Assessing Resource System Vulnerability to Climate Change Methodology

Assessing Resource System Vulnerability to Climate Change: Methodology By Yongyuan Yin1,2, Nicholas Clinton2, Bing Luo3 and Liangchung Song4 Lead author-Adaptation and Impacts Research Group (AIRG), Environment Canada, and Sustainable Development Research Institute (SDRI), University of British Columbia (UBC), 2029 West Mall, Vancouver, B.C Canada V6T 1Z2; Telephone: (604)822-1620; Fax: (604)822-3033; Email: yongyuan.yin@sdri.ubc.ca International Institute for Earth Systems Science (ESSI), Nanjing University, Nanjing China Faculty of Engineering, University of Regina, Saskatchewan, Canada Meteorological Bureau of Gansu Province, Lanzhou, Gansu, China Abstract One challenging issue in resource planning and management is to design and apply integrated approaches to estimate resource system vulnerabilities to climate variability and change, and to identify desirable options that could be used to reduce these vulnerabilities Given the complexity and uncertainties in resource management, it will be difficult to identify those most vulnerable regions Research on assessing resource system vulnerability can provide scientific information and understanding necessary for insuring regional sustainability Vulnerability of resources system reflects a complex set of interrelations involving biophysical, social, and economic factors on both the demand and supply sides of the resource use equation These biophysical and socio-economic factors may limit or facilitate resource supply and demand In that sense they become determinants as to whether resources can provide various functions to meet many societal demands These functions can serve various human values, preferences, and aspirations to meet multiple demands from a variety of users such as agriculture, water, and ecosystem When a system’s propensity is to undergo impacts and lead to disruptions in its nominal functionality as a result of climate variation or change, we assume that the system is vulnerable to climate 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 In this study, several major factors which influence resource system vulnerability in the Heihe River region of China will be considered In other words, resource vulnerability is a function of these factors including: climate, economic activities in the region, land users, size of resource use activities, resource use efficiency, the price elasticity of supply and demand, environmental protection, policy options (economical, technical, or policy), lifestyle associated with income increasing, and population growth This paper focuses on methodology development for land and water system vulnerability assessment using the Heihe River Basin as a case The paper introduces methods for the formulation of indicators for agricultural land and water resource system vulnerability to climate variation and change Indicators are discussed in relation to their specificity, descriptive power, thresholds, and capacity for geographic allocation using ancillary or modeled data Resource system vulnerability is addressed with a description of applicable indicators, literature references, and geo-spatial data requirements for the mapping of the indicators 1 Introduction 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 paper, 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 The purpose of this paper is to develop methods to assess land and water system vulnerability The paper will first highlight some major determinants of the resource vulnerability This will then serve to relate these determinants to vulnerability indicators and to focus and organize discussion on developing appropriate methodologies for resource vulnerability assessment A vulnerability assessment framework will be developed and applied to a case study in the Heihe River Basin in Northwestern China In particular, the paper will address the following questions:  What are the important climatic and non-climatic exposures operating on, or expected to operate on, the land and water system of study region?  What factors are driving the changes of the system vulnerability?  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? With barriers such as extremely fragile ecological conditions, fewer financial resources, poorer infrastructure, lower levels of education, and lesser access to technology and markets, the Heihe River region has been suffering from climate variations and will experience severer impacts of climate change on food production, water uses, and human health Moreover, the region’s adaptive capacity is lower than in the coastal region of China The region is facing substantial and multiple stresses, including rapidly growing demands for food and water, large populations at risk to poverty, drought, degradation of land and water quality, and other issues that may be amplified by climate change The time horizon considered for vulnerability assessment by AS25 project will be two: beginning with a careful assessment of the present-day climate vulnerability, which can then be used to assess resource system vulnerabilities to future climate change This paper, however, focuses on current climate vulnerability assessment Major Determinants of Resource System Vulnerability Vulnerability of resources system reflects a complex set of interrelations involving biophysical, social, and economic factors on both the demand and supply sides of the resource use equation These biophysical and socio-economic factors may limit or facilitate resource supply and demand In that sense they become determinants as to whether resources can provide various functions to meet many societal demands These functions can serve various human values, preferences, and aspirations to meet multiple demands from a variety of users such as agriculture, water resources, and ecosystem In this study, several major factors which influence resource system vulnerability in the Heihe River region of China will be considered In other words, resource vulnerability is a function of these factors including: