Prediction and Evaluation of Hydrological Factors and Drought Indices under Climate Change using CMIP6 in Monsoon Regions

Climate change is increasing the uncertainty of global hydrological cycles, leading to an increase in extreme weather events such as heatwaves, droughts, and floods. While global and continental-scale hydrological analyses based on CMIP6 (Coupled Model Intercomparison Project Phase 6) climate change scenarios have been actively conducted, detailed analyses for specific regions are still lacking. Furthermore, comparing model predictions with observation data for the initial 10-year period (2015-2024) of climate change models is important for validating short-term forecast accuracy and enhancing the reliability of long-term climate prediction. This study evaluates the performance of the CMIP6 prediction models for air temperature, evapotranspiration, precipitation, and soil moisture during the 2015-2024 period under the SSP 2-4.5 and SSP 5-8.5 scenarios. To validate the scenarios, a comparison with GLDAS (Global Land Data Assimilation System) and ERA5-Land reanalysis data is executed. Subsequently, SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) were calculated for the early (2015-2040), mid (2041-2070), and late (2071-2100) periods of climate change, to analyse the intensity and frequency of future droughts. The results show that air temperature and evapotranspiration exhibited a strong correlation, while precipitation and soil moisture showed relatively weak correlations. Based on this study, quantifying bias in climate models can contribute to improving the performance of regional climate predictions. Furthermore, it is expected to provide important information for future climate change forecasting.

 

Keywords: Climate Change, CMIP6, Drought Indices, Monsoon Regions

Acknowledgement

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266).