Theor Appl Climatol DOI 10.1007/s00704-014-1234-8
ORIGINAL PAPER
Assessment of drought vulnerability of the Tarim River basin, Xinjiang, China Qiang Zhang & Peng Sun & Jianfeng Li & Mingzhong Xiao & Vijay P. Singh
Received: 7 November 2013 / Accepted: 16 July 2014 # Springer-Verlag Wien 2014
Abstract The Tarim River basin is dominated by arid climate, and agriculture plays a key role in the regional socioeconomic development. The basin is subjected to frequent droughts which are a common natural hazard. Based on Standardized Precipitation Index at 3-, 6-, and 12-month timescales, drought hazard index and composite drought vulnerability indices, drought risk and drought vulnerability are evaluated. Results indicate that (1) drought hazard is higher in northern and eastern parts of the Tarim River basin at 3- and 6-month timescales, while it is high in central and northwestern parts at a 12-month timescale and (2) drought vulnerability is higher in northwestern and Q. Zhang (*) : M. Xiao Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, Guangdong Province, China e-mail:
[email protected] Q. Zhang : M. Xiao Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou 510275, Guangdong Province, China Q. Zhang : M. Xiao Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, Guangdong Province, China J. Li Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China V. P. Singh Department of Biological & Agricultural Engineering, Texas A & M University, College Station, TX 77843-2117, USA V. P. Singh Department of Civil and Environmental Engineering, Texas A & M University, College Station, TX 77843-2117, USA P. Sun College of Territorial Resources and Tourism, Anhui Normal University, Anhui 241000, China
southwestern parts of the basin, and the highest drought vulnerability is identified in the southwestern part. Results also indicate significant relations between drought vulnerability and the percentage of farmers, dependency ratio, and the percentage of drought-induced agricultural loss. Results of this study can be useful for drought hazard mitigation as well as for planning and management of agricultural activities and agricultural irrigation in the Tarim River basin.
1 Introduction Drought is a period dominated by abnormally dry weather that lasts long enough to produce a serious imbalance in the water cycle (Zhang et al. 2011b). Droughts can have a devastating effect on agriculture, water supply, and the economy, causing deleterious impacts on human society. They are ranked as one of the costliest and yet one of the least understood natural disasters (Kao and Govindaraju 2010). It is estimated that the global economic losses caused by droughts are as high as US$6–8 billion each year, being far more than other meteorological disasters (Wilhite 2000). Nowadays, it is accepted that human-induced global warming is accelerating the global hydrological cycle (Allen and Ingram 2002; Alan et al. 2003), altering spatiotemporal patterns of precipitation. The result is increased occurrences of extremes (Easterling et al. 2000; Dore 2005) and in turn increased occurrences of floods and droughts in many regions of the world (e.g., Easterling et al. 2000; Mirza 2002). Strategies for drought mitigation and preparedness are therefore needed in order to reduce vulnerability (Knutson et al. 2001). Drought vulnerability assessment is the first step in this direction (Kelly and Adger 2000; Zarafshani et al. 2012). Some studies have focused on the impact of climate change on water resources (e.g., Chen et al. 2006) and hydrological droughts in the Tarim River basin (e.g., Sun et al. 2012). Critical to agricultural development in the arid region is water