climate, economic activities in the region, land users, size of resource use activities, resource use efficiency, the price elasticity of supply and demand, environmental protection (e.g control desertification and salinization), policy options (economical, technical, or policy), lifestyle associated with income increasing, and population growth In addition, seasonal variations in supply and demand require the study to take account of the temporal aspect of the above factors The biophysical functions that resource systems can provide include life-support for biomass growth, biological diversity, and wildlife habitat; an assimilating function or resilience to absorb chemical wastes and pollutants through its biological chains and chemical cycles; and hydrological and microclimatic regulations The economic functions of resource systems are: supplying useful raw minerals and energy as inputs for economic production; food and water for human consumption In addition, resource systems provide a stream of social functions that are essential for supporting human welfare such as housing, employment, defence, recreation, health, cultural, scientific, educational, and aesthetic services (d'Arge, 1972) 2.1 Biophysical Determinants of Land Resource System Vulnerability In resource systems, natural ecosystems are transformed into hybrid agro-ecosystems or water use system for the purpose of food, fiber, and other production These hybrid systems are directly dependent on biophysical factors and essential ecological functions for sustainability (Conway, 1985) The major biophysical determinants that are related to the land and water use are listed in Figure (Biophysical System) Climate variables (temperature and precipitation), the slope of land, as well as soil type and fertility, are some critical biophysical determinants relating to the supply of land for agriculture and forestry The quantity, quality, and distribution of resources, and the way they are utilized, are other essential factors for resource use decision making The availability of land and water resources for meeting increasing demand in each particular area is limited The existence of possible absolute biophysical constraints on resource use activities implies some limits to exploit the resource base Therefore, the alleviation of the threat of resource scarcity requires that resources be used within their biophysical limits or capability (Page, 1977; Daly, 1984) There is increased concern about the effects of climate change arising from economic activity on the availability and suitability of resource systems for agriculture, forestry, and wildlife (IPCC, 2001; Parry et al., 1987) Climate change by changing the biophysical determinants of resource use could most directly affect various functions of resources Changes in these biophysical determinants would likely result in negative impacts on productive, hydrological, and other functions of the land resource system The implication is that climate change will affect the supply of resources with respect to their availability, suitability, and distribution 2.2 Economic Determinants of Resource Use Economic determinants provide another set of opportunities or limitations to the resource use (Figure 1, Economic System) Advanced technology and managerial skill have raised land productivity In turn, this may have eased resource system vulnerabilities If future technology and managerial skill can continue to improve the productivity of resource systems at a rate faster than the growth rate in demand, no additional resources will be needed for production (Pierce, 1990) Population growth and urbanization will contribute to the growth in demand for resources The earliest popular description of the concept of carrying capacity as related to human population was by Thomas Malthus and may be referred to as the limit of a given resource system to support the demand and consumption levels associated with a human population This concept has also been applied to establish economic, social, and behavioral thresholds beyond which the environmental quality will deteriorate and user enjoyment will decline (Mitchell, 1989) With high standards of living, people in developed countries demand low density housing which often translates into sprawling urban development and costly services In addition, modern societies desire more open space for recreation and public parks Many of these developments occur in productive farmlands, forestry lands, and areas which are perceived to be of natural, historical, cultural, scenic, or scientific importance Economic development and urbanization in both developed and developing countries are often accompanied by increasing stress on the resource system and cause significant adverse effects on the ecosystem (Hufschmidt, 1983) Many economic development activities not pay sufficient attention to resource depletion and environmental deterioration Resource degradation, such as erosion, salinization, desertification, and water pollution has caused a decline in crop yields and an increase in production and environmental costs 2.3 Social Determinants of Land Use People with different cultural and historical backgrounds perceive and value resources in different ways Moreover, their perceptions and valuations with respect to land and water resources have changed over time (Rees, 1985; Tisdell, 1989) Different perceptions and valuations often lead to alternative land and water uses For example, the environmental movement has influenced resource use patterns through an increase set aside of wilderness areas Other social factors affecting resource use include policy, development programs, government regulation, inter and intra-generational equality issues (Figure 1, Social System) Biophysical System Functions/ Characteristics: Variables: Economic System Functions/ Characteristics Variables Social System Functions/ Characteristics Variables: Biogeochemical and hydrological cycling, habitat, bio-diversity, resilience, production, Climate, soil, resource quantity and quality, location or Net Return, stability of