address risk must also recognize that risk, of either type, is a confluence of different factors – the hazard or physical disturbance, the exposure of populations and assets, and the vulnerability (or conversely, adaptive capacity) of the




exposed elements that makes them susceptible to the hazard. It is here where the FORIN approach becomes valuable – the roots of exposure and vulnerability to hazards are deeply embedded in the community history and development, and will therefore require such probing investigation if they are to come to light.

This document presents a case study applying a CCA-‐‑DRM FORIN approach to Metro Manila, implemented under the Manila Observatory project entitled “Harmonizing FORIN for Climate Change Adaptation and Disaster Risk Management to Develop Multi-‐‑sectoral Narratives for Metro Manila.” The FORIN Project Report by IRDR describes several templates for constructing in-‐‑depth research projects, which include the Critical Cause Analysis, Meta-‐‑Analysis, Longitudinal Analysis and Scenario Analysis. (For more details, please see the complete FORIN report, by IRDR, 2011.) However, because the goal of a forensic investigation is to analyze and address the systemic drivers of risk, such research necessarily requires significant time and resources. Thus, the FORIN approach also provides for a shorter “narrative” template.

The FORIN narrative is a template employed here in a preliminary scoping endeavor for future more in-‐‑depth and exhaustive forensic investigations. As described in the FORIN Report, a narrative “can frame the broad outlines of the processes embedded in the developmental forms and strategies of a society that result in vulnerability construction and disaster causation (IRDR, 2011).” The narrative

format can help with identifying gaps in available data or previous research, while also highlighting the trends and issues for which more comprehensive analyses is required.

This compendium of multi-‐‑sectoral narratives contains four studies of Metro Manila representing the physical, social, economic and health sectors. The physical sector addresses mainly the hazard component of risk while the other sectors address aspects of exposure and vulnerability in more details. Each narrative is structured as appropriate for that particular sector, but all are developed so as to contain two basic parts – a description of the approach, and the application to the context of Metro Manila. The description of the approach includes a discussion of how the proposed general CCA-‐‑DRM FORIN framework is concretely operationalized in the sector, and a discussion of the sectoral dimensions of risk (given the two categories of risk and the components of risk discussed previously). This section also identified connections and interfaces with other sectors in the study of risk and resilience.

The application of the framework to Metro Manila includes a contextualization to frame the Metro Manila experience within the larger context of developments (e.g. national trends), and a discussion of the evolution of risk in Metro Manila. This section is longitudinal in nature, tracing historical, current and if possible, projected components of risk. It applies the CCA-‐‑DRM FORIN framework to analyze the




factors driving the levels and distribution of risk.

Following the narratives is a brief integration that summarizes common themes and critical processes, and discusses

challenges and lessons learned from the multi-‐‑sectoral scoping process. A more detailed discussion of the proposed CCA-‐‑DRM FORIN approach can be found in Volume 1 of the project output documents.




References

Integrated Research on Disaster Risk. (2011). Forensic investigations of disasters: The FORIN Project (IRDR FORIN Publication No. 1). Beijing: Integrated Research on Disaster Risk.

Integrated Research on Disaster Risk. (2009). Report of the ad hoc working group, IRDR Forensic Investigations. Toronto: International Council for Science.

White, G. F., Kates, R. W., & Burton, I. (2001). Knowing better and losing even more: The use of knowledge in hazards management. Environmental Hazards, 3(3-‐‑4), 81-‐‑92.




Chapter 1: Increasing risk to disasters due to the effects of climate change and urban development

Framework and Approach to CCA-‐‑DRM FORIN in the Physical Sector

The narrative of the Physical Sector and its role in Climate Change and Disaster impacts adapts in general the fundamental framework and ideas of the new special report of the Intergovernmental Panel on Climate Change (IPCC) on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX, IPCC 2012), see Figure II.1. The framework illustrates the importance of not just weather and climate events in disaster risk but also the role of non climatic factors (exposure and vulnerability) in determining the potential disastrous impacts of weather and climatological hazards. More

importantly, and relevant to this narrative, the IPCC SREX framework links climate change and disasters to development. Figure II.1 illustrates that:

• Pathways to development dictate greenhouse gas emissions that consequently result in anthropogenic climate change.

• Both human induced changes in climate and natural variability influence or affect weather and climate events, especially extreme weather events. The IPCC in its fourth assessment report has stated that in globally warmer world, extreme events are more likely to occur, with greater than 90% probability (very likely) for more frequent and heavy precipitation events (Adger et al., 2007).

• The risk to disasters, which is the

Figure II.1. Disaster risk framework in the IPCC Special Report on Extreme Events (IPCC 2012). (Figure taken from IPCC 2012).




compounding effect of hazard, exposure, and vulnerability, can threaten development but at the same time, the way cities develop affects the city’s or society’s level of risk to the impacts of extreme events.

This narrative will hence analyze the physical hazard in the risk framework by first focusing on the changing characteristics of weather and climate extremes and variability in light of climate change. It will then expound on the compounding effects of urban development to flooding that increases the risk to disasters of urban cities to the extreme weather events. The discussions will illustrate:

a. the historical impacts of climate change and variability on rainfall trends, especially rainfall extremes, as the main threat and hazard to development and;

b. how local development increases the risk to and the complexity of the disastrous nature of urban flooding due

to extreme rainfall events.

Climate change, variability and extremes and Urbanization The climate has been changing locally in the Philippines, especially in the urban areas. In the same trend as the global average temperatures, the average annual temperature of the Philippines has also steadily increased since the 1950s. In particular, urban areas have shown an average warming of about 0.8oC from 1950-‐‑2005 based on 23 stations located in the different cities of the country (including Manila, Baguio, Legazpi, Tacloban, Mactan, etc), see Figure II.2.

Both changes in the global and local temperatures in the Philippines can have other climatic impacts including changes in rainfall and tropical cyclone characteristics. In particular, rainfall variability in the Philippines is affected by local-‐‑scale and synoptic systems, such as monsoons, tropical cyclones, and El Nino Southern Oscillation, and hence any change in these

Figure II.2. Average annual surface temperatures from 1951 to 2005 based on data from observation stations located in urban areas in the Philippines. (Data: PAGASA)

!0.8%

!0.6%

!0.4%

!0.2%

0%

0.2%

0.4%

0.6%

0.8%

1%

1951% 1961% 1971% 1981% 1991% 2001%

Anom

aly((°C)(

Years(

Annual(surface(temperature(anomalies(from(1951(to(2005(%in(Urban(Areas((the(Philippines((




systems can influence trends in rainfall.

Monsoonal influence on rainfall is indicated in the geographical pattern in the rainfall trends. Based on the analysis of seasonal total rainfall values from 1951 to 1992 by Jose et al. (1996), there is a decreasing trend in eastern Luzon, most areas in Visayas and in Mindanao, which may be attributed to changes in the northeast monsoon (from October to March), while a general increasing trend in rainfall is found over western Luzon, particularly during the southwest monsoon (SWM) season (from April to September).

However, in a more recent analysis of rainfall data from 1961 to 2010, Cruz et al. (2012) found a general decreasing trend in the SWM rainfall total and its spatial variability, particularly in the western part of the Philippines. Although, two out of nine stations indicated a very minimal increasing trend (0.008% per decade in Science Garden and 0.017% per decade in Iloilo), these trends were found to be not significant (i.e. without statistical confidence) given the insufficient number of years of the dataset used in the study (Cruz et al. 2012).

The warm and cold phases of the El Niño Southern Oscillation is also known to influence seasonal rainfall in the Philippines, wherein drought is experienced in many areas during El Niño years, while there is heavy rainfall and flooding during La Niña years. However, there can be a reversal in the sign of the seasonal response of rainfall to ENSO between boreal summer (from July to September) and boreal

autumn (from October to December) for both El Niño and La Niña years (Lyon et al., 2006). During the boreal summer of an El Niño year, statistically significant above-‐‑median rainfall is recorded over north and central Philippines before the onset of anomalously dry conditions in the boreal fall. On the other hand, below-‐‑median rainfall is recorded during the boreal summer of a La Niña year, which is followed by anomalously wet conditions. Lyon et al. (2006) found that this rainfall response is not associated with the transitions in ENSO during the boreal spring, but because of the influence of ENSO on the atmospheric circulation over the western North Pacific.

While heavy rainfall events over western Luzon correspond to strong, moist southwest winds, which can occur as early as May (Moron et al., 2008), tropical cyclones can also affect the intensity of SWM rainfall indirectly. Cayanan et al. (2011) found that the southwesterlies from the basic monsoon flow can be either strengthened by the southwesterly winds generated by tropical cyclones located over northeast of Luzon, or be converged with the northwesterlies generated by tropical cyclones located over north or north-‐‑northwest of Luzon. These winds interact with the Cordillera Mountain ranges along western Luzon resulting to enhanced vertical motion leading to heavy rainfall over western Luzon (Cayanan et al., 2011).

Heavy rainfall can also be directly associated with tropical cyclones, which can have destructive impacts (Ribera et al., 2008). Typhoons within the vicinity of the




Philippines have a maximum occurrence during August and September, followed by July and October with the few events between January and March (Ho et al., 2004; Ribera et al., 2005). The highest passage frequency of typhoons occurs over the northern and northeastern part of Luzon. However, there have been some observed recent changes in these tracks. Ho et al. (2004) found a significant decrease in the typhoon passage frequency over the Philippine Sea and East China Sea with a slight increase over the South China Sea. The interdecadal variations of typhoon tracks during the boreal summer reveal that the major shift started in the late 1970s over the East and South China Sea whereas a constant rate of about -‐‑9% per decade can be seen over the Philippine Sea (Ho et al., 2004). These changes can be attributed with the westward expansion of the subtropical northwestern Pacific high (Ho et al., 2004). Similarly, the analysis of Wu et al. (2005) reveals a substantial westward shift in the two prevailing typhoon tracks in the Western North Pacific, possibly due to the change in the mean translation velocity of typhoons and the large-‐‑scale steering flow,

causing an increased influence over the Subtropical East Asia. In terms of tropical cyclone landfall numbers in the Philippines, while no long-‐‑term interdecadal trend is found, annual landfall numbers is observed to decrease (increase) in El Nino (La Nina) years during low Pacific Decadal Oscillation phase (Kubota and Chan, 2009).

Preliminary analysis of archival data together with more recent typhoon tracks data from the Joint Typhoon Warning Center (JTWC), appear to indicate a clear increase in the number of tropical cyclones (TCs) forming in the Western Pacific Region (Formation, blue line), an increase in the TCs approaching or within the Philippine Area of Responsibility (Approach, green line), and slight increase in the TCs that make landfall in the Philippines (Landfall, red line). It is noted though that the methodology for documenting the tracks of the TCs before the second world war in the early 1940s , and as reflected in the TC counts in the Manila Observatory Archival records, will most likely be different from the current systems of tracking TCs.




Table II.1. Eleven of the most destructive typhoons in the Philippines from 1947-‐‑2009; (Data taken from typhoon2000.com, except for Sendong estimates.)

NAME YEAR HIGHEST WIND SPEED (KPH)

DEATHS DAMAGE (Php B)

1 Rosing 1995 260 936 11 2 Reming 2006 320 754 5 3 Frank 2008 172 938 13 4 Uring 1991 95 5101 1 5 Pepeng 2009 120 492 27 6 Ondoy 2009 85 464 11 7 Nitang 1984 220 1363 4 8 Ruping 1990 220 748 11 9 Sisang 1987 240 979 1

10 Sening 1970 275 768 2 Sendong 2011 ~100 ~1000+ ~1

Figure II.4. Number of typhoons that a) form in the Western Pacific Region (blue); b) approach and are within the Philippine Area of Responsibility (green); and c) make landfall in the Philippines (red) from 1941-‐‑2011 based on from the JTWC best track.

0"

10"

20"

30"

40"

1945" 1955" 1965" 1975" 1985" 1995" 2005"

Num

ber'

Forma+on'

Approach'

Landfall'

Tropical Cyclone Formation, Approach, and Landfall (1945-‐‑2011)




Table II.2. Top 10 Natural Disasters in the Philippines from 1900-‐‑2012 sorted by the number of people affected. (Table taken from http://www.emdat.be/result-‐‑country-‐‑profile; EM-‐‑DAT: The OFDA/CRED International Disaster Database.)

Hence, declining trends in the passage of tropical cyclones across the Philippines and within the Philippine area of responsibility are not indicative of the resulting disasters due to these extreme events. This

emphasizes the importance of investigating the nature of the hazard and the nature of the exposed and vulnerable elements to the hazard. On the characterization of the weather hazard, for example, there is a shift




now in the warning system of the Philippine Weather Bureau (PAGASA) from a purely wind strength based warning to a system that incorporates rainfall warning signals. This is a response to the fact, which was noted earlier, that the more recent “slow-‐‑moving” storms, with wind speeds of about 85-‐‑100kph, were disastrous because of the extreme amount of rainfall associated with the tropical cyclones. Figure II.5 shows that PAGASA rainfall warning signals that range from a yellow warning (flooding is possible); orange warning (flooding is threatening); and red warning (serious flooding is expected in low lying areas and hence evacuation measures should be implemented).

Equally important to characterizing the changing nature of the weather hazard is understanding the nature of the exposed elements and how these have evolved through time, especially with urban growth, development, and expansion. This narrative will not deal with the socio-‐‑economic variables related to exposure and vulnerability. Rather, it assesses urban exposure in terms of the secondary consequence of physical urban morphology changes that affect in turn the crucial translation of extreme rainfall events into

the secondary and potentially disastrous impacts of flooding.

It is important to understand the impact of urbanization on surface hydrology, particularly since the growth of urban areas is often accompanied by increased incidences of flooding (Bankoff, 2003; Shi et al., 2007). Heavy rainfall events also tend to be more frequent over urban areas compared to rural areas (Kishtawal et al. 2009). The conversion of lands to urban areas results in changes in the characteristics of the land surface, which affect the local climate, including the surface temperature, surface moisture availability and runoff (Carlson and Arthur, 2000). Paved and concrete roads and drainage systems increase surface impermeability and divert natural flows leading to changes in surface runoff (Booth, 1991). Further, urbanization can intensify flood hazard because the high impermeability of urban surfaces can result in more surface runoff, faster runoff confluence times and high maximum flood discharge (Shi et al., 2007). The evolution of development therefore can potentially compound the impacts of extreme weather events, especially flooding.

Figure II.5. Rainfall warning system of the PAGASA. (Image taken from: http://newsinfo.inquirer.net/files/2012/08/color-‐‑code-‐‑warning-‐‑signal.jpg)




Hence, in light of climate change increasing the probability of occurrence of extreme rainfall events, it is important to include in disaster risk analysis the compounding effects of urban development to the secondary impacts of flooding. Together with analyzing variability and trends in changes in temperature and rainfall, the evolution and trends of urban growth and expansion should also be studied. Figure II.6 shows the more detailed framework for the Physical Sector, which builds on the concept on how urban development can compound the effects of extreme rainfall events and increasing the risk to disastrous flooding. Climate change will affect long term trends in temperature and rainfall. These changes will affect the probability of flood events in cities as an increasing trend in rainfall can produce “new normals” or new averages. Return periods of rainfall extremes can, in time, change such that

previous 50 or 100 return period rainfall events can happen at more frequent

intervals of less than 50 years. Extreme events are then more likely to occur, leading to higher risk to disasters. But the increasing risk is not solely due to changing climate but also to the changing nature of the urban landscape. Unplanned and disorganized urban development and expansion can lead to the narrowing of rivers and waterways that can hamper the flow of water through cities to the bay and hence leading to higher potential for city flooding. Urbanization also increases paved areas, which reduces water infiltration to the ground, and can also increase the speed of water runoff. Watershed deforestation decreases the ability of the ground to absorb and hold water and also causes river siltation, thereby decreasing the depth of waterways through time. These factors increase the complexity of flooding in urban cities and increases the risk to disastrous impacts of flooding due to extreme weather events.

Interfaces with Other Sectors Based on the framework in Figure II.1, the physical sector provides input to the analyses in the economic, social, and health sectors. These other sectors tackle more the aspects of exposure and vulnerabilities where economic variables can be indicators of development and hence exposure either in terms of infrastructure or people or both. The social and health sectors on the hand will illustrate crucial information and indicators of human and societal vulnerabilities that can increase the risk to disasters.

Application to Metro Manila

Figure II.6. Climate change and extreme weather events lead to higher risks to flooding but complex and unplanned urban development can compound flooding impacts and further increases the risk to disasters.




Contextualization

Geography and Demographics Metro Manila, also known as the National Capital Region, is the country’s financial and economic center. It covers 636 square kilometers of land that sits on a “semi alluvial flood plain formed by sediments

flowing from the Meycauayan and Malabon-‐‑Tullahan river basins” (Bankoff 2003). It has a tropical wet and dry climate/tropical savanna climate (WWF 2009). Inhabited by more than 11.8 million people (NSO 2010), it is one of the most populated coastal megacities in Asia, see Table 3.

Table II.3. Population and area of the major coastal cities in Asia.

Megacity Population (in millions)

Area (sq. km)

Dhaka, Bangladesh 13 154 Jakarta, Indonesia 8.5 662 Metro Manila, Philippines 11.8a 636 Calcutta, India 15 1886b Phnom Penh, Cambodia 2 375 Ho Chi Minh, Vietnam 9 2095c Shanghai, China 20 6400 Bangkok, Thailand (including Greater Bangkok)

12 1500

Hong Kong, China 7 1100 Kuala Lumpur, Malaysia (including Klang Valley)

7.2 244

Singapore, Rep. of Singapore 4.7 710 Source: Mega-Stress for Mega-Cities: WWF 2009 a NSO 2010 b Kolkata Metropolitan Development Authority c wikipedia.org

Climate Vulnerability Through the years, Metro Manila has been a passing point of tropical cyclones that cross the archipelago. According to Bankoff (2003), two of at least five identified main typhoon tracks are in NCR: one that crosses to the north of Manila, one that traverses south of the capital. A study made by the World Wildlife Fund on “Mega-‐‑Stress for Mega-‐‑Cities” analyzed the ranking of major coastal cities in Asia, including Metro Manila, in terms of climate vulnerability.

The study collected information that are mostly sourced from the IPCC’s latest reports, specifically, “Working Group I: The Physical Science Basis” and “Working Group II: Impacts, Adaptation and Vulnerability”. Other data came from the “Climate Change Vulnerability Mapping for Southeast Asia” report by Anshory and Francisco (2009). The criteria used for ranking was comprised of three major categories: environmental exposure, socioeconomic sensitivity and adaptive capacity. Encompasses in the environmental




category are storm threat (which includes threats from tropical cyclones and storm surge), sea-‐‑level rise and subsidence, and water-‐‑related threats (whether due to flooding or drought). The socioeconomic sensitivity variables include population, wealth (GDP) and contribution to national

GDP. The adaptive capacity analysis includes existing examples of capacities and per capita GDP. Table 4 shows that Metro Manila, in this study, is ranked third in terms of overall vulnerability and hence has higher risk to disaster and climate change impacts.




Table II.4. Ranking of overall vulnerability of megacities in Asia based on environmental exposure, socioeconomic sensitivity and adaptive capacity.

Megacity Overall Vulnerability

Environmental Exposure (Storm Threat, Sea-level

Rise, Flooding/Drought)

Socioeconomic Sensitivity

(Population, Assets Threatened)

Inverse Adaptive Capacity

Dhaka, Bangladesh 9 8 [(4+9+10)/3] 8 [(6+10)/2] 10 Jakarta, Indonesia 8 6 [(2+8+9)/3] 10 [(10+9)/2] 7 Metro Manila, Philippines 8 9 [(10+8+1/3)] 7 [(5+9)/2] 7 Calcutta, India 7 6 [(3+8+7)/3] 7 [(7+6)/2] 7 Phnom Penh, Cambodia 7 4 [(2+1+10)/3] 6 [(1+10)/2] 10 Ho Chi Minh, Vietnam 6 8 [(6+10+3)/3] 6 [(4+7)/2] 3 Shanghai, China 6 8 [(7+10+7)/3] 9 [(9+8)/2] 2 Bangkok, Thailand (including Greater Bangkok)

5 5 [(2+7+7)/3] 7 [(5+9)/2] 4

Hong Kong, China 4 7 [(7+7+6)/3] 6 [(3+8)/2] 1 Kuala Lumpur, Malaysia (including Klang Valley)

4 3 [(1+1+7)/3] 5 [(3+7)/2] 3

Singapore, Rep. of Singapore

4 4 [(1+6+5)/3] 6 [(2+10)/2] 1

Note: Some figures are rounded up. Source: Mega-Stress for Mega-Cities: WWF 2009

Evolution of Risk in Metro Manila The historical tracing of changes and developments that has increased the risk of Metro Manila to the potentially disastrous effects of extreme weather events and climate will focus on how urban growth in and expansion of the city has progressed rapidly within the last decades.

Urbanization Expansion: Pervious vs. Impervious Estimates The evolution of urban morphology and development is assessed through primary satellite-‐‑based data analysis. As mentioned earlier, urbanization has an undoubted relationship with the way land is utilized. Here, for the case of Metro Manila, the changing character of land use is reflected

in the ratio between pervious and impervious, which has rapidly changed since the 1970s. The spread of paved areas in the city was analyzed using five multi-‐‑date Landsat images that were processed using different RS techniques such as image classification (maximum likelihood) and

Figure II.7. Satellite image analysis show the decreasing ratio between urban and vegetated areas through time.




masking. The final layout was done through GIS spatial mapping (del Castillo et al., 2012). Initial results, Figure 7, show that through the years, the total area of impervious surfaces such as residential, commercial and industrial regions have continued to rise while forest lands and other vegetated regions (considered as pervious areas) have continuously decreased. In 1972, more than 50% of the area of Metro Manila (~301 sq. km of land) were vegetated, including agricultural areas and bare land. The remaining part comprised of various impervious surfaces (residential, commercial and industrial areas) had a total of 239 sq. km. In 7 years, vegetation dropped to 238 sq. km while impervious surfaces rose to 329 sq. km (~60%). After a decade, only 174 sq. km (30%) of land remained vegetated and impervious surfaces increased to 455 sq. km (70%). In 1999, only 115 sq. km (>21%) of land remained permeable/vegetated and

impervious areas continued to rise to 455 sq. km. By 2009, areas with vegetation have decreased to about 20% and approximately 80% of the city is considered urban and can be characterized mostly by impervious surfaces. Only 107 sq. km (<20%) of land was permeable/vegetated while impervious areas had a total of 475 sq. km.

Climate Change and Extremes in Metro Manila As Metro Manila, grew and expanded, the local climate has also been changing. The PAGASA station data in Science Garden, Quezon City are analyzed for indicators of local climate changes in the metropolis. Results show that the average temperature has, in general, steadily increased since the 1980s, Figure II.8. The city has been warmer than normal in the past 30 years. This can be both due to global warming and the urban heat island effect.

Figure II.8. Average temperature recorded in the Science Garden station of PAGASA in Metro Manila shows a steady increase since the 1980s. (Data: PAGASA)

y"="0.0319x"*"0.5085"

*1.5"

*1"

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1961" 1966" 1971" 1976" 1981" 1986" 1991" 1996" 2001"

Mean%Tempe

rature%ano

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Temperature%anomaly%rela5ve%to%1961:1990%baseline%%(with%5:year%running%mean)%

Science%Garden%




Rainfall trends have also changed and observation data indicate that the recent decades have been wetter than normal. Figure II.9 shows the annual precipitation ratio (relative to the 1961-‐‑1990 climate). The blue bars in the figure denote the times when the year had more rain than the climatological average. For example, 2011 had rainfall that was about more than 50% of the average rainfall that the station in Science Garden normally receives. Since the 1980s, the precipitation ratios show that more years have rainfall that were higher than average. This suggests that there has been a general increasing trend in rainfall in Metro Manila. It is noted though that results are still to be tested for statistical significance and that more data might be needed for better confidence in the trends analysis.

The wetter characteristic of the recent decades appear to be supported by the decadal trends of rainfall during the

southwest monsoon (SWM) season, or the rainy season during the months of June-‐‑October. Figure II.10 shows that beginning in the 1980s, the SWM season had more rains than normal, (as indicated by the linear (increasing) trendlines in the figure). Hence, both the annual precipitation ratios and the decadal trends of the SWM rainfall indicate wetter conditions in Metro Manila.

It is important to examine the nature of the increasing trend in rainfall, i.e. specifically, whether embedded in the trend is a signal that indicates an increase in the frequency of extreme rainfall events, which is the primary driver for heavy flooding. In this study, this is done by analyzing the decadal trends in the 90th and 95th percentile of rainfall amounts (i.e. the heavier rainfall amounts). Results show that the number of days when these heavier rainfall amounts occur have been increasing through the decades from the 1960s to the 2000s, see Figure II.10. Therefore, rainfall change in

Figure II.9. Annual precipitation ratio (relative to the 1961-‐‑1990 baseline) show that the recent decades have been wetter than normal. (Data: Science Garden Station, PAGASA)

0"

20"

40"

60"

80"

100"

120"

140"

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1961" 1971" 1981" 1991" 2001" 2011"

Ra#o

%(%)%

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Metro Manila in the last 50 years point to average wetter conditions, higher amounts of rainfall during the rainy months of the

SWM season, and an increase in the occurrence of the heavier rainfall events.

Decadal trends of southwest monsoon rainfall

Figure II.10. Decadal trends of rainfall during the Southwest Monsoon Season. (Data: Science Garden Station, PAGASA)

Figure II.11. Frequency of events of the 90th and 95th rainfall percentile.




In the previous section, it was mentioned that, considering only the period of 1945-‐‑2011, there is a slight decline in the number of tropical cyclones that made landfall and that approached or entered the Philippine area of responsibility. Here, the number of TCs that cross within a 600km radius from the center of Metro Manila is analyzed. Further, the TCs are categorized according to intensity based on the classification by the Joint Typhoon Warning Center: tropical depression (intensity < 63kph), tropical storm (63kph<intensity<117kph), typhoon (118kph<intensity<239kph), and super typhoon (intensity>240kph). Results in Figure II.12 show that there appears to be no clear trend in the behavior of the different categories of TCs. Although a closer study of trendlines would show that the number of tropical depression events have increased through the decades while the other categories show very slight declining trends in frequency. Overall, the total number of TCs show a slight increasing trend since the 1960s.

