Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed, Northwestern China

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1 December, 2011 J. Resour. Ecol (4) DOI: /j.issn x Journal of Resources and Ecology Vol.2 o.4 Article Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed, orthwestern China LOG Xin 1, 2, ZHE Lin 1, CHEG Shengkui 1 * and DI Suchuang 3 1 Institute of Geographic Sciences and atural Resources Research, CAS, Beijing , China; 2 Graduate University of Chinese Academy of Sciences, Beijing , China; 3 Beijing Hydraulic Research Institute, Beijing , China Abstract: Though drought is a recurrent phenomenon in the Jinghe watershed, very little attention has been paid to drought mitigation and preparedness. This article presents a method for the spatial assessment of agricultural drought risk in the Jinghe watershed of western China at a 1- grid scale. A conceptual framework, which emphasizes the combined roles of hazard and vulnerability in defining risk, is used. The Z index method in a GIS environment is used to map the spatial extent of drought hazards. The key social and physical factors that define agricultural drought in the context of the Jinghe watershed are indentified and corresponding thematic maps are prepared. Risk is calculated by the integration of hazard and vulnerability. Results show that the risk gradient follows a north-south and west-east tendency and that agricultural droughts pose the highest risk to northern and northwestern sections of the Jinghe watershed. Key words: agricultural drought; natural hazards; vulnerability; risk assessment; Jinghe watershed 1 Introduction Drought is a normal, recurrent feature of climate that affects virtually all countries to some degree. The number of drought-induced natural hazards has grown significantly since the 1960s, largely as a result of increasing vulnerability to extended periods of precipitation deficiency rather than because of an increase in the frequency of meteorological droughts. This increase in drought-induced natural disasters has resulted in a considerable growth of interest in drought mitigation and preparedness worldwide. High economic costs and social vulnerability to droughts has led to increasing awareness of drought vulnerability in recent years. Agricultural drought risk assessment is essential for drought management. Previous studies are weak because of the subjectivity in the selection of risk assessment indicators and the focus on evaluation index systems and evaluation models, usually conducted on an administrative scale. Administrative scale-based risk assessment models lack accuracy to some degree. The average area affected by drought in China is about million ha per year, accounting for 60% of the total area affected by all types of meteorological disasters and annual grain losses due to drought are up to 10 billion kg (Wu et al. 2011). From , data from the China statistical yearbook showed that affected area of arable land induced by drought accounted for 77.92% of the total area affected by all types of meteorological hazards (Fig.1). The Jinghe watershed is located in a semi-arid region and characterized by high inter-annual and intra-seasonal rainfall variability. This region is regarded as one of the most drought-prone areas in China. Low and erratic rainfall, and soils with low water-holding capacity are major features of the Jinghe watershed region, where winter crops are often exposed to varying intensities of terminal drought. Agriculture in the Jinghe watershed is largely rain-fed, low-input based and highly resource dependent. Almost every year, this region experiences drought hazards, which impose high impacts on many aspects of society and environment. To help decision makers reduce the impacts Received: Accepted: Foundation: ational Key Technology R&D Program (o. 2008BAK50B05), ational Key Project for basic research (973) (o.2009cb421106) and the Knowledge Innovation Program of CAS (o. KZCX2-EW-306). * Corresponding author: CHEG Shengkui. chengsk@igsnrr.ac.cn.