Q. Zhang et al.
(Huang et al. 2012), and the availability and variability of water determine agricultural production (Sharma and Minhas 2005). However, excessive exploitation of water resources causes environmental degradation and may be counterproductive to agricultural development in the long run (Huang et al. 2012). For example, in northwest China, agricultural water consumption accounts for approximately 90 % of the total water use (Li et al. 2010), but the average available water in northwest China is less than 1,635×108 m3 per year, which is only 5.8 % of the national average available water in China. Besides, increased population shifts and shrinking water supplies have exacerbated competition among different water users (Huang et al. 2012), and all these factors combine to make water resources management in the Tarim River basin a challenging task, especially when water resources are limited. Standardize Precipitation Index (SPI) is a powerful and popular tool to study meteorological drought (McKee et al. 1993). Zhang et al. (2012) used SPI to analyze the droughts for 1957–2009 in Xinjiang, China, and they indicated that the severity and duration of drought in the southern part of South Xinjiang were increasing. Mansouri Daneshvar et al. (2013) applied SPI to assess the drought impacts on wheat cultivation in Iran. Livada and Assimakopoulos (2007) applied SPI to study droughts in Greece. Since drought hazard is not only a natural phenomenon but also a socioeconomic phenomenon, therefore, its evaluation requires consideration of more factors including drought hazard itself, as well as drought vulnerability and risk (e.g., Shahid and Behrawan 2008; Kim et al. 2013). Zarafshani et al. (2012) applied face to face interviews to collect data on vulnerability indices from farmers and applied Me-Bar and Valdez’s vulnerability formula to assess the vulnerability of wheat farmers during drought. Kim et al. (2013) developed the effective drought index to assess the drought hazard itself and used the drought risk concept from the National Drought Mitigation Centre, USA, to assess the drought vulnerability and risk in South Korea. Although drought hazard in Tarim River basin was studied as parts of results of previous studies (e.g., Zhang et al. 2012; Zou et al. 2005; Zou and Zhang 2008), the objective of this study is to evaluate drought vulnerability of the Tarim River basin based on precipitation data, population structure, and so on. The current study may serve as a case study for similar arid regions of the world.
2 Case study and data The Tarim River is the longest inland river in China with an annual flow of 4–6 billion cubic meters, being fed by glacial/ snow melt water, which accounts for 48.2 % of the total water volume in the basin (Chen et al. 2007). About 10 million population including ethnic minorities: Uyghurs and Mongolians, live in the river valley. The river basin, characterized by
low precipitation and high potential evaporation, has an arid climate (Zhang et al. 2010; Sun et al. 2012). In the arid lowlands of the basin, the Tarim River is the most important source of water, with more than 8 million people living in oases clustered along its banks and in an alluvial plain downstream (Zhang et al. 2010). Due to its exceptional role in the sustainable development of local socioeconomy, the central government of China launched a 5-year emergency water diversion program in 2000 with ¥10.7 billion (US$1.3 billion) earmarked for the reclamation of the river and Taitema Lake (Tao et al. 2008). Monthly precipitation data for a period of 1960–2008 from 21 rain gauging stations in Xinjiang, shown in Fig. 1, were analyzed. The data were obtained from the National Climate Center (NCC) of the China Meteorological Administration (CMA). The missing precipitation data were filled in following the method used by Zhang et al. (2011a). The 1–2 days were replaced by the average precipitation of neighboring days, while the consecutive days with missing values were filled by the long-term average of the same days of other years. Also, the demographic data from 42 counties within the Tarim River basin were collected, such as male, female, illiteracy rate, population of age between 0 and 14, population of age between 15 and 59, and population of age above 60. The agricultural data collected included arable land area, effective irrigation area, drought-affected area, food production, direct agriculture loss due to agriculture, gross domestic product, and so forth. The population and agriculture data were collected from statistical yearbooks, and some were provided by the Bureau of Tarim River basin.
