Agricultural Drought Vulnerability Research Articles

Identifying influence patterns of regional agricultural drought vulnerability using a two-phased grey rough combined model

PurposeA core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.Design/methodology/approachA two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.FindingsThe results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.Originality/valueThe proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.

Study of the spatiotemporal variability in agricultural drought vulnerability based on a dynamic classification projection pursuit model

Studies of the temporal and spatial variabilities in agricultural drought vulnerability can help researchers better understand the potential risks of regional agricultural drought. In this paper, projection points with approximate numerical values are grouped into one class, and the degree of dispersion is measured by the information entropy rather than the standard deviation. Then, the projection pursuit model is improved. Finally, the model is applied to assess the spatiotemporal variability in agricultural drought vulnerability for 10 counties in Qiqihar, a typical semi-arid region in China. The model analysis shows that the improved model avoids the cutoff radius, which is one of the difficulties of traditional models. Moreover, information entropy is superior to the standard deviation in measuring data. The vulnerability results show that regional drought vulnerability exhibits an increasing trend over time, the vulnerability in the northern part of the region is relatively high, and the vulnerability in the central and southern parts of the region is relatively low. The improved model can enrich the theory of projection pursuit, and the temporal and spatial variability of vulnerability results can provide a reference for resisting agricultural droughts in semi-arid areas.

Evaluating the vulnerability of agricultural drought in Hetao Irrigation Area of Inner Mongolia Based on super efficiency DEA

Drought vulnerability has become a key point for agricultural sustainable development in such a changing context. In this study, Hetao Irrigation Area which is the major grain-producing area is selected as the research area, where frequent drought threatens food production. Firstly, super efficiency data envelopment analysis (SEDEA) model is used to evaluate the agricultural drought vulnerability of Hetao Irrigation Area from 1997 to 2016. And then the spatial and temporal differences of vulnerability are analyzed. Moran’s I Index is also adopted for further spatial autocorrelation analysis. The results show that the vulnerability decreases with the investment of agricultural science and technology, and the drought vulnerability in different irrigation fields are affected by the irrigation area coverage rate. The drought vulnerability is much higher in the east of Hetao Irrigation Area than that in the west due to the worse water supply in the east. And the vulnerability in Wuyan is the highest due to the largest cropping sown area, while Linhe stays the lower value due to the highest GDP.

Identifying key factors of regional agricultural drought vulnerability using a panel data grey combined method

Regional agricultural drought vulnerability (RADV) is a complex problem caused by the interaction of various factors, and the combination of multiple dimensions of each subregion, factor index and time affects the RADV. Therefore, panel data should be used to reflect the actual situation of the region objectively and comprehensively. Current research on identifying key factors of affecting RADV is relatively scarce from the perspective of panel data. In view of this, in order to classify and identify the key factors, a new panel data grey combined method of comprehensive grey relational analysis (CGRA) and Max-CGRA clustering is proposed, which is applied to identify the key factors of RADV in China’s Henan Province. According to the identification results of key factors, the reasons for the change of RADV are further discovered, and the corresponding drought policies and countermeasures that need to be strengthened and controlled are presented. In addition, these results can also provide scientific basis for regional agricultural drought risk control.

Agricultural Drought Risk Assessment in Southwest China

Drought disasters jeopardize agricultural production and are expected to become more serious in the context of global climate change. However, in China, little attention has been paid to evaluating agricultural drought risk in humid areas (such as in Southwest China), which have also been affected by severe drought in recent years. In this work, we used the Standardized Precipitation Evapotranspiration Index (SPEI), which was computed from high-quality monthly precipitation and temperature data from 92 rain-gauge stations across Southwest China, to study the drought characteristics (e.g., intensity, duration, and frequency) and their decadal variations from 1960 to 2017. Furthermore, we applied a widely accepted conceptual model that emphasizes the combined role of drought hazard (calculated by the intensity and frequency of drought) and agricultural drought vulnerability (integrated with high-resolution soil properties, climate, topography, irrigation, and gross domestic product) to conduct a spatial assessment of agricultural drought risk at a 1-km grid scale. The results revealed that drought has become more serious and frequent in Southwest China, especially since the 2000s. About 27.4% of the agricultural area has been exposed to an extremely high risk of drought, 33.5% to a high risk, 22.5% to a moderate risk, and 16.6% to a low risk. The extreme agricultural risk areas were located mainly in northeastern and southeastern Chongqing, southwestern Sichuan, northeastern and eastern Guizhou, and central and eastern Yunnan. Our findings highlighted that more attention should be paid to the agricultural drought risk in humid regions of China. Furthermore, this work could set the stage for policy makers and practitioners to take measures to reduce the agricultural drought risk in Southwest China.

