December, 2011 Journal of Resources and Ecology Vol.2 No.4

J. Resour. Ecol. 2011 2(4) 338-344 Article DOI:10.3969/j.issn.1674-764x.2011.04.007 www.jorae.cn

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

LONG Xin1, 2, ZHEN Lin1, CHENG Shengkui1* and DI Suchuang3

1 Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China; 3 Beijing Hydraulic Research Institute, Beijing 100048, 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-km 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

The average area affected by drought in China is about 1 Introduction 21.593 million ha per year, accounting for 60% of the total Drought is a normal, recurrent feature of climate area affected by all types of meteorological disasters and that affects virtually all countries to some degree. annual grain losses due to drought are up to 10 billion kg The number of drought-induced natural hazards has (Wu et al. 2011). From 1978–2009, data from the China grown significantly since the 1960s, largely as a result statistical yearbook showed that affected area of arable of increasing vulnerability to extended periods of land induced by drought accounted for 77.92% of the total precipitation deficiency rather than because of an increase area affected by all types of meteorological hazards (Fig.1). in the frequency of meteorological droughts. This increase The Jinghe watershed is located in a semi-arid region in drought-induced natural disasters has resulted in a and characterized by high inter-annual and intra-seasonal considerable growth of interest in drought mitigation and rainfall variability. This region is regarded as one of preparedness worldwide. the most drought-prone areas in China. Low and erratic High economic costs and social vulnerability to rainfall, and soils with low water-holding capacity are droughts has led to increasing awareness of drought major features of the Jinghe watershed region, where vulnerability in recent years. Agricultural drought risk winter crops are often exposed to varying intensities of assessment is essential for drought management. Previous terminal drought. studies are weak because of the subjectivity in the selection Agriculture in the Jinghe watershed is largely rain-fed, of risk assessment indicators and the focus on evaluation low-input based and highly resource dependent. Almost index systems and evaluation models, usually conducted every year, this region experiences drought hazards, which on an administrative scale. Administrative scale-based risk impose high impacts on many aspects of society and assessment models lack accuracy to some degree. environment. To help decision makers reduce the impacts

Received: 2011-09-18 Accepted: 2011-11-01 Foundation: National Key Technology R&D Program (No. 2008BAK50B05), National Key Project for basic research (973) (No.2009CB421106) and the Knowledge Innovation Program of CAS (No. KZCX2-EW-306). * Corresponding author: CHENG Shengkui. Email: [email protected]. LONG Xin, et al.: Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed of Northwestern China 339

45 80.00 2 Study area 40 70.00 Jinghe River is one of the ten water systems of the Yellow 35 60.00 30 River, and also the biggest branch river of Weihe River 50.00 (Fig.2). It is 451 km long, with a total basin river area of 25 % 40.00 4 2

M ha 20 4.54×10 km . The watershed extends from 105°49′ to 30.00 15 108°58′ E longitude and from 34°14′ to 38 °10′ N latitude. 20.00 10 The Jinghe watershed is a typical loess plateau hilly area 10.00 5 with high conflicting human-land relationships (Zhen et 0 0.00 al. 2005). The watershed involves 31 counties, of which 1978 1980 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 five counties belong to the Hui Autonomous Affected area Ratio of drought to all Region, 13 counties belong to Shaanxi Province and 13 Fig. 1 Area affected by drought (1978–2009). counties belong to Province. The total area of the 31 involved counties is 70 039 km2. In this study, the of drought, it is important to improve our understanding of Jinghe watershed refers to these 31 counties. We selected the characteristics and parameters that cause drought and the 31 counties as the study area because risk management also risk assessment spatial distributions. is normally based on administrative units and some data It is important to quantitatively analyze the spatial used in this study is county-based. The altitude of the characteristics of agricultural drought risk at a fine area varies from 384–2923 m. The geographical location resolution and comprehensively estimate potential threats of the study area and an east-to-west and south-to-north and direct losses attributable to these phenomena on winter elevation gradient (about 2500 m) determine several wheat production. There is an urgent need to develop a climatic gradients and precipitation gradients. Climatically, region-wide agricultural drought risk assessment model. Jinghe watershed belongs to the temperate region where Therefore, this study aims to determine the key factors typical temperate continental climate prevails throughout in agricultural risk assessment and build a gird-based the year with an average temperature of 8℃. The climate agricultural risk assessment model for the dominant crop, is characterized as semi-humid (in the south) to semiarid winter wheat, in the Jinghe watershed using ArcGIS. (in the north), great annual temperature variations, highly This study also aims to characterize the spatial pattern of variable precipitation, and drought are dominant natural drought hazards, identify the vulnerability of agriculture hazards. The precipitation of the study area varies between to the impact of droughts, and map drought risk zones 350–660 mm, gradually decreasing from south to north. in the Jinghe watershed. With a map of agricultural risk, These gradients mainly determine the study area’s land use decision makers can better visualize the hazard and characteristics. Social-economically, the Jinghe watershed communicate the concept of risk to agricultural producers, has a total population of 932.24×104, and the natural natural resources managers and others related to drought population growth rate is between 6‰–12‰. The average management. Gross Domestic Product (GDP) per capita of this area in