The amount of rainfall associated with tropical cyclones is next analyzed by getting

the ratio between the total amount of rainfall (per category) and the number of events (per category). In this way, the average precipitation per each event (of a category) is obtained and hence indicates the “intensity” of the rain associated with the TC. For example, a total of 1000mm of rainfall associated with four typhoon events will give a ratio of 250mm/typhoon. On the other hand, there will be less “intense” rainfall associated with TCs if there is a total of 1000mm of rainfall for 10 typhoon events, resulting in a ratio of 100mm/typhoon.

Figure II.13 shows that the rainfall ratios (with the number of TCs) for the slow moving categories of tropical depression and tropical storm have been increasing from the 1990s to the 2000s. On the other hand, the average rainfall associated for each event of typhoons and super typhoons has been slightly decreasing. Hence, in recent decades, the slow moving TCs have been bringing more rain. In particular, tropical depression and tropical storms had more rain than typhoons in the 2000s.




Figure II.12. Number of tropical cyclones that cross within a 600 km radius from Metro Manila,

classified according to the different categories of the JTWC.

Figure II.13. Ratio between the total rainfall associated with each tropical cyclone category and the total

number of events per category.

0 20 40 60 80

100 120 140 160 180

1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 Num

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Tropical Depression Tropical Storm Typhoon Super Typhoon

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1961-1970 1971-1980 1981-1990 1991-2000 2001-2010

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Tropical Depression Tropical Storm Typhoon Super Typhoon




Compounding effects of increasing rainfall and urban expansion in Metro Manila The general increasing trend in rainfall, the wetter conditions during the southwest monsoon season, and the greater amount of rainfall associated with slower moving tropical cyclones all indicate that Metro Manila is facing an increasing hazard in terms of a new wetter “normal” for rainfall and the more frequent occurrence of extreme rainfall events, which are both indicators of a changing climate. Together with these climatic and extremes changes, the landscape of the metropolis has also changed, with impervious surfaces replacing more and more of the porous soils associated with natural vegetation areas. Development has also resulted in the narrowing of river channels, lost esteros and waterways, the siltation of rivers due to watershed deforestation, land subsidence, all of which greatly increases the potential for flooding in Metro Manila.

Bankoff (2003) has linked the increase in flood incidence in Metro Manila not just to climatic patterns and topography but also to the pattern of urban development. Similarly, Zoleta-‐‑Nantes (2000) identifies the increase in impervious areas, the encroachment of floodplains and waterways, increase in run-‐‑off due to watershed denudation as major reasons on why urbanization contributes to severe flooding. Both Bankoff (2003) amd Zoleta-‐‑Nantes (2000) illustrated timelines on the evolving nature of flooding in Metro Manila. Figure II.14 shows the sequence of events from Bankoff (2003) coupled with the analysis on the observed trend in rainfall

and development. Interestingly, Bankoff (2003) shows that severity in flooding has already begun in the 1960s because of the deforestation of the Marikina and Montalban watersheds. By the 1970s and onward, siltation, urban expansion to the west (areas of Cainta, Pasig, Pateros, and Taguig), siltation of Laguna Lake further exacerbated flood potential and impacts. It is also shown in Figure II.14 that the timeline of the increasing number of factors that lead to more serious flooding coincide with the changing landscape as observed in the satellite image analysis and the increasing trends in rainfall beginning in the 1980s. The chronology of expansion of flooded areas documented by Zoleta-‐‑Nantes (2000) also correlate well with the satellite-‐‑based urban expansion patterns. In the 1960s-‐‑1970s, according to Zoleta-‐‑Nantes (2000), flooding expanded to Manila, Quezon City, Pasay, San Juan, and Caloocan. These are also the urban areas identified in the image analysis. Moreover, flooding further expanded to the low-‐‑lying areas of Pasig, Marikina, Taguig, and Pateros (Zoleta-‐‑Nantes, 2000), which are also the areas of urban expansion in the 1980s.

Current and Projected Risk to Flooding Tropical Storm Ketsana in 2009 was a clear illustration of how disastrous flooding can be in Metro Manila due to the extreme weather event and the complex nature of development that compounded the effects of the heavy precipitation. Metro Manila is at high risk to flooding because of the increasing nature of the hazards, which are primarily the extreme rainfall and




secondarily the translation of heavy precipitation into ground flooding. Because of the way Metro Manila has developed, the city has now a lower threshold for rainfall amounts that could result into heavy and disastrous flooding.

Climate change projections over the Philippines using the A1B scenario of the ECHAM5 global climate model downscaled by a regional climate model (RegCM3) show that the northern parts of the country, including Metro Manila, will have wetter than normal rainfall conditions, Figure II.15. The rest of the Philippines are projected to have a general decrease in rainfall,

especially Mindanao. Further analysis shows that during the rainy season, there can be an increase of about 5-‐‑20% in the northwestern regions of the country, including Metro Manila and that the increase is more likely to be due to the frequent occurrence of heavy rainfall events (Villafuerte, 2009). It is noted though that these climate projections are from a single regional climate model driven by one global climate model scenario. It is best to have an ensemble of projections from different models and scenarios to better understand the potential future changes in climate.




Figure II.14. Sequence of events from Bankoff (2003) coupled with the analysis on the observed trend in rainfall and development.




Conclusions and Recommendations

Both climate and the urban landscape have changed in Metro Manila In the recent decades. There is a general increasing trend in rainfall and also an increase in the occurrence of heavier rainfall amounts. Tropical depressions and storms have also brought more intense precipitation.

Further, Metro Manila has rapidly grown and expanded since the 1970s. The climate changes and unsystematic urban development have translated into a greater risk to flooding that Metro Manila has been and is now facing, and will continue to face in the future, especially in light of a globally warmer world.

The analysis in the Physical Sector has illustrated how changes in climate trends

Figure II.15. Climate change projections show that the northern parts of the country, including Metro Manila, will have wetter than normal rainfall conditions by 2020, as denoted by the blue colored regions. (Courtesy of the Manila Observatory; Dynamic downscaling using the regional climate model RegCM3A1B driven by the A1B scenario of the ECHAM5 global climate model.)




and extremes and how the urbanization factors that compound flooding have increased the physical hazard and consequently have increased the risk to disasters. Zoleta-‐‑Nantes (2000) asserted that flood control infrastructures are inadequate because the effects of urbanization are not incorporated in the design. This study highlights this argument and also emphasizes the need to incorporate climate change impacts in future infrastructure developments to address city flooding. To support future development plans and mitigating measures to flooding, a more in depth scientific analysis on how Metro

Manila has developed should be done with clear identification and spatial profiling of existing causes that exacerbate flooding. Although previous studies have indicated the general factors that lead to more severe and disastrous flood events, these can be supported by more research with greater detail and focus, especially in the most vulnerable areas, so as to form the basis for specific policy actions. Climate change projections should also be done using multi-‐‑models and an ensemble of scenarios to assess the range and probabilities of the climatic impacts over Metro Manila.

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Bankoff, G., 2003. Constructing vulnerability: The historical, natural and social generation of flooding in Metropolitan Manila. Disasters, 27: 95-‐‑109.

Booth, D.B., 1991. Urbanization and the Natural Drainage System – Impacts, Solutions, and Prognoses. The Northwest Environmental Journal, 7: 93-‐‑118.

Carlson, T.N., and S.T. Arthur, 2000. The impact of land use – land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and Planetary Change, 25: 49-‐‑65.

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Kubota, H., and J. C. L. Chan (2009), Interdecadal variability of tropical cyclone landfall in the Philippines from 1902 to 2005, Geophys. Res. Lett., 36, L12802, doi:10.1029/2009GL038108

Lyon, B., Cristi, H., Verceles, E.R., Hilario, F.D., Abastillas, R., 2006. Seasonal reversal of the ENSO rainfall signal in the Philippines. Geophys. Res. Lett. 33, L24710. http://dx.doi.org/10.1029/2006GL028182.

Moron, V., A. Lucero, F. Hilario, B. Lyon, A. W. Robertson and D. DeWitt (2008), Spatio-‐‑temporal variability and predictability of summer monsoon onset over the Philippines, Clim. Dyn., 33, 1159-‐‑1177, doi:10.1007/s00382-‐‑008-‐‑0520-‐‑5

Ribera, P., R. García-‐‑Herrera, L. Gimeno, E. Hernández, 2005. Typhoons in the Philippine Islands, 1901-‐‑1934. Clim. Res., 29, 85-‐‑90.

Ribera, P., R. Garcia-‐‑Herrera, and L. Gimeno, 2008: Historical deadly typhoons in the Philippines. Weather, 63(7), 194-‐‑199.

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Chapter 2: Tracking the Health Impact of Climate Change in Metro Manila: Understanding the Risks on the Health System




Framework and approach to CCA-‐‑DRM FORIN in the Health Sector

Framework The recommended framework to CCA-‐‑DRM FORIN in the health sector is an epidemiological framework. This framework would include both a descriptive and an analytic component. In the descriptive component, the magnitude of the morbidity and mortality events due to climate change and to extreme events would be measured using frequency parameters. The distribution of these events would also be described in terms of person, place, and time (e.g., who, where, and when).

Approach 1. The main health outcomes are morbidity and mortality events associated with climate

change and with extreme events. a) Magnitude (i.e., exposure) is the frequency of an event over a period of time and may be

measured as: (1) Incidence (for communicable diseases) or the number of new cases per population at

risk over a specified period of time. The burden of disease (BOD) due to climate change has been measured (Campbell-‐‑Lendrum et al, 2006). There are completed examples for: cardiovascular mortality, diarrheal disease, malnutrition, natural disaster health impacts, vector-‐‑borne diseases. The procedure consisted of the following: identification of health outcomes, quantification of the dose-‐‑response relationship at baseline, definition of future exposure scenarios, and estimation of the BOD: those attributable to a risk factor and those avoidable by plausible reductions in risk factor. For chronic diseases, prevalence is the preferable parameter, i.e., the number of existing cases per population over a specified period of time.

(2) The magnitudes of health events due to climate change and extreme events have been measured in several studies. (a) For example, Knowlton has developed a model for predicting attributable deaths

due to changes in ozone concentration (Knowlton, 2004). The model is as follows:

M= (P/100,000) x B x CRF x E Where: • M = estimated number of deaths attributable to O3 concentrations • P = estimated county population during time period of interest • B = estimated baseline county-‐‑level daily mortality rate in June-‐‑ August (per 100,000

population)




• CRF = concentration-‐‑response function, which quantifies the magnitude of the proportional change in daily mortality that would be expected in response to a given daily O3 concentrations, based on results from epidemiologic literature

• E = daily 1-‐‑hr max O3 concentrations in June-‐‑August in each county, interpolated from Giss/MM5/CMAQ model outputs1

(b) The WHO has reported that children suffer more from environmental hazards to

health. The number of healthy life years lost by children is five times greater compared to the total population. In developing countries, children lose eight times more healthy life years per capita than their counterparts in developed countries from environmentally caused disease (WHO, 2006).

(c) There is a 5-‐‑7% expected increase in the population at risk for malaria in Africa due to the spread of mosquitoes into higher altitudes and a 16-‐‑28% increase in total number of person months of exposure. Climate change is thought to have caused 0.3% of global deaths annually and 5.5 million lost DALYs annually (0.4%) by 2000. Climate change is responsible for 2% of the burden of malnutrition (Haines et al 2006).

b) A state transition model may be hypothesized in which people may assume one of four states of health: healthy, sick from climate change and extreme events, sick from other causes, and dead (WHO, 2004).

c) These CCA-‐‑DRM-‐‑related health events may also be described in terms of their distribution over person, place, and time. (1) For example, social disparities, lifestyles, and economic inequities all affect the risks

of disease and death. Floods have resulted in many adverse outcomes (Alderman et al, 2012). Those at highest risk for death were: ethnic minorities, those living in floodplains and in unstable dwellings, females, the very young and the very old. On the other hand, injuries were most common among young and middle-‐‑aged males. Toxins were prevalent in flood-‐‑impacted industrial or agricultural areas. Communicable diseases resulted from the destruction of water supply systems, poor hygiene facilities, inadequate provision of clean drinking water, power shortages, and post-‐‑flood resettlement (i.e., overcrowding, compromised quality of water and hygiene). Vector-‐‑borne diseases resulted from increased exposure to vectors (i.e., sleeping outside, overcrowding), changes in vector habitat, and compromised vector control programs during floods. Psychological disorders were associated with the degree of exposure, previous flood experience and disaster preparedness, being

1 GISS - Global circulation model: Goddard Institute for Space Studies. MM5 - Regional Climate Model: Penn State/ National Center for Atmospheric Research Mesoscale Model 5. CMAQ - Ozone simulation: Community Multiscale Air quality model.




female, older age, lower socio-‐‑economic status, family structure, religion, social support, self-‐‑reported physical health, baseline personality factors, and rural residence.

(2) The risk factors associated with mortality varied with the type of disaster. Heat wave deaths were associated with increased age, urban location because of the urban island heat effect and increased pollution, and an uncoordinated response. Flooding deaths were associated with living close to rivers, residential areas located beside agricultural or industrial areas, low-‐‑income countries because more people live in flood plains and coastal areas and there is limited public health infrastructure, male gender, and being elderly. Feco-‐‑oral diseases during flooding were associated with living in a low income country and in those residing in a flooded home or yard. Mental health consequences during flooding -‐‑ anxiety and depression were most common in high and middle income countries, among individuals aged 35-‐‑75 years old, among those with higher levels of pre-‐‑flood depressive symptoms, and those in the lower socioeconomic groups (Haines et al, 2006)

(3) On the other hand, infectious diseases were most common in the following situations: (a) disruption of public health services and health-‐‑care infrastructure, (b) damage to water and sanitation networks, (c) changes in population density (in crowded shelters), (d) population displacement and migration, (d) increased environmental exposure due to damage to dwellings and (e) ecologic changes. Factors unique to developing nations that are more likely to favor disease emergence were: (a) high endemic rates of disease, (b) low immunization rates, (c) poor access to clean water, (d) poor sanitation, (e) prolonged crowding in shelters, and (f) inadequate nutrition (Ahern et al, 2006).

(4) Finally, adverse psychological outcomes was associated with: (a) severity of individual exposure, (b) female gender, (c) age (children, old adults), (d) lower socioeconomic status, (e) pre-‐‑disaster functioning (Schulz et al, 2004)

2. Under the epidemiologic framework, after describing the magnitude and distribution of the health outcome, the factors that are associated with increasing or decreasing the risk of occurrence of the health outcomes are analyzed. Risk factors may be divided into harmful factors (i.e., risks) or protective factors (i.e., adaptive capacity). a) Harmful factors may be classified as physical and non-‐‑physical.

(1) Physical factors (i.e., hazards) may be further sub-‐‑classified as geophysical, hydro-‐‑meterological, and climatological. (a) Geophysical hazards include earthquakes, volcano eruptions, and mass

movements (dry). In one study, the risk factors for mortality from a landslide are: female gender, younger age (5-‐‑14 years old), knowledge of landslides occurring elsewhere and of natural warning signs (Sanchez et al, 2009).

(b) Hydro-‐‑meteorological refer to floods, mass movement (wet), and storms (i.e., tropical cyclones, extra-‐‑tropical cyclones, and local storms). In one study, Knowlton predicted that considering an increase in population growth and




anthropogenic O3 precursor emissions, much greater changes in summer mortality are projected: regional summer O3 related mortality would increase by a median of 59.9% in the 2050s compared with the 1990s. These larger impacts are dominated by the growth in population at risk (Knowlton 2006). The health outcomes due to floods may be classified as short-‐‑term or long-‐‑term (Alderman et al, 2012). (i) Short-‐‑term health outcomes include:

(a) Mortality due to drowning and acute trauma (direct exposure) (b) Non-‐‑fatal injuries and exacerbation of chronic illnesses (direct

exposure) – nonfatal injuries: cuts, falls, struck by falling debris or objects moving quickly in flood water; being bitten or stung

(c) Toxic exposure (indirect exposure) (d) Communicable diseases (indirect exposure) (e) Water borne diseases (indirect exposure) – GI diseases: diarrheal

diseases, cholera, Norvo-‐‑virus-‐‑based gastroenteritis, Hepatitis A and E, respiratory and skin infections: upper respiratory infections, skin rashes, earaches, inflammatory dermatoses, infectious skin conditions, infections caused by moulds, leptospirosis

(f) Vector-‐‑borne diseases (indirect exposure): dengue, malaria, dengue hemorrhagic fever, yellow

(ii) Long-‐‑term health outcomes include: (a) Non-‐‑communicable diseases: cardio-‐‑vascular disease, cancer,

chronic lung disease, diabetes (b) Psychosocial health: mental health disorders such as post-‐‑

traumatic stress disorder (PTSD), depression, anxiety; psychosocial symptoms such as earache, headache and bodily pain.

(c) Malnutrition (d) Birth outcomes – by affecting physical and mental health of

pregnant moms and their ability to access health services, floods may impact on the health of newborns. There are also increased levels or prenatal stress and increased maternal risk. Other associated outcomes include: behavioral problems, psychiatric conditions, low and preterm births, and autism spectrum disorders.

(c) Many climatological and health associations have been hypothesized. (i) Common risk factors identified include: temperature change (i.e., heat wave,

cold wave, extreme winter condition), precipitation change (i.e., drought), sea level rise, and extreme events (i.e., wildfires, forest fire, land fire). Heat waves were associated with increased cardiovascular, cerebrovascular and




respiratory deaths, especially when humidity is high, e.g., Western Europe in 2003. Flash floods and slow-‐‑rise river flooding increased morbidity and mortality due to: drowning, spread of chemicals from storage or contained in particular areas (e.g., heavy metal soil contamination after flooding of Meuse river during 1993-‐‑1994), diarrheal and respiratory diseases (especially in areas of crowding), and anxiety and depression. Droughts affect nutrition, infectious disease spread, forest fires, and air pollution (Haines et al 2006).

(ii) Floods were associated with the following outcomes: (1) increased mortality (the number of deaths from acute events such as drowning and trauma are dependent on speed of onset, depth and extent of food) and injuries, (2) an increase in feco-‐‑oral disease (increased cases of cholera, cryptosporidiosis, nonspecific diarrhea, poliomyelitis, rotavirus, and typhoid and paratyphoid), (3) increased incidence of vector-‐‑borne diseases (e.g., malaria, filariasis, West Nile virus), and (4) mental health problems: anxiety and depression, behavioral changes and bedwetting among children 2-‐‑9 years old, PTSD, depression and dissatisfaction with life among 11-‐‑20 year olds, and suicide (limited evidence). However, the evidence for diarrheal deaths is inconclusive (Ahern et al, 2005).

(iii) Dengue fever cases increases with: rise in temperature, rise in humidity, higher rainfall, and decreased sunshine. Mosquito density rises with temperature rise and increased rainfall (Pham et al, 2011)

(iv) The impacts of climate change on non-‐‑communicable diseases (NCDs) have also been reviewed (Frie at al, 2010). These NCDs include: cardiovascular disease, respiratory disease, cancer, mental health, and injuries.

(a) Cardiovascular diseases increase through two exposure pathways: (i) Direct: air pollution and extreme temperatures, particularly heat events and increases in temperatures and (ii) Indirect: changes to dietary options (because of changes in living, hunting and eating patterns and reduced physical mobility)

(b) Respiratory diseases occur due to the following pathways: (i) Climate change compromises outdoor air quality by increasing production of tropospheric ozone resulting in increased respiratory tract irritation, chronic pulmonary disease hospitalizations, and lung disease mortality, (ii) Fine particle air pollution increases the risk of acute respiratory infections, (iii) Extraordinary spring pollen counts and earlier arrival of spring results in extended spring allergy and asthma seasons, and (iv) More intense bushfires increase the risk of respiratory illness among susceptible groups (i.e., asthmatics, children, and the elderly)




(c) Cancers may be caused through the following pathways: (i) Alteration of ambient ultraviolet radiation (UVR) and its spectral composition plus the alteration in sun exposure behavior, (ii) Increased contamination of some foods and greater human exposure from pesticides (secondary to changes in pest biodiversity and ecosystems), and (iii) Aflatoxin contamination from warming of climate

(d) Mental health is affected through (i) increased stress and anxiety from extreme temperature and weather events, increased competition for scarce resources, social inequities, and perception and fear of climate change and (ii) Effects of disasters and weather events on jobs, lives, etc.

(e) Injuries result from (i) Direct injury from weather events and (ii) temperature extremes affect physiological functioning and accident-‐‑proneness

(v) Infectious diseases may also increase in incidence (Barrett et al, 1998). Warmer climates led to increased coastal algal blooms and thus increased proliferation of V. cholerae. Temperature and humidity changes increase malarial vector reproduction.

(2) Non-‐‑physical (i.e., vulnerabilities) may be classified as biological, social, and economic. (a) Examples of biological risk factors include epidemics (i.e., viral, bacterial,

parasitic, fungal, and prion), insect infestations, and animal stampedes. Many studies have described the effect of these factors on climate change, extreme events, and health. Hanta virus re-‐‑emerged due to six years of drought followed by heavy rains. An increase in pine nuts and grasshoppers led to more deer mice. Mice were forced above ground by rains (Epstein, 1995). The effect of climate change on public health is mediated through: geographical variation, technological advances and socio economic status that decrease infectious disease cases, and population size and age profile (Haines et al, 2006). Climate change affects malaria through the following factors: (a) Basic reproduction rate (Ro): average number of secondary infections produced when a single infected individual is introduced into a potential host population in which each member is susceptible and (b) Transmission potential (TP): reciprocal of the critical mosquito density or vector density threshold. Main components of TP: human biting rate: daily biting rate of a female mosquito, human susceptibility: efficiency with which an infective mosquito infects a human, mosquito susceptibility: chance that an uninfected mosquito acquires infection from biting an infectious person, daily survival probability of the mosquito, and incubation




period for the parasite inside the mosquito. Factors i-‐‑iv are dependent on vector species, while factors i, iv, and v are temperature-‐‑dependent.

(b) Social factors also mediate the health and climate change-‐‑extreme event relationship. Tapsell has developed a social vulnerability flood index (SVFI) (Tapsell, 2002) which consists of social factors – gender, young children, ethnicity – and inancial deprivation – elderly, lone parents, pre-‐‑existing health problems, Townsend index. Risk factors for increased cardiovascular mortality during extreme temperatures include: rural areas, less availability of medical resources, older people, people with disabilities, aborigines, cold temperatures (Pei-‐‑Chih Wu, 2010). The qualitative effects of flooding have been reported as: physical and mental health effects, security, community cohesion, attitudes to authorities, vulnerable groups, changes in behavior (Tapsell, 2002). Psychological distress explains some of the excess physical illness after floods (Reacher et al). More media attention and more disaster relief resources are routed to victims of lightning, storms, and floods even though they cause less deaths than extremes of heat and cold (Thacker et al, 2008).

(c) The economic risk factors associated with decreased incidence of malaria were increased GDP per capita, increase per capita expenditures on healthcare, lower Gini inequality index, lower hospital bed per 1000 people (Egbendewa-‐‑Mondzozo, 2001). In a study by Tseng, willingness to pay (amount allocated to reduce the risk for dengue fever) was used as an economic measure. This was correlated with the degree of education and income. With higher probability in acquiring dengue fever, respondents were willing to pay more to avoid it or to reduce the risk. (Tseng et al, 2009)

b) Protective factors (i.e., adaptive capacity), on the other hand, reduce the risk of morbidity and mortality due to climate change and extreme events. (1) Adaptive capacity may be increased by addressing the existing gaps in healthcare.

(a) In terms of early detection – Early detection systems that track issues such as food and water-‐‑borne outbreaks due to bacteria, virus and parasites. Establish baseline prevalence potential of common pathogens (specified were: Giardia lablia, Cryptosporidium sp., Virbio parahemolyticus, west nile virus, pumula virus, Borrelia spp., Brucella spp., Echinococcus spp., Toxoplasma spp. and other protozoa) to guide early detection and intervention strategies.

(b) In terms of complex interactions -‐‑ implications of changing climatic patterns in relation to health on economics, human health, and wellbeing. Climatic effects on health in socio-‐‑economically disadvantaged nations

(c) In terms of the economics of climate induced diseases -‐‑ Lack of data on the economics relating to adaptations to climate change and overall health expenditure. Risk management and economic assessment of climate change and its impact on health, based on empirical evidence such as: socioeconomic status, exposure-‐‑response function, effects and policy measures.




(d) In terms of raising awareness – Health impacts of climate change on children. (e) Pertinent questions – there is a need to assess the consequences of climate on

own populations. Given the severe weather events, the questions that should be addressed are: What are the economic consequences of these natural disasters? What mitigating measures have been taken within the country and the region? What improvements are there in early warning systems? How effective are the early warning systems that we do have? What measures have been taken to protect disadvantaged populations?

(2) Research gaps also need to be bridged in order to improve a country’s adaptive capacity to climate change and extreme events (WHO, 2009). (a) Assessing the risks

(i) Improved evaluation of current climate-‐‑related health risks, rather than a primary focus on risks over very long timeframes

(ii) Identification of vulnerable populations and life stages (iii) Quantification of the fraction of morbidity and mortality attributable to

climate hazards, and to climate change (iv) Better assessment of neglected climate-‐‑health linkages

(b) Identifying the most effective interventions (i) Systematic reviews of the evidence base for interventions (ii) Methodological research to improve analytical tools for cost-‐‑effectiveness

analysis (c) Guiding health-‐‑promoting mitigation and adaption decisions in other sectors

(i) Improve methods for assessment of the health implications of decisions in other sectors

(ii) Health implications of climate change mitigation: energy and transport sectors

(iii) Health implications of climate change adaptation: water, food, and agriculture sectors

(iv) Improved integration of climate change mitigation, adaptation and health through “settings-‐‑based” research

(d) Improving decision-‐‑support (i) Research to improve vulnerability and adaptation assessments (ii) Improvement of operational predictions (iii) Improved understanding of decision-‐‑making processes

(e) Estimating the costs of protecting health from climate change (i) Definition of harmonized methods to estimate costs and benefits (ii) Assessment of the health costs of inaction and the costs of adaptation (iii) Improved economic assessment of the health co-‐‑benefits of climate change

mitigation




(3) There is also a need to close the gap between scientific and common knowledge (Akerlof, 2010). In a survey of three diverse countries, the following findings were reported: (a) Does the public believe that climate change poses human health risks, and if so, are they

seen as current or future risks? Majority of people in all three nations said that it poses significant risks. About one-‐‑third of Americans, ½ of Canadians, and ⅔ of Maltese said that people are already being harmed. Majority in all three countries said that in the future, climate change will likely cause poverty/ reduced standards of living, water shortages, and disease (US and Malta), and more severe/ frequent hurricanes and heat waves (US, Canada)

(b) Whose health does the public think will be harmed? About a third or more of people in the US and Canada saw themselves (US, 32%; Canada, 67%), their family (US, 35%; Canada, 46%), and people in the community (US 39%; Canada 76%) as being vulnerable to at least moderate harm from climate change. About 31% of Maltese said they were most concerned about the risk to themselves and their families. Canadians said that the elderly (45%) and children (33%) are at heightened risk of harm. Americans were more likely to see people in developing countries as being at risk than people in their own nation

(c) In what specific ways does the public believe climate change will harm human health? Canadians and Maltese said that climate change can cause: respiratory problems (78-‐‑91%), heat related problems (75-‐‑84%), cancer (61-‐‑90%), infectious diseases (49-‐‑62%). Canadians also mentioned sunburn (79%) and injuries from extreme weather events (73%). Maltese mentioned allergies (84%). No questions addressed this topic in the US survey

(d) Climate change appears to lack salience as a health issue in all three countries: relatively few people answered open-‐‑ended questions in a manner that indicated clear top-‐‑of-‐‑mind associations between climate change and human health risks.