income, food, fibre and water supply timber, mineral, Housing, employment, defence, public park, education distribution and energy supply amenity Relative location, resource use pattern, population, technology, managerial skill Land tenure, policy, regulation and program, equality, living standard Resource Vulnerability Indicators: food sufficiency, water supply, income, arable land protection, soil erosion control, ecological footprint, wetland protection, water conservation Resource Vulnerabilities Figure Determinants (Variables) of Resource System Vulnerabilities Assessing Resource System Vulnerability 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 In this respect, some methods are presented below for assessing agricultural land and water system vulnerability 3.1 Resource vulnerability indicators In resource vulnerability assessment, indicators are used as decision criteria or standards by which the degree or the group of resource vulnerability class can be identified Efforts to assess resource vulnerability must first to identify and specify multiple indicators Some operationally useful key indicators in vulnerability and adaptive capacity assessment are listed in Table Table Potential determinants (climate and other variables with the forcing) and resource vulnerability indicators in Heihe River region Climate and other related Related system attributes and Resource vulnerability indicators determinants (forcing) options Rainfall - variability Drought Temperature - max Soil moisture Water flows, storage volumes, and quality Palmer drought severity index Evaporation Food sufficiency ratio Farm income Water scarcity (withdraw ratio) Drought hazards Temperature - Wind Cold snap Heat stress days Accumulated degree days Cropping area Population growth Economic growth Technology Consumption Urban expansion Resource management Government policies Soil moisture Irrigation Land conversion Land use plan Adaptive capacity Adaptation options and policies Groundwater stress Hydro power Arable land loss Salinity Soil erosion Grassland deterioration Water quality Wetland area Water use conflicts CO2 and CH4 emission It is obvious that economic return is one of the most important indicators for measuring vulnerability and adaptive capacity Developed countries possess high adaptive capacity Improvements in economic return will enhance adaptive capacity In China, providing enough food for the 1.3 billion population is always a big challenge There has been an increasing concern about China’s food supply and its ability to feed itself The provision of adequate food on a continual basis is a major indicator of regional sustainability The agricultural production indicator reflects the ability of the land base to maintain in perpetuity a given flow of goods and services Agricultural production can also be considered as a security indicator to achieve higher levels of self-sufficiency and/or it may be used to represent a vulnerability indicator to check whether the resource base can provide enough food supply Many of the industrial and housing developments occur in productive farmlands, forestry lands, and wildness areas How to slow down the conversion of farmland to urban and industrial uses is critical for regional sustainability in China Thus, a further indicator to protect and conserve arable land reflects this concern It is now generally realized that an environmental concern should be incorporated in decision making in an effort to achieve sustainable development (WCED, 1987) There are a large number of parameters that can be used as indicators of ecological vulnerability For example, environmental concern may mean protection natural resources, or it may mean minimizing the concentration of atmospheric carbon dioxide at a global scale In Western China, the environmental concern is reflected in the indicators of soil erosion, desertification, greenhouse gas (CO2, CH4) emission, and sand storm There is an increasing concern about the implications of climate change for water management (Gleick, 1990) There is an increasing concern about water use conflicts in semi-arid region of Western China Dealing with potential water use conflicts with changing climate is therefore considered as an important indicator The fight over access to water resources in the Heihe Basin has led to disputes, confrontation, and many cases of violent clashes The growing water use conflicts have posed a big challenge for Chinese government agencies to implement some effective water allocation policies Global warming may change average and extreme high and low river flow Changing water supply induced by climate warming may increase water use conflicts in the region 3.2 Identifying Critical Thresholds for Indicators The critical thresholds (CT) for indicators will be set to compare with indicator values of different areas to identify there vulnerability levels against these indicators If indicator values not exceed the threshold level, we assume that the system will have relatively benign experience and beyond which the system will feel significant stress under climate variation and/or change For example, drought hazards are based on rainfall amount, or aridity index, and if conditions remain below the threshold levels for a sustained period, drought hazards are declared The threshold levels (drought index) can be used as criteria in measuring the frequency of drought hazards over time The thresholds can also be used to measure the level of damages that droughts may cause Another example is annual water withdraw ratio indicator WMO suggests that the withdraw ratio exceeds 20% and 40% of annual water availability be considered as medium and high water stress respectively In Northern China, however, the threshold of this indicator is much higher, a high stress level at 60% In vulnerability and adaptive capacity measurement, many of the indicators can be expressed in numerical terms, particularly for those climate and physical variables It is also recognized, however, many indicators cannot be quantified, and many of the threshold levels can