A Novel Approach to Identify the Characteristics of Drought under Future Climate Change Scenario

Climate change is one of the primary drivers that alters the natural balance of hydrologic cycle and leads to the onset of hydrologic extreme situations. In general, the climate change induces the hydrologic extreme drought more frequently as compared to the other catchment-scale hydrologic processes. In this context, it is indispensable to study the implications of climatic and catchment alterations on different types of drought processes. The present study analyzed the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Streamflow Drought Index (SDI), and Agricultural Standardized Precipitation Evapotranspiration Index (aSPEI) across multiple time scales of the future climate change scenarios. Further, this study attempted a correlation-based approach to identify the suitable drought index to characterize the agricultural drought and critical drought index estimates over the study region for both historical time periods and future climate change scenarios under different Representative Concentration Pathway (RCP) scenarios of RCP 4.5 and 8.5. The aSPEI is proved to be an improvement over the conventional SPEI for analyzing the agricultural drought characteristics across all the time scales. The 6-month time scale is found to be the most suitable reference period for drought monitoring with highest correlation estimate of 0.69 across all the three test locations. Individually, catchment and climate variables failed to represent the drought dynamics over the catchment, whereas the combined model adequately represented the drought dynamics. The relative impact of different process components revealed that the precipitation in the climate model and baseflow index in catchment model have significant impact on short-term drought prediction, while in the combined model, the baseflow index alone is sufficient. The methodology suggested herein could be adopted in any global catchment to represent the drought process, and subsequently, the drivers of drought could be identified with utmost accuracy.