(e) Recommendations: public health communication initiatives that increase salience of the human health consequences associated with climate change; suggested strategy: simple clear messages, repeated often, by a variety of trusted public health voices within a wider policy environment that support greenhouse gas reduction behavior and healthy lifestyles.

(4) In the domain of climate change and malaria, the research gaps are: (a) development of quantitative indicators of vulnerability to malaria and other infectious disease outcomes, (b) improvement of the parameterization of rainfall in malaria models, and (c) development of region-‐‑ or national-‐‑specific models to identify more accurately populations at risk based on regional environmental and socio-‐‑economic changes (Van Lieshout et al, 2004).

(5) In addition, the knowledge of local and indigenous peoples – often referred to as local, indigenous or traditional knowledge – is increasingly recognized as an important source of climate knowledge and adaptation strategies. Indigenous




knowledge focuses on elements of significance for local livelihoods, security and well being, and as a result is essential for climate change adaptation. (Nakashima et al.)

(6) However, there are constraints to adaptive capacity. Adaptive capacity varies between Asian countries depending on social structure, culture, economic capacity, and level of environmental degradation. Areas of concern include water and agriculture sectors, water resources, food security, biodiversity conservation and natural resource management, coastal zone management, and infrastructure (African Development Bank).

(7) Climate change strategies/interventions in different sectors that may affect NCD (Friel et al, 2010): (a) Energy sector: need to change power generation and energy use

(i) Energy generation -‐‑ reduce fuel-‐‑based sources of power generation (e.g., coal, oil, gas)

(ii) Domestic energy use -‐‑ reduce indoor smoke from inefficient burning of biomass fuels by introducing low-‐‑emission household cook-‐‑stoves; improve energy efficiency within buildings and homes (e.g., improving insulation, retrofitting existing homes, and constructing energy-‐‑efficient buildings)

(b) Urban planning: need to ensure that current and future urban development is done in such a way that prevents NCDs, reduces poverty, and builds societies that live within environmental limits

(c) Urban design and the built environment (i) Compact, walkable urban environments can promote physical activity and

reduce BMI, car travel, and air pollutions (ii) Position vital community resources near residential and commercial areas;

workplace proximity encourages walking (iii) Incorporate green spaces within communities, focused along transportation

routes and floodplains: promotes CO2 bio-‐‑sequestration; positively impacts general health, stress, BP and mental health; encourages physical activity and social connectedness, improving air quality, and reducing urban heat island effects; reduces health inequities.

(d) Transport systems (i) Neighborhoods that are easily accessed and navigated by active transport

(e.g., walking, cycling), combined with high-‐‑quality transport (e.g., renewable energy-‐‑powered) for longer journeys are vital for climate change mitigation and, in addition, increases physical activity, reduces obesity and CVD, traffic injuries, and levels of respiratory illness.

(e) Food and Agriculture Sector (i) Changes in animal source food production and consumption

(a) Production of foods from animal sources is the major contributor to green house gas emissions from the agricultural sector.




(b) A 30% reduction in consumption and production of animal source foods can result in a reduced healthy life years lost from ischemic heart disease by an estimated 15%, will allow selected national emission targets to be met, and may also reduce cancer and obesity incidence

(ii) Food system diversification -‐‑ New food production techniques and improved food storage facilities are needed.

(8) Media has both positive and negative effects on the aftermath of disasters. Negative effects include: (a) degree of disaster-‐‑related TV viewing was positively associated with PTSD and depression, (b) extensive media coverage of disasters with related characteristic symptoms play a role in enhancing the spread of mass psychogenic or socio-‐‑genic illness, (c) media hypes can increase the number of persons who attribute their health problems to a disaster, and (d) news waves fuel fear and anxiety among people involved in the aftermath of disasters. The positive effects include: (a) media can inform, educate, or communicate with people (e.g. following terrorist attacks, media can be used to broadcast accurate info to an anxious population.) (Vasterman et al, 2004)

(9) West et al argues that the problems of climate change and air pollution have the same causes, thus strategies that address air pollution also help with climate change. Steps that can taken to tackle both include: (a) create an existing database for all information on the costs and air pollutants and greenhouse gas emission reductions associated with different control strategies, (b) implement decision-‐‑support tools that can be used to analyze cost-‐‑effective strategies for targeting multiple pollutants simultaneously, (c) create user-‐‑friendly tools and train government workers to use these programs. The benefits of simultaneously planning urban air pollutant and greenhouse gas mitigation are small since additional carbon dioxide constraints are often met by investing in measures that target carbon dioxide but with modest changes in emissions of local pollutants (West at al, 2003).

(10) Prevention and control of public health consequences of tropical cyclones should include the following actions: forecasting, warning (with accurate risk perception), evacuation -‐‑ strategies for sequenced evacuation and assessment of personal and property risk, safe shelters, land-‐‑use planning, building design and construction, preparedness behaviors, accurate risk perception, and identification of vulnerable populations (Schulz et al, 2005)

(11) A public health early warning system should be added to existing surveillance mechanisms and effective response capabilities (Ebi et al, 2004). Its aim would be to reduce current vulnerability and increase resilience to future extreme events. However, a basic requirement for such a system would be an existing adequate public health and social infrastructure, including political will, to undertake design and implementation. The components of such a system would include: meteorologic identification and forecasting of extreme events, prediction of possible health




outcomes, effective and timely response plan, and ongoing evaluation of system and its components




Application to Metro Manila

This section applies the framework and approach described above in the context of Metro Manila. Factors, harmful and protective, that drive the magnitude and distribution of the risk are discussed. The spatial and temporal changes in these factors are also noted. Interventions are mentioned. Finally, the current status of Metro Manila in along the continuum of desired CCA-‐‑DRM outcomes are discussed.

In recent years, Metro Manila has grown accustomed to flash floods that slow down inadequate public transportation, disrupt restricted school activities, cut on marginal business profits, burden tight government expenditure, and delay critical health services. The city of Manila and its neighbouring coastal cities, as well as communities along the banks of the Marikina-‐‑Pasig River system, are primarily threatened of inundation with prolonged rainy days. Major thoroughfares in the vicinities of Pureza, G. Araneta Ave., San Marcelino, Taft Ave., portions along Quezon Ave to Espana, Quirino-‐‑Sauyo, Aurora and P. Tuazon in Cubao, E. Rodriguez Ave., NLEX Balintawak, South Super Hi-‐‑Way-‐‑Buendia Ave., and Commonwealth Ave. -‐‑Philcoa among others are usually reported to be catch basins within minutes of a heavy downpour. Efforts at de-‐‑clogging canals and sewers and cleaning up the major tributaries to the Pasig River in conjunction with an intensifying waste management campaign of local governments have not significantly reduced the extent and duration of flooding, it seems. Further, the

intermittent pounding of super typhoons as Ondoy in September 2009 and super monsoons as Habagat in August 2012, have demonstrated the limited capacity of the metropolis to address the risks associated with climate-‐‑related disasters.

With better appreciation of the complex nonlinear mechanisms of climate change, the government and the business sector begin to put in place structures and processes that will allow continuity of operations once disasters strike. Sustainable development initiatives have shifted the discussions from an initially heavy prevention strategy to more mitigation and adaptation strategies toward the effects of increasingly harsh climate-‐‑related challenges. Furthermore, reviews of projects, programs, and policies with better success in dealing with the impact of climate change highlight the importance of adaptation-‐‑specific and result-‐‑based management frameworks (UNFCCC, April 2010). Interestingly, scientists and policy makers have drawn attention to urban centres as scales of mitigation responses, as they recognize climate change impacts that are particular to or emphasized in cities (Hunt and Watkiss, December 2010). This direction becomes relevant as more than half of the world population live in urban dwellings.

Cities at Risk Metro Manila or the National Capital Region (NCR), home to about 12M people and with a population density of close to 20K individuals per square kilometer, illustrates a case of cities at risk of the impact of climate-‐‑related disasters.




Geographically, the region faces threats from more devastating flooding from all directions. In particular, stronger typhoons and extreme weather conditions are bound to magnify the effects of rising sea levels on the western coastal communities along the bay of Manila, which includes Malabon and Navotas in the north and Paranaque, Pasay and Las Piñas in the south. On the east side, where south-‐‑west monsoon rains generally pass, the denuded mountains of Montalban and portions of the Sierra Madre dump volumes of silt along with runoff water into the Marikina river that eventually overflow into the Pasig-‐‑Marikina Basin, a large area that covers eastern Metro Manila. This flooding disrupts private, social, and commercial activities along the riverbanks and, in extreme cases, extends into the inner low-‐‑lying central districts. South of the metropolis is Laguna Lake that drains to the same Pasig-‐‑Marikina river system. With poorly regulated fishing practices and horrendous drainage systems of industries around the lake, it has deteriorated and diminished its containment capacity and contributes its polluted water to the floods that affect the southern cities of Pateros, Taguig, Muntinlupa, and Pasig, as well as the suburban Rizal and Laguna. The industrial zones of Quezon City, Caloocan City, and Valenzuela City on the northern border experience their own version of inundation resulting from clogged canals and sewerage systems that inefficiently drain precipitation even on average rainy seasons. With urbanization, much of the metropolis surface area is covered with concrete and asphalt that impede ground absorption of rainwater during continuous

rain days, especially with bursts of heavy downpour.

Typhoons and floods pose as both immediate and indirect health hazards. As flood water carries toxic substances from improperly collected and disposed wastes, including harmful biologic agents, Metro Manila residents will be exposed to mild irritants to potentially fatal infections such as leptospirosis. Statistics show that more than 2000 cases of leptospirosis and close to 200 deaths from its complications were recorded from 15 hospitals during and a few weeks after typhoon Ondoy (Virola, November 2009). Though unclear, variations in water temperature, humidity, and frequency of rain affect the proliferation and behaviour of pathogens and their vectors. Strains of E. coli, salmonella, cholera, rotavirus, and several parasitic worms and microorganisms contaminate household water supply during and after the floods. Worse, these food borne and waterborne diseases are inadvertently brought to the evacuation centers as part of relief operations. With crowds building up in the holding area for the displaced population, respiratory infections spread faster and affect more people in a given area.

More immediate risks are physical injuries from debris, vehicular accidents, accidental falls, and drowning from rising and sometimes rampaging floods.

A Vulnerable Population Climate-‐‑related risks are not limited to typhoons and floods. The air quality of NCR, though improving, remains far from




acceptable standards (DENR-‐‑EMB 2008). Suspended particles in the atmosphere brought about by natural processes and human activities have been associated with respiratory diseases and their complications. Emissions from vehicles and industrial processes are major sources of these particles. Sulphur and nitric oxides from combustion of fossil fuels are released in the atmosphere and react with water vapor to produce acids that precipitates with rain, exposing Metro Manila residents and commuters to irritating substances that may lead to intermittent and chronic respiratory pathologies. Less direct health impact of contaminated precipitations is their effect on the ecosystem since the resulting acidity and salinity variations can disrupt the reproduction and growth of other organisms, more importantly, the food supply chain. While NCR imports most of its food resources from the provinces, it ultimately shares its atmosphere with the provincial sources as wind and typhoons disperse polluted air to contiguous areas.

As Metro Manila attracts more migrants from the less developed regions in the country, its carrying capacity continues to be challenged. A dense population demands more housing and transportation facilities. The concrete paving of roads and the proliferation of residential and commercial establishments has significantly changed the landscape. Concrete and asphalt covers, not only do they decrease the capacity of the ground to absorb rainwater, increase surface temperature by absorbing more heat that magnify risk of heat fatigue and heat stroke, especially

during the very hot summer. A closely associated challenge is the increased demand for energy with a larger population and an even higher consumption of energy for cooling systems, again, during the dry spells. Stable power sources are also critical in maintaining services in hospital facilities and relief centers during times of disasters. Access to clean and potable water is also a challenge in a fast growing metropolis. As mentioned, typhoons and floods can contaminate sources and limit its availability and use during prolonged rains storms. Moreover, a highly populated metropolis that utilizes more resources is bound to produce more wastes. For decades now, the MMDA has been finding ways to minimize and manage tons of solid municipal waste that, not only make their way to sewerage and waterways, are breeding grounds for pests and disease vectors.

The sheer volume of people in NCR poses health and other problems, beyond space and resource requirements. Increasing number of moving particles in a fixed and defined space is bound to have more collisions. Crowding favors faster transmission of diseases such as tuberculosis and respiratory infections. During typhoons, when mobility is restricted, households and evacuation centers provide increased opportunities for human contact, hence, the higher probability to spread some diseases faster. More people using more vehicles that run on inadequate roads are at risk of accidents and injuries whether in the regular rush of commuting to work or in the uncoordinated trips to safety during disasters and




calamities. With these specific challenges, Metro Manila will have to assess its disaster preparedness and contingency plan.

Adaptability and Sustainability of Health Systems A robust health system must be in place to address the health impact of climate change. Efficient and effective service delivery must be in place and remain uninterrupted when typhoons and related disasters strike. Basic services such as outpatient consultations and wellness programs must remain available to the general population. However, quality care should be ensured to the displaced subpopulation and that the service they are provided is safe, adequate, timely, and equally accessible. Together with services, health and medical supplies should be readily available and accessible to the population. It is important that a comprehensive information system can identify and monitor the inventory of supplies and services and that this system is accessible at all times to field personnel and decision-‐‑makers. Within the information system is a surveillance system that tracks emerging disease patterns during and after the disaster period. Evacuation center personnel can respond more timely and appropriately to the needs of the evacuees when they have access to health information upon registry into these holding areas. It would be ideal if high-‐‑risk populations are pre-‐‑instructed and assigned to specific centers in the event of disasters. Health and medical teams will then be prepared to refill prescriptions of those with pre-‐‑existing conditions as well as disaster-‐‑specific emergency needs. Also, specific

skills and expertise can be made available to pre-‐‑profiled response centers.

In NCR, the high-‐‑risk population is also the most financially vulnerable. Access to health care before, during, and after displacement is a perennial challenge to the marginalized sectors of society who are also in climate hazard zones. It is even more pressing when typhoons or earthquakes destroy their limited properties and resources. Metro Manila might find it practical to invest in traditional and micro-‐‑finance system that will provide the capacity for these sectors of society to mitigate their vulnerable conditions. While government has the legal responsibility to address these risks, it is the moral responsibility of every member of society to care for each other.

Beyond the Borders of Metro Manila and Climate Change The leadership and the governance of NCR and the rest of the Philippines face the daunting task to manage the risks of climate-‐‑related disasters and of the broader challenge of climate change itself. The range of work can be overwhelming and the demanded resource mobilization appears very discouraging. It is important, then, that the leadership acts decisively and deliberately. Evidence-‐‑based decision-‐‑making becomes a rational and practical approach to set priorities, especially with limited budget, manpower, and time. Lessons learned from experiences around the world show that there are better outcomes with preparedness plan tailored to specific localities. While general measures of sustainable strategies can




improve outcomes in the long term, specific mitigation responses scaled to target populations and areas translate to more favorable outcomes for the people.

Metro Manila and the rest of the country take on the responsibility of ensuring health systems that are efficient and effective. At all times, the system should be able to provide services of the best quality equally accessible to all sectors of society. In time of disasters and emergencies, special services should be available without interrupting basic health care. In the same manner, products and supplies like medicines and hospital equipment should have secure supply chains from production to distribution to utilization. Patent roads and uninterrupted communication systems must also be reliable. Information as flood maps, location of relief distribution centers, medical personnel distribution, incidence and prevalence of diseases, population demographics, and volunteer groups among other data bases remain valuable for decision-‐‑makers and planners, even more so during extraordinary times as super typhoons or super monsoons. Health personnel are often stressed during time of calamities. They are as much put to dangers when they are disposing of their duties. The system must ensure their safety and well being as they are given the responsibility to take care of others. Providing them with appropriate skills and expertise improves outcomes of their response. But they also need to be cared for and to be satisfied in their jobs. Health expenditures can double or triple in times of calamities. Both service providers and clients must have access to financial resources, particularly in

emergency situation when usual finance systems are operating with limited capacity. With a large number of the population live on daily subsistence, emergency funds must be accessible to them through just mechanisms. All these structures and processes are achievable with good governance and pro-‐‑active leadership. Prioritization of programs and spending that will not prejudice sectors will result from a system that nourishes trust and cooperation among the different stakeholders. A system that constantly strives to learn and respond better than from the last disaster is bound also to provide better opportunities during less stressful times.

Finally, to address the health impacts of climate change is beyond preparing for disasters. Health is a continuum. One can be well today and be ill the next day. In ordinary times or during disasters, health is a fundamental right and basic service. Health systems, then, must allow individuals and populations to recover and re-‐‑integrate with society so that they can participate in the primary role of the system, which is to keep everyone healthy.


An epidemiological framework can be used to describe the magnitude and distribution of the health outcomes arising from climate change and extreme events. Magnitude is equivalent to the exposure of populations to these events. The framework also incorporates the determination of risk factors for disease and death due to climate




change and extreme events. These factors may be harmful (hazards and vulnerabilities) or protective (adaptive capacity). Metro Manila is used as an example of the application of this framework.

As an initial exercise in estimating the magnitude of climate change and extreme

event-‐‑related disease, a burden of disease study should be undertaken for climate change and for the most common extreme events in Metro Manila. Aside from identifying the number of disability-‐‑adjusted life years (DALYs) that is lost, the DALYs that can be gained by effective interventions can also be estimated along with the costs for these interventions.




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Tseng, Wei-‐‑Chun, Chi-‐‑Chung Chen, Ching-‐‑Cheng Chang and Yu-‐‑Hsien Chu. “Estimating the economic impacts of climate change on infectious diseases.” Climatic Change 92 (2009): 123-‐‑140.

United Nations. “Review of the Yokohama Strategy and Plan of Action for a Safer World.” Paper presented at the World Conference on Disaster Reduction, Kobe, Hyogo, Japan, January 18-‐‑22, 2005.




United Nations. “Yokohama Strategy and Plan of Action for a Safer World: Guidelines for Natural Disaster Prevention, Preparedness and Mitigation.” Paper presented at the World Conference on Natural Disaster Reduction, Yokohama, Japan, May 23-‐‑27, 1994.

van der Berg, Bellis, Linda Grievink, Joris Yzermans and Erik Lebret. “Medically Unexplained Physical Symptoms in the Aftermath of Disasters.” Epidemiologic Reviews 27 (2005): 92-‐‑106.

van Lieshout, M, R S Kovats, M T J Livermore and P Martens. “Climate change and malaria: analysis of the SRES climate and socio-‐‑economic scenarios.” Global Environmental Change 14 (2004): 87-‐‑99.

Vasterman, Peter, C Joris Yzermans and Anja J E Dirkzwager. “The role of the Media and Media Hypes in the Aftermath of Disasters.” Epidemiologic Reviews 27 (2005): 107-‐‑114

West, J Jason, Patricia Osnaya, Israel Laguna, Julia Martinez and Adrian Fernandez.. “Co-‐‑control of Urban Air Pollutants and Greenhouse Gases in Mexico City.” Instituto Nacional de Ecología, México, 2003.

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Yusuf, Shahid, Kaoru Nabeshima and Wei Ha. “Income and Health in Cities: the Messages from Stylized Facts.” Journal of Urban Health: Bulletin of the New York Academy of Medicine 84 (2007): i35-‐‑i41.

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Chapter 3: Valuing economic damages due to natural calamities in the Philippines




Introduction

The purpose of the economic study under the Forensic Investigation of Disasters Project (FORIN CCA/DRM) is to examine the interrelationship between disaster risk management (DRM) and climate change adaptation (CCA) in understanding the broad effects of the severe changes of the natural environment particularly on the economic well-‐‑being of human beings and their communities (CCA-‐‑DRM Addendum to the FORIN Project, 2012). It is being undertaken with the view that DRM and CCA programs are separate research areas. However, this view has become more untenable as the socio-‐‑economic impacts of disasters due to natural calamities have increased and intensified as a result of more frequent and severe climactic conditions that have occurred in the past few decades.

The natural disasters that the study focuses on are typhoons. The study examines the impacts of changes of the physical environment on economy and the different socio-‐‑economic sectors (including households and industries) in the Philippines. Its key contribution is developing a framework of valuing the damages due to typhoons from the perspective of the macro-‐‑economy, the households, and the various industries.

The second part of the paper examines the relationship between disasters and the macroeconomic variables, including output and its components. It conducts a cursory examination of how typhoons had affected selected economic variables. The analysis relies on data gathered by authorities tasked

to minimize the risk of damages due to natural calamities such as typhoons and the adjustment costs of affected individuals and communities. As such, the analysis can only go as far as the availability and quality of the asembled data permits, which motivates the need for developing a methodology for this type of analysis and for identifying the data requirement to implement the analysis using it. This part examines the economic situation in Metro Manila and the role of typhoons in affecting regional output and welfare.

In developing the methodology, Section 3 of this paper reviews the literature on the relationship between natural disasters, climate change and the economy for its modeling approach to reflect the research that has been done not only on disasters and both their direct and indirect impacts, but also, on their implications on specific macroeconomic variables. The discussion moves from the impacts on consumption and investment, then on specific national economy indicators including prices, trade and foreign exchange flows and government taxation and spending. Then, it examines the eventual impact on households and on their asset base. The impacts of disasters on urban areas are also examined. And lastly, the interaction of climate change and natural disasters is explored. The review of literature is summarized by causal loop model.




The Philippine economic performance and natural disasters caused by severe weather disturbances

Overview of Philippine economic performance The Philippines economy, as measured by its gross domestic product (GDP), grew at the average annual rate of 4.7% from 1999 to 2011. The services sector accounted for 62.4 % or the bulk of such economic growth, while the relative contributions of industry and agriculture had been 29.7%, and 7.9% respectively. Since 1999, the share of the service sector to GDP has been high and rising from 52 % in 1999 to 56 % in 2011. Agriculture on the other hand contributes the lowest share at 14% in 1999, which fell to 11% in 2011. Figure IV.1 shows the same information at constant prices.

The more important concern is the relatively low capacity of both the industrial and manufacturing sectors to create jobs and business opportunities for rural residents migrating into urban centers. In the course of economic development, the agriculture’s contribution to the GDP is expected to fall, and the sector releases labor resources for the other parts of the economy. While both are considered most resilient to weather disturbance hazards, these sectors have also gone down from 34

% in 1999 to 32 % in 2011, in favor of services.2

Corresponding to its contribution to the GDP, the services sector accounts for the largest share of employment. The figure ranges from 51 to 52 %. However, the informal sector employment is highest in the sector found in transportation, retail and personal services and even in related rural agriculture activities. The 2008 Informal Sector Survey of the National Statistics Office (NSO) has estimated a total of 10.5 million informal sector operators in the country with almost 90% as self-‐‑employed workers. On the other hand, the share of employment in manufacturing has been stagnant. Instead most of the workers released by agriculture got found jobs in the services sector. However, the quality of jobs provided is of low value. Additionally, the fastest-‐‑growing type of jobs is not permanent wage employment but casual wage jobs. There has been weak growth in productivity, wages and earnings for workers in general.

Although agriculture has the lowest share in GDP, it accounts for 34 % of employment, the second highest in the Philippines after services. The data implies that a significant part of the country’s population continues to depend on agriculture activities despite the fact that it has already the lowest to total income or GDP. This has immediate implications to livelihood outcomes of the

2 Source: NSCB Revised/Rebased ANNUAL National Accounts posted 12 April 2012 (Own calculations at constant 2000)




relatively poor sector of the economy. Compared against industry with a low 15 % share in 2010-‐‑11 in labor employment, it has the second highest share of total income/GDP at 32 % in 2010-‐‑11. High productivity, as expected, had characterized the industrial sector, albeit only accounting for 15 % of the labor force but contributing 32 % to total output or income. With the 4.7% annual average growth rate, the Philippine economy has shown solid growth, albeit modest compared to East-‐‑Asian performance over the 2000s. The country, however, is still far from translating this into inclusive growth, one that can benefit the entire population including the poor and marginalized sectors in society. Despite opportunities created by the economic growth in the period, many

people remain poor (20.9 % of families as of FIES 2009 or 3,855,730 families) and unemployed (7.3% unemployment and 18.7% underemployment), and investments in the country are still below the regional standard of its Asian neighbours.

In summary, the Philippines economy has been slow to transform towards achieving sustain growth powered by increasing overall productivity. Historically, the modern industrial and manufacturing sectors serve as the backbone of increasing productivity and sustaining growth. However, the country ironically went through a de-‐‑industrialization process as shown by Figure IV.1 since 1984 and urban expansion and growth relied instead on the expansion of the services sector activities.

Figure IV.1 Shares of Agriculture, Industry and Service Sectors in GDP, 1946-‐‑2010




Manufacturing stagnated and so were the jobs generated by the sector. The deteriorating political uncertainties in the 1980s, coupled with liberalization of the country’s trade regime could have downward pressure, but also made cleansing effects on industries that are protected, inefficient and are not sustainable or not in a position to compete in a more open market mechanisms. Nevertheless, this has accelerated the process to a serviced-‐‑based economy buoyed by the expansion of business process outsourcing (BPOs) sector. As mentioned, the service sector currently contributes more than half of the economy’s output, employment and overall growth.