3 Methodologies Hazard risk is the product of occurrence frequency of a hazard and the expected loss. Drought risk is determined by the real drought intensity and also the degree to which the droughtaffected bodies are prone to drought hazard. Thus, drought risk evaluation should include occurrence frequency and spatial extent of droughts, drought intensity, regional droughtmitigation facilities, and the degree to which the socioeconomic system responds to drought mitigation. In this study, drought vulnerability evaluation will be assessed based on analysis of drought hazard index and vulnerability of drought-affected bodies. The details of the assessment method can be found in Shahid and Behrawan (2008). 3.1 Drought hazard index By definition, there are generally four types of drought: (a) meteorological drought, (b) hydrological drought, (c) agricultural drought, and (d) socioeconomic drought (Heim 2002; Zhang et al. 2012). This study focuses on the meteorological
Drought vulnerability of the Tarim River basin
Fig. 1 Locations of the precipitation stations in the Tarim River basin, China
drought which is defined as lack of precipitation over a region for a period of time. The SPI (McKee et al. 1993) has been widely used to reveal meteorological droughts (e.g., Moreira et al. 2006) and has proved to be a useful tool in the estimation of intensity and duration of drought events. The timescales of SPI are usually 1, 3, 6, 12, and 24 months. Persistence is usually evident in the monthly precipitation series. To avoid persistence effects, precipitation of the same month, such as March, is singled out to form a series, and the SPI values are computed using the gamma distribution (Zhang et al. 2009). Then, the SPI values for a specific year can be obtained, and the persistence effects are also considered. The computation can be elucidated as follows: Assume X as a monthly precipitation series, Xw is the cumulative precipitation series for w timescale, and w=1, 3, 6,…, Xmon denotes cumulative monthly precipitation of a w specific month (mon) for w timescale (w), e.g., X86 denotes the cumulative precipitation amount of August for 6-month timescale. The SPI can be obtained as follows: SPImon ð1Þ ¼ φ−1 F X mon w w where F denotes gamma function and φ−1 denotes the inverse function of the standard normal distribution function. Since SPI is based on the standard normal distribution, different categories of droughts should be related to a specific theoretical occurrence probability. The SPI values of a specific drought hazard and related occurrence probability can be referred to Table 1. SPI of different timescales has different implications. For example, SPI of shorter timescales can mirror prompt changes of soil moisture which is important for agricultural production. A short-term drought in the Tarim River basin has considerable negative impacts on agricultural
activities and can reflect the influence of spring drought, summer drought, and autumn drought on agricultural production. SPI of longer timescales can indicate streamflow changes which is important for reservoir operation (Wu et al. 2001). In this study, the timescales for SPI were 3, 6, and 12 months, or denoted as SPI3, SPI6, and SPI12. Based on the classifications of SPI-based droughts, as shown in Table 1, the real occurrence probability of droughts of each category for 3-, 6-, and 12-month timescales was calculated, and weights were assigned to each SPI-based drought. Within each drought intensity, the SPI-based droughts were classified into four ratings based on the real occurrence probability (Shahid and Behrawan 2008). In this study, the weights and ratings of mild drought are added based on the classification of Shahid and Behrawan (2008), and the division within mild drought is done by natural break method (Table 2). The drought hazard index (DHI) was defined as follows: DHI ¼ ðM1 Di M1 Dw Þ þ ðM2 Di M2 Dw Þ þ ðSDi SDw Þ þ ðEDi EDw Þ
ð2Þ
Table 1 Classification scales by SPI SPI index
Drought intensity
Occurrence frequency
(−1.0, 0) (−1.5, −1.0) (−2.0, −1.5) ≤−2.0
Mild Moderate Severe Extreme
0.341 0.092 0.044 0.023
Q. Zhang et al. Table 2 Weights and ratings assigned to drought severity Drought intensity
Weight
Frequency (%)
Rating
Mild
1
Moderate
2
Severe
3
Extreme
4
≤16.0 16.1–18.0 18.1–20.0 ≥20.1 ≤9.0 9.1–10.0 10.1–11.0 ≥11.1 ≤3.5 3.6–4.5 4.6–5.5 ≥5.6 ≤1.5 1.6–2.0 2.1–2.5 ≥2.6
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
where M1Dr denotes the rating of mild drought, M1Dw denotes the weight of the mild drought, M2Dr denotes the rating of the moderate drought, M2Dw denotes the weight of the moderate drought, SDr denotes the rating of the severe drought, SDw denotes the weight of the severe drought, EDr denotes the rating of the extreme drought, and EDw denotes the weight of the extreme drought.