Assessment of agricultural drought vulnerability to climate change at a municipal level in South Korea

This study aimed to analyze the future vulnerability to agricultural drought of the Korean administrative units of cities (Si) and counties (Gun) following the climate change phenomenon. To assess the vulnerability in a quantitative manner and also to deal with different physical and socioeconomic data on the occurrence of agricultural drought, principal component analysis (PCA), a multivariate statistical analysis method, was adopted, and a vulnerability index, regional vulnerability index to agricultural drought (RVIAD), was suggested. RVIAD having a range from 0.0 to 1.0 was calculated by rescaling the weighted summation of principal component scores. The analysis was performed with all 21 standardized variables in each administrative unit of Si and Gun: 3 sensitivity variables, 8 exposure variables, and 10 adaptation variables. It resulted in four principal components explaining about 85.7% of the total variance, and the third principal component, comprised of only climate variables, was used for applying future climate data from the RCP 8.5 scenario. The districts of Chungchongnam-Do (M1), Jeollabuk-Do (L1), and Jeollanam-Do (L2) were evaluated as having the highest vulnerability to agricultural drought based on the climate change scenario, not only in the present but also in future. Despite the limitation inherent in the PCA, the approach in this study could reflect different factors other than climate factors on minimizing subjective interruption, and such is expected to contribute to improving the decision-making for diagnosing the drought adaptation capacity in a region and developing measures to mitigate the drought damage.

Satellite Remote Sensing for Monitoring Agriculture Growth and Agricultural Drought Vulnerability Using Long-Term (1982–2015) Climate Variability and Socio-economic Data set

Climate variability significantly impact the agricultural growth, stress, cropping pattern, phenophase and its vulnerability. Satellite derived indices, climate and socio-economic data sets have been used to study the time series trend of agricultural NDVI and agriculture drought vulnerability for two states of India namely Andhra Pradesh and Telangana. The study uses NOAA AVHRR GIMMS NDVI.3g. v1 (1982–2015) data set. The trend analysis of climate and soil moisture was carried out to understand their impact on the agriculture growth/stress, length of the growing period (LGP) and projected agriculture NDVI for IPCC climate AR5 2050 RCP 2.6 scenario. A novel approach is applied to the integrated data sets i.e. satellite and climate variables including socio-economic to assess the agricultural drought vulnerability at the district level, and at the tehsil level of united Telangana and Andhra Pradesh states for the recent-past. We further projected the vulnerability using IPCC AR5 2050 and 2070 climate RCP 2.6 scenario. The study has revealed that climate and soil moisture have a significant impact on LGP and agriculture condition. The predicted agricultural NDVI are near like normal years (2007 and 2013) indicating climate change signatures are not expected in near future. There is a need to improve the understanding using higher resolution soil moisture data to plan appropriate adaptive and mitigation strategies for the agricultural drought conditions in changing climate scenario.

Assessing Agricultural Drought Vulnerability by a VSD Model: A Case Study in Yunnan Province, China

Drought vulnerability of agriculture is significant to economic development and sustainable food production. In this paper, we proposed a framework to evaluate the regional agricultural-eco environment in the face of drought caused by climate change. Based on a vulnerability scoping diagram (VSD) model, we built up a comprehensive system to evaluate the agricultural drought vulnerability of Yunnan Province in China. The model highlights the human-land relationship by considering both natural conditions and human activities. Twelve indicators were generated to construct three components of the model: exposure, sensitivity, and adaptive capacity. During the construction of the VSD model, the entropy and the analytic hierarchy process (AHP) comprehensive analysis method were adopted to generate the weights and to compute the composite index for each section. Furthermore, the factor analysis method was used to determine the dominant factors of different cities and the main indicators driving the system. The results indicated a spatial pattern that the vulnerability value was high on the eastern and western sides, but low in the middle of Yunnan Province. Most of the vulnerable regions were concentrated in remote areas. Indicators such as population density, irrigation level, annual average precipitation, cultivation land ratio, and difficulty of water supply were the main driving factors. This means that there is a deep connection between agricultural drought vulnerability and urbanization. The evaluation system developed during this research will provide guidance for drought mitigation in regions of complex terrain.