2 % LOG Xin, et al.: Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed of orthwestern China 339 M ha Affected area Ratio of drought to all Fig. 1 Area affected by drought ( ) of drought, it is important to improve our understanding of the characteristics and parameters that cause drought and also risk assessment spatial distributions. It is important to quantitatively analyze the spatial characteristics of agricultural drought risk at a fine resolution and comprehensively estimate potential threats and direct losses attributable to these phenomena on winter wheat production. There is an urgent need to develop a region-wide agricultural drought risk assessment model. Therefore, this study aims to determine the key factors in agricultural risk assessment and build a gird-based agricultural risk assessment model for the dominant crop, winter wheat, in the Jinghe watershed using ArcGIS. This study also aims to characterize the spatial pattern of drought hazards, identify the vulnerability of agriculture to the impact of droughts, and map drought risk zones in the Jinghe watershed. With a map of agricultural risk, decision makers can better visualize the hazard and communicate the concept of risk to agricultural producers, natural resources managers and others related to drought management Study area Jinghe River is one of the ten water systems of the Yellow River, and also the biggest branch river of Weihe River (Fig.2). It is 451 long, with a total basin river area of The watershed extends from to E longitude and from to latitude. The Jinghe watershed is a typical loess plateau hilly area with high conflicting human-land relationships (Zhen et al. 2005). The watershed involves 31 counties, of which five counties belong to the ingxia Hui Autonomous Region, 13 counties belong to Shaanxi Province and 13 counties belong to Gansu Province. The total area of the 31 involved counties is In this study, the Jinghe watershed refers to these 31 counties. We selected the 31 counties as the study area because risk management is normally based on administrative units and some data used in this study is county-based. The altitude of the area varies from m. The geographical location of the study area and an east-to-west and south-to-north elevation gradient (about 2500 m) determine several climatic gradients and precipitation gradients. Climatically, Jinghe watershed belongs to the temperate region where typical temperate continental climate prevails throughout the year with an average temperature of 8. The climate is characterized as semi-humid (in the south) to semiarid (in the north), great annual temperature variations, highly variable precipitation, and drought are dominant natural hazards. The precipitation of the study area varies between mm, gradually decreasing from south to north. These gradients mainly determine the study area s land use characteristics. Social-economically, the Jinghe watershed has a total population of , and the natural population growth rate is between The average Gross Domestic Product (GDP) per capita of this area in ingxia Gansu Legend County boundary Provincial boundary Shaanxi E 80 E 90 E 100 E 110 E 120 e 110 E 120 e Legend ational boundary Study area Provincial boundary 80 E 90 E 100 E 110 E 120 e 110 E Fig. 2 The extent of the study area and the location of the Jinghe watershed, China.

3 was CY , while the average value of that of the ational level is CY , meaning the region is economically undeveloped. The dominant crop in the study area is winter wheat and summer maize. Industry in this area is dominated by energy chemical engineering, which is represented by the branch company of Petro China of Changqing Company in Xifeng District of Qingyang City. 3 Materials and methods Since the dominant crop in the study area is winter wheat, this study was based on the assumption that all the arable land is cultivated by winter wheat, and the vulnerability and risk assessment results are based on 1 1 grid. 3.1 Materials Data used for the hazard assessment were downloaded from the China Meteorological Data Sharing Service system. Monthly precipitation data for hazard assessment from and daily precipitation data, highest and lowest temperature, wind speed, relative humidity and insolation duration from the year were also obtained from the China Meteorological Data Sharing Service system. Other data, such as data for validation of the risk assessment model came from Gansu Statistical Yearbook, Shaanxi Statistical Yearbook, the ingxia Hui Autonomous Region Statistical Yearbook and Gansu Rural Statistical Yearbook. Landuse data for the year 2000 was derived from Landsat TM/ETM remote sensing image at a 1: scale, with a 92.7% positioning accuracy (Liu and Buheaosier 2000) from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. Data on soil attributes and types and vegetation types were also from Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. 