Synthetic impact response functions for flood vulnerability analysis and adaptation measures in...

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Natural HazardsJournal of the International Societyfor the Prevention and Mitigation ofNatural Hazards ISSN 0921-030XVolume 59Number 2 Nat Hazards (2011) 59:967-986DOI 10.1007/s11069-011-9812-x

Synthetic impact response functions forflood vulnerability analysis and adaptationmeasures in coastal zones under changingclimatic conditions: a case study inGippsland coastal region, AustraliaDushmanta Dutta, Wendy Wright &Philip Rayment

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ORI GIN AL PA PER

Synthetic impact response functions for floodvulnerability analysis and adaptation measures in coastalzones under changing climatic conditions: a case studyin Gippsland coastal region, Australia

Dushmanta Dutta • Wendy Wright • Philip Rayment

Received: 15 October 2009 / Accepted: 3 April 2011 / Published online: 17 April 2011� Springer Science+Business Media B.V. 2011

Abstract There is an increasing concern that the current management practices for many

coastal regions are unsustainable. Very few countries have planned to deal with the

exacerbation of environmental decline in the face of sea level rise. It is therefore necessary

to assess socioeconomic and environmental impacts of sea level rises to better understand

the vulnerability of coastal zones, as part of devising adaptive and integrated management

principles. This paper presents a systematic approach by which relevant stakeholders can

be actively engaged in prioritising flood impact issues and deriving information for

quantification of impacts for adaptation measures and demonstrates the approach through

implementation in the Gippsland coastal region. As outcomes of the project, we have

identified key issues of concern for this region for flood impacts and constructed synthetic

response functions for quantification of impacts of floods on some of the key issues in the

region. The analysis also showed that stakeholders consider that some of the issues are not

likely to be significantly affected by floods and thus may not require adaptation measures.

The analysis did not provide high agreement on some issues. Different approaches are

required to assess the importance of these issues and to establish impact response functions

for them.

Keywords Synthetic impact response functions � Floods � Coastal zones �Stakeholder engagement � Climate change

1 Introduction

Coastal areas are one of the most important regions from social, economic, and environ-

mental viewpoints. They are home to a large and growing proportion of the world’s pop-

ulation. They include important ecosystems such as coastal floodplains, mangrove forests,

D. Dutta (&) � W. Wright � P. RaymentSASE, Monash University, Churchill, VIC 3842, Australiae-mail: [email protected]

D. DuttaCSIRO Land and Water, Black Mountain, ACT, Australia

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Nat Hazards (2011) 59:967–986DOI 10.1007/s11069-011-9812-x

Author's personal copy

marshes, and tideflats, as well as beaches, dunes, and coral reefs (Costanza et al. 1997). The

coastal zone is also important for marine fisheries because the bulk of the world’s marine

fish harvest is caught or reared in coastal waters (Wilkinson 2000). Coastal areas help

prevent erosion, filter pollutants, and provide food, shelter, breeding areas, and nursery

grounds for a wide variety of organisms. Coastal regions also provide critical inputs for

industry, including water and space for shipping and ports; opportunities for recreational

activities such as fishing and diving and other raw materials, including salt and sand.

Coastal regions are undergoing environmental decline due to the large growth of human

populations, rapid urban and industrial development, overexploitation of natural resources,

and poor management. It is expected that, by 2025, around 75% of the world’s human

population will live within 200 km of a coastline (Creel 2003). There is an increasing

concern that the current management practices are unsustainable. Of particular concern are

low-lying areas, which are also affected by sea water intrusion. The Intergovernmental

Panel on Climate Change predicts that the global mean sea level may rise as much as

88 cm by the end of the twenty-first century (Houghton et al. 2001). Several coastal zones

are facing severe socioeconomic and environmental problems due to their lower elevation.

Very few countries have planned to deal with the exacerbation of these problems in the

face of sea level rise.

A holistic and sustainable approach is needed for coastal zone management to resolve

the conflicting demands of society for products and services, taking into account both

current and future interests and the ideal of sustainable coastal resources and environments

(Post and Lundin 1996; Neumann and Livesay 2001; Walsh 2004; APN 2005; Dutta et al.

2005). Agenda 21 and in particular its Chapter 17 ‘‘Protection of The Oceans’’ reaffirmed

this need. The real challenge in achieving optimal sustainable management strategies in

coastal zones relies on the ability to design, develop, and implement an integrated man-

agement program that not only maximizes the benefits to society and its economy based on

accurate understanding of the impacts of changes in physical processes, but also ensures

that the ecosystems are adequately protected or preserved. The requirement for ecosystem

protection is based on recognition of the inherent value of natural systems as well as a

utilitarian recognition that degraded ecosystems cannot provide many of the products and

ecosystem services that have previously been taken for granted. It is therefore necessary to

assess socioeconomic and environmental impacts of sea level rises to better understand the

vulnerability of the coastal zones, for the purpose of devising adaptive and integrated

management principles.

