An exhaustive investigation of changes in projected extreme precipitation indices and streamflow using CMIP6 climate models: A case study

This study draws attention to the better comprehension of spatio-temporal analysis of climate changes based on precipitation extremes and projection of future streamflow for efficient management of water resources in the Krishna River Basin (KRB), India. The concept of symmetric uncertainty (SU) is employed to select the top five Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project future precipitation extreme indices under different Shared Socio-economic Pathways (SSPs). Grid-wise trend analysis reveals that there is more number of decreasing trends in extreme precipitation indices than increasing trends. From the results, it is observed that the percentage contributions of maximum one-day (RX1day) and five-day (RX5day) precipitation indices to the annual total precipitation indices are more important. In future periods, the precipitation extremes are expected to increase, especially the heavy precipitation indices such as R95p, R99p, RX1day, and RX5day, which are increasing significantly along with R50. The projection of future streamflow in the KRB is done using a Support Vector Machine (SVM) and is expected to increase under different SSPs. These precipitation extremes may increase the chance of hydrological calamities across the basin in the future.

Spatio-temporal analysis of extreme precipitation indices is carried out using CMIP6 climate model simulations over KRB.