Natural calamities and the economy How have severe weather disturbances in the Philippines affected overall growth and output of the economy in the same period from 1999 – 2011? From the review of related literature, the answer to the question has proved to be difficult to obtain given that there are no standard systematic or modelling instrument to measure disaster effects to the economy. Official data on economic damages, as shown below, were direct estimates of physical impact. Indirect effects of these climatic disturbances on productivity or macro-‐‑economic variables were done but only in one or two commissioned studies. Indeed, the contribution of this narrative is to provide a synthesis of estimating economic damages of severe weather disturbances that directly affected Metro Manila, while referring as well to the same in a country wide basis.

In practice, economic impacts of disasters in the Philippines have been assessed primarily in terms of physical damage. The source of information is the National Disaster Risk Reduction and Management Council (NDRRMC) or its predecessor before. The agency depends upon other national government agencies in coming up with the data. In agriculture for example, the NDRRMC has counted on the Department of Agriculture to assess the damage of typhoons or other calamities on the standing crops.

There have been few assessments of the longer-‐‑term impacts of natural disasters as had been done in the case of Tropical Storm (TS) Ondoy and Typhoon (TY) Pepeng in 2009. These assessments had been undertaken by a multilateral and inter-‐‑agency team working on a Post-‐‑Disaster Needs Assessment (PDNA).

The team came up with the monetary value of the damages caused by severe weather disturbances. When the indicator is correlated with the GDP growth for the period from 200 to 2010 as shown in Figure IV.2, the result suggests a lack of any systematic relationship between the damage and overall economic growth. This may imply either that these disasters have had little impact on growth rates in the Philippines. It may also suggest that the damage figures alone are a poor indicator of the economic impact of these calamities.

Benson (1997) believed that physical damage maybe a poor indicator for these reasons. Firstly, damage estimates may themselves be inaccurate, most notably




because costs are defined in terms of reported losses only. Secondly, the focus on levels of damage effectively captures only the stock impacts of disasters and ignores the potentially considerable their larger effects on income flows. Disasters could potentially affect longer-‐‑term growth rates as well as a host of other variables such as the budget deficit, the trade balance, level of external debt or the incidence of poverty.

After reviewing the methodologies on disaster impact estimation, Okuyama (2009has observed that estimated disaster impacts demonstrate that there are no particular trends or correlations among damages, losses, and higher order effects between the types of disaster and the intensity of disasters. Economic impacts of disasters depend on the structure of economy that a hazard hits and where and how the economy is damaged. Okuyama further advocated that the first step for promoting disaster related economic analysis is to create the standard methodology for assessing damages and losses, such as the ECLAC methodology, and the standard framework for estimating the higher-‐‑order effects. However, Hallegatte and Przyluski (2010) also concluded that it is impossible to define “the cost” of a disaster, as the relevant cost depends largely on the purpose of the assessment. The best definition and method, according to them, obviously depend on whether the assessment is supposed to inform insurers, prevention measure cost-‐‑benefit analyses, or international aid providers.

Severe weather disturbances and their economic effects in the national capital region The economy of Metro Manila or the national capital region (NCR) has consistently posted the highest GDP share among all regions increasing from 31% in 2000 to 35.7% in 2011. In 2011, Metro Manila has 26 % of the 820,254 total business establishments all over the country. The regions around the national capital region had the next largest numbers of establishments. Region 4A or CALABARZON was second to NCR with 15 %, and Region 3 or Central Luzon following with 10 %. Roughly the same pattern was observed with regards to employment statistics. In 2010-‐‑2011, out of the total 36,036,000 employed persons in the country Metro Manila’s share was 12%, next to that of Region 4A with 12.5 %, and Region 3 coming third again with 10.3 %.

Metro Manila has 2.6 % poverty incidence, the lowest in the country. It is followed by Region 4A with 10.3 %, and Central Luzon with 12 %. The highest poverty incidence is in Caraga with 39.8 %, followed by ARMM with 38.1 %, and Zamboanga Peninsula with 36.6 %. However, Metro Manila has the the highest proportion of households living in informal settlements with 9.88 % based on the data from the NSCB and the Department of Labore and Employment Bureau of Labor Statistics. A relatively far second is Western Visayas with 5.04 % and followed by Zamboanga Peninsula with 4.64 %.

The latest census of population in 2010 by the NSO shows that the Philippines has a




total population of 92,337,852 with 12.8 % or 11,855,975 individuals living in Metro Manila or the National Capital Region. This is followed by the population of Region 4A with 13.7 % or 12,609, 803 and Central Luzon or Region 3 that had 11 % or 10,137,737 persons.

In terms of the average family income at current prices, Metro Manila has an annual average of P356,000 way above the national average of P206,000 based on the 2009 Family Income and Expenditure Survey (FIES) of the NSO. Regions 4a and 3 came after Metro Manila with Php 249,000 and Php 221,000 average family income. The ARMM was reported to have the lowest average family income with Php 113,000.

In the period from 1999 to 2011, there were about a hundred severe weather disturbances that inflicted some economic damage to various regions of the country, including Metro Manila. These include typhoons (TY), tropical storms (TS), and tropical depressions (TD). Out of these, 36 directly affected Metro Manila as shown in Table IV.1. The years 2009 and 2002 posted the highest number of individuals affected considering that the first was caused by Tropical Storm Ondoy that devastated Metro Manila with unprecedented flooding and the latter by Typhoon Milenyo, which directly passed through the metropolitan area. Figure IV.3 graphically shows an increasing occurrence of severe weather situations in the national capital region.

The number of residents in the national capital region who got affected by these weather disturbanes appears to have risen, as shown in Table IV.2. The rising number may indicate that these weather disturbances have apparently become more and more intense. In 2009, Metro Manila nearly made the record of a million of its residents who suffered losses due to TS Ondoy. However, seven years earlier in 2002, TY Milenyo, a super typhoon, affected nearly 400 hundred thousand, indicating a difficulty to establish a pattern. It must be pointed out that these figures differ from those reported in the PDNA report on TS Ondoy and TY Pepeng. That report placed the number of individuals affected by TS Ondoy at 4,901,234 persons or 993,227 families. It is therefore unclear if the NDRRMC is reporting households or individuals.

In Figure IV.4, the average number of residents affected by these weather disturbances is shown. It is difficult to pin down whether or not there has been a rising number of residents affected by typhoons. In 2009, the average number of individuals affected per typhoon was 165, 498. In the two years following that the number fell to only thousands. Besides in 2002, the average number was higher compared to the intervening years until 2009.




Table IV.1 Severe weather disturbances that affected Metro Manila

1999 2000 2001 2002 2003 2004 2006 2007 2009 2010 2011

1 Helming Biring Feria Juan and Kaka

Chedeng Winnie Caloy Egay Feria Basyang Bebeng

2 Luding Edeng

Milenyo Onyok Marce Florita Hanna Isang Juan Chedeng

3 Neneng

Yoyong Glenda

TS Chedeng and Dodong

Kiko

Falcon

4

Milenyo

Maring

Juaning 5

Ondoy

Kabayan

6

Pepeng

Mina Source: NDRRMC

Figure IV.3 Frequency of Severe Weather Disturbances that Hit Metro Manila, 1999-‐‑2011




Table IV.2 Number of individuals in Metro Manila affected by severe weather disturbances

1999 2000 2001 2002 2003 2004 2006 2007 2009 2010 2011

210 3,106 600 6,020 721 11,066 5 136 1,485 877 275

1,730 25,255

394,782 11,144 28,610 1,220 7,317 3,420 2,802 370

46,485 24,040 28,481 5,858

535

121,118

22,071

1,173

960,152

2,725

72,687 Source: NDRRMC

Figure IV.4 Average Number of Residents of Metro Manila Affected by A Severe Weather Disturbance, 1999-‐‑2011




Severe weather disturbances and their economic effects in Philippines However on a countrywide basis, there appears to an increasing trend in the number of residents affected (Figure IV.5). The same pattern may be observed from the total estimated physical damages as

reported by the NDRRMC (Figure IV.6). In Figure IV.7, the same increasing trend is also observed on the second half of the period. The difference is that the biggest spike in the number of houses damaged was on year 2006 because of the combined effects of Typhoons Milenyo and Reming.

Figure IV.5 Number of Affected Individuals, Philippines, 1999-‐‑2011




Figure IV.6 Total Estimated Damage (constant 2000 prices), Philippines, 1999-‐‑2011

Figure IV.7 Number of Houses Affected by Severe Weather Disturbances, Philippines, 1999-‐‑2011




Effect of Weather Disturbances on Prices in Metro Manila The number of individuals affected could be the first step in assessing indirect productivity effects. The loss of short term income stream due to inaccessibility of the workplace, temporary work shutdowns, sickness, exhaustion (the need to rest after the physical strains of coping with the weather disturbance), failure to work because of immediate necessary house repairs and taking care of sick family members -‐‑-‐‑ all of these reduce productivity and incurring opportunity cost of loss incomes. For example in the case of TS Ondoy devastating Metro Manila, an estimated number of missed working days of the estimated 960,000 individuals affected, adjusted according to the proportion of working age in the average, could have been the first step in estimating indirect effects or losses.

The PDNA report used a methodology developed originally by the United Nations Economic Commission for Latin America and the Caribbean (ECLAC) in the early 1970s. It is used to determine the value of lost assets, define reconstruction requirements, and assess the impacts on each sector. A two part Handbook for Estimating the Socio-‐‑Economic and Environmental Impact of Disasters by ECLAC was published in 2003. The following schematic summary below was presented by Okuyama on estimating losses (indirect impact) and secondary effects or macroeconomic impact following the said handbook.

In order to estimate losses and higher-‐‑order effects, damages need to be transformed into flow measure. This process was carried out using capital-‐‑to-‐‑output ratio, based on the available and estimated physical capital data. Then, the transformed flow measure can be considered as losses (output decrease due to the damages). This is further converted to demand change, since the model used social accounting matrix (SAM) which is a demand driven model; this conversion is done by multiplication with the inverse of diagonal elements in direct input matrix. SAM or IO for each case is aggregated mostly to the following sectors: Agriculture, Manufacturing, Utilities, Construction, Commerce, Services, and Others to match with sector scheme in the damage assessment reports. SAM includes household sector in institution category so that the induced effects of household income changes are included (Okuyama 2009, 2010).

Given this framework, it is posited that productivity losses may push the average price level. In assessing the relationship between prices and severe weather disturbances in Metro Manila, the data on monthly inflation rate were correlated with the number of individuals affected during the particular months. This is done for the three years, 2006, 2009 and 2011. These years were selected because of the severity of weather disturbances that occurred in this period.




No definite pattern of a relationship had been observed. Hallegatte and Przyluski (2010) had pointed out the need for a deeper understanding of the interaction between the economic intrinsic dynamics (e.g., business cycles and financial crises) and external shocks (e.g., natural disasters), as analyzed in Hallegatte and Ghil (2008). The coexistence of these two dynamics explains why it is so difficult to “extract” the effect of natural disasters from macroeconomic data series (Strobl, 2008; Noy, 2009). A better understanding of their interactions would allow for a better measurement of disaster cost and for a better understanding of relevant processes.

In testing further if there is any relationship between the damage due to weather

disturbances and economic characteristics of the various regions in the country, regression tests were conducted. The explanatory variables of the regions affected include population, proportion of households in agriculture, household income, household size, housing characteristics, household head education, regional GDP, and Region dummy variables. The results show that the explanatory variables are insignificant in explaining total damage per region, the number of individuals affected and total houses damaged. The only significant variable found to explain total houses damaged as reported is the proportion of houses made of makeshift materials. The fitted line graph is also shown below (Figure IV.12).

Damage Data

Loss (Output)

Demand Change

Total Impact Model

Capital to Output Ratio

Inverse of Direct Input Coefficient

Figure IV.8 Transformation of Damages Into Flow Measures




Figure IV.9 Monthly Inflation and Number of Residents Affected by Severe Weather Disturbances, Metro Manila, 2011






Figure IV.12 Proportion of Makeshift Housing and Total Houses Damaged, 1999-‐‑2011

0

100000

200000

300000

400000

500000

600000

Tota

l dam

aged

hou

ses

.5 1 1.5 2 2.5 3Proportion of Makeshift Housing

Total houses affected Fitted values




Review of Literature

The economic effects of disasters have been quite large. It has been estimated that total losses in terms of natural disasters had reached US$ 66 billion per year (Benson and Clay, 2002). It has been estimated that in the past ten years, there have been a total amount of 1,430 disasters that have been recorded with over 1 million dead and a total amount of economic losses reaching US$ 1.95 trillion. In the Philippines, for example, natural disasters have estimated to affect 4 to 6 million each year; the 2012 World Disaster Report, using the data from the Center for Research in the Epidemiology of Disasters, which is an internationally known research institute established in 1974 to examine the socio-‐‑economic and long-‐‑term effect of large-‐‑scale disasters, noted that almost 20,000 people have died, 2.7 million people have been directly affected and 94 million people have been affected by natural disasters from 1992 to 2011.

According to the literature (Benson and Clay, 2004; Pelling, Ozerdam, Barakat, 2004; Hallegate and Pryzluzki, 2010), economic impacts of natural hazards include:

• Direct impacts: these are the immediate consequences of disaster phenomenon, especially on physical assets (including raw materials, goods in process and final goods), property, valued at agreed upon replacement prices; the values of these shocks can be observed directly from market goods or their replacement costs.

• Indirect impacts: these are losses are not provoked by the disaster itself but by its consequences, including effects on firm

productivity and household incomes and expenditures over time until assets are fully recovered; there are also additional costs in terms of provision of goods, losses of personal income and partial/ total loss in terms of production, businesses and livelihoods. They also include medical costs of treating victims of the disaster and costs of lower productivity or lost output.

• Macroeconomic ‘secondary’ impacts: these include changes in the over-‐‑all state of economy-‐‑wide indicators, such as the changes in over-‐‑all or sectoral GDP, balance of trade and balance of payments, level of indebtedness, employment, money supply, public finance and capital investments.

Pelling, Ozerdam and Barakat (2004) noted that direct losses have been the focus of most studies on disaster effects because these have been utilized for disaster mitigation and preparedness, and also the provision of insurance and other similar types of financial instruments. Some of the immediate effects of a disaster would be the loss of housing, business and industrial production and crops, and damages to infrastructure, marketing systems, transport and communication.

Indirect impacts are measured mainly in terms of the degree in which the impacts of disasters can spread through economic networks. The loss of critical infrastructure, such as road networks, bridges, electricity, telecommunications and water systems, can impact on the agriculture, industrial and service sectors, even if there is no physical damage of the assets of some (or even most




of) the firms in these sectors. The short-‐‑term decline in commodity exports may also be affected given a loss of productive capacity and the disruptions in domestic and international transportation infrastructure.

Lastly, the economic effects of disasters have longer impacts on the economy and may impact on the production, distribution and consumption of goods and services; these are called the ‘secondary’ effects of a natural shock. Damage to infrastructure causes production to fall, fewer commodities will circulate, and prices for basic commodities will increase. Employment may be displaced as production may be disruption, but urban based industries will recover much faster given that these have better access to insurance and credit. The balance of payments may be adversely affected given the disruptions in export capacity and the need for short-‐‑term imports that would cover interruptions in production. Public expenditures would have to shift towards short-‐‑term relief and rehabilitation, and this may have consequence on the government debt stock.

Tools to Measure Impact There are many ways in which the estimation of economic impacts of disasters have been modelled (de la Fuente, Lopez-‐‑Calva and Revi, 2008; Hallegate and Pryzluzki, 2011); these include the following: determinants of indirect losses from firm and household surveys; microeconomic models at the household level (in which econometric modelling may be required); input-‐‑output models at the regional or national level; and, computable

general equilibrium models at the regional and national levels.

Single event analysis The first type of assessment of disaster impacts analyses the impacts of single events and focuses on the use of firm and household surveys to analyse the direct and indirect impacts of disasters. These measure the amount of damages suffered by communities and enterprises, by infrastructure and by agriculture, industry and service output losses. Both physical and monetary values are measured in the methodology suggested by the United Nations Economic and Social Commission on Latin America (ECLA, 2003) and these are supplemented by expert analysis. Indirect losses are measured from data made available by different sources, such as government agencies and non-‐‑governmental groups.

For example, after Tropical storm Ondoy hit Metro Manila and the surrounding provinces, the economic/ financial impacts are recorded in terms of different types of impacts (Muto, Moroshita and Syson, 2010; Philippine Disaster Needs Assessment Report, 2010). The economic value of total “damages” or direct impacts have been estimated to amount to P 68.2 billion, while the value of “losses” or indirect impacts have been estimated at P 137.8 billion. Around half of the damages are in terms of residential property losses; significant amounts also reflect damages (in terms of loss output and property damage) in the commercial and industrial sector, and also devastated infrastructure nationwide. On the other hand, more than half of the losses




are economic output loss in terms of the commerce/ service sector and a fifth represented the loss of agricultural output.

In the Philippines, most of the analysis of disaster effects have focused on assessing the direct impacts, although there have been some work on evaluating the secondary macroeconomic impacts of single disaster events. The National Disaster Risk Reduction and Management Council (NDRRMC) has a database of damage reports of each typhoon, which is based on the reports provided to them by the local disaster councils. These reports are aggregated through the different politico-‐‑administrative levels so at the national level, the NDRRMC only keeps on reports at the regional level. The estimates on loss and secondary macroeconomic impact are usually undertaken by donors and private institutions, such as the above assessment for the Ondoy storm. Bankoff (2003) and Benson (1997) provide an overview of the extent of the damages from natural disasters in different years, although the latter has a more thorough exposition of the possible macroeconomic effects of disasters.

Baade, Baumann and Matheson (2007) use time series analysis of taxable sales in the Miami area to assess the effects of Hurricane Katrina on the economic outcomes; it found that there is a short-‐‑term positive impact on the community. Other studies, such as West and Lenze (1998) analyse the impact of hurricanes on the effects on the economic output and employment in specific cities or communities.

There are a few micro-‐‑level studies undertaken to assess indirect effects of disasters in communities. Huigen and Jens (2006) provide an overview of the socio-‐‑economic impacts of a typhoon on a municipality in 2003; the loss on agricultural output for that year was particularly assessed.

Econometric assessment Econometric techniques are utilized to analyse different a series of disaster events; at the country or global level, these are undertaken to evaluate the impacts of hazards on economic growth, or other macroeconomic variables such as investments, savings and productivity. At the local level, these are used to measure the changes in economic growth at the provincial level, and to assess the impact on housing values and poverty.

These type of studies examine the impact of disasters on economic output. There are studies similar to that of Skidmore and Toya (2002) which state that disasters have a positive impact on economic output due to the increased spending in terms of reconstruction and production following the hazard event; disasters also increase the utilization of human capital and increase the momentum in utilizing new technologies, which lead to greater productivity. However, studies such as Noy (2009) shows that there are pronounced economic slowdowns following disasters; these studies finds that countries with an increased ability to mobilize resources (because of their superior human capital, greater government spending and openness




to trade) appear to be more robust and less vulnerable to production slowdown.

Econometric approaches at the local level find a small decrease in provincial output (in the case of Vietnam, in Noy and Vu (2009)), and increase regional poverty (in the case of Mexico, in Rodriguez-‐‑ Oriega (2009).

Input-‐‑output and CGE models Economic input-‐‑output (IO) and computable general equilibrium (CGE) techniques assess the impact of disaster on the whole economic system, focusing on the effects on the local production system. In input-‐‑output models, industrial (and probably household) output is constrained by the loss of infrastructure, machinery and other production assets in key sectors that were affected by the natural hazard, and these have ripple effect in the whole economy as the loss of output means the loss of inputs for other productive sectors. Only imports or foreign capital inflows can improve production in the short-‐‑term, while local capacities are being built. Hallegate (2008) proposed an input-‐‑output model that would take into account the production capacities of each economic sector, backward and forward linkages and the integration of adaptive behaviour; the study finds out that the indirect losses exceed the direct losses and therefore the former needs to be taken into account.

On the other hand, CGE models examine the impacts of the changes in the productive system over a longer period, since natural hazards could affect relative prices of goods and inputs, and capital, labor and other

mobile factors could move into disaster stricken areas and the pace of the rehabilitation of devastated areas could be speeded up because of the increase in returns to these factors. In these models, because there is an equilibrium of supply and demand, enterprises, which cannot produce due to infrastructure problems, can be replaced by other producers, while consumers, which may be constrained in the purchase of certain goods, may also substituted by other demanders of the same goods.

Rose and Liao (2005) utilize a CGE framework but revised by the following components: defining business and regional macroeconomic resilience, linking production function parameters to various types of producer adaptations in emergencies, recalibrating production functions to empirical or simulation data, and decomposing partial and general equilibrium responses. They find that it is important to assess the impacts of disasters on local infrastructure to be able to better assess their effects on economic output. Hallegate, Hourcade and Dumas (2005) define a CGE model in order to compute for the economic amplitude of a disaster; their model allows for temporary imbalances between production and demand, labor market disequilibrium due to institutional and technical constraints and short-‐‑run rigid wages. They find that disasters results in production shocks and the effects are amplified by slower productivity and therefore a lower employment rate (due to wage and price rigidities). This eventually decreases profits and remitted share of




savings to firms and therefore reduces consumption and investment.

The IO and CGE models assess the impact of the loss of output of specific sectors, such as utilities and transportation, could have on the whole economy. In some models, interactions with other administrative units in the economy can also be modelled.

Hybrid Models Hallegate and Pryzluzki (2010) review the use of other models such as ‘hybrid’ models in which physical and economic sectors of disasters are combined; ‘idealized’ models examine the transmission of the effects of disasters on technical change and business cycles, while public finance models examine the effect of disasters purely on changes in taxes and expenditures as flows, and public debt as stocks.

Macroeconomic Impacts of Disasters The direct effects of economic disasters are specifically on property damages on affected residential communities and firms. However, there are also other affects that affect the extent of the damage affected by the community, including the following (Benson and Clay, 2010):

1. The type of natural hazard; extremely low or even high and erratic rainfall affect agricultural production, and these have impacts on other hydrologically sensitive sectors such as water supply and hydroelectricity; abnormally severe flooding affects infrastructure and production capacity affects over-‐‑all economic activity.

2. The over-‐‑all structure of the economy, including its natural endowments; the relative importance of the different economic sectors, such as the agriculture, industry and services sectors, the production systems, intersectoral linkages and role of investment would also affect the magnitude of the effects of the disaster event;

3. The geographic size of the country; this would also scope of the effects of the disasters in the economy; in many cases, the Philippines has been pointed out as a country with a significant amount of disasters, but since these are located in a particular area, the scope of the disasters are not that large.

4. The country’s income and stage of development, which includes the linkages made by different sectors or the degree of economic integration, and the ability of government to raise revenues would affect vulnerability of economies to natural disasters; Noy (2008) finds that countries with higher literacy rates, better political institutions and government spending and higher openness to trade are able to better cope with the crisis. a The prevailing economic conditions

in the area, including macroeconomic and social policies, the local and external policy environment is also important.

Initially, the impacts of a hazard event would be the damage that it would result in terms of infrastructure. According to insurance estimates, two-‐‑thirds of all losses in flood-‐‑related events in Asia are




infrastructure losses, including housing, road networks, agricultural systems and firm physical structures. Natural disasters severely impact households in terms of the partial or full damage of their residential areas and on assets that are utilized for generating incomes. Hazard events also may destroy crop systems and forested areas, which would affect the food security of affected communities. Hazard events lead to partial or complete damages among firm physical infrastructure and to the loss of important financial records and there is also some evidence that this would lead to firm relocation (Webb, Tierney, Dahlhamer, 1999; Alesch, Holly, Mittler, Nagy, 2001).

The effects on households would lead to the decline in consumption and investment in favour of activities that would help individuals and families cope with the aftereffects of the natural damage. Private consumption significantly declines after the onset of a catastrophic event, while public consumption also falls but to a smaller degree (Aufret, 2003). At the same time, private investments are diverted to reconstruction and rehabilitation of firm physical capital and infrastructure. The resulting effect would be a short-‐‑term decline in economic output.

Because of the decline in output, there would be a decrease in the demand for labor services, leading to a decline in employment and cuts in formal sector wages and informal sector income (Skoufias ,2003). In some cases, because of disruptions in agricultural output and in the distribution of goods, there would be a

slight increase in prices until the marketing channels are restored.

At the same time, the literature (Benson and Clay, 2004; Pelling, Ozerdam, Barakat, 2004; Hallegate and Pryzluzki, 2010) also suggests that there are several factors that would affect the secondary impacts of the hazard impacts (in terms of economic values): changes in prices, resulting from the disaster shock; length of reconstruction; output effects due to negative ‘network effects’ such as vulnerabilities in transportation infrastructure and utilities (such as water, electricity, gas and transportation) whose effects can be felt throughout the economic system; and, the stimulus effect of disasters, which may be positive or negative depending whether resources are mobilized or diverted (as the case may be) because of reconstruction activities that need to take place.

Disasters have an inflationary effects (Benson, 1997). Because of disruptions in supply, prices could possibly rise, reflecting the loss or damage to goods, and transport and marketing infrastructure. The increase in prices could also be due to an increase in demand for certain goods that are needed in disaster relief and rehabilitation such as canned foodstuff, clothing and housing materials. In some disaster episodes, there may also be a decline in demand for luxury goods, which may temper the general increase in prices, but these comprise only a small %age of the expenditure baskets of ordinary households. In the Philippines, the combined effects of the 1990 Central Luzon earthquake and the 1991 Mount Pinatubo disaster increased prices of fruits and




vegetables by 46 % at the peak of supply disruptions.

In the short to medium term, there is evidence that disasters would affect the redistribution of public finance from capital investments to fund relief and rehabilitation activities, which would affect future economic growth. Governments may be forced to reduce capital spending or even increased public borrowing that would then affect domestic interest rates. Disasters may also affect the inflows of external assistance, as donors would then divert their resources to immediate needs that would then affect other aid objectives.

Disasters may also lead to balance of payments difficulties as exports could be disrupted (because of the loss of agricultural and industrial output and the damage on infrastructure and port facilities) and as demand for imports especially related to rehabilitation efforts would increase. This could then lead to an increase in external borrowing, especially in cases where foreign exchange reserves are limited, with consequences for debt servicing and future government spending and growth. Changes in foreign exchange would also result, affecting the country’s competitiveness and the ability to meet the foreign supply needs of domestic industries.

The resulting loss in production would lead to slower economic growth in the short-‐‑term. Thus, disasters would see a decline in savings and investment, and therefore lower capital stock in the economy. Noy (2011) showed that there is a strong

relationship between the amount of damage suffered in a disaster and negative economic growth; developing countries and smaller countries, on the average, are more adversely affected.