only be qualitatively described For instance, with respect to the CT level of the soil erosion indicator, the soil loss tolerance value, or T-value, can be used as a target level of the soil loss indicator Soil loss tolerance is a useful concept in the relationship between erosion and productivity The degree to which a unit quantity of soil loss reduces yield is dependent on a range of soil characteristics, which may be summarised as "soil loss tolerance" Soils with a concentrated distribution of nutrients in the topsoil, and shallow rooting depths, are usually sensitive in yield loss to soil erosion, and thus will have a low T-value This denotes a soil with low tolerance to erosion Soil with good structure, and deeply weathered with good nutrient reserves, will be less sensitive to erosion, and thus have a higher T-value (FAO, 1984) The T-value expresses a "tolerable" soil loss limit in order to retain productivity of the soil affected by erosion Yohe and Tol (2002) suggest that the relationships between adaptive capacity and its determinants are difficult to quantify Functional representations of these relations are only useful when they can offer insights to the complexity Many of the vulnerability and/or adaptive capacity indicators cannot be quantified, and many of the functions can only be qualitatively described With this respect, stakeholders, policy makers and analysts jointly identify CT levels is commonly used CT levels can be specified using a range of simple to very complicated methods As mentioned earlier, CT level for drought indicator can be determined by the amount of rainfall required in a specific region It also can be set using complex way such as the accumulated deficit in irrigation allocations over a number of seasons (Jones and Page, 2001) 3.3 Measure Vulnerability Resource system vulnerability is closely linked to environmental risk which can be expressed in the following simple formulas: Environmental Risk (ER) = exposure (e) frequency (probability)  consequence Consequence = f{intensity, sensitivity (s), adaptive capacity (a)} Where: The frequency or probability of an environmental stress can be expressed as the likelihood of a specific hazard (e.g climate extreme) The consequence is the damage or adverse impacts of the environmental stress It is the function of intensity of the stress, and the sensitivity and adaptive capacity of the exposure system The resource system vulnerability can therefore be expressed by the relationship: Vulnerability = P(s)*P(e)*[1-P(a)] Where: P is probability With zero probability of any one of these factors, it can be suggested that the system is not vulnerable In many cases, however, we cannot obtain quantitative data of the probability distribution function for these factors Linguistic representations could be used, such as very frequent, reasonably probable, unlikely and extremely unlikely, to reflect the probability parameters It is also often to assign numbers (e.g extremely unlikely could be assigned to a in 1000 year event) In fact, the assumption that vulnerability can somehow be expressed in terms of various combinations of these three attributes is a necessary simplification for investigation of vulnerability in a variety of forms Current vulnerability can be expressed as a statistical measure of the extent or duration of a resource system failure under climate stresses, should a failure occur The extent of a system failure is the amount an observed statistic value exceeds or falls short of the critical threshold For example, agricultural system vulnerability can be measured to show a system's failure to meet an operational goal, such as food supply for a region, or continually generating income above a minimum level for farmers Water system vulnerability can be measured with a system's success or failure to meet demand for a certain amount of water for a municipality, or to continually release water above a minimum flow rate from a reservoir Water system vulnerability can also be measured as the average deficit occurring during failures to meet a target, as well as the severity of failures If we use river flow, F, as an indicator to measure vulnerability, the water system vulnerability can be calculated by: EVf = Max [0, LFt-Ft, Ft-UFt] Where: EVf is water system’s maximum-extent vulnerability based on river flow indicator; LFt and UFt are the lower and upper critical thresholds of the coping range respectively; and Ft is the observed river flow data If the observed data (system performance indicator values over time) are within the upper and lower thresholds (within the coping range), we will then assume that the range of values are satisfactory, acceptable or un-vulnerable Statistics or observed data above the upper threshold or below the lower limit are considered as unsatisfactory or vulnerable It should be noted that these copying ranges may change over time Vulnerability can also be measured as the maximum duration of failure 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 Other methods for calculating vulnerabilities of some key indicators are listed below for illustration purpose  Average annual water withdrawal ratio can be used to identify those sub-units which are under water stress  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)  The FAO CROPWAT model can be used to estimate some critical values of crop growth and water requirements The computation of indicators of crop stress or yield index can be achieved by using (Allen et al 1999):  Yield Index = ETc-stressed/ETc-max With respect to land system vulnerability, the soil erosion rate can be used as another indicator Soil loss rate can be calculated by Universal Soil Loss Equation (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 Once the vulnerability measures of the time-series values are defined, they can be applied to project indicator values over years into the future This can estimate resource system vulnerability under climate change scenarios If over time the measures of vulnerability are increasing, the resource system is getting more vulnerable