3.2 Methods Risk has many definitions. According to the International Strategy for Disaster Reduction (ISDR) conceptual framework, risk assessment can be broken down into a combination of hazard severity and vulnerability. Agricultural drought risk depends on a combination of the physical nature of drought and the degree to which agriculture is vulnerable to the effects of drought. To assess the risk of drought, it is essential to study the frequency, severity and spatial extent of drought hazards as well as closely related factors to agriculture, which in this study refers to winter wheat. Step 1: identify the drought hazard with regard to its spatial extent, frequency and severity; Step 2: identify and quantify the vulnerability of agriculture exposed to drought hazards; Step 3: calculate drought risk pattern and analyze its spatial characteristics. The following is a brief description of the data and Journal of Resources and Ecology Vol.2 o.4, 2011 methods used for agriculture drought risk assessments in the Jinghe watershed. Based on a literature review, the proposed model was built to possess the following characteristics. First, the model needed to be cropspecific. The model was developed specifically for winter wheat as it is the dominant crop in this region. Second, the model used for agricultural risk assessment related to phonological stages during winter wheat development, providing risk information in a timely manner. Third, the agricultural drought risk model was applied with both grid data and county-based data due to the data deficiency Agriculture drought risk assessment framework According to the ISDR conceptual framework, risk is the combination of hazard severity and vulnerability. Also, Blaikie et al. (1994) defined risk as the product of hazard and vulnerability. Therefore, the risk assessment carried out in this study falls within the above two frameworks Identification of drought hazard pattern Droughts, like other natural phenomena, have spatial and temporal dimensions. In assessing drought, many researchers have used the capability of geographic information systems (GIS) to store and analyze large volumes of remotely sensed data. To investigate the spatial and temporal extent and severity of drought occurrence in the study area we used the Z index method to identify drought hazard patterns in the Jinghe watershed. The Z method has been used widely to assess drought hazard severity (Tang et al. 2009; Yang et al. 2010). The Z index method is based on the assumption that precipitation of a certain period is fitted to a Possion-III distribution, which will be transformed into a standardized normal distribution with the variable Z. In order to compute the Z index value, historic rainfall data of each station are fitted to the Possion- III distribution function: Z i s Cs 6 Cs = Fi C 2 C 6 where C s is the coefficient of skew and Φ i is the standardized variable. The value of Z index is divided into four classes which are according to study of Tang et al. (2009), as shown in Table 1. The area vulnerable to drought is identified on the basis of occurrence. For the mapping of the spatial extent of rainfall, Australian ational University Spline (AUSPLI) was used. The AUSPLI package has Table 1 Drought categories defined for Z values. Grade Drought category Z Value s 1 o drought Z 2 Mild drought Z < Moderate drought Z < Severe drought Z < (1)

4 LOG Xin, et al.: Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed of orthwestern China 341 been designed to provide a facility for transparent analysis and interpolation of noisy multivariate data using thin plate smoothing splines (Hutchinson 2003). In this paper, we defined drought hazard over four categories (no drought, mild drought, moderate drought and severe drought) according to the results of the calculation. Each severity theme is given a particular weight and each feature of the theme is given a rating to compute drought severity of the integrated layer. The drought hazard index of integrated layer is calculated by the following formula: DHI = n ki Fre i (2) i=1 where DHI is the drought hazard index, k i is the weight of class i drought and Fre i is occurrence of class i drought during Weight of mild drought, moderate drought and severe drought is 1, 2 and 3, respectively Identification of vulnerability of agriculture The use of the terms vulnerable and vulnerability is often vague and equated with poor and poverty (World Food Programme 1996). Vulnerability refers to the potential for loss (Cutter 1993). Etkin et al. (2004) defines vulnerability as the propensity to suffer some degree of loss from a hazardous event, whereas Turner et al. (2003) defines it as the degree to which a system is likely to experience harm due to exposure to a hazard. Downing and Bakker (2000) stated that vulnerability is a relative measure, and the analyst must define its critical levels. Factors influencing drought vulnerability are numerous and their inclusion may depend on data availability (Wilhelmi and Wilhite 2002). Mapping of vulnerability began in the late 1970s (Currey 1978). However, a large increase in the