Capital assets and resources would also be affected by natural disasters, and this would affect long-‐‑term economic output. However, there is some evidence of the “productivity effect” (Hallegate and Pryzluzki, 2010) that the capital investments that are destroyed can be replaced by investments and infrastructure that could be replaced with the most recent technology which would increase the returns to the infrastructure more than that before the disaster.

Impacts on Urban Areas In cities such as Metro Manila, the presence of public infrastructure increases economic development, and the scarcity of land leads to high density occupation; residential areas with greater population concentrations, especially those with large building sizes, may cause a higher level of disaster risk (Lall and Deichmann, 2009).

Also, cities generally attract firms in industries and services in order to attract access to specialized suppliers and expertise that will only be supported at some relative minimum scale, or to benefit from the existence of different industries due to the diversity of their skill and expertise that encourages innovation. These further increases the economic and demographic density, which also increases risks brought about by natural disaster.




City managers usually respond to the density by imposing restrictions in land use; but these result, in a majority of cases, in lower economic productivity (as it limits the sizes of production areas) and temporarily increase the prices of land and housing services. At the same time, this also encourages the growth of urban informal settlements, where the urban poor are generally affected in terms of the vulnerabilities to disaster risk. Mapping studies on urban areas show that poorer households have a disproportionate share of hazard risk because they are located in more vulnerable areas (near rivers or creeks, or earthquake faults) which generally are cheaper in terms of housing costs. Other factors that affect the risk of these households include poor infrastructure, weak property rights, and the lack of planning standards and adequate infrastructure and weak institutional oversight of land usage.

The effects of disasters in the rural areas also affect urban areas. With disasters, agricultural productivity declines, leading to lower agricultural incomes and increased rural to urban migration.

Impacts on Households Hallegate and Pryzluski (2010) note that households may lead to “poverty traps”, where disasters negatively affect assets and incomes, and result in lower productivity, which would make it more difficult to build these assets and savings. This may even lead to “inter-‐‑generational” poverty where disasters negatively impact on long-‐‑term diminution of cognitive and psychological abilities.

According to the literature, the impacts of disasters on households depend on the different assets that households have acquired. Lopez-‐‑Calva and Levi (2009), which examined the relationship between disasters and poverty, note that the ability of households to utilize their asset endowments. These assets can be collectively owned by the communities or privately owned by households and individuals. The ability of these assets to mediate the impact of disasters depends on the ‘rate of utilization’ of these assets and the ‘availability and exchangeability’ of the assets with each other. Households have the ability to utilize or invest in these assets, and are able to derive benefits from these assets by their use or by exchange with other assets.

The ability of households to diversify across different types of assets is important. Dercon (2005) noted that rural families in many developing countries engage in the planting and harvesting of many low-‐‑risk crops, different types of livestock and activities in order to reduce the risk of being too dependent on one activity; however, at the same time, this affects the ability of these families to earn sufficient incomes given that many of these low-‐‑risk activities are also produce very little returns.

Households’ human capital assets, such as education and health, are important to take advantage of livelihood generation activities. Higher levels of education allow individuals to be able to switch to different types of work in the face of the loss of firm employment given a disaster that would affect industrial level assets. At the same




time, improved health outcomes are important; detrimental effects of a disaster on the physical bodies of individuals (i.e., disabilities, sickness) would lead to a negative impact on the ability to cope with a disaster.

The physical assets of the household are also important; these include the type of residential housing and may also include other material assets that allow them to earn a living. Informal settlements are particularly vulnerable to disaster risk because the light materials utilized for constructing their homes. Households which have access to home-‐‑based assets that can be utilized to earn a living can also help households cope with disasters.

The diversity of financial assets of households is also important in order to cope with disaster risk. Dercon (2005) noted that the micro-‐‑insurance, in which low-‐‑income households can purchase to guard against the loss of assets, can improve the ability of families to spring back after a typhoon or earthquake. Savings and investments in bonds and stock can also reduce covariant risks that can be caused by downturns in consumption.

Other types of assets can also help communities able to cope with disaster effects. The ability of households to connect with organizations and institutions that can help them cope with the impacts on their welfare. The important role of NGOs, local social institutions and the private sector in uplifting the situation of marginalized groups has been repeatedly emphasized in the literature.

Ultimately, the long-‐‑term social and human capital impacts of disasters on different communities would have to be affected by several factors (Lindell and Prater, 2003). These include the coping mechanisms that are available at the household, community and regional levels, which may include informal, market or public mechanisms. Financial assistance can be provided, for example, by the local governments or by non-‐‑government organizations that may easily allow individuals to recover, or there may even be behaviour, such as finding additional work, that can also affect the probability of households to recover.

At the macroeconomic level, government policies and programs can mitigate the impacts of disasters and also influence how different households cope with the negative impacts of a shock.

Skoufias (2003) provided some lessons in undertaking public programs after a natural disaster or an economic downturn. Ex-‐‑ante programs are more effective than programs undertaken after a disaster had occurred because they improve welfare and also reduce poverty. Second, market liberalization reforms that are undertaken together with improvements in the provision of goods and services through private markets and public agencies are important in reducing the adverse effects of a crisis. Third, the institutionalization of targeting mechanisms that would support the implementation of programs are also important. And finally, the design of public programs that aim at specific members of the household such as women or children




should take into consideration the role of families.

The policy environment can also improve the ability of households especially in urban areas to cope with disaster risk. Lall and Deichmann (2006) suggest that the deregulation of land markets may allow poor people to access land and housing rental markets. Improving the land titling process and strengthening the ability of the poor to access formal titles may provide the following effects:

“Assurance” effects in that the presence of land titles would allow households to feel more secure in their dwelling, and would increase investments in their housing in order to better cope with disasters; this can be witnessed in the urban housing programs undertaken for informal settlers which shifted from light materials to cement and other heavier materials in reconstructing their homes following the receipt of their land titles;

“Collateralization” effect in which households would be able to use their land titles in order to be able to obtain loans that can be utilized to build their assets;

“Realizability” effects that allow households to better transfer ownership of their dwelling to others, further increasing investment in their homes that would allow them to improve their dwelling areas.

Diversification of a country’s sources of economic output can also help. Benson and Clay (2004) noted that the transformation of Dominica, an island country in the Carribean, was facilitated by the rapid

development of public infrastructure which strengthened the development of the tourism and financial services industries. The country, which was dependent on banana exports as their main source of income and foreign exchange and thus was vulnerable to the natural disasters, was able to better invest in disaster mitigation mechanisms. In Bangladesh, the removal of restrictions on private agricultural investments resulted in the rapid expansion of irrigated rice, displacing highly risk rainfed rice and jute, and resulting in better exports.

Impacts of Climate Change and Relationship with Natural Disasters The relationship between climate change and various macroeconomic and welfare indicators is more complex. The Stern Review on the Economics of Climate Change (2006) noted that the significant impact of the climate change would be a disruption in the natural capital of most countries; more particularly the impacts would result in the following:

1. Floods and droughts have caused disruptions in agricultural output, resulting in lower food production; this will in turn affect malnutrition rates across different countries;

2. Water supply disturbances, affecting agricultural and industrial output; also, persistent water shortages exist in many large urban areas;

3. Increase in the loss of forest cover, which would disrupt the livelihoods of




individuals that are dependent on subsistence activities.

All of these would affect economic output of all countries around the world in the medium and long run. However, at the same time, the report also acknowledges that climate change can result in increase in the magnitude and intensity of flooding and other natural disasters, and thus would heighten the impacts of these disasters on the economy.

Urban centers are particularly vulnerable to climate change (Gaspar, Blum and Roth, 2011) as urban areas are particularly located near coastal areas where physical infrastructure are vulnerable to the rise of sea levels and associated land erosion. Climate change can affect water quality which is important in the production of economic goods; it can also affect the ability to produce electricity. In the Philippines, for example, a significant portion of power comes from hydroelectric sources, which supplies the bulk of Mindanao’s needs; in the past several years.

Hallegate, Henriet and Coffe-‐‑Marlot (2011) provide an overview of the possible impacts of cities on cities; see Table IV.3 below.

Long-‐‑term changes in temperature would lead to changes in energy consumption. Asset losses will also be felt due to sea level rise, and the occasional hurricanes, typhoons or even storm surges; major losses will also be seen due to an increased rise in the sea level in major cities which are based in coastal areas. Non-‐‑quantifiable effects would also include increased mortality and morbidity due to frequent heat or cold waves, and there would be a greater %age of the population that will be affected due to increased storm.

Indirect effects in cities would be the changes in tourism activity due to changes in temperature, the fall in worker productivity due to health impacts, and long-‐‑term population and migration patterns.

Also, the rise in water levels also poses a threat to the transportation networks that have been established in the urban areas. Highways, bridges and road tunnels in and around Metro Manila are particularly vulnerable during periods of high storm intensities. Tourism infrastructure, because of their proximity to natural resources, is also affected during thunderstorms and landslides.




Table IV.3 Types of climate change impacts on cities

Impacts Direct Indirect Climate mean

changes Climate variability

changes Catastrophic

changes Market Change in energy

consumption due to heating/ cooling

Rise/fall in tourism due to temperature changes

Asset losses due to sea level rise

Asset losses due to hurricanes or storm surges

Major asset losses due to catastrophic sea level rise

Effects of decline in tourism activity

Fall in worker productivity due to health

Spatial or sectoral diffusions

Effects on long-term economic development

Non-market

Increased mortality and morbidity due to increase in global mean temperature

Loss in thermal comfort

Population at risk due to sea level rise

Number of deaths due to frequent heat wave/ stresses

Population at risk in coastal cities due to increased storminess

Cultural losses and migration, including ethical aspects induced by catastrophic sea level rise

Effect of climate change on water shortages

Inequality deepening; loss of human security

Source: Hallegate, Henriet and Corfee-Morlot (2011). Climate change has also caused socio-‐‑economic dislocation of a significant number of people. The declining amount of fish stocks and declining amount of water resources available for crop production, for example, have forced fishery and agricultural-‐‑based households to move to urban centers to look for informal sector work. The Stern report warned that as much as half a billion people might be affected by the rise of sea waters in low-‐‑lying coastal areas caused by an increase in temperature by 3 degrees Celsius.

Summary and Causal Loop Diagram Given the review of the literature above, the causal link tree can now be illustrated

below (see Figure IV.13 below). The initial effect of hazard as a discrete event in urban areas would be to negatively impact housing and other residential assets, public infrastructure and industrial capital stock. The decline in housing assets would negatively impact household consumption, at least in the short-‐‑term, as household income would be diverted towards replacing the assets, reducing demand and therefore industrial production and employment. The short-‐‑run decline in job demand would then have a negative impact on wages and income.

The loss of industrial capital stock would adversely impact output, which would lead




to supply bottlenecks and eventually might lead to an increase in consumer prices. The loss of industrial production would also lead to a slowdown in the export capability of industry, affecting the balance of payments and foreign exchange. The loss of industrial output, together with the short-‐‑run decline in income, may affect public revenues and could eventually lead to a decline in expenditures, affecting the ability of governments to replace the public assets that were lost in a disaster.

Government spending in public infrastructure in itself could lead to a greater increase in complimentary private investments in urban areas, increasing land values and strengthening agglomeration. This could lead to an increase in informal settlements, affecting the vulnerability of these settlements to disasters.

At the household level, the loss of income could lead to a decline in the stock of financial and human capital assets in the households, affecting their ability to respond to a natural disaster. Therefore, the nature of the disaster and the characteristics of households would eventually affect the transmission of the shock to these households. Household vulnerability would then be affected also by the proportion of the population in unsafe areas and the proportion of the population in sub-‐‑standard housing.

On the other hand, climate change as a slow-‐‑onset incident would affect the vulnerability of physical and natural resources to respond to one-‐‑off extreme events. This would also impact on the ability of physical infrastructure to cope with stresses brought about the effects of the extreme events.




Figure IV.13 Causal Loop Diagram on the Economic Impacts of Climate Change and Disasters

tropical cyclone

intensity

tropical cyclone

magnitude

temperature change

sea level rise

housing and

residential assets

damage

public

infrastructure stock

industrial capital

damage

public infrastructure

damage

industrial capital stock

housing and

residential stock

industrial investments

consumption/demand commodity prices

industrial production

urban employment

urban wages/income

private expenditures in

education and health

education levels

DALY/VSL (health)

household tax

household savings

public spending

public spending in

education and health

household financial

capital stock

industrial sector tax

export supply balance of payments foreign exchange

importsforeign supplydomestic supply

energy demandenergy prices

land investments land values urban densityproportion in

unsafe areas

proportion in

sub-standard houses

agricultural productionagricultural income rural to urban migration

Household

Vulnerability

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The supply side of economic estimation of disaster impacts has increased with recent frequent and severe disasters around the world. Disaster, as a phenomenon, is quite different from any other economic events: sudden occurrence of the event; loss of lives; negative externalities; non-‐‑uniform distribution of damages over space; and a dynamic trend change from large negative shocks to a positive demand injection for recovery and reconstruction (opposite effect). Studies and research done worldwide on the estimation of economic impact of disasters has improved over the decade in the same period that this narrative covers data of severe weather disturbances in the Philippines and in particular Metro Manila. This does not mean, however, that the methodologies in economic estimation of disaster impact are easily accessible, doable, and replicable.

Given above, a casual attempt of an economics impact narrative investigation of weather disasters in Metro Manila is constrained foremost by quantity and quality of data overtime, and the ease and familiarity in combining economic estimation methodologies to be employed. Much capacity has to be developed in installing this capability among individuals and institutions in government and independent private institutions. This is very obvious to researchers writing this narrative report.

On the practicalities of the demand side, the economic estimations must address the information need for emergency management by officials (ex-‐‑post response), policy makers for preparedness and mitigation policies (ex-‐‑ante response), for development planning, and for a wide range of researchers of different disciplines engaging in disaster impact analysis. In this vein, the PDNA of TS Ondoy and Peping must be assessed by users of how the study was useful. For example, did the flow analysis estimation of impacts helped in emergency management, and policy makers were able to grasp the size and extent of the devastation on productivity and future stream of incomes. The users in this case may have demanded the credibility, not necessarily the accuracy of the estimation (Okuyama 2010).

Meanwhile, in order to advance our understanding of disasters and their impacts in Metro Manila and in the Philipines, it is essential that a wide range of researchers cooperate together. The FORIN interdisciplinary approach is a first step towards that direction. All the above discussions lead toward the necessity of reliable data collection of disasters, and it appears now that having standardized definitions and data of damages and losses for all the disasters remains a dream. West and Lenze (1994) suggested that the more sophisticated impact models become, the more precise numerical data will be required, while imperfect measurements of the damages and losses of a disaster are often the case. In international settings, definitions of damages and losses are often




different across countries, let alone the definition of economic impacts.

Whereas the demand for details are different among the users discussed above, disaster data have to be standardized, reliable, and consistent to some extent for all disasters in order to make cross-‐‑disaster analysis. While collecting such data retrospectively for the past cases is hardly affordable and unrealistic, it is never too late to set and implement some international standards for future events, such as the method devised by the Economic Commission for Latin American and the Caribbean (ECLAC, 2003). On this front, it may also be useful to link economic models for disaster with other established indicators of development, so that the data collection and analysis can be streamlined and linked together, and that the models and the resulting analyses will be much more useful to the disaster community, researchers, and practitioners a like.

With capacity and capability to provide the supply side of estimation methodologies of economic impact of disaster, and collection of standardized and reliable data on disaster, it is only then that a detailed forensic investigation of disasters, such as in Metro Manila can be done.




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Bankoff, Greg (2003). “The ‘Costs’ of Hazard in the Contemporary Philippines,” in Greg Bankoff (ed.) Cultures of Disaster : Society and Natural Hazards in the Philippines. London: Routledge Curzon.

Benson, Charlotte (1997). “The Economic Impact of Natural Disasters in the Philippines,” Overseas Development Institute Working Paper 99.

Benson, Charlotte and J. Edward Clay (2004). “Understanding the Economic and Financial Impacts of Natural Disasters,” World Bank Disaster Risk Management Series Paper No. 4.

Carter, Michael, Peter Little, Tewodaj Mogues and Workneh Negatu (2004). “Shocks, Sensitivity and Resilience: Tracking the Economic Impacts of Environmental Disaster on Assets in Ethiopia and Honduras,” typescript.

Dercon, Stefan (2005). “Vulnerability: A Micro Perspective,” QEH Working Papers qehwps149, Queen Elizabeth House, University of Oxford.

de la Fuente, Alejandro, Luis Felipe López-‐‑Calva and Aromar Revi (2008). “Assessing the Relationship between Natural Hazards and Poverty: A Conceptual and Methodological Proposal,” Document Prepared for ISDR-‐‑UNDP Disaster Risk-‐‑Poverty Regional Workshops in Bangkok, Thailand (April 2008) and Bogotá, Colombia (June, 2008).

Gasper, Rebecca, Andrew Blohm and Matthias Ruth (2003). “Social and Economic Impacts of Climate Change on the Urban Environment,” Current Opinion in Environmental Sustainability 3, pp. 150–157.

Hallegatte, Stéphane, Fanny Henriet and Jan Corfee-‐‑Morlot (2010). “The Economics of Climate Change Impacts and Policy Benefits at City Scale: A Conceptual Framework,” Climatic Change 104, pp. 51–87.

Hallegatte, Stéphane and Jan Corfee-‐‑Morlot (2011) “Understanding Climate Change Impacts, Vulnerability and Adaptation at City Scale: An Introduction,” Climatic Change 104, pp. 1–12.




Hallegatte, Stéphane and Valentin Przyluski (2010). “The Economics of Natural Disasters: Concepts and Methods,” World Bank Policy Research Working Paper 5507.

Hallegatte, Stephane , Jean-‐‑Charles Hourcade and Patrice Dumas. “Why Economic Dynamics Matter in Assessing Climate Change Damages: Illustration on Extreme Events,” Ecological Economics 62 (2), pp. 330-‐‑ 340.

Hallegatte, Stéphane, Fanny Henriet, Anand Patwardhan, K. Narayanan, Subimal Ghosh, Subhankar Karmakar, Unmesh Patnaik, Abhijat Abhayankar, Sanjib Pohit, Jan Corfee-‐‑Morlot, Celine Herweijer, Nicola Ranger, Sumana Bhattacharya, Murthy Bachu, Satya Priya, K. Dhore, Farhat Rafique, P. Mathur, Nicolas Naville (2010). “Flood Risks, Climate Change Impacts and Adaptation Benefits in Mumbai: An Initial Assessment of Socio-‐‑Economic Consequences of Present and Climate Change Induced Flood Risks and of Possible Adaptation Options”, OECD Environment Working Papers, No. 27, OECD Publishing. http://dx.doi.org/10.1787/5km4hv6wb434-en

International Federation of Red Cross and Red Crescent Societies (2012). World Disaster Report 2012. Geneva, Switzerland: IFRCS.

Kumpulainen, Sato (2006). “Vulnerability Concepts in Hazard and Risk Assessment. Natural and technological hazards and risks affecting the spatial development of European regions.” Geological Survey of Finland, Special Paper 42, pp. 65–74.

Kim, Chul-‐‑Kyu. “The Effects of Natural Disasters On Long-‐‑Run Economic Growth,” Michigan School of Business Journal 4 (1), pp. 11-‐‑ 49.

Kousky, Carolyn (2012). “Informing Climate Adaptation: A Review of the Economic Costs of Natural Disasters, Their Determinants, and Risk Reduction Options,” Resources for the Future Discussion Paper.

Lall, Somik and Uwe Deichmann (2009). “Density and Disasters Economics of Urban Hazard Risk,” World Bank Policy Research Working Paper 5161.

Lindell, Michael and Carla S. Prater (2003). “Assessing Community Impacts of Natural Disasters,” Natural Hazards Review 4 (4), pp. 176-‐‑ 185.

Lopez-‐‑Calva, Luis Felipe and Ortiz-‐‑Juarez, Eduardo (2009). “Evidence and Policy Lessons on the Links between Disaster Risk and Poverty in Latin America,” MPRA Paper No. 18342 Online at http://mpra.ub.uni-muenchen.de/18342/.

Muto, M., Morishita, K., and Syson, L. (2011). “Impacts of Climate Change upon Asian Coastal Areas: The case of Metro Manila,” Japan International Cooperation Agency with the




cooperation of Integrated Research System for Sustainability Science (IR3S), Institute for Global Change Adaptation Science (ICAS), CTI International, ALMEC Corporation, Ateneo de Manila University, and National Statistics Office, Republic of the Philippines.

Noy, Ilan (2009). “The Macroeconomic Consequences of Disasters,” Journal of Development Economics 22 (2), pp. 221-‐‑ 231.

Okuyama, Yasuhide (2009-‐‑10). “Critical Review of Methodologies on Disaster Impact Estimation,” Graduate School of International Relations, International University of Japan, Niigata, Japan.

Pelling, Mark, Alpaslan Özerdem and Sultan Barakat (2002). “The Macro-‐‑economic Impact of Disasters,” Progress in Development Studies 2 (4) pp. 283–305.

Rose, Adam (2004). “Defining and Measuring Economic Resilience to Disasters,” Disaster Prevention and Management 13 (4), pp. 307-‐‑ 314.

Skoufias, Emmanuel (2003). “Economic Crisis and Natural Disasters: Coping Strategies and Policy Implications,” World Development 31 (7), pp. 1087-‐‑ 1102.

Stern, Nicolas (2007). The Economics of Climate Change. Cambridge, UK: Cambridge University Press.

Webb, Gary, Kathleen Tierney and James Dahlhamer (1999). “Businesses and Disasters: Empirical Patterns and Unanswered Questions,” University of Delaware Disaster Research Center Preliminary Paper 281.

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Chapter 3: Social Sector: Narratives on Flooding and Climate Change in Metro Manila




Frameworks

The vulnerability frameworks of Birkman et al (2010) and Cutter et al (2003) are utilized in this narrative in order to examine the social factors associated with the causes and consequences of disaster and climate change on the vulnerable populations of Metro Manila.3The framework used in examining these narratives is given below:

Cutter (1996) in analyzing the impacts of flooding in the United States used householdsas units of analysis while Bankoff (2003)used historical-‐‑spatial approaches in examining the hazards of floodinginMetro Manila. Meanwhile, Porio (2011, 2012) examined the vulnerability, adaptation and resilience of marginal communities in the three flood basins of Pasig-‐‑Marikina, Manggahan and the KAMANAVA4 areas (Kaloocan, Malabon, Navotas, and Valenzuela).

3Metro Manila is composed of 17 cities and municipalities by virtue of Presidential Decree No. 824 issued in 1975. 4Part of the 17 cities and municipalities comprising Metro Manila.

Methodology/Data Sources

The social sector paper used mainly secondary sources plus the following primary data sources:

1. Disaster and Climate Change Study (2008-‐‑2009) conducted by Dr. Emma Porio in 15 marginal communities located in the three flood basins of Metro Manila. This study utilized household and community profiling surveys, key informant interviews, and focus group discussion (FGDs);

2. APN-‐‑supported study “Enhancing Medium-‐‑ and Long-‐‑Term Planning AmongLocal Governments” (Porio 2012). This study utilized household and community surveys, business and industrial establishment survey, key informant interviews and focus group discussions (FGD);

3. Post-‐‑Disaster Needs Assessment (Typhoons Ondoy and Pepeng)/PDNA Study (Institute of Philippine Culture, 2009). This study utilized household surveys, community profiling, and FGDs;

4. Social Impact Assessment Study of Ondoy and Pepeng (Institute of Philippine Culture, 2011). This studyutilized household surveys, community profiling, and FGDs.

While the social sector narratives also utilized other studies, the quantitative and qualitative assessments of the causes and impacts to the different stakeholders and affected communities were largely lifted out

Figure V.1. Framework for the Social Sector Narrative




of Porio (2011) and Porio (2012) as well as from the some un-‐‑analyzed portions of the data sets obtained by these two studies.

For a coastal and riverine city like Metro Manila, the hazards and sources of disaster risks are floods, sea level rise, and storm/tidal surges, earthquakes and subsidence.

Risk

Disaster risk is a product of the interaction of the hazard (event) and the vulnerability conditions of the society or elements exposed. As a result, the need for a systematic linkage between disaster risk reduction (DRR) and climate change adaptation (CCA) to advance sustainable development, and finally human security is being discussed within the ongoing climate change negotiations as well as within the disaster risk community, for example, in the framework of the Intergovernmental Panel on Climate Change special report on ‘Managing the risks of extreme events and disasters to advance climate change adaptation’ (Birkmann et al, 2010).

Social Vulnerability

The definition of the term ‘vulnerability’ has been widely debated in the literature. Several definitions have been advanced depending largely on their disciplinary orientations (Cutter, 1996).Vulnerability and adaptation is relative to the degree that a particular group is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes(Carter and Kankaanpaa, 2003 and

IPCC, 2001). Downing et al (1995) refer to vulnerability as an outcome: “the degree of loss (ranging from 0 % to 100%) resulting from a potentially damaging phenomenon”. Adger (1999) in a similar way argues that “the exposure of groups or individuals to stress, as a result of social and environmental change, where stress refers to unexpected changes and disruptions to livelihoods”. However, vulnerability is also seen as a set of indicators that condition outcomes in terms of susceptibility to impacts (Lewis, 1999). The work of Blaikie et al (1994) defines vulnerability as: “characteristics of person or group in terms of their capacity to anticipate, cope with, resist, and recover from the impacts of natural hazards”. Adger (2000) provides an alternative definition for this: “the presence or lack of ability to withstand shocks and stresses to livelihood”.

The definition given by Downing sees vulnerability in terms of exposure to hazards. Studies focusing on this tend to ‘focus on the distribution of some hazardous condition, the human occupancy of this hazardous zone . . . and the degree of loss (life and property) associated with the occurrence of a particular event’ (Cutter, 1996). In a way, this is what can be termed as the “physical-‐‑ecological vulnerability”. In turn, Blaikie’s definition sees vulnerability as the variations in people’s ability to cope with the hazards. Studies focusing on this tend to highlight the social construction of vulnerability (Cutter, 1996). Studies have shown that the socio-‐‑political processes by which people are made vulnerable are the most relevant to disaster and mitigation strategies (Few, 2003; Pelling, 1999).




