Outline

1. Resource System Vulnerabilities to Climate Variation and Change(Presentation at 2004 Asia-Pacific Region WorkshopNovember 2-5, 2004, Manila, Philippines) By Yongyuan Yin1, Nicholas Clinton2, and Bin Luo3 1 Adaptation and Impacts Research Group and Sustainable Development Research Institute/UBC, Canada, and ESSI, Nanjing University, Nanjing, China 2 International Institute for Earth Systems Science (ESSI), Nanjing University 3 Faculty of Engineering, University of Regina, Canada

2. Outline • Concept: the resource system vulnerability • Need for new methods in vulnerability assessment • Major questions addressed • Determinants of resource vulnerabilities • IA research methodology • Resource vulnerability indicators • Identifying Critical Thresholds for Indicators • Measure Vulnerability • Vulnerability Classification by the Fuzzy Set Model • Mapping Vulnerability • Some examples for illustration • Prioritizing adaptation options or policies • Acknowledgement

3. Study Site – Heihe River Basin • 128,000 Square kilometers (inland watershed) • Diversity of land cover types • Diversity of population densities • Geographically variable climate

4. Concept of Resource System Vulnerability A system’s vulnerability is related to a system’s resilience defined as the capability of the system to maintaining its functionality in the face of a particular environmental change. In this connection, the vulnerability of a system is defined as its propensity to undergo impacts or lead to disruptions in the nominal functionality of the system as a result of climate variation or change.

5. Need for new methods in vulnerability assessment The current status of climate vulnerability research and vulnerability assessment show a lack of designing new methods to meet the increasing demand of policy makers. The main goal of vulnerability assessment is to develop effective methods to measure vulnerability and to assess the environmental risks in dealing with climate stresses.

6. Major Questions Addressed • What factors are driving the changes of system vulnerability? How might these factors change in the future and what implications would that have for future vulnerability? • What are the important climatic and non-climatic exposures operating on, or expected to operate on, the land and water system of study region? • How can vulnerability indicators be used to assess resource system vulnerabilities to present climate variations and future climate change? • Can thresholds in climatic variables be identified which, if surpassed, would pose substantially greater risk of harm to the land and water system or sub-systems than would be expected if the thresholds are not surpassed? • Does vulnerability (or adaptive capacity) to climatic exposures vary in character or degree for different sub-units of the resource system?

7. Determinants of Resource Vulnerability Table 1 Potential determinants (climate and other variables with the forcing) and resource vulnerability indicators in Heihe River region

8. IA Research Methodology • Climate scenarios and extremes • Dr. Xu Ying’s presentation • Socio-economic scenarios • CAREERI • Data collection: RS, GIS, field work, literature review, and survey • Vulnerability and adaptation assessment

9. Methods Vulnerability and Adaptive Capacity Assessment • Selecting vulnerability and adaptive capacity indicators • Identifying critical thresholds for indicators • Setting priorities to vulnerability indicators • Vulnerability classification by the fuzzy set model • Adaptive capacity classification by the fuzzy set model

10. Some Examples of Calculating Vulnerabilities • Average annual water withdrawal ratio can be used to identify sub-units under water stress. WMO suggested that the ratio exceeds 20% and 40% of annual water availability be considered as medium and high water stress respectively. • Reservoir system vulnerability defined as the magnitude of a water supply failure as a fraction of annual yield can be computed by: Vrf = 0.452 * (S/Y)1.27(Where: Vrf is reservoir vulnerability, S is the reservoir storage capacity, and Y is annual reservoir yield) • Vulnerability can also be measured as the maximum duration of failure, should a failure (i.e., an unsatisfactory value falling outside of the coping range) occur. The Maximum Duration-Vulnerability can be calculated by: Maximum Duration-Vulnerability (p) of DVF = Maximum duration (number of time periods) of a continuous series of failure events for indicator F, occurring with probability p or that may be exceeded with probability 1-p • With respect to land system vulnerability, the soil erosion rate can be used as another indicator. Soil loss rate can be calculated by USLE or wind erosion model. The soil loss tolerance value can be used as the threshold level. Soil loss tolerance is a concept in the relationship between erosion and productivity.

11. Some Examples for Illustration Input Data: Land cover, population density, rainfall, temperature, soils, DEM, AVHRR • Sources: heihe.westgis.ac.cn, edcdaac.usgs.gov, www.orbit.nesdis.noaa.gov, www.ornl.gov

12. Computation of Indicators: Part I • The “Rational Runoff Equation,” Q=cia, to partition rainfall • c = f(curve number, slope, imperviousness, intensity) • i = measured rainfall (interpolated to a grid) • a = 1 square kilometer (one pixel) = X Runoff

13. Computation of Indicators: Part II • Evapo-transpiration under irrigated conditions • Implementation of FAO methodology in GIS • Inputs: monthly temperature, average range as measured at the Jiuquan station, crop location data, crop growth parameters Monthly Temperature Monthly Evapotranspiration Crop location Avenue Avenue

14. Computation of Indicators: Part IIIa • In this example, rainfall infiltration is subtracted from crop water requirements to derive moisture deficit (irrigation requirement)

15. Computation of Indicators: Part IIIb • In this example, runoff for year 2000 is divided by population to create an indicator for water resource availability.

16. Vulnerability Classification by the Fuzzy Set Model The sets, U, of classification criteria and V of vulnerability levels can be specified as follows: U = {(temperature), (rainfall), (low flow event frequency), (low flow event duration), (causality and/or injury), (damage to ecosystem), (water use conflicts), …} V = {(extremely vulnerable), (high risk), (moderate risk), (low risk), (acceptable)} The problem under consideration is how to assign different land units into proper categories of overall vulnerability level on the basis of the given data and criteria, and thus partition the whole region into several sub-regions with unique vulnerability patterns.

17. Adaptive Capacity Classification by the Fuzzy Set Model The sets, U, of classification criteria and V of adaptive capacity levels can be specified as follows: U = {(economic return), (industry productivity), (technology advancement), (regulated annual supply), (institutional frameworks), (water storage capacity), …} V = {(extremely adaptive), (high adaptive), (moderate adaptive), (low adaptive), (acceptable)} Since factors influencing adaptive capacity may be different from vulnerability indicators, criteria selected in the U set equation are thus different from the vulnerability criteria set. The factors affecting a system’s adaptive capacity are usually those economic, technological, and social in nature.

18. Prioritizing Adaptation Options or Policies Adopt a multi-criteria decision making technique, Analytic Hierarchy Process (AHP), to identify desirable adaptation options to reduce climate vulnerabilities and to improve adaptive capacity.

19. Identify desirable adaptation options which can be used to reduce climate change vulnerability Inventory of climate change impacts and adaptation options RANKING: 1. Government Relief 2. Farm-level Adjustment 3. Research & Education 4. Water Management Prioritized ranking of options, indicating overall preference AHP multi-criteria evaluation of options by stakeholders using online survey

20. Acknowledgements The research project and participation of this workshophave been made possible through the financial support of the AIACC, Adaptation and Impacts Research Group/Environment Canada, and Sustainable Development Research Institute/University of British Columbia.