Process-based and distributed mathematical models can be utilized to capture changes

in hydro-biogeochemical processes in coastal zone systems in the context of climate

change and anthropogenic forcing (Gordon et al. 1996; Hong et al. 2002; Nakayama et al.

2004; Dutta et al. 2007); thus, contributing to our knowledge about the vulnerability in

coastal zone systems. Such modeling approaches can provide better understanding of

changes in the complex and interrelated biogeochemical and physical processes in coastal

zones such as nutrient flux, salinity, floods, erosion, and sedimentation. In order to assess

vulnerability for the development of adaptive management strategies, it is necessary to

identify and prioritize the important issues and to quantify the impacts of the changes in

physical processes due to floods and high water levels associated with sea level rise. In

order to quantify impacts, it is necessary firstly to identify relationships between charac-

teristics of the floods and associated quality variables and key issues and secondly to

quantify these relationships (Berning et al. 2000).

Impact response functions are essential components of vulnerability and impact

assessment models, which relate impacts of flood inundation and water quality variables to

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key issues (Krzysztofowicz and Davis 1983; Smith 1994). There are several types of

hazards for coastal areas associated with climate change and sea level rise. In this study,

the hazard of interest is flooding. The flood inundation variables which govern the impact

characteristics and which are considered for stage-damage functions are as follows: flood

depth, duration, velocity and frequency, and water quality. The response functions are

usually derived in one of two ways. Damage data from past floods may be incorporated

into the model, but if such information is unavailable or unreliable, an alternative approach

is to generate synthetic response functions from hypothetical analyses of flood events based

on land cover and land use patterns and the key issues for the region (Das and Lee 1988;

Smith and Greenway 1988; Smith 1994). Berning et al. (2000) call for incorporation of

social and environmental components into these models, but damage functions for these

elements of a model are difficult to estimate (Dougherty and Hall 1995; Kang et al. 2005).

According to Viljoen et al. (2001), including the environmental impact, dimension into the

holistic damage assessment methodology should render further benefit. Various authors

including Dougherty and Hall (1995) suggest use of expert advice in determining synthetic

response/loss functions. In this study, information regarding the likely impact of inundation

on key issues with social, economic, or environmental values was generated by surveying

stakeholders with experience of past flood events.

This project involves a vulnerability analysis for a key coastal zone in Gippsland,

Victoria, under climate change conditions. The focus of the study is on flooding caused by

extreme rainfall and sea level rises associated with storm surge and global warming. Floods

due to other extreme events such as tsunami are not considered here. The vulnerability

analysis required the identification of relevant flood hazard parameters and key issues for

the study region and the synthesis of impact response functions using expert and stake-

holder opinion. The outcomes of the vulnerability analysis are potentially useful as a basis

for the development of adaptation measures for the region. The project required the

engagement of experts and key stakeholders in order to identify and prioritize the key

issues and to generate synthetic response functions. A significant outcome of the project is

an insight into how stakeholders’ knowledge and expertise (at regional and local levels)

might be utilized for establishing such response functions for quantification of the likely

impacts of climate change in coastal regions. This paper details the methods, analysis, and

outcomes of the project.

2 Gippsland coastal region

The Gippsland coast is home to thousands of people who live in or near one of the many

coastal towns and settlements located between San Remo on the eastern extent of Western

Port Bay and Mallacoota near the New South Wales border. Despite these built-up areas,

the Gippsland coast remains in a largely natural state, being characterized by diverse

natural and cultural values, and including important habitat for a range of fauna species

protected by National Parks, reserves, and public foreshore land (GCB 2008).

About one-third of the study area covers agricultural land. Grazing of cattle and sheep is

the most widespread form of agriculture in the coastal area and is vulnerable to floods.

Cropping is of limited extent and largely for vegetable growing, for example on alluvial

flats in East Gippsland, and for the production of grain for stock feed (Aldrick et al. 1984).

The region includes the Gippsland Lakes System (Fig. 1) that is a series of coastal

lagoons—large areas of shallow water almost wholly sealed off from the sea by a coastal

dune system. ‘‘The Lakes’’ are Australia’s largest navigable inland waterway and include

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three main water bodies: Lake Wellington (138 km2) in the west, fed by the La Trobe,

Thompson, Macalister, and Avon Rivers and linked by the McLennans Strait to Lake

Victoria (110 km2) and Lake King (92 km2) (Boon et al. 2007). These lakes are now well

recognized as a major natural resource with economic, environmental, and cultural sig-

nificance. However, the benefits and attractions offered by the lakes have been compro-

mised over the years by many undesirable changes in water quality caused by various

stressors, such as land use (mainly agriculture), land use change, pollution, and eutro-

phication (Green 1978; Pitt and Synan 1987; Harris et al. 1998; Webster and Wallace

2000; Longmore 2000; Grayson et al. 2001; Webster et al. 2001).