When we use a set of vulnerability indicators to measure a system’s vulnerability, it is possible that vulnerability results of some indicators will be improving while others will be worsening 3.4 Vulnerability Classification by the Fuzzy Set Model A fuzzy pattern recognition model can be applied to calculating the aggregated ratings of vulnerability ranks for all the land units in the region In the vulnerability fuzzy classification, each indicator is considered to be a fuzzy criterion, and the membership function is expressed in parametric form The advantage of fuzzy classification is its strength in relating many criteria simultaneously Through pattern recognition modelling, the general risk level of a land unit is identified by analyzing a generated fuzzy vector The vector also contains the membership degrees of other risk levels which provide further information on the exposure risks 3.5 Mapping Vulnerability Mapping the spatial distribution of system vulnerability is part of the study to facilitate policy makers identifying the most vulnerable sub-units To geographically distribute the indicators, several spatial scales have been considered ranging from square kilometre, county level, subbasin, to the whole basin based on data availability and other logistical reasons Thus data that represent various system vulnerabilities may be mapped at different scales Vulnerability Assessments in the Heihe River Basin The Heihe River Basin case study of vulnerability assessments is presented below for illustration purpose It should be noted that results presented in this chapter are mainly from current vulnerability assessment This section provides information on the geographical distribution of current climate vulnerability levels of the region Thus, the following applications show how methods described in section can be employed in vulnerability assessment In this section, several vulnerability indicators listed in Table are selected to measure resource vulnerability under current climate condition Geographic information system (GIS) or mapping tool is employed to identify the spatial distribution of system vulnerability in the region Maps, tables, and figures provide visual displays of resource system vulnerability, which can facilitate policy makers to identify the most vulnerable sub-units It should be indicated that there have been few practical applications of such approach yet particularly in climate vulnerability research In this sense, the methodology developed by this study can provide an introduction to a useful computer technique for climate vulnerability assessment Results of current vulnerability assessment will establish a baseline set of measurements and observations that can be used to measure progress toward reducing vulnerability to future climate change Once these vulnerability measures are identified for various vulnerability indicators, they can be applied to project potential vulnerabilities of the resource systems to future climate change scenarios Thus, the research on present vulnerabilities and adaptive capacities of resource systems will provide insights into potential impacts and vulnerabilities associated with future climate change While results shown in the case study are mainly based on current conditions, the research team of the AS25 project is now applying methods to investigate climate change vulnerabilities in the region 4.1 The Heihe Region The Basin is located in a region with the latitude of 35.4-43.5°N and the longitude of 96.45102.8°E Figure is a map of the study region The study area is the second largest inland river basin in the arid region of Northwestern China The Basin includes parts of two provinces (Qinghai and Gansu) and Inner Mongolia Autonomous Region With an area of 128,000 square kilometres, the region is composed of diverse ecosystems including mountain, oasis, forest, grassland, and desert Heihe River flows from a headwater on Qilian Mountain area to an alluvial plain with oasis agriculture, and then inters deserts in Inner Mongolia, representing the upper, middle and lower reaches of the Basin respectively Figure Map of the Heihe River Basin 4.2 Measuring and Mapping Vulnerability in the Heihe Region The Palmer Drought Severity Index The Palmer drought severity index (PDSI) was introduced by Palmer (1965) for measurement of meteorological drought It has been widely used in different regions of the world to study severity of drought hazards (Briffa et al., 1994; Kothavala, 1999; Ntale and Gan, 2003) Because PDSI can simulate monthly soil moisture content, it is thus suitable to compare the severity of drought events among regions with different climate zones and seasons (Makra et al., 2002) The computation of the PDSI begins with a climatic water balance using historic records of monthly precipitation and temperature Soil moisture storage is considered by dividing the soil profile into two layers The indicator operates on a monthly time series of precipitation and temperature to produce a single numerical value between +4 and -4 that represents the severity of wetness or aridity for a particular month Any PDSI values above +4.00 or below -4.00 fall into the "extreme" category of wet spell or drought Figure illustrates the trend in growing season PDSI for lower reach of the study basin It shows that the lower reach area has been dryer in the past decade This trend would continue under the changing climate Figure illustrates the trend in growing season PDSI for middle reach-lower part of the study basin It also shows that this area has a trend of becoming dryer in the past decade Figure illustrates the trend in growing season PDSI for middle reach-upper part of the study basin The drought trend for this area in the past decade is not very obvious since this area is much close to the high mountains and annual average precipitation is relatively higher Figure illustrates the trend in growing season PDSI for upper reach of the study basin The drought trend in this area has little changes in the past decades due to higher annual average precipitation