number of studies on assessment of spatial vulnerability occurred in the last decade for two reasons: the recognition of the importance of vulnerability in hazard assessment and disaster management, and the availability of GIS technology with the capacity to integrate data of different types and from different sources, analyze data and present results in a timely and appropriate manner for environmental and agricultural decision making (Wilhelmi and Wilhite 2002). As to vulnerability, we selected four factors to assess the vulnerability of agricultural drought, which is closely related to the growth of the dominant crop in the study area. The Kriging method was used for interpolation of the station data. Kriging is a geostatistical method that uses known values and a semivariogram to predict values at some unmeasured locations. ArcGIS v9.3 offers several Kriging models: simple, ordinary, universal, indicator, disjunctive and probability. We selected the ordinary Kriging method during vulnerability calculations. The vulnerability of agriculture drought in the Jinghe watershed was calculated by the formula: V agri =W mete F mete +W soil F soil +W terr F terr +W irri F irri (3) where V agri is the agricultural vulnerability index and W mete, W soil, W terr and W irri are weights of climate factor, soil factor, topographical factor and irrigation factor, respectively. F mete, F soil, F terr and F irri are values of climate factor, soil factor, topographical factor and irrigation factor, respectively. Climate. The assumption underlying the approach taken in this study was that the best characterization of the climate factors is the probability of seasonal crop water deficiency (SCWD). Seasonal crop water use thresholds for well-watered crops were estimated using the evapotranspiration (ET) mathematical model recommended by the Food and Agriculture Organization of the United ations (FAO). SCWD was used to calculate the percentage of water difference between ET and Precipitation to ET, calculated using the formula: ET-P SCWD = (4) ET where SCWD is seasonal crop water deficiency, ET is seasonal crop water evaporation and P is the precipitation during the crop growing season. Soil. Soil water-holding capacity is a significant agricultural drought vulnerability factor (Wilhelmi and Wilhite 2002). The geographic pattern of soil waterholding capacity is important for studying water stress in plants and critical to water management planning for irrigation and dryland crops (Kern 1995; Klocke and Hergert 1990). The plant-available water-holding capacity of soil is estimated as the difference in water content between field capacity and permanent wilting point. Field capacity is the amount of water retained by a wetted soil after it has been freely drained by gravity for some period of time. The water-holding capacity of the soil is mostly dependent on soil porosity, which, in turn, depends on soil texture and structure. In the Jinghe watershed, soils vary from fine texture and high water-holding capacity, to sandy soils with coarse texture and low water-holding capacity. In general, most of the study area has a poor water-holding capacity, which we can see from average soil erosion modulus data, viz t -2. We used available water capacity (AWC) as a significant vulnerability factor to identify soil of different abilities to buffer crops during periods of deficient moisture. The digital map of available water-holding capacity for the Jinghe watershed was in an ArcGIS v9.3 format with 1 spatial resolution and Albers Equal Area projection. Irrigation. The spatial and temporal ill-distribution of precipitation means that the water needs of crops in the study area are barely met. Data showed that in the northeast and northern semi-humid region of China wheat is greatly affected by water deficiency. In the northwest

5 342 Table 2 Categories defined for irrigated land to total county land at county level. Irrigation ratio class Irrigated area compared to total county land area (%) umber of counties Percentage of counties in the 31 areas (%) Very high High Moderate Low Very low of China, no irrigation means little yield (Gao 2006). Although there is no consensus in the literature whether irrigation reduces or increases vulnerability to drought (Lockeretz 1981; Opie 1989; Jackson 1991), in most cases, and especially during a short-term drought, irrigation farming provides more security for crop growers. In this study, irrigation factor was represented by percent of irrigated land compared to total county land area for each of the 31 counties. The natural break method was used to classify the percentage of irrigated land to total into five classes: very low, low, moderate, high and very high. The weights and ratings used for integration are given in Table 2. Topography. Slope was selected as the indicator Legend Low Relatively low 25 High 0 Extremely high Fig. 3 Spatial extent of agricultural drought in the Jinghe watershed Table 3 Categories defined for slopes. Slope value Journal of Resources and Ecology Vol.2 o.4, 2011 Slope categories < >25 5 to represent topography because different types of physiognomy affect land use type, inputs and irrigation conditions (Shi 2006). Further, land use is one of the driving forces behind water demand and critical factors of agricultural drought vulnerability. We viewed topography as a factor influencing agriculture vulnerability. We selected the slope as the indictor, which was divided into five classes, according to the classification system of arable land slope in Loess Plateau (Table 3). We assigned arable land with a slope no more than 3 the value 1, which means it has the least vulnerability and arable land with a slope more than 25 the value 5, which means it has the highest vulnerability. 