Drought projection using GCM & statistical downscaling technique: A case study of Sirohi District

Abstract

The natural hazard that has the greatest impact on people and is considered to be the most complicated but least understood is drought. Thus, the projection of drought is extremely necessary to be prepared against possible damage. There are many indices available for the assessment of drought severity, but the result of SPEI is more reliable than any other indices. The objective of the study was to predict drought in the Sirohi district (India) using different meteorological variables like temperature, precipitation, Potential Evapotranspiration (PET), etc. For the projection of drought, the statistical downscaling technique was applied by using observed data and Global Climate Model (GCM) data. Drought years in the period 2020–2099 were identified and the probability of occurrence of the different drought classes has been evaluated. Finally, the relationship between El Niño and droughts was investigated using Sea Surface Temperature (SST) anomaly data. Sirohi district has a 75.0 % chance of drought during El Niño years. The model can predict droughts with a 60 % accuracy using the statistical downscaling technique. From 2019 to 2099, there are 13.75 % chances of moderate droughts and 12.5 % chances of mild droughts. According to this research, there will not be any severe or extreme droughts in the future. This study concludes that the projection of drought can be done accurately by statistical downscaling techniques using GCM data. Results showed that major droughts in the South-West region of Rajasthan occurred during El Niño years. This will help the policymakers to prepare policies, drought contingency plans, and rules according to the drought conditions of the particular region.