3.2 Drought vulnerability index The concept of vulnerability to natural hazards was formulated by the United Nations (United Nations 1991) that the vulnerability to a natural hazard is the degree of loss as a result of potential hazard in a given region. The drought vulnerability in this study was defined as the potential maximum loss of life and property due to drought hazards in a given region during a given time period. In an economically developed region, hazard-affected bodies are large, densely distributed, and have higher economic costs. Hence, the mortalities and economic losses should be larger when this region is subjected to natural hazards. However, the economically developed regions are dominated by good hazard mitigation facilities and well-educated people, and financial investment for hazard mitigation can be massive. All these factors greatly reduce possible losses due to natural hazards. Besides, vulnerability to natural hazards is closely related to living conditions, infrastructure facilities, public strategy and management, organizing capacity, social inequality, sexual distinction, economic model, and so forth. Based on what is mentioned above, six socioeconomic vulnerability indicators and three physical vulnerability
indicators were used to comprehensively evaluate drought vulnerability of the Tarim River basin. The socioeconomic vulnerability indicators include the percentage of agricultural economic loss to gross domestic product (GL), the female to male ratio (FMR), population density (PD), illiteracy rate (IR), dependency ratio (DR), and percentage of agricultural population to total population (AO). Three physical vulnerabilities are the percentage of effective irrigation area to the total arable land area (IL), unit area yield (UY), and the percentage of drought-affected area to the total arable land area (DA). The natural breaks technique was used to classify each drought vulnerability index into four ratings (Smith 1986; Shahid and Behrawan 2008), which is the same as the classification in DHI. Among these nine vulnerability factors, larger IL and UY mean higher agricultural productivity and also higher ability to defend drought hazard, and hence lower drought vulnerability. For the rest of the vulnerabilities, larger index values indicate higher drought vulnerability. Therefore, the larger the values of UL and UY, the smaller their ratings are, while for other indices, the larger the values, the larger the ratings are. In this case, drought vulnerability index (DVI) was obtained as follows:
DVI ¼
GLr þ FMRr þ PDr þ IRr þ DRr þ AOr þ ILr þ UYr þ DAr 9
ð3Þ
where GLr, FMRr, PDr, IRr, DRr, AOr, ILr, UYr, and DAr denote the ratings of GL, FMR, PD, IR, DR, AO, IL, UY and DA, respectively.
3.3 Drought risk index Based on the drought risk index (DRI) proposed by United Nations (1991), the drought risk index of the Tarim River basin was obtained as follows:
DRI ¼ DHI DVI
ð4Þ
where DHI denotes the drought hazard index of each county considered in this study and DVI the drought vulnerability index of each county. The drought risk index will be 0 if no drought events occur or the drought vulnerability is very low, e.g., deserts or mountainous areas in the Tarim River basin where the droughts are in high frequency; however, drought vulnerability is very low due to sparse human settlements, thus drought vulnerability in these regions will be 0.
Drought vulnerability of the Tarim River basin 75°E
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35°N
Fig. 2 Spatial patterns of occurrence frequency (%) of a mild, b moderate, c severe, and d extreme droughts at 3-month timescale
4 Results 4.1 Drought hazard index The spatial patterns of SPI3-based mild, moderate, severe, and extreme droughts in the Tarim River basin are shown in Fig. 2 which shows that occurrence frequency of mild drought is higher in the southwest than in other regions. The highest frequency of mild drought can be identified in the Shache 75°E
80°E
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(A) Mild drought
County and Zepu County. The highest frequency of moderate droughts can be observed in the northeastern part of the basin and in the Hejing County and Yanqi Hui Autonomous County in particular (Fig. 2b). Higher frequency of severe droughts can be found in the middle and northwestern parts of the basin and particularly the Qiemu County (Fig. 2c). Higher frequency of extreme droughts can be identified in the northern parts of the basin, particularly the counties of Bachu, Aksu, Awat, Kalpin, Wensu, Luntai, and Kuqa (Fig. 2d). SPI6-based mild 75°E
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Occurrence of severe drought (%) 6-month time scale ≤ 3.5 4.6 - 5.5 35°N 3.6 - 4.5 ≥ 5.6
Occurrence of extreme drought (%) 6-month time scale ≤ 1.5 2.1 - 2.5 1.6 - 2.0 ≥ 2.6