106°E 107°E 108°E 109°E 70°E 80°E 90°E 100°E 110°E 120°e 110°E 120°e N

N 38°N 38°N

Legend Ningxia County boundary Provincial 40°N 37°N

37°N boundary Shaanxi 30°N 36°N 36°N

Legend National boundary 35°N 35°N Gansu Study area 20°N Provincial boundary 0 25 50 km 106°E 107°E 108°E 109°E 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. 340 Journal of Resources and Ecology Vol.2 No.4, 2011 2007 was CNY 10 114, while the average value of that of methods used for agriculture drought risk assessments the National level is CNY 18 665, meaning the region is in the Jinghe watershed. Based on a literature review, economically undeveloped. The dominant crop in the study the proposed model was built to possess the following area is winter wheat and summer maize. Industry in this characteristics. First, the model needed to be crop- area is dominated by energy chemical engineering, which specific. The model was developed specifically for winter is represented by the branch company of Petro China of wheat as it is the dominant crop in this region. Second, Changqing Company in Xifeng of City. the model used for agricultural risk assessment related to phonological stages during winter wheat development, 3 Materials and methods providing risk information in a timely manner. Third, the Since the dominant crop in the study area is winter wheat, agricultural drought risk model was applied with both grid this study was based on the assumption that all the arable data and county-based data due to the data deficiency. land is cultivated by winter wheat, and the vulnerability 3.2.1 Agriculture drought risk assessment framework and risk assessment results are based on 1km × 1 km grid. According to the ISDR conceptual framework, risk is the combination of hazard severity and vulnerability. Also, 3.1 Materials Blaikie et al. (1994) defined risk as the product of hazard Data used for the hazard assessment were downloaded and vulnerability. Therefore, the risk assessment carried from the China Meteorological Data Sharing Service out in this study falls within the above two frameworks. system. Monthly precipitation data for hazard assessment 3.2.2 Identification of drought hazard pattern from 1957–2009 and daily precipitation data, highest and Droughts, like other natural phenomena, have spatial lowest temperature, wind speed, relative humidity and and temporal dimensions. In assessing drought, many insolation duration from the year 1971–2010 were also researchers have used the capability of geographic obtained from the China Meteorological Data Sharing information systems (GIS) to store and analyze large Service system. Other data, such as data for validation of volumes of remotely sensed data. To investigate the spatial the risk assessment model came from Gansu Statistical and temporal extent and severity of drought occurrence Yearbook, Shaanxi Statistical Yearbook, the Ningxia Hui in the study area we used the Z index method to identify Autonomous Region Statistical Yearbook and Gansu Rural drought hazard patterns in the Jinghe watershed. The Z Statistical Yearbook. Landuse data for the year 2000 was method has been used widely to assess drought hazard derived from Landsat TM/ETM remote sensing image at a severity (Tang et al. 2009; Yang et al. 2010). 