Climate change, sea level rise, and coastal subsidence all have the potential to signif-

icantly impact on the Gippsland coast, affecting both natural values and built infrastructure

on private and public land. Coastal erosion, flooding, and large scale changes to Gipps-

land’s coastline caused by climate change not only have the potential to impact on a very

broad range of environmental and cultural values, but may also pose a direct threat to an

array of physical assets along the Gippsland coast. Physical assets associated with town-

ships and potentially at risk range from isolated boat ramps and jetties to valuable private

properties fronting prime foreshore land. The most vulnerable coastal sites are low-lying

areas and/or those that have a high potential for erosion, and hence shoreline retreat (GCB

2008).

Fig. 1 Map of the Gippsland Lakes catchment (Source DSE)

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Phase 1 of the Gippsland Climate Change Study (McInnes et al. 2005a, b, 2006) and

earlier work by CSIRO indicate that the major impacts of climate change on weather

systems along the Gippsland coast include:

– Increased dominance of south-westerly frontal synoptic weather patterns

– Increased wind speed

– Increased storm surge height—up to 19% by 2070

– Increased frequency and intensity of extreme events by approx. 10%.

– More severe and more frequent storms

Phase 2 of the Gippsland Climate Change Study (GCB 2008) indicated that sea level

rise will result in coastal recession as beaches equilibrate to the new wave and tidal

regimes. Shoreline recession of between 40 and 79 m can be expected along the Gippsland

coast, based on a 0.79 cm sea level rise scenario.

According to Malcolm (2010), the 2006/2007 fire and flood episodes in Gippsland are

examples of how one severe event (fire), closely followed by another (flood), can lead to

increased runoff and soil erosion. The events in the Macalister River catchment led to a

mobilization of sediment of a magnitude not previously recorded and a reduction of an

estimated 6–7% in the storage volume of Lake Glenmaggie due to silt deposition in the bed

of the lake. Nearer to the coast, the flood resulted in high turbidity levels in the Gippsland

Lakes, which persisted for 4–5 months (June–October 2007) and an algal bloom that

commenced in November 2007 and persisted until May 2008. These two events resulted in

almost 12 months of severe light attenuation in the lakes and there was a 50–75%

reduction in area of sea grass.

3 Project approach and methods

A systematic approach has been implemented throughout the project, from the selection of

experts and stakeholders to the design and distribution of the questionnaires and to the

statistical analyses of the data provided by stakeholders, which in turn enabled the pri-

oritization of issues and impact response functions for adaptation measures. The roles of

the experts and stakeholders were to identify relevant flood hazard parameters and key

issues for the region and to provide data for the generation of synthetic response functions

for impact analysis. The questionnaire was devised as an instrument to collect data for

generating synthetic response functions.

The overall framework of the project is shown in Fig. 2, which presents a flow chart of

the important activities, approaches, and outcomes of the project. Each aspect of the

project activities and approach is discussed below.

3.1 Identification of hazard parameters and key issues

Two groups were formed in order to identify relevant flood hazard parameters and key

issues for the study region. A ‘‘stakeholder reference group’’ was formed by inviting

stakeholders from government, non-government, and industry sectors familiar with the

region and its natural resource management issues. The main criteria used for selection of

stakeholders were as follows: interest in the topic, familiarity with regionally relevant

issues, and appropriate educational qualifications and/or work experience in relevant

projects. The second group was formed by recruiting international water and coastal zone

experts from six countries in the Asia–Pacific region. Members of this expert group had

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been working collaboratively on a project on coastal zones and climate change (Dutta

2007).

Each of the two groups was engaged in brainstorming meetings in order to identify the

most important flood inundation and water quality parameters (hazard parameters) asso-

ciated with coastal zone flooding that could be simulated by a process-based model. In

addition, the groups identified the key social, economic, and environmental issues on

which these hazard parameters could impact.

The sets of key issues and hazard parameters were finalized in a brainstorming work-

shop among members of the expert group. Twelve experts from six countries (Australia,

Bangladesh, Japan, Sri Lanka, Thailand, and Vietnam) with expertise in hydrology, water

resources modeling and management, flood risk management, project management, ecol-

ogy, coastal zone modeling and management, and aquaculture participated in the work-

shop. Most of the experts had experience in climate change-related projects in catchment

and coastal zones. In the brainstorming workshop, the expert group members first pre-

sented issues identified as important and relevant in the coastal areas of their own countries

based on their own experience and that of the members of the country-based stakeholder

reference groups. The six countries had different priority issues for coastal zone man-

agements. After a lengthy deliberation on relevance of various issues for the project, it was

agreed by the expert team to consider the issues that were important across the six

countries using the triple bottom line concept. Twenty-two issues were identified as key

issues related to economic, social, and environment aspects of coastal zone management.

After finalizing the 22 key issues, the twelve-member expert team deliberated on various

flood hazard parameters already identified and finalized seven parameters as the most

Identification of hazardparameters andissues/sectors

Impact Responsefunctions

Prioritizationof issues

Questionnairedesign

StakeholderReference Group

InternationalExpert Group

Stakeholders’survey

Statistical analysisof responses

Sensitivityanalysis: changes

vs. impacts

Fig. 2 Project framework

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relevant parameters across the six countries pertaining to the 22 issues. The proceedings of

the brainstorming workshop are presented in Dutta 2007.