Fig. 3 Spatial patterns of occurrence frequency (%) of a mild, b moderate, c severe, and d extreme droughts at 6-month timescale
Q. Zhang et al. 75°E
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Occurrence of severe drought (%) 12-month time scale ≤ 3.5 4.6 - 5.5 35°N 3.6 - 4.5 ≥ 5.6
Occurrence of extreme drought (%) 12-month time scale ≤ 1.5 2.1 - 2.5 1.6 - 2.0 ≥ 2.6
Fig. 4 Spatial patterns of occurrence frequency (%) of a mild, b moderate, c severe, and d extreme droughts at 12-month timescale
drought is subject to lower occurrence frequency than SPI3based mild drought (Figs. 2a and 3a). Higher frequency of SPI6-based mild drought can be found in the northwest and Kalpin County in particular. Higher frequency of SPI6-based moderate drought can be identified in the eastern, southwestern, and northwestern parts of the basin. Comparison between Fig. 3a, b indicates that larger areas of the basin are dominated by higher frequency of SPI6-based moderate drought than SPI6-based mild drought. However,
higher frequency of SPI6-based severe drought can be observed mainly in the western part of the basin, and the highest frequency can be found in the Wuqia, Qira, and Yutian counties (Fig. 3c). Figure 3d indicates that the basin is largely dominated by SPI6-based extreme drought with the highest frequency being in the central part of the basin. Lower frequency of SPI6-based extreme drought is observed in the Minfeng and Qiemo counties (Fig. 3d). SPI droughts of larger timescales, e.g., 12 months, are
Fig. 5 Spatial distribution of drought hazard index of a 3-month, b 6-month, and c 12-month timescales
Drought vulnerability of the Tarim River basin 75°E
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(A) Agriculture economy loss
0
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(B) Female to male ratio
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0.71 - 0.77 0.78 - 0.93 0.94 - 0.97 0.97 - 1.10
0.16 - 3.06 3.07 - 6.86 6.87 - 14.06 35°N 14.07 - 27.79
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1.02 - 2.26 2.26 - 3.46 3.46 - 5.31 5.31 - 10.00
0.18 - 5.66 5.67 - 16.30 16.30 - 31.68 35°N 31.69 - 2541
35°N
30.98 - 41.90 41.90 - 48.89 48.89 - 56.29 35°N 56.29 - 60.38
39.84 - 53.93 53.93 - 72.16 72.16 - 84.22 84.22 - 98.42
Fig. 6 Spatial distribution of socioeconomic drought vulnerability indicators based on county territory for a percentage of agriculture economy loss to GDP, b percentage of female to male ratio, c population density, d Illiteracy rate, e dependency ratio, and f percentage of agriculture population
subject to higher occurrence frequency over a larger area (Figs. 2, 3, and 4). It can be seen from Fig. 4a that the highest occurrence frequency of SPI12-based mild droughts can be detected in Kuqa, Xayar, Xinhe, and Kashgar counties. Generally, higher frequency of SPI12-based mild droughts can be found in the eastern part of the basin and lower frequency in the southern and western parts of the basin (Fig. 4a). In comparison with SPI12-based mild droughts, large areas of the basin are dominated by higher frequency of SPI12-based moderate droughts, such as the eastern, northern, and northwestern parts; specifically, higher frequency of SPI12-based moderate droughts is observed in the Baicheng, Wensu, and Wushi counties (Fig. 4b). Higher frequency of SPI12-based severe droughts is found in the western parts of the basin and mainly in the northern and western parts. Specifically, the highest frequency can be observed in Korla, Luntai, Kuqa, and Xayar counties and also in the western parts of the basin (Fig. 4c). The spatial patterns of the highest frequency of SPI12-based extreme droughts are similar to those of other grades of SPI12-
based extreme droughts; i.e., the highest frequency is found in the south, west, and northeast parts of the basin (Fig. 4d). Drought Hazard Index (DHI), based on Eq. (2), classification of drought hazards of different timescales is illustrated in Fig. 5a, c. It can be seen from Fig. 5a that higher DHI of SPI3-based drought is found in the northern (north slope of the Tianshan Mountains) and northwestern parts of the basin and lower frequency in the southern and eastern parts (Fig. 5a). The high DHI of SPI6-based droughts are observed in the northern and western parts of the basin. Regions dominated by high DHI of SPI6-based droughts are larger than those by SPI3-based droughts with lower DHI of SPI6-based droughts in southwestern parts (Fig. 5b). Figure 5c illustrates that the high DHI of SPI12-based droughts is found mainly in the central and northwestern parts of the basin and lower DHI in the southeast parts (Fig. 5c). Generally, the high DHI of SPI droughts with timescales of 3, 6, and 12 months is observed mainly in the northern and western parts of the basin.
