Comparison of meteorological, hydrological and agricultural droughts for developing a composite drought index over semi-arid Banas River Basin of India

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-04-03 DOI:10.1007/s00477-024-02704-4

Divya Saini, Omvir Singh

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Abstract

This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (r = − 0.81), SDI-3 (r = − 0.78) and VCI-5 (r = − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems.

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比较气象、水文和农业干旱，以制定印度半干旱巴纳斯河流域的综合干旱指数

本研究试图通过整合印度拉贾斯坦邦半干旱的巴纳斯河流域的气象、水文和农业干旱来开发一种综合指数。为此，利用 2000 年至 2020 年期间的站点和遥感数据，在 1 个月、3 个月、5 个月、9 个月和 12 个月的时间尺度上使用了标准化降水指数 (SPI)、溪流干旱指数 (SDI) 和植被状况指数 (VCI)。为了确定上述干旱的发生情况以及在不同时间尺度（1 个月、3 个月、5 个月、9 个月和 12 个月）上最易发生干旱的时期，SPI、SDI 和 VCI 与粮食产量和历史干旱年份的发生情况进行了验证。结果表明，SPI-3（r = - 0.81）、SDI-3（r = - 0.78）和 VCI-5 （r = - 0.80）时间尺度的验证效果显著。随后，利用主成分分析 (PCA) 得出的权重对这些时间尺度进行合并，以编制综合干旱指数 (CDI)。以这种方法得出的年度综合干旱指数还通过粮食产量和历史干旱年的发生情况进行了进一步验证。综合干旱指数的结果表明，轻度干旱的面积最大（73%），其次是中度（21%）和重度（4%），而湿润条件下的面积很小（2%）。最后，这项研究表明，单个干旱类型（气象、水文、农业）并不能恰当地反映干旱的严重程度，因此，使用基于多重干旱的综合指数来有效评估和监测水文系统中的干旱更为现实。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.