The key issues were used to develop a set of criteria, indicators, and appropriate

response functions relating to various scenarios where the intensity of the flood hazard

parameters varied due to climatic and anthropogenic changes in the study area (Belfiore

2003). Tables 1 and 2 show the flood inundation parameters (4), water quality parameters

(3), and the key issues (22) identified for impact analysis, respectively.

3.2 Questionnaire design

A questionnaire was designed to gather information regarding stakeholders’ views of the

likely impacts of various levels of coastal inundation on key issues and assets in the study

area. For the purpose of structuring the questionnaire, magnitudes of different flood

inundation and water quality parameters were classified into three categories: low, med-

ium, and high. The stakeholder and expert groups were both consulted regarding the

suitability of these categories, and a range of references were used to finalize realistic

magnitude ranges for the flood inundation and water quality parameters within these three

categories for coastal lakes and wetlands in Victoria (VGG 1988, 1996; DEWR 2007; EPA

2007). These take account of generally accepted standards for aquaculture, wetland bio-

diversity, recreational activities, etc. Tables 3 and 4 show the magnitude ranges of dif-

ferent flood inundation and water quality parameters for the three categories.

The items comprising the questionnaire were designed by the group of international

experts in order to generate data reflecting stakeholders’ assessments of the different

impacts of the three categories of flood inundation and water quality parameters (as given

in Table 1) on key social, economic, and environmental issues (as given in Table 2). The

data collected were used in the formation of synthetic response functions relating the level

of flooding to the level of impact. The main purposes of the questionnaire were as follows:

• to investigate

• which issues (assets) were of most concern to stakeholders;

• whether the intensity (high, medium, or low) of flood parameters affects those

issues of most concern

• to facilitate development of synthetic response functions

3.3 Administration of the questionnaire

Stakeholder reference group participants provided anonymous responses to the question-

naire. In total, over 300 stakeholders were identified to participate in the survey. Question-

naires were distributed to the participants either by email or surface mail. In the latter case, a

reply-paid envelope was provided for return of the completed questionnaire. Other respon-

dents chose to scan and return their questionnaires to the research team via email. The total

number of completed questionnaires received was 33, a response rate of about 10%.

Table 1 Flood inundation and water quality parameters to be modeled by process-based model underclimatic change conditions

Flood inundation parameters Water quality parameters

Depth, duration, velocity, frequency Nutrients (TN, NO2, NO3, TP, PO4), salinity, turbidity

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Table 2 Key issues in coastalareas identified for climatechange impact analysis

Key issues (with abbreviations)

Infrastructure Drainage (Dr)

Roads (Rd)

Railways (Rl)

Ports & Harbors (Pt)

Dykes (Dy)

Coastal protection structure (Co)

Landuse planning (LU)

Buildings Residential (RB)

Non-residential (NR)

Potable water (PW)

Water quality (WQ)

Erosion (Er)

Tourism (To)

Population Short-term displacement (SD)

Long-term resettlement (LD)

Agriculture (Ag)

Fishery (Fi)

Fish habitat/distribution (FH)

Wetland health Extent (WEx)

Flora biodiversity—no. of veg.species (WFl)

Fauna biodiversity—no. of birdspecies) (WFa)

Mangroves (Ma)

Table 3 Flood inundation magnitude scale

Depthcategory

Magnitude(m)

Durationcategory

Magnitude(days)

Velocitycategory

Magnitude(m/sec)

Frequencycategory

Magnitude(return period)

Low \0.6 Low \0.5 Low \0.5 High \5 years

Medium 0.6–1.5 Medium 0.5–2 Medium 0.5–1 Medium 5–20 years

High [1.5 High [2 High [1 Low [20 years

Table 4 Water quality magnitude scale

Category TN(lg/L)

NO2-

(lg/L)NO3

-

(lg/L)TP(lg/L)

PO43-

(lg/L)Salinity(l S/cm)

Turbidity(NTU)

Low \350 \10 \10 \10 \5 \30 \5

Medium 350–750 10–50 10–50 10–30 5–10 30–100 5–20

High [750 [50 [50 [30 [10 [100 [20

TN total nitrogen, NO2 nitrite, NO3 nitrate, TP total phosphorous, PO4 phosphate, salinity measure ofconcentration of total dissolved solids in water, lg/L micrograms per liter, mg/L milligrams per liter, l S/cmmicro siemens percentimetre, NTU nephlometric turbidity units

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Due to the complex nature of the questionnaire, stakeholders with highly relevant

academic and professional backgrounds from the Gippsland region were invited to par-

ticipate. Table 5 shows the background of the 33 stakeholders, who responded to the

survey. These 33 stakeholders had relevant professional experience across a good mix of

the research, government, and private sectors.

