Assessing Socioeconomic Drought Based on a Standardized Supply and Demand Water Index

Socioeconomic drought is a phenomenon of water shortage caused by an imbalance between the supply and demand of water resources in natural and human socioeconomic systems. Occurrence of these droughts is closely related to sustainable socioeconomic development. However, compared with meteorological drought, hydrological drought and agricultural drought, socioeconomic drought has received relatively little attention. Therefore, this paper proposes a universal and relatively simple socioeconomic drought assessment index, the Standardized Supply and Demand Water Index (SSDWI). Taking the Jianjiang River Basin (JJRB) in Guangdong Province, China as an example, socioeconomic drought characteristics and trends during 1985-2019 were analyzed. The return period of different levels of drought were calculated using a copula function to estimate the risk of socioeconomic drought in the basin, and the relationship between socioeconomic, meteorological, and hydrological droughts and their potential drivers were discussed. The results showed that: (1) SSDWI was a better index for characterizing socioeconomic drought in the JJRB. 29 socioeconomic droughts occurred in the basin during the past 35 years, with an average duration of 6.16 months and an average severity of 5.82 per events. Socioeconomic droughts mainly occurred in autumn and winter, which also had more severe droughts than other seasons. (2) In the JJRB, the joint return periods of ‘∪’ and ‘∩’ for moderate drought, severe drought and extreme drought were 8.81a and 10.81a, 16.49a and 26.44a, and 41.68a and 91.13a, respectively; (3) Due to the increasing outflow from Gaozhou Reservoir, the risk of socioeconomic drought and hydrological drought in the JJRB has significantly declined since 2008. The reasonable operation of the reservoir has played an important role in alleviating the hydrological and socioeconomic drought in the basin.

Drought Assessment Based on Fused Satellite and Station Precipitation Data: An Example from the Chengbi River Basin, China

Drought poses a significant constraint on economic development. Drought assessment using the standardized precipitation index (SPI) uses only precipitation data, eliminating other redundant and complex calculation processes. However, the sparse stations in southwest China and the lack of information on actual precipitation measurements make drought assessment highly dependent on satellite precipitation data whose accuracy cannot be guaranteed. Fortunately, the Chengbi River Basin in Baise City is rich in station precipitation data. In this paper, based on the evaluation of the accuracy of IMERG precipitation data, geographically weighted regression (GWR), geographic difference analysis (GDA), and cumulative distribution function (CDF) are used to fuse station precipitation data and IMERG precipitation data, and finally, the fused precipitation data with the highest accuracy are selected to evaluate the drought situation. The results indicate that the accuracy of IMERG precipitation data needs to be improved, and the quality of CDF-fused precipitation data is higher than the other two. The drought analysis indicated that the Chengbi River Basin is in a cyclical drought and flood situation, and from October to December 2014, the SPI was basically between +1 and −1, showing a spatial pattern of slight flooding, normal conditions, and slight drought.

Meteorological Drought Assessment in Sharjah, UAE Using Drought Indices

Drought is a natural disaster that significantly affects environmental and socio-economic conditions. It occurs when there is a period of below average precipitation in a region, and it results in water supply shortages affecting various sectors and life adversely. Droughts impact the ecosystems, crop production, and erode livelihoods. Monitoring drought is essential especially in the United Arab Emirates (UAE) due to the scarcity of rainfall for an extended period of time. In this study, drought is assessed in Sharjah UAE using monthly precipitation and average temperature data recorded for 35 years (1981-2015) at the Sharjah International Airport. The standardized precipitation Index (SPI), and the Reconnaissance Drought Index (RDI) are selected to predict future droughts in the region. SPI and RDI are fitted to the statistical distribution functions (gamma and lognormal) in an annual time scale and then, a trend analysis of index values is carried out using Mann-Kendal test. The correlation between SPI and RDI indices was found to be high where both showed high drought frequencies and a tendency to get drier over time, thus indicating the need of appropriate drought management and monitoring.

Drought Assessment in São Francisco River Basin, Brazil: Characterization through SPI and Associated Anomalous Climate Patterns

The São Francisco River Basin (SFRB) is one of the main watersheds in Brazil, standing out for generating energy and consumption, among other ecosystem services. Hence, it is important to identify hydrological drought events and the anomalous climate patterns associated with dry conditions. The Standard Precipitation Index (SPI) for 12 months was used to identify hydrological drought episodes over SFRB 1979 and 2020. For these episodes, the severity, duration, intensity, and peak were obtained, and SPI-1 was applied for the longest and most severe episode to identify months with wet and dry conditions within the rainy season (Nov–Mar). Anomalous atmospheric and oceanic patterns associated with this episode were also analyzed. The results revealed the longest and most severe hydrological drought episode over the basin occurred between 2012 and 2020. The episode over the Upper portion of the basin lasted 103 months. The results showed a deficit of monthly precipitation up to 250 mm in the southeast and northeast regions of the country during the anomalous dry months identified through SPI-1. The dry conditions observed during the rainy season of this episode were associated with an anomalous high-pressure system acting close to the coast of Southeast Brazil, hindering the formation of precipitating systems.

Drought Vulnerability Assessment and Cluster Analysis of Island Areas Taking Korean Island Areas at Eup (Town) and Myeon (Subcounty) Levels as Study Targets

The purpose of this study is to conduct drought vulnerability assessment and cluster analysis of Korean island areas at eup (town) myeon (subcounty) level. Drought vulnerability assessment was conducted using factor analysis and entropy method, and cluster analysis was analyzed using K-means, a nonhierarchical cluster analysis method. Vulnerability consisted of climate exposure, sensitivity, and adaptive capacity. Twenty-two indicators were used to evaluate and analyze vulnerability of drought in small island areas. The results of entropy method showed that winter rainfall, no rainfall days, agricultural population rate, cultivation area rate, water supply rate and groundwater capacity have a substantial impact on drought assessment. The overall assessment of vulnerability indicated that Seodo-myeon Ganghwa-gun, Seolcheon-myeon Namhae-gun, and Samsan-myeon Ganghwa-gun were most vulnerable to drought. The cluster analysis was evaluated by categorizing the regions into three clusters, and policy support and planning are needed to suit the characteristics of each cluster was observed.