This has particular meaning for the whole basin since most of the water resources in the study basin are from the upper reach of the basin 10 growing season PDSI Poly (growing season PDSI) Growing season PDSI -2 -4 1961 196419671970 19731976 19791982 19851988 199119941997 2000 Year Figure Trend in growing season PDSI for lower reach of the study basin growing season PDSI Poly (growing season PDSI) -2 -4 19 61 19 64 19 67 19 70 19 73 19 76 19 79 19 82 19 85 19 88 19 91 19 94 19 97 20 00 -6 Year Figure Trend in growing season PDSI for middle reach-lower part of the study basin -1 -2 -3 Growing season PDSI -4 -5 growing season PDSI Poly (growing season PDSI) 1961 19641967 19701973 19761979 19821985 19881991 19941997 2000 Year Figure Trend in growing season PDSI for middle reach-upper part of the study basin growing season PDSI Poly (growing season PDSI) -1 -2 Growing season PDSI -3 -4 1961 19641967 1970 19731976 1979 19821985 1988 19911994 1997 2000 Year Figure Trend in growing season PDSI for upper reach of the study basin Water Use Conflict Water use conflicts can be measured to show a system's success or failure to meet certain operational goals, such as supplying a certain amount of water for multiple users In the Heihe region, various water use policies and plans have been implemented or designed to limit or prohibit the utilization of certain amount water by sectors or regions Controversies have occurred, of course, as a result of such policies For the water diversion policy in Heihe Basin, farmers in upper and middle reaches of the river already argue that less water for irrigation has led to detrimental consequences in agricultural sector, while others have indicated that the new policy has been able to revive a dried lake located in the downstream region Obviously, water policies or regulations may make some sectors or regions worse off and others better off because of their re-distributive nature It is this re-distributive nature of policies that often aggravate water use conflicts Figure illustrates the trend of water use conflict in the study basin It shows that the trend of water use conflict has been increasing in the past decade The trend of this social indicator suggests that water shortage in growing season becomes more and more serious due to decreased water supply, and increasing population and per capita water use 45 40 35 30 25 20 15 Water-use conflict 10 19711973197519771979198119831985198719891991199319951997199920012003 Year Figure Trend of water use conflicts in the study basin Mapping Vulnerability in the Heihe Basin The following indicators were computed from a wide variety of ancillary datasets These data will be described for each indicator The indicators focus on weather, agriculture, and water resources While numerous assumptions are made in the analysis, it is important to keep in mind that the objective is to elucidate geographic trends Thus, relative differences between regions are the important trend under investigation The following map (Figure 8), from data created through the International GeosphereBiosphere Program (IGBP), shows (in light blue) the approximate distribution of cropland in the Heihe River Basin The agricultural sector relies heavily on irrigation water, mainly from river flow and groundwater sources, due to aridity of the region For this reason, vulnerability of this sector is largely a function of water resources To evaluate agricultural vulnerability, both crop demand and water resource availability were assessed Figure Map of land use in the Heihe Region Precipitation and temperature data were collected from meteorological stations distributed through the Heihe Basin The data were averaged, on a monthly basis, over the period from 1995 to 2000 in order to represent current conditions Using a method similar to Tan et al (2002), these data were interpolated to one-kilometre grid cells and partitioned into infiltration and runoff fractions (in millimetres) using the “rational” method Rational runoff coefficients were created from inputs of a DEM, soil type, and land cover The runoff was routed to a theoretical channel network in order to assess monthly values of discharge, geo-located at one-kilometre resolution Estimated crop evapotranspiration was computed using the FAO method described in FAO Drainage and Irrigation Paper 56 (Allen et al 1999) Crop types were based on the data in the land cover dataset from IGBP Weather conditions were based on a combination of the measured data and the CLIMWAT database The simulated evapotranspiration data were compared to the estimated rainfall, on a monthly basis It is assumed that all infiltrated rainfall is available for crop growth, while runoff is not This analysis indicates areas of crop water deficit, meaning that the infiltrated rainfall is insufficient to meet crop demand Thus, the deficits indicate the amount of irrigation water needed to maximize crop growth Areas of high deficit will place more pressure on irrigation infrastructure and neighboring areas of surplus Figure is a map illustrating the distribution of these areas of high demand The units are in millimetres of deficit Figure Map illustrating areas with high irrigation water demands The map indicates that there are geographic differences in demand for irrigation In the likely event of climate change, areas with high water deficits will be more affected by fluctuations in the supply of irrigation water Thus, this deficit can serve as an indicator of vulnerability However, irrigation will compete with humans for available water The Landscan dataset was utilized in the analysis of per capita water supply in the Heihe region (Dobson et al., 2000) It was assumed that none of the infiltrated water was available for humans, and that human consumption would rely entirely on runoff Similar to the analysis of crop water requirements, the areas of low per capita water yield indicate areas of high demand These areas may exert more pressure on neighboring regions with a surplus of water resources However, there may be cumulative impacts associated with