Global vulnerability to agricultural drought and its spatial characteristics

As an important part of agricultural drought risk, agricultural drought vulnerability helps effectively prevent and alleviate drought impacts by quantifying the vulnerability as well as identifying its spatial distribution characteristics. In this study, global agricultural cultivation regions were chosen as the study area; six main crops (wheat, maize, rice, barley, soybean, sorghum) were selected as the hazard-affected body of agricultural drought. Then, global vulnerability to agricultural drought was assessed at a 0.5° resolution and finally, its distribution characteristics were revealed. The results indicated that the area percentages of different grades of global vulnerability to agricultural drought from low to very high were 38.96%, 28.41%, 25.37%, and 7.26%, respectively. This means that the total area percentage of high and very high vulnerability zones exceeded 30% of the study area. Although high and very high vulnerability zones were mainly distributed in arid and semi-arid regions, approximately 40% of those above were distributed in humid and semi-humid regions. In addition, only about 15% of the population in this study was located in the high vulnerability regions. Among the vulnerability factors, water deficit during the growing season and the irrigation area ratio are the key factors affecting regional vulnerability. Therefore, the vulnerability could be reduced by adjusting crop planting dates and structures as well as by improving irrigation level and capacity.

Assessment of Agricultural Drought Vulnerability in the Guanzhong Plain, China

The Guanzhong Plain, as an important traditional agricultural area, is suffering from high frequency droughts and a trend towards more serious drought. In this paper, eight factors, precipitation, evapotranspiration, surface water availability, depth to groundwater, well yield capacity, slope, potential water storage of soil, and GDP from agriculture, are integrated into an index to represent drought vulnerability based on the overlay and index method. In this approach, according to the internal connections between factors, precipitation and evapotranspiration are integrated into the moisture index, and depth to groundwater and well yield capacity are integrated into groundwater availability. To improve the rationality and accuracy, normalization is employed to assign rating values, and the analytic hierarchy process is introduced into the weighting scheme. Two local drought monitoring datasets endorses the results of the model. The map removal sensitivity analysis indicates the vulnerability index has low sensitivity in removing each layer. The single-parameter sensitivity analysis indicates the major contribution to the vulnerability index is meteorology followed by groundwater availability and surface water availability. The vulnerability map shows the low vulnerability coincides roughly with irrigation districts on the terraces and floodplains. The northwest tableland generally has moderate vulnerability, due largely to inefficient groundwater withdrawal. The high vulnerability is concentrated at the peripheries of the plain, where agriculture is generally rain-fed without irrigation and groundwater support, and land is rugged with high slopes.

Drought vulnerability mapping using AHP method in arid and semiarid areas: a case study for Taft Township, Yazd Province, Iran

Drought is an insidious hazard of nature. It is often referred to as a “creeping phenomenon,” and its impacts vary from region to region. Studies show that the best approach to managing drought is to apply a comprehensive drought management plan. This system necessitates collecting reliable information to make decisions to decrease the risk and damages caused by drought. Therefore, the first step of the comprehensive drought management is accomplished by preparation of the drought vulnerability map. The main object of this study was to provide for agriculture drought vulnerability mapping for Taft town located in Yazd Province, Iran. To do so, with regard to factors affecting on agricultural drought, maps were prepared for each factor and analytic hierarchy process technique was applied to determine the importance of each map as an input parameter on agricultural drought vulnerability. All input maps and the output of this research, an agricultural drought vulnerability map, were provided and analyzed by geographic information systems. Then, to evaluate and validate how accurately the vulnerability map shows the degree of proneness to agricultural droughts, some ground control points (GCP) were taken into consideration. Results indicated that there is a good agreement between agricultural drought vulnerability map and GCPs in many cases. Also, results showed that mountainous regions of the study area are more vulnerable to agriculture droughts compared with plains. These results can be helpful for decision makers to mitigate agricultural drought impacts over the region, although implementing a drought risk management plan requires a detailed study covering different aspects of drought.

Geospatial approach for assessment of biophysical vulnerability to agricultural drought and its intra-seasonal variations.

