Web-based vulnerability analysis in central provinces of Viet Nam

Pham Van CuNguyen Trung KienBui Quang ThanhNguyen Quoc Huy

PGIS design

The PIS system provides 3 functions: (1) Storage of various data: A page introduces project and background information (2) Data analysis : a GIS-based Analyst (spatial analysis) tool (3) Information communication: A map-centred communication tool (interactive page for communication between

stakeholders) integrated in a cohesive manner.

The three components are accessible through a user friendly interface web system.

User interface

Storage + Viewer

Web-based Analyst tool

Map-centered communication

tool

PGIS design

1. The purpose of the Background information ( storage of data) is to provide background information on the CC-induced problems being addressed and references for further reading. This section structurally comprises of a single page introducing the initiative, objectives of the system, web structure/ sitemap, registration page if needed. Topics covered within this section include CC-induced disasters (drought, salinity intrusion, heavy rainfall, flood inundation) and related documents. This section also serves as a repository of downloadable spatial data.

2. A GIS based analyst tool designed to involve local participation in conducting spatial analysis that are GIS fundamental functions. The page should help local users to make sense of their own experiences and indigenous knowledge. The UI might look similar as below picture.

3. The communication component enables sharing of local/scientific knowledge and guidance of decision makers. Data analysis by scientists, information verification and feedbacks from the users), the information system will be getting more and more enriched. This section might include a forum for experience sharing if needed.

WebGIS

Clients access system via web browsers. GeoEXT and Openlayers javascript library loaded from server side

Geoserver acts as a mapping server that produces maps as requested by clients. Geoserver configurations comply with OGC standards

PostgreSQl/Post GIS used as spatial database management system.

Geo Webcache accelerates the delivery of images and data by dividing the area in to tiles and caching for fast delivery.

wps

PGIS design

Climate change vulnerability

IPCC vulnerability framework:V = f(E, S, AC)

E = ExposureS = SensitivityAC = Adaptive Capacity

Exposure

• The degree of climate stress upon a particular unit of analysis

• Climate stress: – long-term climate conditions– climate variability– magnitude and frequency of

extreme events

Sensitivity• The degree to which a

system will respond, either positively or negatively, to a change in climate.

Adaptive Capacity

• The capacity of a system to adjust in response to actual or expected climate stimuli, their effects, or impacts.

The degree to which adjustments in practices, processes, or structures can moderate or offset the potential for damage or take advantage of opportunities created by a given change in climate.

Interpretation 1:

• Vulnerability analysis as a means of defining the extent of the climate problem

• Vulnerability = Impacts – Adaptations• Adaptability defines vulnerability• Vulnerability is the end-point of the analysis

Interpretation 2:

• Vulnerability analysis as a means of identifying what to do about climate change.

• Vulnerability is shaped by adaptive capacity.• Vulnerability determines adaptability• Vulnerability is the starting point of the analysis.• Under this interpretation, we need measures of the

social processes that contribute to vulnerability.

Implications

• End point: We need better GCM scenarios, better process models, and better quantifications of adaptation;

• Starting point: We need better understanding of coping capacity, adaptive capacity, outcomes of social processes, and measures of well-being.

Measuring vulnerability:Practical challenges

• How should indicators be chosen?• Are adequate data available?• How should composite indicators be developed?• How can measures of vulnerability be validated?

Choosing indicators: Deductive approach

• Theory driven: Start from theory or hypothesis; find indicators that might support or reject the hypothesis.

• Example: Adger and Kelly (2000) hypothesize that the architecture of entitlements is a key determinant of vulnerability in Vietnam; thus they identify income levels, income inequality and diversity of livelihood as key indicators.

Choosing indicators: Inductive approach

• Data driven: Examine lots of data, look for patterns and examine correlations or statistical relationships. Generalizations can be used to develop conceptual models and theories.

• Example: Ramachandran and Eastman (1997) analyzed 92 variables to explain the need for food assistance in West Africa. Using statistical methods, they identified the contributions of different variables to vulnerability.

Reality:• Eriksen and Kelly (submitted) point out that in most

national level assessments of vulnerability, the selection of indicators is based on a ”rudimentary theoretical appreciation of vulnerability (which is often, it is only fair to say, all that is available)”. Few ”inductive” indicator studies explicitly discuss implications of findings for vulnerability theory.

• Most studies that measure vulnerability are ”not easily distinguishable as either deductive or inductive…”

Data

• Need for reliable, readily available, and representative data for desired indicators of vulnerability.

• Compiling national data is difficult. National level vulnerability assessments often rely on existing global data sets (FAO, World Bank, UNDP, WRI, etc.)

• More detailed data usually available for sub-national assessments (e.g., census data)

Data

“Data are usually treated unproblematically except for technical concerns about errors. But data are much more than technical compilations. Every data set represents a myriad of social relations.”

(Taylor and Johnston 1995, p. 58)

Case study

Case study

Case studyID Indicators Unit Min* Max*

Exposure

1 sdT – Sd of Average anual temperature oC 0,2 0,9

2 sdP – Sd of Avarage anual Precipitation mm 1,5 8

3 rT – Temperate Min-Max difference oC 2 5

4 Nhot – Frequency of extreme hot months ( above 37oC)

Nr 1 3

5 Ncold – Frequency of extreme cold months (under 10oC)

Nr 1 5

6 Ndry – Frequency of extreme dry months Nr 1 3

7 Ndisaster – Number of disasters Nr 1 9

Case studySensitivity

S1- Average irrigated area per person m2 1,5 15,3

S2 – Number of agricultural households Nr 1 147

S3 – Percentage under 5 years old % 1 15

S4 – Percentage above 65 years old % 5 10

S5 – Death rate under 5 years old % 1 10

S6 – Number of deaths by disasters % 1 5

S7 – Number of injured by disasters Nr 1 60

Adaptability

A1 – Average expense per person VND Currency

1.000.000 4.000.000

A2 – Percentage of high school education % 10 60

A3 – Believe in local government % 10 85

A4- Participation in politics % 1 100

Case study

Case study

Case study

Case study

Case study

Case study