Q. Zhang et al. 75°E
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33.04 - 53.72 53.72 - 69.68 69.68 - 89.25 35°N 89.25 - 100.0
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crop area to total farm land
2098 - 4626 4626 - 6327 6327 - 7401 7401 - 8934
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4.55 - 17.27 17.28 - 25.85 25.86 - 44.51 44.52 - 68.81
Fig. 7 District-based level maps of physical/structural drought vulnerability indicators. a Percentage of irrigated land to total land. b Food production. c Percentage of drought-affected crop area to total farm land
4.2 Drought vulnerability indicators Figure 6a–f shows the spatial patterns of socioeconomic vulnerability indicators defined by the percentage of agricultural loss due to agricultural drought, the ratio of female to male, population density, illiteracy rate, dependency ratio, and the percentage of people dependent on agriculture. The percentage of agricultural loss due to agricultural drought mirrors the percentage of agricultural loss to the total GDP of the entire Tarim River basin. It can be observed from Fig. 6a that the percentage of agricultural loss to the total GDP is the highest in the Awat, Xinhe, and Kuqa counties and higher percentage in the Xayar, Baicheng, Wushi, Kalpin, Moyu, and Pishan counties (Fig. 6a). Sexuality is related to drought vulnerability due to different responses of male or female groups to influences of drought hazards (Vaughan 1987; Bord and O’Connor Fig. 8 Spatial distribution of integrated drought vulnerability index across the Tarim River basin
1997). Higher female vs. male ratio is detected mainly in the southeastern and western parts of the basin, specifically, Artux, Jiashi, Kashgar, Shule, Yopurga, Yengisar, Makit, Zepu, Yecheng, Pishan, Hotan, and Qira counties (Fig. 6b). Higher population density is identified mainly in the northwestern and southwestern parts of the basin and lower population density in the southeastern and southern parts (Fig. 6c). These regions dominated by higher population density are more sensitive to drought hazards than the regions dominated by lower population density. Illiteracy rate refers to the percentage of illiteracy to the total population with age above 15 years. The highest illiteracy is found in the Xayar, Bachu, and Jiashi counties. The lowest illiteracy is found in the Bayangol Mongol Autonomous Prefecture in the northeastern basin (Fig. 6d). Higher illiteracy implies weak human mitigation to drought hazards
Drought vulnerability of the Tarim River basin 75°E
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0
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Fig. 9 Spatial distribution of drought risk at a 3-month, b 6-month, and c 12-month timescales
and means higher drought vulnerability. The dependency ratio denotes the percentage of working age (15–59) to nonworking age (below 15 or above 60). Lower dependency ratio implies high working efficiency and higher contribution to the development of socioeconomic. The highest dependency ratio can be found in Akto, Jiashi, and Kalpin counties while the lowest dependency ratio is detected in the eastern parts (Fig. 6e). Generally, the percentage of agricultural population in the basin is relatively high. The percentage of agricultural population to the total population in the western and northeastern Tarim River basin is relatively low, and a higher percentage of agricultural population is detected in the middle and western and also southern parts of the basin (Fig. 6f). This spatial distribution of the percentage of agricultural population implies that these regions are dominated by agriculture activities, and these regions are sensitive to drought hazards. The matter vulnerability in this study refers to the percentage of effectively irrigated fields to the total arable land area, the percentage of drought-affected crops to the total arable land area, and the spatial distribution of these Table 3 Percentage of counties dominated by different drought risks in the Tarim River basin Timescales (months) Percentage of counties under different drought risks
3 6 12
Mild
Moderate
Severe
Extreme
14.3 21.4 7.1
42.9 35.7 42.9
33.3 33.3 31.0
4.8 9.5 19.0
indictors as illustrated in Fig. 7a–c. Figure 7a indicates a high percentage of effectively irrigated crop land in most regions of the basin. The highest percentage is identified in the Qiemo, Minfeng, Yutian, and Hotian in the south Tarim River basin and also cities in the southwest. Effective irrigation crop area is important to ensure food production and food security in arid regions. A higher percentage of effective irrigation crop area implies enhancement of human mitigation of drought hazards. Grain production per unit area also reflects agricultural production. Large grain production per unit area is found in counties of the northern parts in the basin, such as Hejing, Baicheng, and Wensu counties (Fig. 7b). The highest percentage of drought-affected crop area to the total arable land is detected in Ruoqiang, Minfeng, and Pishan counties and the lowest in the Kalpin, Awat, and Xinhe counties (Fig. 7c). Based on Eq. (3), the integrated drought vulnerability index is obtained, and its spatial patterns are shown in Fig. 8. It should be noted here that the Tarim River basin is composed of oasis, desert, Gobi desert, grass land, and so on. Droughts have no impacts on deserts, on Gobi desert, and also on mountains; thus, the integrated drought vulnerability index is evaluated mainly for oasis and agricultural regions (Fig. 8). It can be seen from Fig. 8 that the highest integrated drought vulnerability index is found in the western parts of the basin, specifically, the Minfeng, Hotan, Moyu, Pishan, Yecheng, Yopurga, Jiashi, Kalpin, Wushi, and Xinhe counties. The lowest drought vulnerability is detected in the central and east parts (Fig. 8).
