Vulnerability mapping: approaches from continental to local scales

Lelys Bravo de Guenni*, Raúl Ramirez , Elvin Barreto and Isabel Llatas Universidad Simón Bolívar, Caracas, Venezuela *e-mail:lbravo@cesma.usb.ve

Acknowledgements

• Water System Analysis Group of the University of New Hampshire led by Prof. Charles Vörösmarty

• Graduate students Jhan Rodríguez and Pedro Sequera from the Statistics graduate program. Universidad Simon Bolivar, Caracas-Venezuela

Contents

• The role of the scale in vulnerability mapping

• Geophysical and socioeconomic data sets: From global to local scales

• Vulnerability quantification process

• Conclusions

The vulnerability concept (after Downing, et al., 1999)

Vulnerability: is the degree of loss (from 0 to 100 percent) resulting from a potentially damaging phenomenon. It depends on the capacity of a person, a group or a component of a natural system to resist and/or to recover from and/or to cope with the impacts of an adverse event

Risk: is the expected losses (of lives, person injured, property damage and/or disruption of economical activity) due to a particular hazard for a particular location and time period. In other words, risk can be expressed as R= Vulnerability x Pr(Hazard), i.e., the expected losses due to a particular hazard

Note: In Disease Mapping Analysis: Risk = Vulnerability

Focus at the Continental scale

• What changes in precipitation characteristics and population growth for South America have occurred in recent years?

• What are the implications of these changes for population exposure, vulnerability and risk to rainfall anomalies in South America?

• How can we assess the marginal contributions of precipitation and population changes to population vulnerability and risk?

Scenarios: Constant Population (1960)

Early Climate (1960-1970)

Late Population & Late Climate

Tabular Impact of: Population Change

Climate Change

Interactions

CRU

Precipitation

Time Series

1960-2000

CRU

Precipitation

Climatology

1960-2000

Urban and

Rural Predicted

No. Affected

Population Grids

1960-2000

5-yrs increments

MAP Riskier Places to Live

Flood Damage DATA

6 Minute

River Network

Upstream

Average

Time Series

Upstream

Average

Climatology

River Networked

Anomaly

Time Series

Urban and Rural

Exposure CRED Flood Database

213 Events

State and Country

Urban and Rural

Vulnerability

Functions

(weighting factor)

Scenario

Exposure Scenario Affected

(weighted exposure)

Equations

CLIMATE DATA

POPULATION

DATA

VULNERABILITY

MODEL

NETWORKED PRECIPITATION DATA

FOR SOUTH AMERICA

Monthly precipitation time series and

monthly rainfall climatologies (1960-

2000) from the Climate Research Unit

(CRU) monthly grided data set were

coupled to the 6 min. South America

Simulated Topological River Network to

produce Networked Rainfall Time Series

by calculating the average precipitation

on contributing grid cells upstream any

given cell.

6 min Simulated Topological

River Network for South America

(Fekete et al., 2001)

NETWORKED PRECIPITATION CLIMATOLOGIES

Population centers with River Networked Rainfall Anomaly for April 1999.

0 1 2 3 4 5

05000

00

1500

000

2500

000

anom oly

Number of people affected by rainfall anomaly category using the “count back” method

Rainfall Anomaly Category

Cu

mu

lati

ve E

xpo

sure

(m

illio

ns)

Vulnerability Model

Area: 916.445 km2

Population Density: 23,95 inhab./km2

Views from Vargas State: Event, Dec. 1999

Motivation of the Problem at the National Level

• The main objectives of this research are to have a

better knowledge of population vulnerability to hydrometeorological related hazards in Venezuela, enabling its spatial analisys for more and better interregional and district comparisons.

MAIN OBJECTIVE MAPPING VULNERABILITY

Events Data Set Map of total number of affected during the period 1960-2000

Total affected per district

Events Data Set

Map of total number of events in the period 1960-2000

Total Events per Distric for the period (1960-2000)

Total Events per Distric

0

1 - 3

4 - 10

11 - 18

19 - 33

´

Areal Model for Vulnerability We consider the variable as following a Poisson distribution of the form:

).(~ iii RREPoisY

iY

•Yi is the number of people affected in the district i •Ei is the expected number of people affected. This value is calculated as Ei = ni x p*, where ni is the average number of people living in district i and p* is the national proportion of affected calculated from all observations •RRi is the relative risk calculated as pi/p*, where pi is the proportion of people affected (to be estimated). •The value of RRi is the quantity of interest and pi is the vulnerability as defined before.

If RRi >> 1 the district i has a Relative Risk greater than the National Mean

Elevation Above Sea Level (in meters)

Distance from each point to the closest river (river network density)

MODEL FIT OBSERVED AFFECTED

Mean values of the posterior predictive distribution of people affected

DATA SCALE PROBLEMS: Rainfall Anomalies

Guasdualito Grid Rainfall Anomaly: 1.4

Mean Rainfall Anomaly Páez District:

1.1

Guasdualito, August 1996: 7,000 people affected

Rio Apure

Vargas State, December 1999: 300,000 people affected

Carmen de Urea Grid Rainfall Anomaly: 1.45

Mean Rainfall Anomaly Vargas District:

1.33

Local scale: Community based early warning system

• The problem and the location

• Adaptation and Mitigation Measures to Hidrometeorological extremes

• Components of the Early Warning System

• Experiences with the Community

Study Area: 15,000 inhabitants, located 45 min away from Caracas

Naiguatá Camurí Grande

Community based early warning system: Example

of Camurí Grande and Naiguatá Basins

For each community the following tasks are developed:

Evaluation of existing hazards

General information about mitigation measures

Establishing a local risk task force

Developing a local risk and emergency plan

To define the role of the community

Communal early warning system tools (portable radios, alarms,

communal rain and water level gages)

Promotion activities (workshops, posters, fliers, broadcasting etc.)

Simulations (evacuation drill)

Community based hazard map: Camurí Grande

Community based hazard map: Naiguatá

Conclusions