What are the indicators of social vulnerability? One of the most popular is poverty level (Few, 2003; Chan and Parker, 1996; Adger, 1999, Porio, 2011). Communities with a higher incidence of poverty tend to be more vulnerable to environmental hazards. The poor tend to occupy the more flood-‐‑prone environments, and they have fewer resources upon which to draw to counteract the impacts of flooding (Few, 2003; Chan and Parker, 1996). However, poverty is NOT the only indicator of vulnerability to environmental hazards. Vulnerability to the impacts of hazards has social, political, institutional, and cultural dimensions as well (Few, 2003; Cutter, 2000; Pelling, 1997). A list of indicators to social vulnerability has been constructed by Cutter (2000), and it involves: “access to information and knowledge, access to political power and representation, and beliefs and customs”. Pelling (1997) argues that the neighbourhoods who are most vulnerable to flooding are those with low household incomes, poor housing quality, and low levels of community organization (Few, 2003). Maskrey (1999) also sees the multi-‐‑dimensional nature of social vulnerability that includes “geographic location, economic capacity in terms of asset levels, reserves and access to loans, levels of social cohesion and organization, its cultural vision of disasters, and many more…” However, Maskrey (1999) warns of treating hazard victims as homogeneous, since communities are definitely heterogeneous in terms of vulnerabilities and needs. There are many variations in social vulnerability within different communities that have variable socio-‐‑economic status, gender, age,

ethnicity, and religious affiliation (Few, 2003).

Porio (2011) utilizing Nicholls (1998) in examining the vulnerability of riverine communities in Metro Manila distinguished between natural system’s vulnerability and socio-‐‑economic vulnerability to climate change. More significantly, she argued that the dynamic interaction between these two systems can intensify the vulnerabilities of different social, political, and economic groups. Therefore, the socio-‐‑economic vulnerability of certain social classes or groups is also partly dependent on the society’s technical, institutional, economic and cultural ability to prevent or cope with these impacts, i.e., capacity to adapt within the timescale of natural changes, (Sairinen and Peltonen (N.D.). Meanwhile, vulnerability can also be defined as the degree to which a system or unit is likely to experience harm due to perturbations or stresses (environmental, economic, technological) and the responses of, and impacts on social groups, ecosystems, and places (De Sherbinin et al. 2007).

Social Factors that Determine Risk to Communities

Flooding in Metro Manila is not caused by physical and environmental factors alone. There are also human-‐‑induced factors, brought about as a result of human activities (Bankoff, 2003; Porio, 2011). The dynamic interaction between physical and socio-‐‑economic factors can intensify the vulnerabilities of various social, political, and economic groups (Porio, 2011). There are four main social factors that contribute




to risk of communities in Metro Manila: (1) population growth, (2) encroachment of informal settlers on waterways, (3) improper disposal of waste, and (4) poor maintenance of flood control systems (Bankoff 2003; Porio 2011, 2012; Zoleta-‐‑Nantes 2000).

Metro Manila’s population expanded from 5.93 million in 1980 to 7.95 million in 1990, 9.93 million in 2000 and is projected to reach 19.43 million in 2020. In 2007, the National Statistics Office (NSO) reported that Metro Manila has 12 million residents but the average daytime population is about 16 million (Porio, 2011). The high population density has strained the existing sewerage and drainage systems in the city (Porio, 2009; Zoleta-‐‑Nantes, 2000).

There are two major sewage systems in

Metro Manila. One is the Central Manila Sewer on the north and south banks of the Pasig River, whichcovers 1,850 hectares and with a total length of 240 kilometers. It was initially designed to serve 500,000 people, but it recently services more than a million people. Another sewage system is located in Quezon City and Makati, measuring approximately 140 kilometers in length. It was designed to serve 350,000 individuals, but currently serves about half a million people (Zoleta-‐‑Nantes, 1999).

Because most of the people who migrated to the metropolis were poor, they could not afford the high costs of land, housing materials, and construction. Consequently, migrants have to find housing in the informal housing sector, living along the banks of rivers, canals, and esteros (Bankoff, 2003; Porio, 2011; Zoleta-‐‑Nantes, 2000). In

Figure V.2. Population Trends of Metro Manila (1970 – 2020)




fact, a study conducted by the HUDCC revealed that in 2007, the total informal settlers in the country number 550,771 households, with 36% or 199,398 households found in Metro Manila (Cruz, 2010). These makeshift houses often encroach on to available waterways, narrowing their flow capacity and diminishing their volume of discharge (Bankoff, 2003; Porio, 2011; Zoleta-‐‑Nantes, 2000).

Finally, residential communities are also responsible for generating about half the total volume of Metro Manila’s solid wastes. Unfortunately, only 71 % of the wastes are properly collected by the dump trucks and taken to landfills. The remaining wastes are simply left on the streets, dumped on vacant lots, or thrown into storm drains, creeks, or rivers. As a result, an amount of about 55 to 157 tons of solid wastes each day clog the network of drainage canals, thus increasing the likelihood of flooding (Bankoff, 2003).

Transitions in History that Changed the Distribution of Impacts in Metro Manila.

Floods have always affected coastal and riverine cities like Manila whose earlier socioeconomic development was very much based on its location Manila Bay and the Pasig River. Manila Bay provided an ideal port location for both foreign/local ships/boats to dock in Manila Bay and near the mouth of the Pasig River to transport of people and goods. Ports served as the transport hub for import and export of goods starting with the Galleon trade

between the Philippines and Europe. Meanwhile, the Pasig River served as the main channel for bringing people and goods from the hinterlands of Luzon to the city. This all changed at the turn of the century with the increasing shift to road-‐‑based transport and the decline of water-‐‑based transport along the Pasig River and those docking in Manila Bay.

The destructive impacts of floods became more discernible with the rapid increase of the population after WWII. Rapid urbanization started in the 1950s and reached its height in the 1990s with more than half of the population living in cities. By the 1980s, Metro Manila has become almost hundred percent urban (Porio 2009).

The following section will describe the flood patterns and trends from 1950s to 2000.

Post-‐‑WWII to the 1960s: Emergent Social and Environmental Trends In the 1950s, floods often devastated Tondo, Sta. Cruz, Sampaloc, and Sta. Mesa, Manila. During this time, floods were mostly attributed to nature such as high rainfall intensity, city’s flat terrain, and high tide events in Manila Bay (Zoleta-‐‑Nantes, 2000). Furthermore, tidal channels had become shallower and narrower (from 12 meters to 3.6 meters) with increased siltation. Mass housing complexes and commercial establishments had been constructed all over the city. Government paid little attention to flood problems in Metro Manila (Leuterio, 1956).

In the 1960s, the areas inundated by floods have expanded. Almost 70% of the city of




Manila was drenched with flood waters (Zoleta-‐‑Nantes, 2000). Pasig River, which used to have a mean depth of about 4.8 to 5.5 meters, accumulated silt to the point that the depth had been reduced to 3.6 to 4.3 meters (Pope, 1967). A number of makeshift houses along riverbanks have also been constructed and blamed for the floods.

The Manila Council blamed the floods to massive deforestation initiated by the Philippine Congress in the Marikina and Montalban watersheds. On the other hand, the Congress blamed city government’s poor drainage system. In fact, 20-‐‑meter wide esteros had been reduced to 0.6 meter wide canals. The number of paved roads also increased flooding (Rodriguez, 1967).

1970s-‐‑1980s: Urbanization Intensifies, Increasing Environmental Degradation and Social Services Deficit In the 1970s, soil erosion and garbage on waterways have made the colours of flood waters brown and murky dark. Watershed deforestation, uncompleted dike constructions, silting of waterways, and lack of comprehensive flood control programs were the primary causes of flooding in the 1970s. City officials even blamed the flooding on supernatural forces, such as the due to the robbery of the 400-‐‑year old image of the Holy Child, the patron saint of Tondo (Zoleta-‐‑Nantes, 2000).

By the 1980s, chronicles of flooding of riverine communities of Metro Manila became more frequent. For example, Zoleta-‐‑Nantes (2000) described the 1986 and 1988

floods as causing a lot of destruction in Marikina Valley and along Laguna de Bay. Heavy siltation had reduced the water holding capacity of Laguna de Bay by 64% (Ressurreccion-‐‑Sayo, 1988). To control the destructive flood impacts, the government started constructing flood control infrastructure such as flood gates, river walls, and main interceptors along the main water channels along the Pasig-‐‑Marikina River System and the rivers/coasts of the KAMANAVA areas. More specifically, it was during the 1980s that the Manggahan Floodway and the Napindan Floodgate were installed (Zoleta-‐‑Nantes, 2000).

1990s-‐‑2000s: Intensification of Impacts of Urbanization and Environmental Degradation It was in the 1990s that the outbreak of flood-‐‑related diseases, such as leptospirosis, cholera, and dengue emerged. Traffic congestion in the mega city also became worse. For instance, a two-‐‑hour travel can translate to 10 hours of being trapped inside one’s vehicle on a flooded street. The depths of floodwaters also had increased in Marikina, Pasig, and Kamanava (Kalookan, Malabon, Navotas, Valenzuela) areas.

It was also in the 1990s, however, that the government provided more transportation assistance to stranded commuters. Timely suspension of school was also observed. The bulk of government response was on construction of food control infrastructures and relief operations (Zoleta-‐‑Nantes, 2000).

It was in this decade that Metro Manila suffered greatly from the flooding brought about by typhoons. In early July 2008, the




World Bank estimated that the Philippines loses P15 billion annually to disasters like typhoons and floods (Porio, 2011). Typhoon Ketsana (Ondoy) wreaked havoc in Metro Manila last 2009, leaving 300 dead and causing nearly P9 billion pesos in total damages. Unfortunately, insufficient funds, mismanagement, and misspending of resources continue to hinder improvement of flood control programs.

DRRM and Climate Change This decade also saw the increasing awareness of people regarding changing climate patterns as manifested in the intensity of typhoons and floods and the variability (or season) of their occurrence during the year. This was also supported by accounts of affected residents who observed the increasing height of flood waters in their midst and the unpredictable patterns of their occurrence compared to climate events

Figure V.4. Map of the Flood Prone Areas of Metro Manila River Basins and the Research Communities.




in the past. They said that they have always come to expect the typhoon season from June to November every year. But urban poor residents in the riverine and coastal communities of Metro Manila remembered that in 2008, they experienced their first typhoon in May.

Porio (2012) also noted as reflected in the table below that in 2011, 10 deadly storms (out of 19) occurred year round (from January to December) causing many deaths and damage to property and infrastructure (bridges, roads). These occurrences prompted residents in the surveyed marginal communities of the Pasig-‐‑Marikina Flood Basin to argue that climate change is indeed very real for them as they constantly suffer from floods and storms more now than in the past.

Pre-‐‑Disaster Social Trends Continues

Because the increasing population of general population and the increasing populations of vulnerable populations such as poor people and due to various socio-‐‑political contexts affecting the vulnerabilities of populations, the trends are continued. Furthermore, the behavior of the affected population changed temporarily after the disaster but went back to their old ways after just a few months. Mismanaged solid waste management systems, land use and habitation patterns in both declared danger zones/areas continue in the metropolis.

Another factor for the continuation of pre-‐‑disaster social trends is the absence of a

Figure V.3. List of Storms/Typhoons and Earthquakes in the Philippines in 2011




continuous-‐‑systematic training and capability program for the staff of the agencies responsible for disaster risk reduction and, more importantly, for the vulnerable, flood-‐‑prone communities and residents.

Government Laws, Measures, and Policies: DRRM and Climate Change

There are a number of existing policies and legislations that respond to disaster risk reduction and management in the country. On June 13, 1956, the Fourth Congress passed Republic Act Number 2056, which prohibited construction of buildings, dams, dikes, and other structures that encroach upon navigable waterways. The punishment was a fine of five hundred to six thousand pesos. This law was poorly observed and enforced (Zoleta-‐‑Nantes, 2000). In September 1973, Presidential Decree Number 296 had directed the demolition of all structures that obstruct the natural flow of water, and specified a heavier penalty: fine of five thousand pesos and imprisonment for two to ten years. Unfortunately, these directives were not executed (Zoleta-‐‑Nantes, 2000).

The Metropolitan Manila Commission was created in 1975 to integrate the provision of public services in Metro Manila. This was later replaced by the Metro Manila Authority (MMA) in 1990, and then to Metropolitan Manila Development Authority (MMDA) in 1995. The MMDA has undertaken development planning, traffic administration, solid waste disposal, and flood control in the city(Zoleta-‐‑Nantes,

2000). An important resolution created by the MMDA with regard to flood mitigation was the 1996 provision on the Pasig River and esterosin Metro Manila. This resolution specified a minimum building setback of 10 meters from the existing banks of major river systems, and 3 meters from existing esterosin Metro Manila (Zoleta-‐‑Nantes, 2000).

In June 1978, Presidential Decree Number 1566 established the National Disaster Coordinating Council (NDCC). The group is mandated to coordinate disaster preparation and management activities of government agencies. The NDCC also advises the President on national disaster preparedness program, disaster operations, and rehabilitation efforts.

In the year 2000, the Local Government Code has institutionalized a systematic allocation of powers and responsibilities between the national and local governments. This decentralization of state power from the national to local governments was instituted to increase the efficiency and effectiveness of local governments in responding to the needs of their constituents (Porio, 2012). Metro Manila comprises of 17 administrative cities, each having autonomous local government units (LGUs), but also managed through the MMDA. In terms of disaster preparation and operations, it is currently the LGUs who are responsible for the coordination of evacuation and relief operations for their respective constituents. They also initiate the trainings of their barangay disaster management teams. Finally, workshops are also conducted for




local residents in order to improve disaster preparation and mitigation.

In order to further address the challenges brought about by disasters, several policies and programs were enacted by Congress. Two overarching frameworks for adapting to climate change were crafted: the 2010 National Disaster Risk Reduction and Management and the 2011 National Framework Strategy on Climate Change. The first provides for the creation of the National Disaster Risk Reduction and Management Council (NDRRMC); the second, for the Climate Change Action Plan approved for implementation by President Benigno Aquino III (APN, 2010).

National Disaster Risk Reduction and Management Plan

In 2010, Republic Act No. 10121 provided a new legal basis for policies, plans, and programs dealing with disasters. It acknowledges the need to “adopt a disaster risk reduction and management approach that is holistic, comprehensive, integrated and proactive in lessening the socio-‐‑economic and environmental impacts of disasters including climate change, and promote the involvement and participation of all sectors and stakeholders concerned.” (NDRRMP, 2011) Thus, the National Disasters Risk Reduction and Management Plan (NDRRMP) has been formulated.

The NDRRMP is a document formulated and implemented by NDRRMC through the Office of Civil Defense (OCD) that sets out goals and objectives for reducing disaster risks. The NDRRMP goals are to be

achieved until 2028, and address the following needs: (1) Need for institutionalizing DRRM policies, structures, coordination mechanisms and programs from national down to the local levels; (2) Importance of mainstreaming DRRM and CCA in the development processes such as policy formulation, socioeconomic development planning, budgeting and governance; (3) Competency and science-‐‑based capacity building activities; and (4) Emphasizes gender-‐‑responsive and rights-‐‑based sustainable development (NDRRMP, 2011).

National Climate Change Action Plan The National Climate Change Action Plan (NCCAP) outlines the agenda for climate change adaptation and mitigation for 2011 to 2038. Consistent with the Climate Change Adaptation Framework, the NCCAP’s ultimate goal is to “build the adaptive capacities of women and men in their communities, increase the resilience of vulnerable sectors and natural ecosystems to climate change and optimize mitigation opportunities towards gender-‐‑responsive and rights-‐‑based sustainable development” (NDRRMP, 2011).

The NCCAP calls for a convergence of approaches and programmes between DRRM and CCA, especially since climate and weather-‐‑based hazards can lead to large scale disasters if processes and communities are not prepared and the risks are not reduced. The NCCAP pursues seven priorities: (1) Food Security, (2) Water Efficiency, (3) Environmental and Ecosystem Stability, (4) Human Security, (5)




Climate-‐‑Smart Industries, (6) Sustainable Energy, and (7) Knowledge and Capacity Development (NDRRMP, 2011).

Disaster Risk Mitigation at the City Level: Marikina City The crafting of local risk reduction and management plans at the barangay (community), city, and provincial levels provide a very promising scenario. These local plans drawn locally and elevated to the city level make design strategies contextually-‐‑specific and more appropriate, responsive and effective for local needs (APN, 2010; Porio 2012).Among local governments in Metro Manila, Marikina is one of the most successful LGUs in constructing flood mitigation structures. In 1992, Marikina’s flooded area was 6.36 sq. kms. But this was reduced to 4.40 sq. kms. in 2004 with the following initiatives: (1) Concreting of roads a priority of the LGU to reduce the amounts of sand, pebbles, and mud entering the drainage system; (2) Construction and rehabilitation of major outfalls which allow flooded areas to recede faster and in the process reduce flood damage and other negative impacts; (3) Regular massive dredging operations allowing faster discharge of floodwaters from residential subdivisions to the creeks/rivers; (4) Demolition of obstructions like the squatter/ informal settlements along rivers and/above waterways (e.g., small creeks, drainage channels); (5) Provision of relocation areas and housing for the informal settlers by the city’s resettlement office, and (6) Improvement of existing water diversion channels (Porio, 2011).

Social Impacts of Flooding in Metro Manila

A hazard like flooding creates a multitude of impacts on populations. These impacts can include loss of homes, livelihood, or loss of life.

In the case of Metro Manila, studies (e.g., Muto 2009; Porio 2011; Porio 2012) suggests that flooding “interacts with the patterns of human activities in the metropolis in more complex ways.” Not all tangible losses are directly observed, because floods “not only affect structures themselves but also their contents and the activities undertaken within them.” Examples of these losses include disruption of traffic and business. Such secondary impacts are damages induced by the direct impacts and may occur outside the flood event in space or time (such as the degradation of materials such as wood and metal when soaked in water). In addition, there are intangible impacts such as health hazards. Cases of these negative effects were presented by Porio (2011) and the IPC Post-‐‑Distaster Needs Assessment Study (Typhoons Ondoyand Pepeng) (2009) from fieldwork done in the affected areas after the devastation of typhoon Ondoy of 2009. The IPC also followed this with a “Social Impact Assessment Study” in 2011 in both rural and urban affected areas in Metro Manila and Luzon.

Impacts on Real Property On the average, the flood waters in their homes reached an average of 20 ft., while in some it reached the maximum height 30 ft. in 2009. But of the two cities, Marikina had




suffered more with 75 percent of their dwelling structures fully or partially damaged while only 35 percent of Pasig had suffered the same (Porio 2012).

Most (81 percent) of their major repairs and reconstruction were concentrated on adding and repairing/repainting the floors, walls, and roofing of their homes while the rest (19 percent) either constructed a new home, fixed their plumbing, drainage system and toilets (Porio 2012).

The length of time needed to repair or reconstruct their houses was dependent on the extent of the damage and the availability of resources. On the average, it took them 30 days to complete the repair and reconstruction of their homes, with a small minority (10 percent) unable to do so because of not having the resources to do it or were prevented by the authorities to reconstruct their homes as these were located in danger zones (Porio 2012).

In Marikina, affected residents spent an average of P141,000 but maximum of P4 million (compared to P12,000 in Pasig City but maximum of P150,000) to repair their damaged homes and appliances. Overall, it took them an average of 2.5-‐‑3 months to completely reconstruct their damaged dwelling structures. The disparity is due to the fact that extreme flooding in Marikina hit more upper/middle-‐‑income households while in Pasig City the extreme floods (in terms of height and length) devastated mostly low and middle-‐‑income households (Porio 2012).

Impact on the delivery of basic services The frequent and increasing intensity of flooding in Metro Manila have severely affected the delivery of basic services like water, drainage systems, sanitation facilities, and electricity (Porio 2012).

In the study conducted by Porio (2011) on the riverine communities affected by typhoons Ondoy and Pepeng, only 39 percentof the respondents have their own electric meter while 42 percent buy their electricity from their neighbor at a higher price. Ten percent obtained electricity through illegal connection from their neighbors, while almost 9 percent use oil/gas lamps and candles for light, heightening the risks of fires in informal settlements. During typhoons and floods, they often experience energy fluctuations, “brown-‐‑outs”, and “grounding” of their electricity sources (Porio, 2011).

Only one-‐‑third (32 percent) of the respondents in the same study have piped water while the remaining two-‐‑thirds buy it from suppliers or neighbors (65 percent) who have water connections or dug/artesian wells. Slightly less than one-‐‑fourth (23 percent) of them have their water supply buried by floods and tidal/storm surges. Thus, they have to buy potable water at higher prices from water suppliers. During floods and storm surges, then, their expenses on water, food commodities, and transportation also increase (Porio, 2011).

Respondents reported that during floods/tidal surges, their toilets get clogged with waste overflowing to their floor and




forced to relieve themselves in the river or in their neighbors’ toilet located far from the flooded area. They also complained that their place smell so bad and quite dirty with floating garbage, plastic bags, and human waste. Aside from the smell, dirt, and environmental pollution, they also observed that sometimes, huge worms or snakes would emerge from their toilets, sewage, and/or drainage… (Porio 2011)”

In addition, the affected residents also reported that their water supply was interrupted for 22 days in 2009 (compared to zero in 2011). In the same manner, electricity could not be delivered for about a month (Marikina) and 14 days (Pasig) to about half of the sample population in 2009 compared to almost nil (1 day) in 2011 (Porio 20120.

Impact on Lost Work or School Days Respondents reported losing from 2 to 98

days in the last rainy season because of illness or floods prevented them from working or pursuing their business/livelihood activities. On the average, they lost from 1–15 workdays (average, 4 days) due to Ondoy. Of those who were not able to work or pursue their livelihood activities, they reported losing earnings, ranging from P98–P2,000 (average of P1,081; median of P500) in the last rainy season (Porio 2012).

On the average, 30 man-‐‑days were lost by the residents in the year 2009 because of the flood damage to their homes, basic services, and infrastructure (roads, bridges and water channels) while in a few (5 percent) households the number of days lost totaled 365 days or equivalent to 1 year due to the same reasons (Porio 2012).

Children also suffered from the monsoon rains, typhoons, and floods, with a third of them being absent (average, 4 days) from

Figure V.5. Children playing in the Flood waters of Marikina during the Habagat rains (Source: Reuters, 2012)




their classes and affecting negatively their academic performance. Children from Napindan and Calzada (Taguig City) lost the most number of days (7) while those from Longos, Malabon reported the least (1.5 days). On the whole, respondents reported their children lost five days of schooling because of floods (Porio 2011).

Impact on Loss of Livelihood In all urban areas visited, the loss of capital and stock caused by Ondoy had a long term impact on small businesses and reduced their profitability. In a number of instances, small businesses closed and could not resume their activities due to heavy losses or to the need to divert capital to meet basic expenditures or conduct urgent housing repairs (Porio 20120.

In rural areas, the destruction of the means of livelihood fields, gardens, fishponds, crops, animals (e.g. carabaos, fowls, pigs), and equipment (e.g. boats, fishing and farming equipment) had significant long term impacts on rural and peri-‐‑urban communities (IPC 2011).

Fishing was no longer a primary source of livelihood in any of the costal or riverine sites visited (as noted in the SIA) due to a steady decrease in catch. Fish farming did, however, play an important role as a complementary source of income. As with farmers, the loss of the investment made in fish farming equipment had long term negative effects. Where fish-‐‑pens were destroyed by Ondoy, fish farmers were unable to secure the capital needed to return to their initial line of activity (IPC 2011).

Impact on Loss of Personal and Household Items Aside from losses in earnings, the respondents also reported damages to their household appliances (refrigerator, TV, washing machine, bed mattress, cabinet or house furniture, radio, electric fan, water dispenser, etc.) and parts of their houses destroyed, costing them from P2–P50,000, with average loss of P4,615 (Porio 2012).

Almost half (45 percent) of the surveyed households reported having lost severely, while 30 percent lost mildly and 16 percent had negligible losses. Severe losses mean losing most of their household appliances and electronic equipment/gadgets. Negligible loss could mean an electric fan becoming inoperable or losing some household utensils/things (Porio 2012).

Impact on Health Respondent s complained of suffering from skin itchiness/allergies, psoriasis, athlete’s foot, fever, colds, diarrhea, typhoid, dengue, and the resurgence of TB infection among children and the elderly. Most (almost 80 percent) of the respondents suffered from coughs/colds and fever due to the floods. About 40 percent complained of diarrhea and stomach ailments. About one third (32 percent) said they suffered from skin diseases/allergies. But it is quite alarming that almost one-‐‑fifth of the respondents suffered from deadly diseases like leptospirosis (18 percent) and dengue (15 percent). Majority (84 percent) of the respondents were also alarmed with the increase of climate/flood-‐‑related diseases




such as diarrhea, dengue, and leptospirosis (Porio 2011, Porio 2012).

The diseases mentioned also happen to be quite costly among medical services. In subsidized government health centers/hospitals, treatment for dengue could cost P35,000 -‐‑ P60,000 while treatment for leptospiroses ranged P10,000 to P50,000. In private hospitals/medical centers, expenses could be double or triple these amounts (Porio 2011, Porio 2012).

On the average, they spent P 6,517 for medical expenses, with some families spending a maximum of P70,000 during the Ondoy floods. Of those who got sick, only half (50 percent) of them said they were benefited by the medical services provided by the government and/or non-‐‑government organizations (NGOs). But majority of them said they were able to receive free medicine, first aid kits and services like water chlorination and dengue-‐‑infested area fumigation or defogging (Porio 2012).

Impact on Personal Finances Only a fifth (20 percent) of the respondents took out loans because of the 2009 floods with an average loan of P10, 517. Of those who have taken loans, about half of them paid interest of less than 10 percent while the other half paid high interest ranging from 12-‐‑30 percent per month. Of those who have loaned money, only half of them (50 percent) were able to pay their debt/loan at the time of the survey. The used the loaned money to build and repair their homes, replace equipment and livelihood losses (Porio 2012).

In summary, the number of absences from school and/or workdays lost rose a 100 percent for both men and women. But their average income losses rose to 300 hundred percent for male-‐‑headed households but a bit lesser (200 percent) for women-‐‑headed households. This is perhaps due to the longer closure of factory-‐‑based work for males while home-‐‑based work for females opened earlier than the former. But the worst is the increase in their expenditure for medicines, sanitation, and health care which rose a thousand percent during the Ondoy floods (Porio 2012).