1: 100 000 scale, with a 92.7% positioning accuracy (Liu The Z index method is based on the assumption that and Buheaosier 2000) from the Data Center for Resources precipitation of a certain period is fitted to a Possion-III and Environmental Sciences, Chinese Academy of distribution, which will be transformed into a standardized Sciences. Data on soil attributes and types and vegetation normal distribution with the variable Z. types were also from Data Center for Resources and In order to compute the Z index value, historic Environmental Sciences, Chinese Academy of Sciences. rainfall data of each station are fitted to the Possion- III distribution function: 3.2 Methods 1 3 Risk has many definitions. According to the International 6Cs6Cs Zi=+Fi1 - +(1) Strategy for Disaster Reduction (ISDR) conceptual C2 s Cs 6 framework, risk assessment can be broken down into where C is the coefficient of skew and Φ is the a combination of hazard severity and vulnerability. s i standardized variable. The value of Z index is divided into Agricultural drought risk depends on a combination of four classes which are according to study of Tang et al. the physical nature of drought and the degree to which (2009), as shown in Table 1. agriculture is vulnerable to the effects of drought. To assess The area vulnerable to drought is identified on the the risk of drought, it is essential to study the frequency, basis of occurrence. For the mapping of the spatial severity and spatial extent of drought hazards as well as extent of rainfall, Australian National University Spline closely related factors to agriculture, which in this study (ANUSPLIN) was used. The ANUSPLIN package has refers to winter wheat. Step 1: identify the drought hazard with regard to its Table 1 Drought categories defined forZ values. spatial extent, frequency and severity; Grade Drought category Z Value Step 2: identify and quantify the vulnerability of agriculture exposed to drought hazards; 1 No drought –0.642 ≤ Z Step 3: calculate drought risk pattern and analyze its 2 Mild drought –1.037 ≤ Z < –0.642 spatial characteristics. 3 Moderate drought –1.645 ≤ Z < –1.037 The following is a brief description of the data and 4 Severe drought Z < –1.645 LONG Xin, et al.: Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed of Northwestern China 341 been designed to provide a facility for transparent analysis Vagri=Wmete · Fmete+Wsoil · Fsoil+Wterr · Fterr+Wirri · Firri (3) and interpolation of noisy multivariate data using thin plate where V is the agricultural vulnerability index and smoothing splines (Hutchinson 2003). agri W , W , W and W are weights of climate factor, In this paper, we defined drought hazard over four mete soil terr irri soil factor, topographical factor and irrigation factor, categories (no drought, mild drought, moderate drought respectively. F , F , F and F are values of climate and severe drought) according to the results of the mete soil terr irri factor, soil factor, topographical factor and irrigation calculation. Each severity theme is given a particular factor, respectively. weight and each feature of the theme is given a rating Climate. The assumption underlying the approach to compute drought severity of the integrated layer. The taken in this study was that the best characterization drought hazard index of integrated layer is calculated by of the climate factors is the probability of seasonal the following formula: crop water deficiency (SCWD). Seasonal crop water n use thresholds for well-watered crops were estimated

