This project applies physics-informed machine learning (PIML) to study how climate change affects hydrological systems, such as rivers, watersheds, and water resources. By integrating physical principles with advanced machine learning techniques, the research aims to improve the prediction and understanding of hydrological processes under changing climate conditions. The project seeks to develop more accurate and reliable models for assessing climate-related impacts on water availability, flooding, droughts, and ecosystem resilience, supporting better environmental management and decision-making.