Impact on Gender and Household Resource Management Porio (2011) in her study for the Asia Pacific Network (APN) summarized the costs of basic services before, during, and post-‐‑Ondoy floods below. As shown in the comparative summary table of costs and losses incurred prior, during, and after the Ondoy floods based on the recollection of respondents during FGDs. The number of absences from school and/or workdays lost rose a 100 percent for both men and women. But their average income losses rose to 300 hundred percent for male-‐‑headed households but a bit lesser (200 percent) for women-‐‑headed households. This is perhaps due to the longer closure of factory-‐‑based work for males while home-‐‑based work for females opened earlier than the former. But the worst is the increase in their expenditure for medicines, sanitation, and health care which rose a thousand percent during the Ondoy floods.

Comparing male and female-‐‑headed households show that women bear a




heavier burden in times of disasters, especially during extreme floods during Ondoy as seen in the tables below. But in non-‐‑extreme situations like that of Pedringand Quiel in 2011, the damages were negligible (Porio 2012).

Perceived Positive Impacts by Community Residents However, though most of these impacts are negative in nature, there can be some positive ones as well. In some areas of KAMANAVA areas and those near Laguna de Bay, informal fisher folk were able to catch fish that escaped from the flooded fish pens and ponds around the area for their own consumption and for selling. Of course, this causes losses to the owners of the flooded fishpens. Furthermore, fisher folk who had their own boats ferried people across flooded streets to enable them to get to work or school. Other transportation providers such as the pedicab (bicycles with

sidecars), and the flood-‐‑adapted tricycles (motorcycles with sidecars mounted on high suspension and wheels) were plying the routes on roads where jeeps and private vehicles cannot pass through due to the high flood waters (Porio 2011).

DRRM and Climate Change Perceptions Among Affected Residents While the above impacts have been mainly attributed to flooding, some survey respondents also recognized climate change as a factor that could have intensified the impacts of flooding. They also reported a change in the climate such as intensity and variability of temperature and heat patterns during the year or during summer and cold months of December-‐‑February; cold months seem to become warmer, etc., (Porio 2012).




Summary of Costs of Basic Needs/Services (in pesos, monthly, US$ 1=P43)

Pre-‐Ondoy Ondoy Period Post-‐Ondoy

Men HH

Women HH Men HH

Women HH Men HH

Women HH

Food P6,000 P5,800 P2,500 + relief goods

P2,000 + relief goods

P6,500 P6,000

Water

• Drinking P50 P45 P240 P240 P60 P50

• Cooking/washing utensils

P80 (well) P500 (piped)


P80 (well, long lines)

P1,500 (piped)

P80 (well, long lines) P1,500 (piped)



Energy/electricity P2,000 P1,800 P5,000 P4,500 P2,000 (wet) P3,000 (dry)

P1,800 (wet) P2,500 (dry)

Sanitation/Laundry (mud, waist deep; cleaning – 2 weeks – one month)

P300 P310 P2,000 P2,000 P360 P320

House repair P1,500 – P15,000

P1,000 – P8,000

Figure V.6. Summary of Costs of Basic Needs/ Services




Percent Increase/Decrease of Costs of Basic Needs/Services in Men-‐ and Women-‐headed Households (monthly)

Pre-‐Ondoy Ondoy Period Post-‐Ondoy

Men HH

Women HH Men HH

WomenHH

MenHH

WomenHH

Food È-‐3% È-‐20% È-‐8%

Water

• Drinking È-‐10% same È-‐17%

• Cooking/washing utensils

0% (well)Ç10% (piped)

0% (well)0% (piped)

0% (well)È-‐5% (piped)

Energy/electricity È-‐10% È-‐10% È-‐10%(wet)È-‐17% (dry)

Sanitation/Laundry (mud, waist deep; cleaning – 2 weeks – one month)

Ç3% same È-‐11%

House repair È-‐33% to -‐47%

Impacts due to Climate Change Aside from floods caused by isolated extreme events such as that of Ondoy, research is also showing that similar impacts can be created by floods due to climate change (sea level rise) and other related causes (tidal surges and land subsidence). Porio (2011) reports that the areas of KAMANAVA are particularly susceptible throughout the year to the effects of sea level rise (SLR) and tidal surges. During the last few years, the residents also reported changes in the climate patterns marked by increases in water levels during tidal/storm surges as seen in the water marks left of their house posts. These pose additional risks to their

household appliances, garments, and higher losses in their work days (Porio 2011).

Data gathered from coastal and riverine cities by Porio (2011) in Metro Manila also show that in flood-‐‑prone areas have also reported changes over time in the intensity of the monsoon rains and the depth of floods in their communities. Tidal waters did not reach their schools before; now their classrooms often are flooded resulting in children going home during the rainy season. Furthermore, residents from KAMANAVA areas (near Manila Bay) and West Mangahan (near Laguna Lake) experience mostly the effects of sea level rise through tidal/storm surges. Residents here reported said that in the previous years

Figure V.7. Percent Increase/ Decrease of Costs of Basic Needs/ Services in Men-‐‑ and Women-‐‑headed Households




the water in their yard would only reach up to their knees but in the recent storm/ tidal surges the water would reach their waists (Porio 2011).

Looking at these impacts that affected a lot of people, one can see that there were cases of an “uneven distribution” of such hazards and impacts. Porio (2011) in particular points out that households near the constructed flood control infrastructures (gates and walls) complained that the flood levels have increased (from ankle deep to waist deep). Respondents reported that there was an observed intensification of flooding with the clogging of the water ways. From the perspectives of those households and communities, increasing water levels, there is a perceived sense of unfairness. Also, according to the local officials, informal settler families (ISFs) should not settle in danger zones (hillsides, creeks, riverbanks and other waterways or sites of soil erosion and subsidence. But continuous eviction/demolition programs by LGUs in these areas have not solved the problem (Porio 2011, Porio 2012b).

These household level flood risks and vulnerabilities also increase with the expansion of residential, commercial, and industrial development in their immediate localities and cities. This could be observed in the rapid expansion of construction projects around the Mangahan River Basin, which do not have the necessary infrastructure support like proper drainage, sewerage, and road systems. The consequent flooding become intense as the areas near the Napindan Channel and Laguna de Bay are not really suitable for

habitation nor for commercial-‐‑industrial use as these are mostly wetlands. Meanwhile, these have not deterred building activities in these areas because developers just fill up the marshy areas and raise the building height of ground floors (Porio 2011, Porio 2012b).

It must be noted however that during Ondoy 2009, both informal and formal settlers experienced more or less the same impacts as shown in the IPC Post-‐‑Disaster Assessment (IPC 2011). But as Porio (2011) noted earlier, aperson’s vulnerability stems from social conditions such as those who endured Ondoywere mostly “very poor households (old, widowed/separated, no income and dependent on the relatives’ food support), also came from these low income communities” which still shows the possibility of discrimination of impacts in the community. This was reinforced in the post-‐‑disaster study of marginal communities in the Pasig-‐‑Marikina flood basin who suffered from the Ondoy, Pepeng and Qiel floods (Porio 2012, Porio 2012b).

Flood Impacts: Intersections of Social, Ecological-‐‑Environmental and Health Vulnerabilities

Individuals and their societies live on specific spatial and temporal locations that are bound by certain contexts or backgrounds. Each location has physical, economic, health, and other contexts that affect the dynamics of societies. In understanding risk and vulnerability of people and populations, one must consider how risks stemming from the physical




conditions of the environment, the economic status of the area, and the overall state of health and socio-‐‑political-‐‑economic status of the families living in the area. In this particular paper, we look at the intersection of all of these forces in cases of flooding in urban cities particularly coastal and riverine cities such as Metro Manila (Porio 2012b).

In several studies that examined the characteristics of vulnerable populations. For example, Porio (2009, 2011) suggested the following characteristics:

“Most of the very poor households (old, widowed/separated, no income and dependent on the relatives’ food support), also came from these low income communities. These urban poor settlements remain very vulnerable because of dilapidated housing structures and absence of services and drainage systems in their water-‐‑logged environments. Most respondents had 8.5 years in schooling and only respondents from Malabon attained some college education (11.20 years). Their low education and income, in part, explain their low levels of formal employment as most of their earnings were from the underground economy.”

Note that apart from social sector variables such as poverty, marital status, age, gender, educational attainment, and income, the description also emphasizes the fact that these populations live in water-‐‑logged areas that lack social services such as potable water, electricity, passable roads and inadequate drainage systems. This study

reports that these seemingly vulnerable populations suffer from a myriad of flood-‐‑related diseases such as those of the skin, dengue, diarrhea, and leptospirosis. These diseases are exacerbated by lack of access to clean water and health care facilities that can help prevent or reduce the risk of getting such diseases. Loss of work days due to these diseases in turn contributes to the reduction of the individual’s ability to become economically productive as also shown by the study. The interrelatedness of these different sectors emphasizes the need to address issues of vulnerability and risk in a multidimensional manner (Porio 2011). More details on the intersections of the vulnerable contexts of marginal communities are given in the figures below:

Cutter (2003), in her study of social vulnerability to environmental hazards, uses variables such as loss of physical infrastructure and lifelines (bridges, sewers), commercial and industrial development, occupation, and availability of health services. These variables can be sourced from the other sectors mentioned. Cutter considers these variables’ interaction with age, gender, race and ethnicity, and socioeconomic status to fully understand social vulnerability.

Community Preparation Efforts

Community Preparation for Hazards Associated with Extreme Events The wealthy residents experience significant flood impacts, and are active in defense of their own interest. They are more capable of finding solutions on a neighbourhood scale,




and do not look for the government to help them. They often also possess enough political connections that can help them direct much-‐‑needed resources towards flood-‐‑proofing their houses (Zoleta-‐‑Nantes, 2002).

In the study conducted by Porio (2011) in the riverine communities of Metro Manila, the wealthy or better-‐‑off families responded to increasing heights of floods by“flood-‐‑proofing” their houses through use of

stronger materials like lumber, zinc and concrete blocks. Better-‐‑off residents also build additional floors to their houses so they can move up when the ground floor is flooded as well as providing rental income from workers in nearby fish port in Navotas City or in nearby factories in Manggahan floodway, Pasig City.

On the other hand, the flood problems of the urban poor are intimately linked with survival problems such as decent housing

Figure V.8. Environmental Vulnerabilities of Places: Sources of Vulnerabilities for Urban poor Households in the Three Metro Manila Flood Plains (Porio 2011).




availability, infrastructure maintenance, and scarcity of financial resources. Their flood losses are smaller in value, but have more adverse impacts on their livelihoods, daily survival, and capability to deal with flood hazards in the future (Zoleta-‐‑Nantes, 2002).

The urban poor living in the KAMANAVA area, who regularly experience tidal/storm surges, have adapted themselves over the years by just packing their clothes in boxes, tying and raising their furniture and appliances to the higher parts of their homes through a makeshift pulley. They have devised platforms for their appliances and household things that they raise when the tidal/storm surge comes. As one respondent quipped, “they are forever in an evacuation mode with their clothes and other necessities or valuables in boxes”! (Porio, 2011).

Community Preparation for Hazards Associated with Changing Climate Adaptation to the hazards involving risks of storm/tidal surges and floods has been the key survival strategy of most residents. They seem to have gotten used to the constant flooding in their premises and crafted a “water-‐‑based lifestyle” — getting used to the regular rise of dirty water in their midst and adjusting their household routines and work patterns according to the demands of the rains, floods, and water surges”. This is particularly true for residents in the KAMANAVA areas where tidal surges often meet flood waters from the upstream (Porio, 2011). However, residents are not really well-‐‑aware of the hazards associated with climate change.

They only hear about climate change through mass media (TV and radio), but they do not really know what it means and how relevant it is for them. As a result, the preparation and adaptation strategies to the changing climate are very rare.

Access to Information on Relevant Hazards Efforts are made by the government, media, and other organizations in order to relay important information on hazards to the people. For instance, in the study conducted after typhoon Ondoy and Pepeng in Pasig City and Marikina City, majority of the respondents (71 percent) obtained their information from television while some heard it from friends/relatives (13 percent), radio (8 percent), government agencies (2 percent) or have read about it in magazines/newspapers (7 percent) (Porio 2012).

However, differences in access to information still persist. In the IPC Social Impact Monitoring Study (IPC 2011) conducted in the summer of 2011, respondents reported that differences in access to information partly explained the uneven distribution of support: “Being ordinary citizens we do not know who to approach!”Participants reported that not everyone was informed about the recovery programs implemented in the community, with some respondents finding out about existing programs because they responded to meetings called by the barangay/organization concerned or were sought out by groups providing support. For others, information about recovery programs arrived too late: “Anyone could




apply. But the application is no longer available”! (IPC, 2011)

Respondents in affected areas often highlighted what they considered to be a “monopoly of knowledge” by community leaders and local officials. The way in which information about the support available circulated in affected areas seemed, therefore, to be highly reliant on personal networks. Respondents reported that there were no instances of LGU putting in place outreach or communication strategies for those in the social margins.Limited access to information also meant that respondents were unable to voice their complaints or grievances about the recovery process (IPC, 2011).

Social and Cultural Barriers to Adaptation and Resilience The study conducted in riverine communities in Metro Manila showed three main barriers to disaster preparation efforts: (1) lack of financial capacity; (2) lack of institutional support; and (3) security-‐‑related concerns.While respondents recognized the risks of floods and storm/tidal surges to their homes and communities, economic (unemployment and loss of livelihood) and social problems (thefts, drugs, domestic conflicts) were their over-‐‑riding concern (Porio, 2011).Their money is usually allocated for basic commodities such as food, water, and medicine. So instead of spending money to flood-‐‑proof their houses, they will just strengthen their house posts and stock up on food (Zoleta-‐‑Nantes, 2002).

Availability of support networks during calamities seemed low among the urban poor. When surveyed what kind of support they received from their relatives, neighbors, friends and community officials, the respondents displayed a high level of self-‐‑reliance (Porio, 2011). One-‐‑third of the respondents consistently assert that they just rely on themselves as no one really helps them. Only a small portion (range of 3–24 percent) received aid from their support networks. Perhaps, their social networks are quite thin and also in similar dire situation (Porio 2011).

Security-‐‑related risks like thefts, fire, and drug abuse in their neighborhoods are also hindrances to disaster preparation efforts of the community. These security-‐‑related issues are closely linked to the physical congestion and economic insecurities (i.e., high unemployment/underemployment) of their communities/ families. Given these socio-‐‑economic risks, their vulnerability to the effects of typhoons, floods, and sea level rise (SLR) or storm/tidal surges increasingly becomes heightened (Porio, 2011).

Community Perceptions of Hazard, Risk and Vulnerability

Flooding as Normal An overall perception that some degree of flooding is part of “normal life” persists among the residents in flood-‐‑prone areas. For instance, in the IPC Social Impacts Monitoring Study conducted in 2011, reported that acceptance of floods as part of daily life in 12 out of the 21 studied sites. There were even instances of under-‐‑




estimationof the potential dangers in the current location as reflected in high degree of pride in having survived previous disasters: “You were not born yet when we went through more ferocious typhoons. But here we are, still alive!”(IPC, 2011).

In a study conducted by Porio (2011), respondents reflect a low perception of risk living in riverine communities: “You do not die from floods or from the rising waters here!” Thus, the risk of catching infection from rat’s urine in the flood (i.e., leptospirosis which can result in massive infection and death) is also not high in the people’s consciousness. This level of awareness about the deadly effects of rats and leptospirosis changed after the Ondoy floods when those affected by the dreaded virus reached hundreds (Porio 2012b).

Increasing Awareness of Risk In the IPC Social Impacts Monitoring Study conducted after typhoons Ondoy and Pepeng, 17 out of the 19 communities reported greater awareness of the dangers of flooding and of possible mitigation measures. Mass media (TV and radio) and schools (for children and young people) were the sources of information about disaster prevention consistently mentioned. There were also isolated instances where written materials provided by the municipality or the barangay were referred to as a useful sources of information (in two of the 19 communities/areas studied).In these areas, the research team observed that respondents were aware of a general disaster response plan (“May sistemanapodyan “(There is a system in place) (IPC, 2011).

Climate Change and Extreme Events In the study conducted among the affected residents of Pasig City and Marikina City from typhoons Ondoy and Pepeng, majority of them (86 percent) have heard of climate change and have accepted it as part of environmental change. In fact, when asked how they understand the phenomenon, majority were able to enumerate the following indications: ozone depletion, sea level rise, air pollution, heavy rains/floods, droughts, water shortage, melting of glaciers and rapid/intense weather changes. But they were not agreed on the causes of climate change as they gave varying responses like: environmental pollution (50 percent), increasing population (36 percent), and God’s wrath/mankind’s sins (14 percent). But on the whole, they acknowledge that the major causal forces of climate change are: people’s lifestyles (68 percent) as mainly responsible, followed by inaction or negative actions of government (20 percent), industries (10 percent), and God (2percent) (Porio 2012, Porio 2012b).

Furthermore, the residents in flood prone areas around Laguna Lake and KAMANAVA observed the effects of climate change (e.g., variable patterns of heat/temperature, rainfall and floods), but they are not necessarily aware that these are already signs of climate change (Porio 2012b). In the study conducted by Porio (2011), residents in the KAMANAVA area have reported changes over time in the intensity of the monsoon rains and the depth of floods in their communities. Tidal waters did not reach their schools before; now their classrooms often are flooded resulting in children going home during the




rainy season. Residents in West Manggahan (near Laguna lake) have reported that in the previous years the water in their yard would only reach up to their knees but in the recent storm/ tidal surges the water would reach their waists. Very few of the respondents attributed these to climate change (Porio 2011).

Education Programs and Trainings in the Community Based on the Social Impacts Monitoring Study conducted after typhoons Ondoy and Pepeng, 11 out of 19 study sites reported the existence of a disaster preparedness plan in their barangays. Furthermore, 15 out of the 19 study sites also reported the existence of trainings on disaster preparedness implemented by the local government units and non-‐‑governmental organizations. Unfortunately, these trainings tended to focus only on LGU officials, with limited outreach especially to vulnerable sectors (urban-‐‑rural poor, women-‐‑headed households, children, senior citizens, poor migrants) of the community level. Hence, future disaster preparedness trainings should include the broader sectors of the community, especially vulnerable groups and not just the local government officials. More importantly, communication strategies and techniques should utilizeand incorporate indigenous/local knowledge as well as mobilize existing social capital and networks in reaching and assisting urban-‐‑rural poor sectors and other vulnerable groups in these communities (Porio 2012b).

Response and Recovery

Majority of the affected population are given assistance and help by the local government. In the study conducted among the affected residents of Pasig City and Marikina City after typhoons Ondoy and Pepeng, an overwhelming 83 percent claimed to have been assisted by the local/city government with food and clothing. They received this assistance on the average after 48 hours, minimum 14 hours and maximum of 504 hours (or about 21 days). Only 1 percent claimed to have received financial assistance of P4,000 (about US$ 9) because of the death of a family member during the flood (Porio 2012).

After the floods, Porio (2012, 2012b) reported that the following measures were taken by the barangay/city government to reduce the impact of flooding in their communities: (1) Evacuation of residents, especially those in informal settlements along the rivers, creeks and other danger areas; (2) Clearing and rebuilding of roads and bridges; (3) Rebuilding of the water supply network; (4) Clearing and rebuilding of water channel and drainage networks (including rip-‐‑rapping of river/creek walls and elevating water dikes); (5) Pumping flood waters out of the area; (6) Relocation/resettlement of affected residents; (7) Acquisition of equipment/supplies necessary during calamities (e.g., rubber boats, fire trucks); (8) Defogging of mosquito infested areas, especially those with high incidence of dengue cases; and (9) Capability building programs (training/seminars, information campaigns/dissemination).




In short, the immediate, medium and long-‐‑term responses of the city/local government can be summarized into: 1) evacuation, 2) restoration of basic services and 3) rebuilding of infrastructural support (Porio 2012).

Given the above interventions, majority of the respondents in the same study (65 percent) feel satisfied with the interventions provided by the national/local government agencies. A small number (35 percent) wished the government could provide better services such as water, sewage and sanitation but most of all sustainable livelihood and land/housing for those displaced by the floods. Thus, when asked who is responsible for preventing and responding to hazards and calamities, an overwhelming majority (70 percent) pointed to the barangay/city LGU as the one

responsible while others identified the family (17 percent), civil society (8 percent) and the private sector (5 percent) as being responsible.

Glaringly highlighted in the identified medium and long-‐‑term responses of the city governments of Marikina and Pasig is the absence of a continuous-‐‑systematic training and capability program for the staff of the agencies responsible for disaster risk reduction and, more importantly, for the vulnerable, flood-‐‑prone communities and residents (Porio 2012b).

Insights from the Study of Antecedent Social Conditions to Risks and Disasters

Figure V.9. A resident being rescued during the Habagatflooding (source: Agence France-‐‑Presse)




People’s memory is short. It seems that the affected community groups, especially the poor vulnerable to floods, easily forget the devastations they experienced during the disaster. Or it seems that they do not adjust/adapt their behaviour in their midst. They continue to go back to their old ways of mismanaged waste and other environmentally-‐‑degrading practices after just a few months. On closer look, however, residents reported that the incentive structures (both positive and negative) are not consistently implemented.This short memory is not only limited to those of affected populations but of institutions (local government units, recovery and rehabilitation agencies) as well. This lack of institutional memory is glaringly reflected in the persistence of outmoded land use policies and building codesin most cities of Metro Manila (Porio 2012b).

Another important lesson from these studies is that the social vulnerabilities (gender, age, socio-‐‑political-‐‑economic status) of the poor in marginal riverine or coastal communities in Metro Manila’s flood basins strongly interact with ecological-‐‑environmental and political vulnerabilities of places/communities and their LGU governance systems highlighting the overall vulnerability of a sector or population (Porio 2012b) as seen in the following description.

Based on the 2008 household survey conducted in the three flood basins of Metro Manila, namely, 1) Pasig-‐‑Marikina, 2) West Mangahan, and 3) KAMANAVA Area. Based on the survey, the respondents were mostly female (86 percent) as they were the ones available/open to be interviewed, compared to the unwilling male members of the household. Their ages ranged from 18-‐‑92 years old, with a median age of 42 years old. They were mostly legally married (61 percent), or were in live-‐‑in/cohabitation arrangements (20 percent), while the rest were widowed, separated or single (18 percent). Their mean household income was P10,033 per month but their median monthly household income was P8,000, suggesting some disparity among the respondents. Most of the very poor households (old, widowed/separated, no income and dependent on the relatives’ food support), also came from these low income communities. These urban poor settlements remain very vulnerable because of dilapidated housing structures and absence of services and drainage systems in their water-‐‑logged environments. Most respondents had 8.5 years in schooling and only respondents from Malabon attained some college education (11.20 years). Their low education and income, in part, explain their low levels of formal employment as most of their earnings were from the underground economy (Porio 2011).




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Appendices

Appendix V.1: Summary of Data Gathered from Different Sources (Social Surveys, Key Informant Interviews and Focus Group Discussions/FGDs and Secondary Sources, namely 1) JBIC-‐‑supported Study of Three Flood Basins (Porio 2011), 2) APN-‐‑supported study “Enhancing Medium-‐‑ and Long-‐‑Term Planning Among Local Governments” (Porio 2012), 3) PDNA Study (Institute of Philippine Culture, 2009), and 4) Social Impact Assessment Study of Ondoy and Pepeng (Institute of Philippine Culture, 2011).

Questions Answers/Sources

3. In general, what are the social impacts of: (i) hazards associated with extreme events; and (ii) hazards associated with gradually changing climate change, on the community? Describe these in quantitative and qualitative terms. a. Which of these social impacts could be

considered as positive, and which can be considered as negative impacts to the community?

SIM/APN Study –Taguig, Pasig and Marikina – on flooding

Negative – destruction of houses/properties,

unable to go to work/classes for a number of days

after the event; presence of water-borne diseases,

disruption of businesses, increased

transportation expenses.(Some or allwere reportedly experienced by both focus group discussion participants and survey respondents.)

Positive – Increased fishermen’s income who used boats in ferrying residents to and from unflooded portions where they can get transportation to and from work/school.(mentioned by four (4) of the six (6) focus group discussions done in Wawa, Taguig)

JBIC Study – Napindan, Taguig – Flooding

Negative – unable to go to work/classes, destroyed properties, unclean water,water-borne diseases, increase in transportationexpenses. (reported by almost all of the surveyrespondents and key informants.)





Positive/Negative– sustenance fishermen had more catch because of fish from overflowing fishponds/fish pens but negative for fishpond/pens owners whose fishpens got destroyed and lost their fish. (from key informants living near the mouth of the Pasig River going to Laguna Lake)

APN Study – Pasig and Marikina – Flooding

Negative – unable to go to work/classes,

destroyed houses/properties, water-borne

diseases, increased transportation expenses,

losses of lives, increase cost of repairs/

replacements for both house and properties. (All survey respondents)

Positive –

-‐ some residents were hired to clean up muddied houses, selling gallons of clean water to residents whose water connections were cut off because of the flood.(from all respondents in Marikina)

-‐ ferrying passengers from their houses to where they can get their rides to and from work/school (as recounted by a few of the respondents from Pasig)

b. Has there been any sense of unfairness or discrimination in the community in relation to the distribution of impacts of the hazards, or in relation to actions taken to address these hazards?

JBIC Study (from key informant interviews and focus group discussions)

-‐ the construction of the sea wall caused excessive flooding to settlers whose houses were situated in low-lying areas. (e.g., Navotas and Malabon)

-‐ construction of access roads which were





built higher than some houses and had inadequate drainage also caused more flooding in the area (Navotas and Malabon)

-‐ informal reclamation/filling up of swampy or waterlogged lands by realtors and better-off neighbours led to flooding among residents who could not afford to make these adaptive strategies or were in low-lying areas

-‐ pumping stations (by the city LGU) which suction flood waters from one barangay caused flooding in other barangay (Caloocan and Navotas)

SIM Study, APN

-‐ Usually, only those who were relatives/friends of barangay officials/staff can get relief goods easily compared to those who did not have strong social and political networks. Other barangay residents had to line up for a long time to get relief goods. (reported by some respondents of each survey barangays in Marikina, Pasig and Taguig).