DHI =∑ ki · Frei (2) using the evapotranspiration (ET) mathematical model i=1 recommended by the Food and Agriculture Organization where DHI is the drought hazard index, ki is the weight of the United Nations (FAO). of class i drought and Frei is occurrence of class i drought SCWD was used to calculate the percentage of water during 1957–2009. Weight of mild drought, moderate difference between ET and Precipitation to ET, calculated drought and severe drought is 1, 2 and 3, respectively. using the formula: 3.2.3 Identification of vulnerability of agriculture ET-P The use of the terms ‘vulnerable’ and ‘vulnerability’ is SCWD = (4) often vague and equated with ‘poor’ and ‘poverty’ (World ET Food Programme 1996). Vulnerability refers to the where SCWD is seasonal crop water deficiency, ET is potential for loss (Cutter 1993). Etkin et al. (2004) defines seasonal crop water evaporation and P is the precipitation vulnerability as the propensity to suffer some degree of during the crop growing season. loss from a hazardous event, whereas Turner et al. (2003) Soil. Soil water-holding capacity is a significant defines it as the degree to which a system is likely to agricultural drought vulnerability factor (Wilhelmi and experience harm due to exposure to a hazard. Downing Wilhite 2002). The geographic pattern of soil water- and Bakker (2000) stated that vulnerability is a relative holding capacity is important for studying water stress measure, and the analyst must define its critical levels. in plants and critical to water management planning for Factors influencing drought vulnerability are numerous irrigation and dryland crops (Kern 1995; Klocke and and their inclusion may depend on data availability Hergert 1990). The plant-available water-holding capacity (Wilhelmi and Wilhite 2002). Mapping of vulnerability of soil is estimated as the difference in water content began in the late 1970s (Currey 1978). However, a large between field capacity and permanent wilting point. Field increase in the number of studies on assessment of spatial capacity is the amount of water retained by a wetted soil vulnerability occurred in the last decade for two reasons: after it has been freely drained by gravity for some period the recognition of the importance of vulnerability in hazard of time. The water-holding capacity of the soil is mostly assessment and disaster management, and the availability dependent on soil porosity, which, in turn, depends on soil of GIS technology with the capacity to integrate data of texture and structure. In the Jinghe watershed, soils vary different types and from different sources, analyze data from fine texture and high water-holding capacity, to sandy and present results in a timely and appropriate manner for soils with coarse texture and low water-holding capacity. environmental and agricultural decision making (Wilhelmi In general, most of the study area has a poor water-holding capacity, which we can see from average soil erosion and Wilhite 2002). As to vulnerability, we selected four -2 factors to assess the vulnerability of agricultural drought, modulus data, viz 507 314 t km . which is closely related to the growth of the dominant We used available water capacity (AWC) as a significant crop in the study area. The Kriging method was used for vulnerability factor to identify soil of different abilities interpolation of the station data. Kriging is a geostatistical to buffer crops during periods of deficient moisture. The method that uses known values and a semivariogram to digital map of available water-holding capacity for the predict values at some unmeasured locations. ArcGIS v9.3 Jinghe watershed was in an ArcGIS v9.3 format with 1 km offers several Kriging models: simple, ordinary, universal, spatial resolution and Albers Equal Area projection. indicator, disjunctive and probability. We selected the Irrigation. The spatial and temporal ill-distribution ordinary Kriging method during vulnerability calculations. of precipitation means that the water needs of crops in The vulnerability of agriculture drought in the Jinghe the study area are barely met. Data showed that in the watershed was calculated by the formula: northeast and northern semi-humid region of China wheat is greatly affected by water deficiency. In the northwest 342 Journal of Resources and Ecology Vol.2 No.4, 2011

Table 2 Categories defined for irrigated land to total county Table 3 Categories defined for slopes. land at county level. Slope value Slope categories Irrigated area Percentage of <3° 1 Irrigation compared to total Number of counties in the ratio class county land area (%) counties 31 areas (%) 3°–7° 2 Very high 0–1.01 4 12.90 7°–15° 3 High 1.01–9.99 4 12.90 15°–25° 4 Moderate 9.99–19.94 7 22.58 >25° 5 Low 19.94–28.39 13 41.94 to represent topography because different types of Very low 28.39–92.73 3 9.68 physiognomy affect land use type, inputs and irrigation conditions (Shi 2006). Further, land use is one of the of China, no irrigation means little yield (Gao 2006). driving forces behind water demand and critical factors of Although there is no consensus in the literature whether agricultural drought vulnerability. irrigation reduces or increases vulnerability to drought We viewed topography as a factor influencing (Lockeretz 1981; Opie 1989; Jackson 1991), in most cases, agriculture vulnerability. We selected the slope as the and especially during a short-term drought, irrigation indictor, which was divided into five classes, according farming provides more security for crop growers. to the classification system of arable land slope in Loess In this study, irrigation factor was represented by Plateau (Table 3). We assigned arable land with a slope percent of irrigated land compared to total county land no more than 3° the value 1, which means it has the least area for each of the 31 counties. The natural break method vulnerability and arable land with a slope more than 25° was used to classify the percentage of irrigated land to the value 5, which means it has the highest vulnerability. total into five classes: very low, low, moderate, high and 4 Results and discussion very high. The weights and ratings used for integration are given in Table 2. 4.1 Drought hazard severity map Topography. Slope was selected as the indicator Drought hazard severity mapping of the Jinghe watershed is shown in Fig.3. The mapping results show that 106°E 107°E 108°E 109°E the distribution of different classes of hazard differs N geographically and quantitatively. The extremely severe class occupies 2.63% of the total watershed area. The 38°N 38°N 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. 37°N 37°N 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

