Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions

Abstract Machine learning (ML) models have been widely used for hydrological simulation. Previous studies have reported that conventional ML models fail to accurately simulate extreme flows which are crucial for design flood estimation and associated risk analysis in the context of climate change. Therefore, this study proposes a joint probabilistic rainfall‐runoff model (JPRR) for improving high‐to‐extreme flow projection. With the aid of …