high demand for water for both domestic and agricultural use The following map (Figure 10) indicates the areas of high demand for domestic water supplies Units are in cubic meters per year, per capita Figure 10 Map shows areas of high demand for domestic water supplies A simple comparison of available runoff and population (what Feitelson and Chenoweth (2002) call “annual per capita internal renewable water resources”) indicates the geographic areas of high demand The above map is designed to show areas where the local demand for water is not met by a “readily available” local supply (from local runoff) The “dependence ratio” indicator suggested by Lane et al (1999) describes this deficit as the fraction of the local demand that must be met through water transfers Areas of supply deficit are likely to be of varying degrees of vulnerability based on the extent of local resources available for remedying this supply-demand imbalance For example, the income per capita and the existing level of water supply infrastructure development will determine how well these areas can import water from elsewhere or otherwise provide local people with a safe source of water It is notable that some areas are barely at the subsistence level in terms of access to water Similar to the indicator for the agricultural sector, the areas of high demand for water will be more affected by any fluctuations in supply that result from changes in climate While the above metrics are fairly indirect indicators of vulnerability, current trends in weather and climate can illustrate the potential exposure to climate change The following indicators are based on the analysis of weather at measurement stations between the years 1999 and 2003 These indicators are computed based on the methods proposed in Kaly and Pratt (2000) It is notable that the periods of wet and dry weather are in the upper indices of vulnerability, while the “heat wave” indicator is much higher than anything discussed in Kaly and Pratt (2000) Figure 11 is a map showing the number of months over the last years during which rainfall was 20% lower than the long term average for that month It is an indicator of drought stress and, as such, should be considered in conjunction with the first two indicators Figure 11 Map showing the number of months over the last years during which rainfall was 20% lower than the long term average The following map (Figure 12) shows the number of days over the last years during which the maximum temperature was more than degrees C higher than the mean monthly maximum It is a “heat wave” map that should also be considered in conjunction with the other indicators, in terms of illustrating the areas of likely weather extremes Figure 12 May showing the number of days over the last years during which the maximum temperature was more than degrees C higher than the mean monthly maximum These indicators can also be scaled and multiplied, to obtain a geographic amalgam of vulnerability To this, the weather indicators (wet periods, dry periods and hot periods) were divided by their relative maxima and arithmetically averaged to obtain a composite indicator of weather extremes This composite was then multiplied by the per capita water indicator and the crop water deficit indicator (the latter two being first scaled between and The result is the following map (Figure 13) of region wide vulnerability, on a scale between and Figure 13 Map showing result of region wide vulnerability, on a scale between and Discussion and Conclusions The chapter seeks to provide answers to some important questions in relation to climate vulnerability assessment It provides information on the geographical distribution of current climate vulnerability levels of the region The results indicate the relative vulnerability levels that land and water system in different areas exposed to current climate stimuli The vulnerability measures for resource system can be applied to project potential vulnerabilities of the resource systems to future climate change scenarios In addition, the paper contributes to methodological development in vulnerability assessment and mapping By using vulnerability indicators, the climate vulnerability of the study region under current climate has been investigated The methods for the compilation of indicators, geographic allocation and synthesis are valid for other regions as well The applications presented here are intended to benefit future studies that aim to assess resource system vulnerability The consideration of scale will be important in the determination of what indicators are necessary and feasible for the inclusion in any potential climate vulnerability assessment It should be mentioned that some data used in assessment are fairly abstract, and not particularly meaningful out of context It is also notable, that the indicator is only mapped over areas of agriculture, since the crop indicator was included in the composite and is undefined over areas without agriculture However, assuming food production to be an important element of society, and a logical starting point for vulnerability reduction, this composite indicator is informative Specifically, the areas of highest vulnerability, as evidenced by the indicators, have been narrowed down to several square kilometers Assuming constraints to the adaptive capacity of the entire region, these places could be designated as high priority in terms of implementing effective adaptation strategies to prevent long term damage from climate change For examining system vulnerability to climate change, a natural resource system representing particular future conditions needs to be proposed For example, water resource system design, operation, and management policy can be specified over time The specification will include assumptions regarding system design, operation, and hydrologic and other inputs and demands that are all key aspects of a water system scenario representative of what could occur in the future Incorporated into that scenario are key indicators of resource vulnerability The uncertainties arising