Weighted multi-model ensemble approach for robust monsoon river flow projections

Abstract

Climate change significantly impacts water resource management in monsoon-dependent regions like Sri Lanka, where increasing variability necessitates robust river flow projections for sustainable planning. Focusing on this critical need, this study evaluates future climate impacts on rainfall and streamflow patterns in the Maha Oya River Basin to guide adaptive water management strategies. A weighted Multi-Model Ensemble (MME) was developed using five Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs), downscaled with the Long Ashton Research Station Weather Generator (LARS-WG). Weighting factors based on Standardized Precipitation Index (SPI) and percentiles were integrated into a Reliability Ensemble Averaging (REA) framework. Hydrologic modeling using the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) projected 2031-2050 streamflow under Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP5-8.5 scenarios. Results show 10-25% wet-season rainfall increases and up to 19% higher October streamflow, indicating elevated flood risks. The MME approach improved precipitation accuracy by 15-20% over conventional methods while reducing uncertainty, demonstrating its superiority for tropical basin projections. The study highlights the urgency of adaptive strategies like flood preparedness and dry-season storage to address climate-induced water variability. These enhanced projections provide critical insights for policymakers developing climate-resilient water management frameworks in vulnerable monsoon watersheds.