4 Results and discussion 4.1 Drought hazard severity map Drought hazard severity mapping of the Jinghe watershed is shown in Fig.3. The mapping results show that the distribution of different classes of hazard differs geographically and quantitatively. The extremely severe class occupies 2.63% of the total watershed area. The severe class occupies 46% of the total area. The moderate class and low-to-moderate class cover 39.60% and 11.78% of the total study area, respectively. Hazard mapping results also show that from north to south, hazard severity decreases, but with some variation. 4.2 Vulnerability assessment results Using formula 3, after the integration of the indicators, we obtained the vulnerability of agricultural drought in the Jinghe watershed (Fig. 4). We can see a clear gradient from the north to the south of the study area. The results showed that only 3.68% of the Jinghe watershed area was classified into the very low category, and 13.77% was classified into the low category. For the moderate category, 29.45% was classified, and 27.87% of the area was classified as the high category, while the very high category includes 25.23% of the total study area. The map also shows that the northern part of the Jinghe watershed is the comparatively high vulnerability region and the southern part of the region is of low vulnerability. We attribute this vulnerability spatial distribution between the north and the south to physiological and social-economic factors. First, the entire Jinghe watershed has a rainfall gradient from north to the south, which is highly correlated with SCWD and AWC. The

6 LOG Xin, et al.: Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed of orthwestern China Vulnearability Very low Low Moderate High Very high Risk Very low Low Moderate High Very high Fig. 4 Spatial variation in vulnerability assessment of agricultural risk in the Jinghe watershed. southern part, particularly counties with a relatively flat topography located in the Weibei plain, have topographical advantages. Second, the south-north spatial variation is a result of better economic conditions in the south. Better economic conditions consequently assure more financial support to alleviate impending droughts and their impact on agriculture yield. Third, the southern region has better irrigation facilities and a higher availability of irrigation water resources than the north. 4.3 Risk assessment results By using the risk assessment of agricultural drought model, the risk assessment results at a 1 grid scale (Fig. 5) show that spatial variation is differentiated from north to south. Generally, the high risk region is located in the north, and low risk region in the south of the study area. There is also a gradient in risk spatial distribution. The northern region is almost calculated as very high and high risk areas; the south is covered very low and low risk areas. This spatial variation in risk may have arisen because of the following reasons. First, high precipitation variation between the north and the south results in different hazard zoning. Second, the SCWD indictor results showed that there is also a similar gradient in the SCWD as for precipitation. AWC results also showed a similar gradient, Fig. 5 Spatial variation in agricultural risk assessment in the Jinghe watershed. not to mention the irrigation factor. As a result of the combination of these factors, the southern part of the study area has low risk. 5 Validations of the risk assessment of agricultural drought in the Jinghe watershed Crop loss induced by drought is a direct reflection of drought impact on agriculture. Since this study mainly focused on the dominant crop of winter wheat we used the average affected area by drought from the year of 13 counties in Gansu province. The data showed that Huanxian County, Zhenyuan County and Qingcheng County have the highest affected area, and Huating County, Chongxin County and Huachi County have the lowest affected area. These results follow a similar tendency to the modeled results. 6 Conclusions An agriculture risk assessment of the Jinghe watershed was carried out at a 1- grid scale by using a GIS-based agricultural drought risk assessment model. This model is based on the ISDR and risk assessment conceptual framework of Blaikie et al. (1994), which specify that risk is a combination of hazard and vulnerability. We applied the Z index method to assess hazard severity in the study area. In our vulnerability assessment we selected