in estimating future demand or operational changes can be comparable to those associated with projecting climate change, and equally complicated for vulnerability assessments As a pilot study, the methods used in this study are subject to critiques When applying vulnerability assessment methods in the case, vulnerability indicator selection and vulnerability measurement were not carried out in a comprehensive and systematic way However, these methods are effective in vulnerability assessment and mapping spatial distributions of resource system vulnerability When future climate change and socio-economic scenarios are available, these methods can also be applied to estimate indicator values in the future This will produce future vulnerability data for each indicator Acknowledgment The research presented in this chapter is part of the AS25 project which is one regional subproject of AIACC The authors are grateful to AIACC and Environment Canada for funding this research References Allen, Richard G., Luis S Pereira, Dirk Raes, Martin Smith 1999 “Crop Evapotranspiration: guidelines for computing crop water requirements.” FAO Irrigation and Drainage Paper No 56 Briffa, K., Jones, P and Hulme, M (1994) Summer moisture availability across Europe, 18921991: an analysis based on the Palmer Drought Severity Index International Journal of Climatology 14: 475-506 Conway, G.R 1985 “Agroecosystem analysis” Agricultural Administration, 20: 31-55 Daly, H.E 1984 "Report on report." Environment 26(7): 25, 27-28 d'Arge, R.C 1972 "Economic growth and the natural environment." In: A.V Kneese, and B.T Bower, (eds.) Environmental Quality Analysis Baltimore: Johns Hopkins University Press Dobson, J.E., Bright, E.A., Coleman, P.R., Durfee, R.C., and Worley, B.A 2000 LandScan: A Global Population Database for Estimating Populations at Risk Photogrammetric Engineering & Remote Sensing, V 66, N FAO, 1984 Erosion and Productivity: a Review of its Effects and Research with Suggestions for Further Investigation FAO Report, Rome Feitelson, E and Chenoweth, J 2002 “Water poverty towards a meaningful indicator” Water Policy Vol 263–281 Gleick, P H 1990 “Vulnerabilities of water system” Pp 223-240 in Climate change and U S water resources, edited by P Waggoner New York: John Wiley and Sons, Inc Hufschmidt, M., James, D.E., Meister, A.D., Bawe, B.T and Dixon, J.A 1983 Environment, Natural Systems, and Development: An Economic Valuation Guide Baltimore: The Johns Hopkins University Press IPCC (Intergovernmental Panel on Climate Change) 2001 Climate change 2001: Impacts, adaptation, and vulnerability Summary for policymakers A Report of Working Group II Geneva, Switzerland: IPCC Jones, R N and C M Page 2001 “Assessing the risk of climate change on the water resources of the Macquarie river catchment” Pp 673-678 in Integrating models for natural resources management across disciplines, issues and scales, Volume 2, edited by F Ghassemi, P Whetton, R Little and M Littleboy Canberra: Modelling and Simulation Society of Australia and New Zealand Kaly U and Pratt C 2000 Environmental Vulnerability Index: Development and provisional indices and profiles for Fiji, Samoa, Tuvalu and Vanuatu Phase II Report for NZODA SOPAC Technical Report 306 89p Kothavala, Z 1999 “The duration and severity of drought over eastern Australia simulated by a couple ocean-atmosphere GCM with a transient increase in CO2” Environmental Modeling and Software 14: 243-252 Lane, Melissa E., Kirshen, P.H and Vogel, R.M 1999 “Indicators of impacts of global climate change on US water resources” Journal of Water Resources Planning and Management July/August: 194-204 Makra, L., Horváth, Sz., Pongrácz, P and Mike, J 2002 “Long term climate deviations: an alternative approach and application on the Palmer drought severity index in Hungary” Physics and Chemistry of the Earth 27: 1063-1071 Mitchell, B 1989 Geography and Resource Analysis Second Edition Essex, U.K.: Longman Scientific & Technical Ntale, H.K and Han, T.Y 2003 “Drought indices and their application to east Africa” International Journal of Climatology 23: 1335-1357 Page, T 1977 Conservation and Economic Efficiency: An Approach to Materials Policy Baltimore: Johns Hopkins University Press Palmer, W.C 1965 Meteorological Drought Research Paper, vol 45, US Department of Commerce, Weather Bureau Parry, M.L., Carter, T.R and Konijn, N.T (eds.) 1987 An Assessment of Climatic Variations on Agriculture Volume I Assessment in Cool Temperate and Cold Regions Volume II Assessments in Semi- Arid Regions The Netherlands: Reidel, Dordrecht Pierce, J.T 1990, The Food Resource Essex, U.K.: Longman Scientific & Technical, 127～201 Rees, J 1985 Natural Resources: Allocation Economics and Policy London: Methuen Tan, C.H., Melesse, A.M and Yeh, S.S 2002 “Remote sensing and GIS in runoff coefficient estimation in China, Taipei” Proceedings of the 23rd Asian Conference on Remote Sensing Kathmandu, Nepal November 2002 Online: http://www.gisdevelopment.net/aars/acrs/2002/pos3/217.pdf Tisdell, C 1989 "Environmental conservation: economics, ecology, and ethics." Environmental Conservation 16(2): 107-112 WCED (World Commission on Environment and Development) 1987 Our Common Future Report of the World Commission on Environment and Development Oxford: Oxford University Press Yohe, G and Tol, R.S.J 2002 “Indicators for social and economic coping capacity – moving toward a working definition of adaptive capacity” Global Environmental Change (12): 2540  ... expected to operate on, the land and water system of study region?  What factors are driving the changes of the system vulnerability?  How can vulnerability indicators be used to assess resource system. .. climate change For examining system vulnerability to climate change, a natural resource system representing particular future conditions needs to be proposed For example, water resource system. .. water system in different areas exposed to current climate stimuli The vulnerability measures for resource system can be applied to project potential vulnerabilities of the resource systems to future