SIM Study

-‐ Some benefits for relocated families were not given by the sending city or what was given them was not enough to sustain them while they were looking for work and host LGUs could not also afford to provide assistance (As complained by more than half of the relocatees from Marikina to Biñan, Laguna)

-‐ Survey respondents and focus group discussion informants reported that there were not enough employment in/near the relocation sites, some relocatees had to go back to former residence to look for work(from most of the relocatees from Marikina to Biñan, Laguna; relocatees from other parts of Manila to Southville 4a, Sta. Rosa, Laguna)

-‐ Some relocated families had gone back to live near their former residence, renting rooms/houses because they had a hard life in the relocation site. However, they also





revealed that they left some of the family members in the relocation site so that their will not lose their entitlement to resettlement. (as gleaned from the key informant interviews in Southville 4a, Sta. Rosa, Laguna; in Langkiwa, Biñan, Laguna and Eusebio Bliss, Pasig)

4. Assess the distribution of impacts due to disasters within the community related to social factors. b. What are the key social factors or

preconditions that determine the risk or resilience of communities? Describe and explain

JBIC Study, SIA, SIM, APN

-‐ Construction of houses near or over waterways by informal dwellers/settlers

-‐ Indiscriminate disposal of garbage by these dwellers/settlers which caused clogging of canals/drainage

e. What social mechanisms or types of assistance are available to the community to recover from discrete events, and to be build resilience and adaptive capacity in the long-run?

JBIC Study

-‐ Relief goods -‐ Flood water pumps (“bombastic”)

SIA, SIM, APN

-‐ Relief goods/medicines -‐ Temporary evacuation centers -‐ Relocation/resettlement -‐ Disaster orientations/trainings

SIA/SIM

-‐ Giving housing materials to verified victims of the flood to repair their damaged houses.

5. What is the state of the community’s access to basic services? a. How has access to basic services evolved

over the past few decades? i. What were the drivers of these

changes in access to basic services?

Negative/Positive: Deterioration of services (water, drainage) but some improvement in garbage collection (recycling and segregation have found greater support among LGUs).

ii. Who are the decision-makers, other actors, and stakeholders responsible? Provide a detailed description of their roles and actions.

Key decision-makers for social services: the service providers like Manila Water or Maynilad, electricity provider like MERALCO, government service providers like the city social services departments (e.g., social welfare and development, solid waste management, land use and planning division) and the local barangay council/committees (DRRM, environment, social services)





b. How has access to basic services in the community affected in the aftermath (short, medium, and long-term) since the disasters occurred?

i. What critical factors and conditions have affected access to services after disasters? Describe and explain.

SIM/APN

-‐ Long lines of residents waiting for relief distributions or medical assistance (reported by almost all of the respondents SIM and APN)

-‐ Doubtful residency or doubtful claims (in cases of the housing materials distributions, mentioned in the focus group discussion and some key informant)

-‐ Affected families have no access to relief distributed (for families who opted to stay on top of their houses, and their houses were located in inner alleys of the settlement), the relief distribution usually took place in dry area or main streets/corners of flooded area. (reported by some respondents from Marikina and Pasig)

ii. List down available resources and networks, both private and government, that will help enable disaster-stricken areas to recover.

SIM/APN

-‐ LGUs (city and barangay) § Prepare residents for future disasters

ü Pasig – had manuals and done trainings on disaster preparation down to the barangay level

ü Marikina –had had orientation seminars on what to watch out for or what to do in times of typhoon/flooding

§ Ensure that laws regarding settlements near waterways be implemented

ü Pasig – relocated river bank/floodway side dwellers to Bliss housing and relocation sites in Laguna

ü Pasig – had pumped out flood waters from flood-ravished barangays

ü Marikina – had also brought to Laguna residents whose houses were destroyed by the flood.

ü Taguig – had already





informed waterways dwellers that they had to move out/some were offered relocation, but their resettlement housing were not ready yet.

-‐ NGOs/CSOs – ü Have conducted seminars/

trainings in risk awareness/reduction/preparation

iii. What is the nature of community recovery (e.g. how fast the recovery process was; which parts of the community recovered first and fastest; which members of the community or social groups recovered more quickly and effectively?)

SIM/APN

-‐ Those who could afford, repaired their houses, and bought new appliances to replace appliances destroyed by Ondoy. (Mariking, Taguig and Pasig)

-‐ Some residents left their houses after it has been destroyed by Ondoy (Provident Village, Marikina)

-‐ Some repaired their destroyed houses a little at a time. (Marikina and Pasig)

-‐ For those who could not afford, they make do without the appliances they use to have. (i.e., kerosene stove in place of their destroyed gas stove; use fans because they could not afford to buy new electric fans)

-‐ Some businesses which were destroyed by Ondoy were not able to operate again due to lack of capital.(Marikina)

As to recovery

-‐ Those who had money to repair their houses/business/appliances and did not suffer death among its family members seems to have recovered better (Marikina and Pasig)

-‐ For those who had lost their houses/businesses, their appliances destroyed did not think they will recover for a long time. Some businesses though have transferred or put up branches in higher places like Antipolo and Laguna. (Marikina and Pasig)

-‐ Those who had suffered deaths, sickness and trauma – think they could not forget about the tragedy, and would probably remember it for





the rest of their lives. (Marikina) iv. Are communities able to move

beyond recovery and “build back better”?

SIM/APN

-‐ Respondents said that rebuilding houses meant adding another storey to which they can run to in case another tragedy like Ondoy strikes..

c. What is the role of access to basic services in reducing risk or strengthening resilience before and after an extreme event, and under a gradually changing climate? Describe and explain.

JBIC/SIM/APN

-‐ Given the Ondoy experience, both the household respondents and key informants believe that although the barangay LGUs had helped them (like giving warnings of the flood, relief distribution, setting up evacuation sites), they themselves should be prepared in anyway they can (like putting up important things and clothes in barrels/big baskets and raised it up to the higher portion in their houses, construction of additional storey to their houses, etc.) (Pasig, Taguig and Marikina)

d. How has the community been able to access information of relevant hazards? How has this information been used for social protection? Describe and explain.

SIM/APN

-‐ Information system throughout the barangay has been expanded (i.e, public address system has been put up around the barangay). Announcements of possible disaster (i.e. typhoon) are made through these public address system (Marikina)

-‐ Baragangay officials going around the barangay using a megaphone to warn people about the disaster (Taguig)

-‐ Informationscomes from the City Rescue Unit to the barangay LGU. The barangay follow the procedures which were contained in their manual.(Pasig)

8. What is the community’s perception and understanding of risk, disaster risk management and resilience, climate change, and climate change adaptation? How do their perceptions affect their decision-making process?

a. What meaning does the community attach with the idea of “risk”?





b. How much does the community understand about the points of convergence and points of difference between DRM and CCA?

c. How does the community assess their personal risk to hazards brought by extreme events and climate change?

d. Does the community believe that climate changes are related to extreme events?

e. Are there any education programs or trainings for CCA and DRM in the community?

f. What is the average literacy level of the community? On the average, how many years of formal education did the community members receive?

g. Is there an overall or prevalent community-shared view of the disaster? Alternatively, are there contrasting or conflicting views? Describe and explain.




Appendix V.2: Accounts of Popular Narratives on the Causes, Impacts and Solutions to the Various Typhoons in Metro Manila Questions Typhoon Bidang

(Nov. 25 – 29, 1974)

Typhoon Gading (July 6 – 10, 1986)

Typhoon Unsang (Oct. 21 – 26, 1888)

Typhoon Rosing (Oct. 30 – Nov. 4, 1995)

Typhoon Milenyo (Sept 27 – 30, 2006)

Typhoon Ondoy/Pepeng (Sept. 24 – 27, 2009 Sept 30 – Oct 10, 2009)

Causes of the disaster

““PAGASA said it expected Bidang’s backlash winds and rains to cause the swelling of the Pampanga swamplands which sprawl over half the province and Nueva Ecija.” (Phil. Daily Express, 1974)

“Executive Secretary Joker Arroyo blamed the floods on the inefficient drainage system inherited from the previous administration…” (Renato Reyes et al, Philippine Daily Express)

“Many residents of Barangay Minahan Interior which alongside the Marikina river were hardly alarmed by the continuous rains spawned by the typhoon since most of the shanties in the community were 20 meters away from the riverbank. However, the heavy overnight downpour caused the river to overflow, its swollen waters sweeping the frail shanties in barangays along the bank, including

“Many bodies were crushed by mudslides from denuded hills…” (John Bello et al, Phil. Daily Inquirer) “One reason is the overflowing of major rivers during heavy downpours, and another is the inefficient and inadequate inland drainage facilities…” (Christina Hermoso, Manila Bulletin) “Indiscriminate dumping of waste materials along canals, rivers, creeks, and esteros has been one of the major causes of flooding…” (First Metro District

“Ferrer said they were apparently trying to check if it is necessary for them to leave their homes and more to higher ground. ‘Witnesses said part of the (Butas) dam [in Cavite] suddenly gave way as the dam was already swollen with water. The victims were then swept away by the water.” (Christine O. Avendano and Marlon Ramos, PDI 30 Sept 2006).

“The rainfall was recorded as approximately 450 mm at the Manila Observatory, an extremely rare occurrence…” (Source: PDNA) “Local governments did not have adequate zoning ordinances, building codes, and standards to mitigate against these risks…” (Urban planner FelinoPalafox)




Minahan interior.” (Noel Bartolome, Malaya 26 October 1988)

Engineer Nardo Cruz)

Factors contributing to the risks

“At least 85 shanties and houses in Metro Manila were destroyed by Bidang as she lashed the country packing killer winds of 220 kph near the center. Most of the destroyed shanties and houses were located in Islang Putting Bato and Luzviminda villages in Tondo and in Barrio La Huerta, Paranaque, Rizal.” (Phil. Daily Express, 1974)

“The howler, packing center winds of 175 kilometers per hour… spawned floods that left thousands homeless in the Ilocos region, Cagayan, Southern Tagalog, and Metro Manila. (Phil. Daily Express, 10 July 1986)

See above, consider fact that people were living along the river.

“Many officials blamed PAGASA’s failure to accurately plot the typhoon’s path for the extensive damage to life and property…” (Negros Governor Rafael Coscollucia) “The sea level in the metropolis had gone up by 30 centimeters as a result of global warming. The low-‐‑lying areas in Metro Manila are in danger of becoming severely flooded within the next 100 years…” (Philvocs director RaymundoPunongbayan, 1992)

“The high population density has strained the existing sewerage and drainage systems in the city…” (Zoleta-‐‑Nantes, 2002). “The forestlands in the Marikina watershed had been transformed into paddies, villages, and residential subdivisions, eroding 25 to 50% of the topsoil and thus silted Marikina and Pasig river systems…” (Zoleta-‐‑Nantes, 2002).

“Expansion of residential, commercial, and industrial development in their immediate localities and cities, which DO NOT have the necessary infrastructure support like proper drainage, sewerage, and road systems...” (Porio, 2011) “Only 71 % of the wastes are properly collected by the dump trucks and taken to landfills. The remaining wastes are simply left on the streets, dumped on vacant lots, or thrown into storm drains, creeks, or rivers…” (Bankoff, 2003)




“Corruption was the lifeboat that should have been there but there were not because the money had been stolen. Corruption was the pile of relief goods that should have been there but was not because it had been stolen. Corruption was the dams and garbage incinerators and drainage systems and relocation areas that should have been there but were not…” (Conrado de Quiros) “Killer floods are not only the result of clogged waterways, inadequate drainage systems, dysfunctional urban management, rising sea levels, or




the gradual sinking of Metro Manila as a whole due to unabated ground water extraction. They are also the product of gross social inequality and mass poverty…” (Randy David, Phil. Daily Inquirer)

Strategies, measures, and policies that will prevent adverse impacts

“Social workers went on immediate alert, most of them rushing to the affected area to help in the massive rescue and relief operation for Central Luzon and Greater Manila area residents whose houses were either toppled or rendered roofless by the strong winds. A stand-‐‑by fund of P1,339,000 was

“President Aquino ordered the release of P10 million from the national budget for the affected Luzon provinces, and another P1 million from the Philippine Charity Sweepstakes Office for Metro Manila… The Ministry of Health said it is dispensing P3-‐‑million worth of analgesics and antibiotics in coordination with other agencies involved in relief operations…”

“President Aquino ordered yesterday the immediate release of a P10 million calamity fund for the victims of typhoon ‘Unsang.’ The President ordered mobilized all government agencies, especially the DSWD, the DND, and the Dept. of Local Governments, in carrying out relief operations in the typhoon-‐‑ravaged areas of the country.

“The Metro Manila Disaster Coordinating Council placed the entire metropolis on red alert in anticipation of super typhoon Rosing… These include plans for evacuating people, especially those living near shorelines, rivers, and creeks, or in low level areas. Rescue and relief teams were also readied…” (Donna Cueto, Phil. Daily Inquirer) “Prospero Oreta, MMDA Chair, mobilized hundreds of

“Adaptation strategies of the households to deal with Typhoon Milenyo were mostly structural, such as reinforcing their houses and household properties… as well as behavioural, such as securing food, water, and other household needs… Some collective action took place in the community, particularly after the typhoon as relief operations

“A community-‐‑based early-‐‑warning system, involving the gathering of data, interpretation of results and dissemination of forecasts and warnings to solicit appropriate response from the affected populace, is in place…” (Yumul, 2011) “The sharing of best practices on being proactive in mitigating the ill-‐‑effects of disasters




also released by the DSW to the provinces that life in the path of the typhoon, the seventh and strongest to hit the country since October 11...” (Phil. Daily Express, 29 Nov 2012) “What to do during typhoons: 1. Watch out for manholes, ditches, and washed out pavements leading to brooks and rivers while walking in flooded in flooded streets and roads. 2. Watch out for flooded areas with toppled electric posts or dangling electric wires. 3. Use bancas with outriggers,

(David Borje et al, 12 July 1986)

The president also directed the Bureau of Customs to make available all seized smuggled goods, including clothes and and food to typhoon victims. “ “[The president] also instructed DOH Sec Alfredo Bengzon to provide all the much-‐‑needed medicines and other first-‐‑aid drugs to the two evacuation sites.” (Lulu Principe, The Manila Times 26 Oct 1988) “US military forces stationed at Clark Air Base took part in the search and rescue efforts for typhoon victims.” “Schools, government offices and many private businesses were closed as strong winds buffeted Metro Manila.”

MMDA personnel for disaster monitoring, rescue, and evacuation work… He ordered deployment of tow trucks and buses to help stranded motorists and commuters. Teams were organized to clear streets of fallen trees and electric wires…” (Dona Pazzibugan and Natasha Vizcarra, Phil. Daily Inquirer)

were mobilized. ” (Linda Penalba and DulceElazegui, EEPSEA)

also has been occurring…” (Yumul, 2011) “The capacities of local government units are enhanced through training, emphasizing the need for their communities to assume ownership of their disaster risk management programmes…” (Oxfam, 2008)




if possible, with reserved paddles and enough food and water supply. 4. When evacuating to the mountains or hills. Be careful about snakes that usually hide in tall grasses in stones or rocks or along the roads. 5. When your house is submerged by floods, cling or hold on a floating object and wait for a rescuer. 6. Handicapped individuals with disability defects such as poor eyesight or hearing should not be allowed to swim for safety for themselves. 7. When in evacuation centers store extra food esp

“Social Welfare Sec MitaPardodeTavera ordered social workers to go on 24-‐‑hour work to assist typhoon victims.” “Times correspondent AnnalynJusay reported that the DOH sent P16.5 million worth of medicines to all its regional offices to check the possible spread of diseases spawned by the typhoons.” (Joe Capadocia and Dante Galapate, The Manila Times 26 October 1988) “President Aquino declared yesterday a state of calamity in six regions, including the National Capital Region, as a result of the destruction caused by typhoon Unsang.”




those that can be eaten without cooking. 8. Do not go swimming or boating in overflowing or flooded rivers or other waterways. 9. Submit yourself immediately to immunization against cholera, dysentery, and typhoid. If wounded by a galvanized iron sheet, see a doctor for immediate immunization for tetanus.” (Phil. Daily Express, 29 Nov 2012)

(Malaya, 26 Oct 1988)

Impacts of the Disaster

Bidang’s Toll: 10 dead, 8 hurt, P40-‐‑M damage. Killer typhoon Bidang left 10 persons dead, four missing and

106 dead, 16 injured, 12 missing, 730,357 people affected P678,493,000 total cost of damages (Source: NDRRMC)

16 pax dead confirmed in mindanao 64 estimated Millions of P in crop and property damage

936 dead, 4152 injured, 376 missing, 4,583,618 people affected P10,850,772,000 total cost of damages (Source: NDRRMC)

“Billboards were torn down and steel scaffolding flung into the streets while sheets of iron were peeled off rooftops by the

929 dead, 736 injured, 84 missing, 9,379,518 people affected P39,248,920,000 total cost of damages




eight injured during her 48-‐‑hour rampage that devastated some P40 million worth of crops and properties across Central Luzon and Greater Manila. 87 shanties on Isla PutingBato (Tondo), Luzviminda Village (Tondo) and in Barrio La Huerta Paranaque, Rizal, and the 120 houses on Constitution Hill in QC were either totally damaged or destroyed. The PLDT reported only a minimal damage to telephone service in Greater Manila.

“Majority of Metro Manila went under water yesterday due to the typhoon-‐‑induced southwest monsoon and the fringe effects of Gading.” (Renato Reyes et al, Philippine Daily Express) “Manila was a Venice yesterday, as flashfloods spawned by typhoon Gading placed nearly 95% of the city under water. Police said that there was not an avenue or street in Manila that was not flooded…” (Renato Reyes et al, Philippine Daily Express) “Meralco said Gading severely destroyed its

Two bridges destroyed while two were damaged in some Mindanao provinces. Malabonand Valenzuela area, crews were on 24-‐‑hour emergency work

“Metro Manila was totally without electricity at the height of the typhoon… There was also no water in many parts of the metropolis… Commerce ground to a halt in the country as the Phil. Stock Exchange and banks were closed… ” (Gemma Cuadro et al, The Manila Times) “PAGASA raised public storm signal 4 in Metro Manila… Rosing smashed squatter shanties, toppled electric posts and signboards, uprooted trees, and broke telephone & electric lines all over Metro Manila… Residents near Manila Bay were evacuated after receiving reports of impending waves as high as 10 feet… Roads and bridges in the provinces south of Manila were

wind… With power out, the Metro Rail Transit and Light Rail Transit halted operations. There were very few buses, jeepneys, and taxis on the road as the typhoon’s fury began to be felt just after noon.” (Anthony Vargas et al, Manila Times) “Strong wind toppled a row of trees on Manila’s famous Baywalk beside Manila Bay, and knocked down billboards near US and Japanese Embassy along Roxas Blvd. where traffic was stalled for several hours. Workers used chainsaws to clear the roads amid howling winds and blinding rains…

(Source: NDRRMC) “Ondoy caused extensive flooding in the Metro Manila area and the neighboring Rizal province, including the cities of Antipolo, Makati, Malabon, Marikina, Muntinlupa, Pasig, Quezon, San Juan, Taguig, and Valenzuela…” (Source: PDNA 2008)




northern line facilities, causing prolonged brownouts in many areas of Metro Manila, Calumpit, Hagonoy, Baliwag, San Rafael and Bustos in Bulacan…” (Phil. Daily Express, 10 July 1986)

destroyed, making it impossible for rescue teams to fight their way to badly hit towns and cities…” (Robert Requintina, Manila Bulletin)

Some residents used makeshift rafts and ferried people across streets through knee-‐‑deep water…” (James Mananghaya et al, September 29, 2006)

Proposed Solutions/ Recommendations

“The department of education announced the suspension of classes, saving students from inconveniences and possible accidents…. Those who were traditionally flooded areas transferred to higher grounds. Families placed supports for their houses and stock up on supplies… The Philippines is situated in the typhoon belt in this part of the

“Floods cannot be avoided until the overall flood control system is completed… The Manggahan Floodway structure in Pasig, still under construction, is designed to intercept rainwaters from high-‐‑lying municipalities…” (Minister Rogaciano Mercado of Public Works and Highways, 1986) “Flood control measures, such as dredging of esteros

Sources had no explicit solutions that were offered but it seems that most measure point to preparations in responding to the calamities.

“Public should heed the government’s call for the proper disposal of garbage…” (DPWH Assistant Director Pablo Bautista, 1994) “Address the problems of deforestation by planting trees and rehabilitating the watersheds.” (John Bello et al, Phil. Daily Inquirer)

“The need for regular widening and de-‐‑clogging of canals and esteros. More flood control projects from the government are necessary… A need coordinated planning and project implementation of the various cities and municipalities in Metro Manila. The mayors in the different cities should work together to arrive at effective solutions for flood problems. There should be a

“Fast track the preparation of multi-‐‑hazard and/or risk maps in high-‐‑risk LGUs… Strengthen the risk modeling and forecasting capacities of scientific/technical agencies…” (PDNA) “Implement a viable urban development plan which would prevent danger zones from being developed. Control the growth of




world. This is our lot which we have to face with fortitude. We must accept the fact that we will be battered by typhoons now and then. With government and the private sector joining hands, we have been – and we will always be – able to build new homes, make our land productive again, and lead new lives.” (PrimitivoMijares, Philippine Daily Express, 30 November 1974 – OpEd Page)

and rivers, and the putting up of earth dikes and tidal gate structures, should be intensified…” (Ministry of Public Works and Highways, taken from Santos Patino,Phil. Daily Express) “Appoint Flood Control Experts at the NCR…” (Minister RogacianoMercado, Ministry of Public Works and Highways, taken from Santos Patino, 1986)

sharing of technological and technical expertise for flood prevention” (Zoleta-‐‑Nantes, 2002).

population in the metropolis by spreading growth to other regions. If growth is inevitable, come up with a new zoning ordinance that would allow high-‐‑rise development.” (Manuel Maximo Lopez del Castillo-‐‑Noche, architect, professor at UST, heritage expert) “Protect the few remaining natural areas that we have. We must set up contingency plans for food and water security, for example, full-‐‑cycle mariculture…” (Jose Ma. Lorenzo Tan, CEO World Wildlife Fund-‐‑ Phils.)




Summary

These narratives of Metro Manila reflect a core principle of FORIN, which is that development and risk are “mutually constitutive” (Oliver-‐‑Smith, 2012). The history and trajectory of urban development influences the level and distribution of risk. This in turn influences (specifically, hinders) development when this risk is “realized” in the form of disasters and lost opportunities due to climate change. For example, both the physical and social narratives assert that the occurrence of hazards over Metro Manila, particularly flooding, is not due to natural factors alone, but is probably even more influenced by human-‐‑induced factors. A key example expounded in the narratives is how urban growth has changed the land cover through the expansion of impervious surfaces, the encroachment of people and infrastructures into floodplains and waterways, and the denudation of the watershed. These factors have significant altered surface hydrology, as well as contributed to the urban heat island effect.

The fact that populations are located in harm’s way (or where they themselves can cause harm) is also a result of the urbanization process. As is discussed in the economics narrative, increasing urban density leads to restrictions in land use and increasing land prices, which drives vulnerable urban populations into informal settlements and hazardous areas. All of these physical and socio-‐‑economic factors lead to living conditions that degrade human health.

These examples also illustrate the cascading or compounding nature of risk and its elements. Primary hazards such as rainfall interact with the city’s physical characteristics on the ground, generating a secondary and potentially, more damaging, hazards in the form of floods. These floods, interacting with water quality and solid waste management problems, pose further hazards, both immediate and indirect to health. Direct economic damages brought about by typhoons and flooding, translate into further indirect losses in productivity and income, and secondary macro-‐‑economic losses. As recognized in the social sector narrative, these dynamic interactions can intensify vulnerabilities of different stakeholder groups.

Indeed, elements of risk compound each other resulting in a “risk trap” – it becomes difficult to escape the cycle of extreme weather events and climate change resulting in significant losses, thus increasing vulnerability, and increasing the probability of repeated disasters and losses. Concerted strategies targeting different sectors in a programmatic and harmonized manner are required to build resilience and adaptive capacities to current climate variability and projected future climate change. These strategies should recognize and aim to strengthen the entire continuum of action from a more proactive approach focusing on prevention, mitigation and adaptation to emergency response to post-‐‑event recovery and rehabilitation, rather than being merely reactive.




Furthermore, as emphasized by the narratives, information, communication and education are crucial factors influencing the success of interventions, and thus, the reduction of risk. For example, the social narrative describes the lack of information dissemination among stakeholders regarding the available capacity-‐‑building options and the appropriate agencies to approach. There is also a prevailing perception of floods being low-‐‑risk events, and a lack of understanding about how past experiences may no longer be a suitable gauge of future trends, in the face of climate change. The drivers of climate change are also unclear among the masses. The health narrative likewise describes a gap between knowledge from research and what is considered “common knowledge” on the ground. Hence, communication strategies, targeting both horizontal and vertical sharing information, represent a basic step towards building resilience across the different sectors. This information, in order to be actionable, must already integrate assessments and evidences across the different relevant disciplines to present a complete picture of the context in which the decision-‐‑making is taking place.

In the field of risk research, an interdisciplinary approach is also crucial in analyzing risk as a complex, multi-‐‑dimensional issue. To facilitate collaboration among the different sectoral and cross-‐‑analyses of different disaster events, shared definitions and methods, and standardized and disaggregated data would be useful. The lack of data is a common challenge amongst researchers. However,

compounding this are the differences in data requirements and data analysis methodologies across the sectors. This can be seen in the different approaches applied by the sectoral narratives of Metro Manila. The economics sector narrative suggests linking models to development indicators, which represent the baseline improvements we aim for, so as to streamline the data collection and analysis process.

But though each narrative highlights a particular sector, it is inevitable for other sectors to be included in the discussion, as is apparent in each narrative, owing to the inherent interfaces and interconnections among the different sectors. More importantly, while, on the one hand, having different paradigms or approaches does pose a challenge for integrative work, on the other hand, offering different perspectives from which to analyze the context of Metro Manila can contribute to a more holistic case study. Results of such a multi-‐‑dimensional approach, if integrated and communicated effectively, can prove to be useful not only to researchers but also to decision-‐‑makers and practitioners on the ground.

This collection of narratives is by no means a comprehensive FORIN case study yet, and is a work in progress. However, by attempting a multi-‐‑sectoral scoping, this endeavor will hopefully act as a catalyst for collaborative research and interventions, and in this way contribute towards risk research as a field of study, and to the understanding of the drivers of risk in Metro Manila in particular.




References

Oliver-‐‑Smith, Anthony. (2012). Disaster Research and the New Ecology: Models of Vulnerability and Resilience. Lecture at the 2012 Advanced Institute on Forensic Investigations of Disasters (FORIN) organized by START and the IRDR International Centre of Excellence (ICoE) in Taipei, together with IRDR International, ICSU and Taiwan’s National Science and Technology Center for Disaster Reduction (NCDR). Held at Academia Sinica, Taipei, Taiwan.

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