The study presents a methodology to assess and map agricultural drought vulnerability during main kharif crop season at local scale and compare its intra-seasonal variations. A conceptual model of vulnerability based on variables of exposure, sensitivity, and adaptive capacity was adopted, and spatial datasets of key biophysical factors contributing to vulnerability were generated using remote sensing and GIS for Rajasthan State of India. Hazard exposure was based on frequency and intensity of gridded standardized precipitation index (SPI). Agricultural sensitivity was based on soil water holding capacity as well as on frequency and intensity of normalized difference vegetation index (NDVI)-derived trend adjusted vegetation condition index (VCITadj). Percent irrigated area was used as a measure of adaptive capacity. Agricultural drought vulnerability was derived separately for early, mid, late, and whole kharif seasons by composting rating of factors using linear weighting scheme and pairwise comparison of multi-criteria evaluation. The regions showing very low to extreme rating of hazard exposure, drought sensitivity, and agricultural vulnerability were identified at all four time scales. The results indicate that high to extreme vulnerability occurs in more than 50% of net sown area in the state and such areas mostly occur in western, central, and southern parts. The higher vulnerability is on account of non-irrigated croplands, moderate to low water holding capacity of sandy soils, resulting in higher sensitivity, and located in regions with high probability of rainfall deficiency. The mid and late season vulnerability has been found to be much higher than that during early and whole season. Significant correlation of vulnerability rating with food grain productivity, drought recurrence period, crop area damaged in year 2009 and socioeconomic indicator of human development index (HDI) proves the general soundness of methodology. Replication of this methodology in other areas is expected to lead to better preparedness and mitigation-oriented management of droughts.

Assessing agricultural drought vulnerability in the Sanjiang Plain based on an improved projection pursuit model

Drought is one of the main natural disasters affecting regional agriculture, and regional agricultural drought vulnerability assessment is necessary to establish regional drought forecast, monitoring, and early warning mechanisms. The results can provide a theoretical basis for the identification of drought hazard and disaster prevention. In this study, the concept of the overall dispersion and local aggregation of projection points was proposed by Friedman and Tukey (IEEE Trans Comput 23:881–890, 1974), and improvements to the projection pursuit model are proposed here by measuring discrete projection points according to the information entropy. This improved model was applied to assess the agricultural drought vulnerability of 18 counties located in the Sanjiang Plain for 4 years (2004, 2007, 2010, and 2013). Information entropy was shown to provide improved measurements in the data discreteness relative to standard deviations, and the cutoff radius was defined between 0 and ln 2, thus allowing the use of the exhaustion method to determine the cutoff radius. The overall agricultural drought vulnerability in the Sanjiang Plain area shows a downward trend over time. The main reason for this result is the reduced regional sensitivity and the increased drought resistance ability each year. Economic development speeds up the urbanization process, decreasing the proportion of agricultural population and the proportion of agricultural GDP each year and increasing the irrigation index, per capita GDP, rural per capita net income and other indicators each year. These developments decrease the sensitivity of the agricultural system, improve the adaptive capacity, and reduce the vulnerability. Spatially, the vulnerability of various regions shows some differences. The vulnerabilities of Hulin, Luobei, Youyi, and Fuyuan are the lowest and showed a downward trend over time. The sensitivities of these regions were also low; the population density, the proportion of agricultural population and other sensitive indicators were significantly smaller than those for other regions. Furthermore, the drought threat is small, the region has many state-owned farms, the economic situation is good, and the drought resistance ability is strong. The vulnerabilities of Baoqing, Muling, Raohe, and Tongjiang are moderate, with high sensitivities but strong adaptive capacity. The vulnerabilities of Yilan, Jidong, Mishan, Fujin, and Boli have changed greatly, mainly due to the rapid economic development in recent years, increasing the agricultural drought resistance. The vulnerabilities of Tangyuan, Suibin, Jixian, Huachuan, and Huanan are the highest, and with little change, these regions are highly sensitive and prone to drought. In addition, the regional economic development level is relatively low, and the agricultural drought resistance is not high.

A study on agricultural drought vulnerability at disaggregated level in a highly irrigated and intensely cropped state of India.

Drought is an important global hazard, challenging the sustainable agriculture and food security of nations. Measuring agricultural drought vulnerability is a prerequisite for targeting interventions to improve and sustain the agricultural performance of both irrigated and rain-fed agriculture. In this study, crop-generic agricultural drought vulnerability status is empirically measured through a composite index approach. The study area is Haryana state, India, a prime agriculture state of the country, characterised with low rainfall, high irrigation support and stable cropping pattern. By analysing the multiyear rainfall and crop condition data of kharif crop season (June-October) derived from satellite data and soil water holding capacity and groundwater quality, nine contributing indicators were generated for 120 blocks (sub-district administrative units). Composite indices for exposure, sensitivity and adaptive capacity components were generated after assigning variance-based weightages to the respective input indicators. Agricultural Drought Vulnerability Index (ADVI) was developed through a linear combination of the three component indices. ADVI-based vulnerability categorisation revealed that 51 blocks are with vulnerable to very highly vulnerable status. These blocks are located in the southern and western parts of the state, where groundwater quality is saline and water holding capacity of soils is less. The ADVI map has effectively captured the spatial pattern of agricultural drought vulnerability in the state. Districts with large number of vulnerable blocks showed considerably larger variability of de-trended crop yields. Correlation analysis reveals that crop condition variability, groundwater quality and soil factors are closely associated with ADVI. The vulnerability index is useful to prioritise the blocks for implementation of long-term drought management plans. There is scope for improving the methodology by adding/fine-tuning the indicators and by optimising the weights.

Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity

Vulnerability of agriculture to drought conditions is closely related to the challenges of sustainable agriculture and food security. Information on agricultural drought vulnerability status at different scales is crucial for prescription, development and implementation of long-term drought management measures. This study is on developing a composite index for measuring crop-generic agricultural drought vulnerability at disaggregated level, in the erstwhile Andhra Pradesh state of India. By analyzing the data on soils, irrigation, farm holdings, rainfall, rainy days, cropping pattern and satellite-based crop condition, 22 input indicators for 1038 spatial units called “Mandals” (within-the-district administrative units) were generated and segregated into exposure, sensitivity and adaptive capacity components of vulnerability. The composite index was computed for each component by generating the weights for inputs through variance approach. Agricultural drought vulnerability index was generated for each Mandal by combining the three component indices and beta distribution was fitted to it to generate vulnerability classes. Results indicate that the vulnerable group of Mandals represents 57% of agricultural area and 67% of rain-fed crop area of the state. The geographical pattern of vulnerability is well captured by the composite index. Districts dominant with vulnerable Mandals showed frequent drought occurrence and higher crop yield variability. The vulnerability map can be instrumental in prioritizing the areas for drought management interventions. Since the methodology was applied over a large geographic area with diverse conditions of weather, soil and crop, its utility over other geographies is also established. Indicators selection and weights generation are the critical aspects for further research.

Analysing agricultural drought vulnerability at sub-district level through exposure, sensitivity and adaptive capacity based composite index

Abstract. Information on agricultural drought vulnerability status of different regions is extremely useful for implementation of long term drought management measures. A quantitative approach for measuring agricultural drought vulnerability at sub-district level was developed and implemented in the current study, which was carried-out in Andhra Pradesh state, India with the data of main cropping season i.e., kharif. The contributing indicators represent exposure, sensitivity and adaptive capacity components of vulnerability and were drawn from weather, soil, crop, irrigation and land holdings related data. After performing data normalisation and variance based weights generation, component wise composite indices were generated. Agricultural Drought Vulnerability Index (ADVI) was generated using the three component indices and beta distribution was fitted to it. Mandals (sub-district level administrative units) of the state were categorised into 5 classes – Less vulnerable, Moderately vulnerable, Vulnerable, Highly vulnerable and Very highly vulnerable. Districts dominant with vulnerable Mandals showed considerably larger variability of detrended yields of principal crops compared to the other districts, thus validating the index based vulnerability status. Current status of agricultural drought vulnerability in the state, based on ADVI, indicated that vulnerable to very highly vulnerable group of Mandals represent 54 % of total Mandals and about 55 % of the agricultural area and 65 % of the rainfed crop area. The variability in the agricultural drought vulnerability at disaggregated level was effectively captured by ADVI. The vulnerability status map is useful for diagnostic analysis and for formulating vulnerability reduction plans.

Assessment on agricultural drought vulnerability in the Yellow River basin based on a fuzzy clustering iterative model

Drought is one of the major natural disasters occurring in China and causes severe impacts on agricultural production and food security. Therefore, agricultural drought vulnerability assessment has an important significance for reducing regional agricultural drought losses and drought disaster risks. In view of agricultural drought vulnerability assessment with the characteristics of multiple factors and uncertainty, we applied the fuzzy comprehensive evaluation framework to agricultural drought vulnerability model. The agricultural drought vulnerability assessment model was constructed based on the multi-layer and multi-index fuzzy clustering iterative method, which can better reveal the drought vulnerability (including sensitivity and adaptation capacity). Furthermore, the cycle iterative algorithm was used to obtain the optimal index weight vector of a given accuracy by setting the objective function. It provides a new approach to weight determination of agricultural drought vulnerability assessment. In this study, agricultural drought vulnerability of 65 cities (as well as leagues and states) in the Yellow River basin was investigated using a fuzzy clustering iterative model and visualized by using GIS technique. The results showed clear differences and regularities among the spatial distribution of agricultural drought vulnerability of different regions. A large number of the regions in the basin consisted of those exhibiting high to very high vulnerability and were mainly distributed throughout Qinghai, Gansu, northern Shaanxi, and southern Shanxi, accounting for 46 % of the total assessment units. However, the regions exhibiting very high vulnerability were not significantly affected by droughts. Most of the regions exhibiting moderate vulnerability (21.5 % of the assessment units) were mainly concentrated among agricultural irrigation areas, where agriculture is highly sensitive to droughts, and drought occurrence in these regions will likely cause heavy losses in the future. The regions exhibiting slight to low vulnerability were relatively concentrated, accounting for 32.3 % of the assessment units, and were mainly distributed in the plains of the lower reaches of the Yellow River, where the economy was rather well developed and the agricultural production conditions were relatively stronger.

