Machine Learning and Bias-Corrected CMIP6 for Improved Streamflow Prediction in the Data-Scarce Kulfo River, Ethiopia
This study demonstrates that integrating bias-corrected CMIP6 climate projections with machine learning models, particularly Random Forest, significantly improves streamflow prediction accuracy in the data-scarce Kulfo River basin, revealing a future trend of rising temperatures and declining annual streamflow with increased hydrological uncertainty under various climate change scenarios.