The lengthy and reasonably complex questionnaire required respondents to indicate

their perceptions of the likely level of negative impact for each of the flood inundation and

water quality parameters (Table 1) on each of the key issues (Table 2) for each of the three

conditions (high, medium, and low) (Tables 3, 4). Respondents used an impact ranking

score in the range 1–5 to indicate predictions regarding the extent of the impact in each

case. The instructions within the questionnaire defined each of the ranking scores as per

Table 6. The participants were explicitly given the option of not completing those sections

of the questionnaire that were perceived as beyond their expertise.

Table 5 Backgrounds of thestakeholders who completed thequestionnaire

Range of academic backgrounds

Civil engineering 12

Hydrology 6

Water engineering 9

Environmental science 7

Biological science 3

Chemistry 3

Environmental policy and planning 1

Planning 1

Rural drainage 1

Range of job sectors

Academic 10

Research 5

Government 10

Semi-government 4

Range of professional experience

\1 year 1

1–5 years 9

5–10 years 4

[10 years 15

Table 6 Impact ranking scoresand their definitions as used inthe questionnaire

Impactranking score

Impact definition

1 No/little impact (0–5% damage)

2 Low impact (5–25% damage)

3 Moderate impact (25–50% damage)

4 High impact (50–75% damage)

5 Extreme impact (75–100% damage)

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3.4 Statistical analysis of the questionnaires

A statistical method was designed to analyze the data obtained from the returned ques-

tionnaires and to create synthetic response functions. In relating the impact ranking score

for a particular hazard parameter, x, on an individual key issue (such as drainage or

agriculture), the impact ranking score (1–5 integer scale) y was analyzed, rather than its

associated predicted percentage damage (Table 6). This was done in order to homogenize

the spreads of response scores across the low, medium, and high levels of magnitude of

each parameter.

For any combination of hazard parameter and issue, the number, s, of stakeholder

survey responses ranged from 21 to 33, since 12 respondents did not complete the

section of the questionnaire relating to the water quality parameters. Denoting the low,

medium, and high responses for the ith individual stakeholder by yLi, yMi, and yHi,

respectively,

bi ¼yHi � yLi

2ð1Þ

is the slope of the fitted least squares regression line (assuming equal spacing of the three

parameter levels).

In the next step of the statistical analysis, responses of all stakeholders were combined

for each hazard parameter. A 95% confidence interval for the underlying slope (CIs) was

calculated as shown in Eq. 2.

CIs ¼ �b� t� � seð�bÞ ð2Þ

Here, �b is the mean value of slope, seð�bÞ is the standard error of �b, and t* is the 97.5th

percentile of the t distribution with (s - 1) degrees of freedom.

The half-width of the above confidence interval was used as a numerical indicator

(termed ‘‘disparity’’) of the level of agreement among respondents, as well as assisting in

developing an inference for the underlying impact.

The quadratic response function fitting the three points ðL; �yLÞ, ðM; �yMÞ, and ðH; �yHÞwas determined for each combination of hazard parameter and issue as a basis for com-

parisons across issues.

3.5 Sensitivity analysis

Relationships between the impact ranking scores for the effects of high, medium, and low

magnitudes for all combinations of flood hazard parameters and key issues were grouped

into the following four classes (Fig. 3):

Class 1: High sensitivity and High Agreement (or low disparity)

Class 2: High sensitivity and Low Agreement (or high disparity)

Class 3: Low sensitivity and High Agreement (or low disparity)

Class 4: Low sensitivity and Low Agreement (or high disparity)

The key issues that show high sensitivity to increasing magnitude for a particular hazard

parameter (i.e., steep slope or high �b value in Eq. 1) and for which there is high agreement

among respondents (i.e., high correlation or narrow confidence interval for the slope, CIs)

are placed in Class 1. All the key issues in this class show a reasonably strong, linear

relationship with increasing magnitude of the particular flood hazard parameters and good

agreement among stakeholder respondents about these relationships. Key issues in Class 2

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appear to be sensitive to the increasing magnitude of the hazard parameters; the opinions of

different stakeholders about these relationships are varied. Class 3 includes key issues

which stakeholders agree are not particularly affected by an increase in magnitude of the

hazard parameters. The key issues in Class 4 also appear to be less sensitive to the hazard

parameters, however, there are more widely varying perceptions among stakeholders about

these relationships. The criterion used to define sensitive issues was �b C 0.5. The criterion

used to define high agreement was a disparity measure of below 0.3. A disparity measure

above or equal to 0.3 was considered to indicate low agreement.

4 Results and analysis

4.1 Prioritization of issues

Table 7 displays the key issues in each of the four classes, for each hazard parameter.

Class 1 (high sensitivity, high agreement) is of most interest since there is good

agreement among stakeholders in terms of the impact of these hazard parameters on certain

key issues, and these impacts are thought likely to increase with the magnitude of the

hazard. The scatter plots showing the sensitivity and disparity of key issues for hazard

parameters: (a) depth, (b) duration, (c) velocity, (d) frequency, (e) nutrients, (f) salinity,

and (g) turbidity are presented in Fig. 4.

The following hazard parameters had at least one issue in this class.

