It is possible to plan suitable measures to mitigate the ill effects of droughts if the severity, recurrence interval, and frequency of droughts can be estimated efficiently. The present study was conducted to assess the drought proneness of various districts of the Saurashtra region of Gujarat state (India) by developing the drought severity–duration–frequency (DSDF) curves using the Standardized precipitation evapotranspiration index (SPEI) using rainfall and temperature data of 40 years (1980–2019) of 36 stations. The drought severities for various return periods ranging from 2 to 100 years were estimated using the best-fit probability distributions to derive district-wise DSDF curves for various districts for durations of 1–4 months. The results revealed that severe droughts are expected to occur once in 29–44 years for various districts of Saurashtra. The districts were ranked based on criteria such as proneness to higher severities for longer drought durations, high severities with short drought duration, and low severities with long drought duration. The finding of the study and DSDF curves can serve as a convenient tool for risk assessment, planning various crop cultivation and irrigation interventions, preparedness, and mitigation against droughts.

The study was carried out to develop rainfall forecasting Models. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for developing Models rainfall of Udaipur city. Two data sets were prepared using 35 year of weather parameters i.e. wet bulb temperature, mean temperature, relative humidity and evaporation of previous day and previous moving average week were used to prepare case I and case II respectively. Gaussian and Generalized Bell membership functions were used to prepare models. Statistical and hydrologic performance indices of ANFIS (Gaussian, 5) gave better performance among developed four models. The study showed that sensitivity analysis revealed wet bulb temperature is most sensible parameter followed by mean temperature, relative humidity and evaporation.

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Drought is a natural hazard which is challenging to quantify in terms of severity, duration, areal extent and impact. The present study was aimed to assess the meteorological drought for Junagadh (Gujarat), India using Standardized Precipitation Index (SPI) and evaluate its correlation with the productivity of Groundnut and Cotton. The SPI was computed for eight durations including monthly (June to August each), 3 monthly (June to August and July to September) and 6 monthly (June to November) time scales for the year1988 to 2018. The results revealed that 54% to 67% of years suffered from drought for SPI-1. Drought years based on SPI-3 and SPI-6 were 48 % to 58%. Among all the eight durations, mild drought was the most dominant drought category. Years 1993, 1999, 2002 and 2012 experienced the most severe droughts for Junagadh. Severe droughts were observed only for SPI-1 (July), SPI-3 and SPI-6. No extreme drought was witnessed in Junagadh. Correlation of groundnut yield with SPI was higher as compared to cotton for all time scales. Kharif groundnut and cotton yield were better correlated with SPI-3 and SPI-6 for Junagadh with significant correlation coefficient ranging from 0.57 to 0.79 for groundnut and 0.46 to 0.56 for cotton. Among monthly SPI, the significantly highest correlation was found for June (0.59) for groundnut and September (0.48) for cotton. The SPI-3 and SPI-6 shown ability to quantify the drought and also shown the potential of yield prediction.

The present study was conducted in the Saurashtra region of Gujarat to demonstrate the development and validation of location and crop-specific composite drought index (CDI) using a linear combination of three parameters including meteorological drought index, vegetation drought index and inverse of maximum consecutive dry days%for major Kharif crops of the region i.e. cotton and groundnut. The performance of nine drought indices including six meteorological and three remote sensing-based vegetation indices wasevaluated in terms of correlation with district scale crop yields.The district-wise expressions of CDI were developed by assigning principal component analysis (PCA) based weights to parameters.Standardized Precipitation Evapotranspiration Index (SPEI)/ Reconnaissance Drought Index (RDI) among meteorological indices and NDVI Anomaly Index (NDVIA)/ Vegetation Condition Index (VCI) among vegetation indices were found suitable for generating district specific CDI expressions. The developed CDI showed higher correlation with Kharif cotton and Groundnut crop yields as compared to various meteorological as well as vegetation indices used in the study and effectively quantified major historic agricultural droughts.The average correlation coefficients of developed CDI with cotton and groundnut yields were 0.71 and 0.77 respectively. The correlations of CDI and crop yields for all CDI expression were highly significant with p<0.01. The method developed in the study will be useful to generate crop and region-specific multi-scalar drought indices by the amalgamation of multiple drought indices for assessing crop production losses.