Q. Zhang et al.
4.3 Drought risk index Drought risk, based on Eq. (4), with timescales of 3, 6, and 12 months is obtained, and the related spatial patterns are shown in Fig. 9a–c. It can be observed from Fig. 9a that the highest drought risk of 3-month timescale is detected in the northern parts of the basin and the high drought risk in the western parts, specifically the Xayar, Xinhe, Awat, and Kalpin counties. The spatial distribution patterns of the risk of droughts with a timescale of 6 months are similar to the drought risk of a 3month timescale (Fig. 9b). However, the number of counties characterized by low and the highest drought risk is increased, and the number of counties dominated by moderate drought risk is decreased (Table 3). The highest and the high drought risk can be identified mainly in the western and northern parts of the basin. In comparison with drought risk with timescales of 3 and 6 months, the number of counties dominated by the highest drought risk with a timescale of 12 months is increased, and the highest drought risk can be identified in the southwestern parts of the basin. High drought risk is observed in the central parts (Fig. 9c). Low drought risk can be found mainly in the eastern and northeastern parts (Fig. 9c).
1. Risk of drought with timescales of 3 and 6 months is high in the northern and western parts of the Tarim River basin, and lower drought risk is in the southern and eastern parts. Moderate high drought risk can be identified in the central and northwest parts of the basin. 2. The drought vulnerability in the northwestern and southwestern parts of the Tarim River basin is the highest when compared to other regions, and the highest drought vulnerability is found in southwestern parts of the basin. The percentage of farmers, dependency ratio, and droughtinduced agricultural loss are in close relations with drought vulnerability. 3. The degree of DHI is the highest in the western parts of the Tarim River basin and is the lowest in the northeastern and southeastern parts. Results of this study are important in planning and management of agricultural activities and also agricultural irrigation.
Acknowledgments This work is financially supported by the Xinjiang Science and Technology Project (Grant no.: 201331104), National Natural Science Foundation of China (Grant No.: 41071020), and Program for New Century Excellent Talents in University (NCET) and is fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK441313). Our cordial gratitude should also be extended to the editor, Prof. Dr. Hartmut Graßl, and also the reviewer for their professional revision suggestions and also for their pertinent and encouraging comments which are greatly helpful for further improvement of the quality of this manuscript.
5 Discussion and conclusions Drought vulnerability and drought risk are analyzed based on precipitation, as well as socioeconomic and physical indicators. The regionalization and classification of drought hazards across the Tarim River basin are obtained. In general, the drought risk is higher in the counties of the western than the southern and northwestern part of the basin. Lower drought risk is in the northeast and southeast parts of the basin. DHI, drought vulnerability, and drought risk are in good agreement in their spatial distributions over the basin. Higher drought vulnerability usually corresponds to higher drought risk. Analysis of drought vulnerability indicates that the percentage of farmers, dependency ratio, and drought-induced agricultural loss are in close relations with drought vulnerability; female vs. male ratio and illiteracy rate can be ranked as the second important factor contributing to drought vulnerability; population density is minor in drought vulnerability. In western and southern parts of the basin, the percentage of irrigated crop area is relatively high. However, irrigation facilities and motor-pumped wells have been partly abandoned, and irrigation efficiency and also the crop yield per unit area in the western and southern parts of the basin are low. Thus, drought vulnerability of these regions is higher than that of other regions of the basin. Specifically, the following conclusions are drawn from this study:
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