4.1.1 Depth

Stakeholders agree on the high sensitivity of many issues to flood depth (Table 7). Of

these, the five most highly sensitive issues with high agreement (or low disparity) are as

follows: short-term displacement (SD), agriculture (Ag), residential buildings (RB),

drainage (Dr), and roads (Rd) as shown in Fig. 4a. Low disparity (or high agreement)

Fig. 3 Grouping of relationshipsbetween flood hazard parametersand important issue

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978 Nat Hazards (2011) 59:967–986

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among stakeholders about the high sensitivity to these issues indicates that these issues are

the ones most likely to be affected by increasing flood depths, thus prioritising them for

adaptive management strategies. It is assumed here that consensus among a relatively

small group of stakeholders reflects reality.

To: TourismSD: Short-term displacementLD: Long-term resettlementAg: AgricultureFi: FisheryFH: Fish habitat/distributionWEx: Wetland health ExtentWFl: Flora biodiversity

- no. of veg. speciesWFa: Fauna biodiversity

- no. of bird species Ma: Mangroves0.400.350.300.250.20

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(a) Depth

(c) Velocity

(e) Nutrients

(g) Turbidity

(b) Duration

(d) Frequency

(f) Salinity

Legends:Dr : DrainageRd: RoadsRl: RailwaysPt: Ports & HarboursDy: DykesCo: Coastal protection structureLU: Landuse planningRB: Residential BuildingsNR: Non-residential BuildingsPW: Potable waterWQ; Water qualityEr: Erosion

Fig. 4 Scatter plot showing the sensitivity and disparity of key issues for hazard parameters: a depth,b duration, c velocity, d frequency, e nutrients, f salinity, and g turbidity

Nat Hazards (2011) 59:967–986 979

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4.1.2 Duration

According to at least some of the stakeholders who participated in the survey, many issues

are quite highly sensitive to the hazard: flood duration, but not all respondents are in

agreement about the effect of this hazard on the various issues. The key issues which were

scored as most highly sensitive were as follows: agriculture (Ag), roads (Rd), drainage

(Dr), short-term displacement (SD), and residential buildings (RB), however, there was

poor agreement (disparity C0.3) among respondents about the effect of flood duration on

all of these issues (Fig. 4b). It is apparent that careful consideration should be made in

assigning cutoff values for agreement levels, since there is a risk of inappropriately dis-

counting sensitive issues where agreement falls below the cutoff level. An alternative

approach would be needed to establish response functions for issues about which there is

some disagreement about the degree of sensitivity and which managers wish to consider

for impact analysis and adaptive measures.

Stakeholders did agree that fisheries (Fi), coast protection infrastructure (Co), ports &

harbors (Pt), and potable water (PW) were key issues with sensitivity to flood duration

(Fig. 4b).

4.1.3 Velocity

The five key issues which scored as most highly sensitive to flood velocity were as follows:

agriculture (Ag), roads (Rd), erosion (Er), drainage (Dr), and railways (Rl) (Fig. 4c).

However, the disparity among stakeholders responding to the questionnaire was high for

these five and for 16 of the remaining 17 key issues. Good agreement was only apparent

regarding a low perceived sensitivity of wetland fauna (WFa) to flood velocity (Fig. 4c).

The low levels of agreement in the stakeholders’ assessment of the impacts of flood

velocity suggest that the effects of this hazard parameter are not well understood. More

research is needed in order to be able to establish response functions for this hazard

parameter.

4.1.4 Frequency

Many issues were regarded as quite highly sensitive to frequency, and there was good

agreement from stakeholders about most of these (Fig. 4d). Five of the most highly sen-

sitive issues were as follows: long-term displacement (LD), agriculture (Ag), land use

(LU), residential buildings (RB), and short-term displacement (SD).

4.1.5 Nutrients

No issues were identified as highly sensitive to nutrients and disparity levels were high in

almost all cases (Fig. 4e). Again, more research is needed in order to be able to establish

response functions for this hazard parameter.

4.1.6 Salinity

Many issues were identified by stakeholders as quite highly sensitive to salinity, and there

was generally good agreement about this perception. Five of the most highly sensitive were

as follows: potable water (PW), water quality (WQ), agriculture (Ag), fish habitat (FH),

and fishery (Fi) (Fig. 4f).

980 Nat Hazards (2011) 59:967–986

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4.1.7 Turbidity

Six issues considered to be most highly sensitive to turbidity were as follows: potable

water (PW), fish habitat (FH), water quality (WQ), fishery (Fi), fauna biodiversity (WFa),

and flora biodiversity (WFl). There was good agreement among stakeholders regarding the

likely effect of turbidity on these issues (Fig. 4g).

