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A comparative analysis of four drought indices using geospatial data in Gujarat, India

机译:印度古吉拉特邦使用地理空间数据对四种干旱指数的比较分析

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摘要

Rainfall deficiency results in drying up of soil, leading to a drought situation, invariably with grave consequences. The magnitude and pattern of drought can be measured with various drought indices using remote sensing and meteorological data. The present study attempts to analyze spatio-temporal pattern of drought severity, and compare meteorological and vegetative drought indices in Gujarat, an agrarian and drought-prone state in western India. Open source remote sensing and rainfall datasets were analyzed for the monsoon and non-monsoon seasons from 1982 to 2001, a period underscored by 12 major droughts. Four indices, namely Standardized Precipitation Index (SPI), Rainfall Anomaly (RFA), Vegetation Condition Index (VCI), and NDVI Anomaly Index (NAI) were used for analysis. Drought patterns, thus delineated, were found to have very good correlation with rainfall. It was observed that both RFA and NAI could be used as an indicator for assessment of the percentage of area affected by meteorological and vegetative drought in the study area. A comparison between the four indices was of much help in understanding the impact of meteorological drought on vegetative drought. Furthermore, multiple regression analysis revealed that RFA and NAI were better-suited drought indices, among the four under consideration.
机译:降雨不足会导致土壤干,,导致干旱情况,不可避免地带来严重后果。可以使用遥感和气象数据,通过各种干旱指数来测量干旱的程度和模式。本研究试图分析干旱严重程度的时空格局,并比较印度西部农业和干旱多发地区古吉拉特邦的气象干旱和植物干旱指数。分析了1982年至2001年的季风和非季风季节的开放源遥感数据和降雨数据集,这一时期由12次主要干旱引起。分析使用了四个指数,即标准降水指数(SPI),降雨异常(RFA),植被状况指数(VCI)和NDVI异常指数(NAI)。因此发现,干旱模式与降雨有很好的相关性。据观察,RFA和NAI均可用作评估研究区域受气象和植物干旱影响的面积百分比的指标。四个指标之间的比较对于理解气象干旱对植物干旱的影响很有帮助。此外,多元回归分析表明,RFA和NAI是正在考虑的四个指标中更适合的干旱指数。

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