7 344 four indictors (climate, soil, irrigation and topography), represented by SCWD, AWC, irrigation ratio and slope. The spatial distribution of hazard indicated that the Jinghe watershed apparently differs from the south to the north, somewhat represented by, but not highly consistent with, precipitation. For the vulnerability assessment we concluded that the north-south difference could be attributed to physiological and social-economic factors. The spatial distribution of vulnerability showed that the Jinghe watershed has a clear north-south differentiation which showed that the northern part of the Jinghe watershed is mainly occupied by the very high and high vulnerable areas and the south part of the Jinghe watershed is mainly covered by very low and low vulnerable areas. Spatial distribution in risk assessment results were similar to those obtained for vulnerability: the north has high risk and the south has low risk. However, since the risk assessment result is a combination of hazard and vulnerability, there is a difference between the vulnerability and risk assessment. The main difference lies in the north of the Jinghe watershed (high vulnerability but moderate risk), which can be attributed to lower hazard severity. References Blaikie P M, T Cannon, I Davis, et al At risk: natural hazards, people s vulnerability, and disasters. ew York: Routledge. Currey B Mapping of areas liable to famine in Bangladesh. Ph. D. dissertation, Manoa, University of Hawaii. Cutter S L Living with risk: the geography of technological hazards. London: Edward Arnold. Downing T E, K Bakker Drought discourse and vulnerability. Chapter 45. In: Wilhite D A (ed.). Drought: A Global Assessment, atural Hazards and Disasters Series. London: Routledge Publishers. Etkin D, E Haque, L Bellisario, et al An assessment of natural hazards and disasters in Canada. The Canadian natural hazards assessment project. Journal of Resources and Ecology Vol.2 o.4, 2011 Public safety and emergency preparedness Canada and environment Canada, Ottawa. Gao Z Y Development and action of irrigation in China. Journal of Economics of Water Resources, 24(1): 36 39, 82. ( in Chinese) Hutchinson M F AUSPLI Version 4.3. Canberra, Australian ational University Centre for Resource and Environmental Studies. Jackson C I Response strategies for the Great Plains: Canadian and U. S. perspectives. In: Wall G (Ed.). Symposium on the impacts of climatic change and variability on the Great Plains. Department of Geography Publication Series, Occasional Paper o. 12, University of Waterloo, Kern J S Geographic patterns of soil water-holding capacity in the contiguous United States. Soil Sci. Soc. Amer. J., 59: Klocke L, G W Hergert How soil holds water, ebguide G90-964, IAR. Lincoln: University of ebraska. Liu J Y, Buheaosier Study on spatial-temporal feature of modern land-use change in China: using remote sensing techniques. Quaternary Sciences, 20(3): ( in Chinese) Lockeretz W The dust bowl: Its relevance to contemporary environmental problems. In: Lawson M P and M E Baker (Eds.). The Great Plains: Perspectives and prospects. Lincoln: University of ebraska Press, Opie J years of climate risk assessment on the High Plains: Which farm paradigm does irrigation serve? Agric. Hist., 63: Shi X M Study on the agricultural geomorphology. Journal of Anhui Agri. Sci., 34 (20): , ( in Chinese) Tang H Y, Wang Z W, Shi J M, et al Analysis of different dryness/ wetness index in orthwest China. Agricultural Research in the Arid Areas, 27 (5): 6 11, 64. ( in Chinese) Turner B L II, Kasperson R E, Matson P A, et al A framework for vulnerability analysis in sustainability science. Proc at Acad Sci USA 100: Wilhelmi O V, D A Wilhite Assessming vulnerability to agricultural drought: A ebraska case study. atural Hazards, 25: Wu J J, He B, Lu A F, et al Quantitative assessment and spatial characteristics analysis of agricultural drought vulnerability in China. atural Hazards, 56: Yang X H, Yang X L Aridity characteristics in East Gansu s Loess Plateau area based on Z index analysis. Agricultural Research in the Arid Areas, 28(3): ( in Chinese) Zhen L, Xie G D, Yang L, et al Land-use change dynamics, driving forces and policy implications in Jinghe watershed of western China. Resources Science, 27 (4): ( in Chinese) 泾河流域农业旱灾风险定量评估及其空间特征 龙鑫 1,2, 甄霖 1, 成升魁 1 3, 邸苏闯 1 中国科学院地理科学与资源研究所, 北京 ; 2 中国科学院研究生院, 北京 ; 3 北京市水利科学研究所, 北京 摘要 : 泾河流域是农业旱灾的多发地区, 但是对该地农业旱灾的发生规律 旱灾影响及旱灾恢复和准备措施方面开展的研究还较少 本文根据自然灾害风险评估的理论框架, 建立了 1 栅格精度的泾河流域农业旱灾风险空间评估模型, 并对农业旱灾致灾因子危险性及农业承险体脆弱性进行评估, 最后综合评估该地区农业旱灾风险 在此基础上, 分析研究区农业旱灾危险性 承险体脆弱性及风险的空间特征 本研究采用 Z 指数方法评估泾河流域农业旱灾致灾因子的强度, 选取农作物生长季缺水率 土壤有效含水量 有效灌溉面积比以及坡度等 4 个指标评估研究区 1 空间尺度的农业旱灾脆弱性 农业旱灾风险综合评估的结果表明, 泾河流域农业旱灾风险的高风险区位于该地区北部, 低风险区位于该地区南部, 且不同等级风险区呈现出自东向西 自南向北逐渐降低的总体趋势, 但不同等级风险区呈现间隔分布的趋势 关键词 : 农业旱灾 ; 自然灾害 ; 脆弱性 ; 风险评估 ; 泾河流域