4.2 Check for sampling bias

It is important to demonstrate that there is no significant bias in the impact assessments of

the stakeholders due to their backgrounds and experience. Otherwise, it could be argued

that the outcomes of the analyses presented in this paper might be quite different with an

alternative mix of stakeholder backgrounds. In order to address this, the distributions of the

individual sensitivity ratings (as computed from Eq. 1) for selected combinations of flood

inundation parameters and environmental factors for subgroup A (15 academics/

researchers) and subgroup B (14 government/semi-government staff) were compared. The

combinations examined were those involving issues with generally high overall sensitiv-

ities. Table 8 displays the two subgroup mean sensitivity ratings (subgroup A first, B

second) and the value of the two independent samples t test statistic for comparing them,

for all analyzed combinations.

In none of the twenty comparisons did the t test provide evidence of a significant

difference in mean sensitivity rating at the 10% level; in fact, the smallest P value across

the tests was 0.12. It is concluded that the stakeholder assessments are relatively insensitive

to their mix of backgrounds across the academic and practitioner sectors.

4.3 Response functions

A response function represents the impact of a hazard parameter on a particular issue. In

this study, response functions were synthesized only for relationships between hazard

parameters and issues classified in Class 1 (high agreement, high sensitivity). The response

Table 8 Mean sensitivity ratings and the value of the two independent samples t test statistic for acomparison of two subgroups of the stakeholder group

Key issues Flood inundation parameter

Depth Duration Velocity Frequency

Drainage 1.21, 0.79 1.14, 0.96 1.00, 0.96 0.55, 0.67

T = 1.65 T = 0.71 T = 0.19 T = -0.37

Roads 0.92, 1.08 1.08, 1.17 1.09, 1.08 0.77, 0.88

T = -0.87 T = -0.39 T = 0.03 T = -0.32

Residential buildings 1.08, 1.08 0.92, 1.04 0.71, 0.88 0.91, 1.00

T = 0.00 T = -0.67 T = -0.92 T = -0.31

Short-term displacement 1.14, 1.17 0.96, 1.04 0.85, 0.67 0.77, 0.92

T = -0.17 T = -0.55 T = 0.97 T = -0.52

Agriculture 0.96, 1.21 0.97, 1.12 1.21, 1.04 0.91, 1.13

T = -1.10 T = -0.59 T = 0.70 T = -0.75

Nat Hazards (2011) 59:967–986 981

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Table 9 Established impact response functions for flood inundation parameters versus issues

Issues Coefficients of impact response functions

Depth Duration Frequency

a b c a b c a b c

Dr 2.52 0.98 -0.04 – – – – – –

Rd 2.84 0.98 -0.12 – – – – – –

Rl 2.44 0.76 0.16 – – – 2.17 0.63 0.25

Pt 2.25 0.85 -0.04 1.83 0.61 0.17 1.96 0.61 0.35

Dy 2.08 0.81 0.38 – – –

Co 2.21 0.88 0.25 1.83 0.67 0.39 1.96 0.61 0.43

LU 2.76 0.96 0.08 – – – – – –

RB 3.08 1.08 0.32 – – – 2.92 0.98 0.13

NR 2.96 0.96 0.24 – – – 2.67 0.9 0.21

PW 2.71 0.67 -0.08 2.61 0.61 0.09 2.43 0.52 0.26

WQ 2.72 0.82 0.04 – – –

To 2.8 0.88 0.16 – – – 2.52 0.88 0.32

SD 2.83 1.19 -0.04 – – – 2.42 0.9 0.21

LD 2.29 0.96 0 – – – 2.79 1.15 0.04

Ag 2.75 1.08 -0.25 – – – 2.36 1 0.08

Fi – – – 1.76 0.5 -0.04 – – –

Ma 1.35 0.57 -0.17 – – – – – –

Table 10 Established impact response functions for water quality parameters versus issues

Issues Coefficients of impact response functions

Salinity Turbidity

a b c a b c

RB 1.29 0.57 0.29 – – –

NR 1.08 0.54 0.46 – – –

PW 2.65 1.37 -0.12 2.76 1.22 -0.28

WQ 2.7 1.31 -0.04 2.88 1.12 -0.38

To 1.5 0.52 -0.04 1.79 0.69 0.04

SD 1.46 0.58 0.17 – – –

LD 1.64 0.7 0.04 1.39 0.52 0.17

Ag 2.44 1.18 0.04 – – –

Fi 2.17 1.02 0.21 2.26 1.11 0.3

FH 2.12 1.06 0.28 2.25 1.15 0.21

WEx 1.78 0.83 0.43 1.65 0.65 0.17

WFl 2.04 0.98 0.3 1.91 1.02 0.13

WFa 1.91 0.91 0.17 1.91 1.04 0.26

Ma – – – 1.48 0.65 0.17

982 Nat Hazards (2011) 59:967–986

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functions were established as described in Sect. 3.4. In all cases, the fitted function was

denoted by

y ¼ aþ bxþ cx2 ð3Þ

where y predicted impact in % to the particular issues, x defined magnitude of a hazard

parameter (coded -1, 0, 1, respectively, for low, medium, and high categories), and a, b,and c are the coefficients.