An Analysis of Vulnerability to Agricultural Drought in China Using the Expand Grey Relation Analysis Method

The identification of agricultural drought vulnerability is an essential step in addressing the issue of drought vulnerability in the country and can lead to mitigation-oriented drought management. In this study, it developed an approach to estimate vulnerability to agricultural drought based on the expand grey relation analysis method (GRA).The new methodology consists of (1) developing the main influence factors of vulnerability to agricultural drought and converting them into quantitative indicators, (2) constructing the evaluation standards and their relative weights based on the collected data on the values of the indicators from 31 provinces and cities in China, (3) computing the vulnerability to agricultural drought of 31 provinces and cities in China in 2009 using expand GRA method. The results indicated that the vulnerability to agricultural drought in the southeast coast of China with more precipitation and irrigation is lower than the central areas; the most vulnerable areas are the west area with less precipitation and irrigation. Some measures for mitigating the vulnerability to agricultural drought in different areas were proposed.

Application of spatial EOF and multivariate time series model for evaluating agricultural drought vulnerability in Korea

This study proposed a methodology using the empirical orthogonal function (EOF) and multivariate time series model for the analysis of drought both in time and space. The methodology proposed was then applied to evaluate the vulnerability of agricultural drought of major river basins in Korea. First, the three-month SPI data from 59 rain gauge stations over the Korean Peninsula were analyzed by deriving and spatially characterizing the EOFs. The shapes of major estimated EOFs were found to well reflect the observed spatial pattern of droughts. Second, the coefficient time series of estimated EOFs were then fitted by a multivariate time series model to generate the SPI data for 10,000 years, which were used to derive the annual maxima series of areal average drought severity over the Korean Peninsula. These annual maxima series were then analyzed to determine the mean drought severity for given return periods. Four typical spatial patterns of drought severity could also be selected for those return periods considered. This result shows that the southern part of the Korean Peninsula is most vulnerable to drought than the other parts. Finally, the agricultural drought vulnerability was evaluated by considering the potential water supply from dams. In an ideal case, when all the maximum dam storage was assumed to be assigned to agriculture, all river basins in Korea were found to have the potential to overcome a 30-year drought. However, under more realistic conditions considering average dam storage and water allocation priorities, most of the river basins could not overcome a 30-year drought.

Quantitative assessment and spatial characteristics analysis of agricultural drought vulnerability in China

In this study, the spatial characteristics of agricultural drought vulnerability in China were investigated using a GIS-based agricultural drought vulnerability assessment model, which was constructed by selecting three agricultural drought vulnerability factors. Seasonal crop water deficiency, available soil water-holding capacity and irrigation were identified as the main indicators of agricultural drought vulnerability in China. The study showed that the distribution of seasonal crop moisture deficiency showed significant differentiation in both north–south and east–west directions, and the agricultural drought vulnerability presented a similar trend. At a regional scale, southern and eastern China typically has a low- and moderate-vulnerability to drought, while high and very high vulnerability to agricultural drought is observed in northern and western China. In terms of China’s agricultural regions, the central part of the southwest region, the area between the southern Huang-Huai-Hai region and the northern part of the Middle and lower reaches of the Yangtze River region, and the northeast region are the areas of low agricultural drought vulnerability in China, while areas of high agricultural drought vulnerability are mainly located in the Inner Mongolia, Loess Plateau and Gan-Xin regions. Due to differences in the physical and social–economic conditions within the agricultural areas, vulnerability to agricultural drought exhibits substantial variability both between different agricultural regions and within the same region. The methodology of grid-cell-based agricultural drought vulnerability assessment, developed in this study, provides a foundation for better description of the differences in regional and even smaller scale.