36°N showed that only 3.68% of the Jinghe watershed area 36°N 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 35°N

35°N also shows that the northern part of the Jinghe watershed Legend is the comparatively high vulnerability region and the Low southern part of the region is of low vulnerability. Relatively low 25 50 100 High 0 km We attribute this vulnerability spatial distribution Extremely high between the north and the south to physiological 106°E 107°E 108°E and social-economic factors. First, the entire Jinghe Fig. 3 Spatial extent of agricultural drought in the Jinghe watershed has a rainfall gradient from north to the south, watershed. which is highly correlated with SCWD and AWC. The LONG Xin, et al.: Quantitative Assessment and Spatial Characteristics of Agricultural Drought Risk in the Jinghe Watershed of Northwestern China 343

106°E 107°E 108°E 109°E 106°E 107°E 108°E 109°E N N 38°N 38°N 38°N 38°N 37°N 37°N 37°N 37°N 36°N 36°N 36°N 36°N

Risk 35°N 35°N 35°N Vulnearability 35°N Very low Very low Low Low Moderate Moderate High High 0 25 50 100 0 25 50 100 Very high Very high km km 106°E 107°E 108°E 106°E 107°E 108°E 109°E

Fig. 4 Spatial variation in vulnerability assessment of Fig. 5 Spatial variation in agricultural risk assessment in the agricultural risk in the Jinghe watershed. Jinghe watershed. southern part, particularly counties with a relatively flat not to mention the irrigation factor. As a result of the topography located in the Weibei plain, have topographical combination of these factors, the southern part of the study advantages. Second, the south-north spatial variation is a area has low risk. result of better economic conditions in the south. Better economic conditions consequently assure more financial 5 Validations of the risk assessment of support to alleviate impending droughts and their impact agricultural drought in the Jinghe watershed on agriculture yield. Third, the southern region has better Crop loss induced by drought is a direct reflection of irrigation facilities and a higher availability of irrigation drought impact on agriculture. Since this study mainly water resources than the north. focused on the dominant crop of winter wheat we used the average affected area by drought from the year 1991–2008 4.3 Risk assessment results of 13 counties in Gansu province. The data showed that By using the risk assessment of agricultural drought Huanxian County, Zhenyuan County and Qingcheng model, the risk assessment results at a 1 km grid scale County have the highest affected area, and Huating (Fig. 5) show that spatial variation is differentiated from County, Chongxin County and have north to south. Generally, the high risk region is located the lowest affected area. These results follow a similar in the north, and low risk region in the south of the study tendency to the modeled results. area. There is also a gradient in risk spatial distribution. The northern region is almost calculated as very high and 6 Conclusions high risk areas; the south is covered very low and low risk An agriculture risk assessment of the Jinghe watershed areas. was carried out at a 1-km grid scale by using a GIS-based This spatial variation in risk may have arisen because agricultural drought risk assessment model. This model of the following reasons. First, high precipitation variation is based on the ISDR and risk assessment conceptual between the north and the south results in different hazard framework of Blaikie et al. (1994), which specify that zoning. Second, the SCWD indictor results showed risk is a combination of hazard and vulnerability. We that there is also a similar gradient in the SCWD as for applied the Z index method to assess hazard severity in precipitation. AWC results also showed a similar gradient, the study area. 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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，邸苏闯3

1 中国科学院地理科学与资源研究所，北京 100101； 2 中国科学院研究生院，北京 100049； 3 北京市水利科学研究所， 北京 100048

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1km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1km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