Each response function predicts the impact of a particular hazard parameter on a par-

ticular issue. Predicted mean impact ranking scores were converted to predicted mean

percentage impacts based on the ranges given in Table 6. The values of the three coeffi-

cients (a, b, and c) for the various response functions are presented in Tables 9 and 10 for

flood inundation and water quality parameters, respectively.

The outcomes from the statistical analysis have revealed the strong views of stake-

holders on the importance of some issues (Class 1) for adaptive measures and no clear

agreements on some other issues (Classes 2 and 4). Furthermore, there are clearly some

issues (Class 3) about which stakeholders agree there is no need for concern. The approach

was beneficial as:

• Synthetic response functions can be generated in the absence of data from previous

flood events. The use of synthetic response functions is therefore particularly useful

where high quality, reliable physical data on past flood impacts have not been collected

or are difficult to obtain.

• The synthetic response functions quantitatively summarize stakeholder opinions about

the likely impact of flood hazards on key issues.

• Key issues of concern for the region were identified along with issues where

understanding appears to be limited and where more research may be needed.

• The outcomes of this methodology could be incorporated into broader investigations of

disaster impacts (Dutta et al. 2003), although not pursued here.

In the recently published report by the House of Representatives Standing Committee

on climate change, water, environment, and the arts of the Parliament of the Common-

wealth of Australia (PCA 2009), the committee recommends development of a consistent

methodology for vulnerability assessments to secure better management outcomes in for

coastal zones in Australia, at a time of environmental change. It notes that in order for

effective climate change adaptation to take place, detailed local vulnerability assessments

are required. Abuodha and Woodroffe (2006) presented a comprehensive review of various

existing approaches, methods, and tools available around the worlds for assessing vul-

nerability of coastal zones such as:

• DIVA (Dynamic Interactive Vulnerability Assessment),

• simCLIM (Simulator of Climate Change Risks and Adaptation Initiatives),

• CVAT (Coastal Vulnerability Assessment Training),

• FUND (Climate Framework for Uncertainty, Negotiations, and Distribution),

• FARM (Future Agriculture Resources Model),

• COSMO (Coastal Zone Simulation Model),

• SURVAS (Synthesis and Upscaling of Sea level Rise Vulnerability Assessment

Studies),

• IPCC CM (Inter-governmental Panel on Climate Change Common Methodology).

Nat Hazards (2011) 59:967–986 983

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While many of these methods and tools are suitable for socioeconomic impact

assessments, their capabilities on environmental impact assessments and applications at

local scale vary widely. The approach presented in this paper provides a clear direction for

enhancing the ability of many of the existing tools in assessment of flood vulnerability and

impacts on various coastal issues related to economic, social, and environmental sectors at

a local scale through effective engagement of stakeholders.

A limitation of the approach is that response functions can only be generated for issues

about which stakeholders are aware. This method is not useful for identifying ‘‘sleeper’’

issues. A particular limitation of this study is that, although considerable effort was made

to identify a large number of stakeholders in the region, only 10% of those approached

responded to the questionnaire. A greater response may have been achieved if the ques-

tionnaire had been designed to be more accessible. It was a lengthy, complex instrument

and required a considerable time investment to complete. It is acknowledged that the

impact response functions would be better estimated on the basis of a larger stakeholder

sample.

5 Conclusions

The paper has presented a systematic approach by which relevant stakeholders can be

actively engaged in prioritising flood impact issues and collating information for quanti-

fication of impacts for adaptation measures and has demonstrated the approach through

successful implementation in the Gippsland coastal region. As an outcome of the project,

we identified key issues of concern for this region for flood impacts and the synthetic

response functions for some of these key issues (with high agreement) for quantification of

impacts of floods on these key issues in the region. The analysis also showed that some of

the issues are considered not to be significantly affected by floods (Class 4) and thus may

not require adaptation measures. The outcomes of the analysis did not provide high

agreement for issues under Classes 3 and 4. Different approaches would be required to

determine the importance of these issues and to establish response functions for these

various relevant hazard parameters.

Synthetic response functions as developed in this study can be used to quantify the

likely impacts of flood hazards of various magnitudes. This allows natural resource

managers and decision makers to better understand the risks associated with sea level rise

and to prioritise adaptive management strategies for coastal regions. Many of the existing

methods for flood impact assessment in coastal regions are limited in their capabilities on

environmental impact assessments. The approach presented in the study can been effec-

tively utilized for enhancing the existing tools in the assessment of flood impacts on

various coastal zone issues related to economic, social, and environmental sectors.

Although not demonstrated in this study, a further application of the response functions

may be to use them in order to analyze the effectiveness of proposed adaptive measures.

Acknowledgments The authors wish to acknowledge the Asia Pacific Network for Global ChangeResearch for financial support, the stakeholder reference group and the international expert group forparticipation in the project, Monash University’s Standing Committee on Ethics in Research InvolvingHumans (SCERH) for review and approval of the questionnaires, Paul McLaren of Monash University’sInformation Technology Support division for valuable assistance with data manipulation, and the twoanonymous reviewers for their invaluable comments.

984 Nat Hazards (2011) 59:967–986

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