Hydrological models, ranging from conceptual to fully distributed frameworks, are essential for understanding and addressing water resource challenges. They offer innovative solutions to stabilize water balances and tackle pressing environmental issues such as droughts, floods, and water scarcity. Complementing these traditional methods, machine learning algorithms (MLAs) have proven highly effective in simulating complex hydrological processes, enabling improved predictions for flood forecasting, drought management, crop modeling, and freshwater allocation. This Special Issue of Water delves into cutting-edge advancements in hydrological modeling, highlighting the integration of remote sensing data and the application of MLAs to enhance the accuracy and efficiency of water resource management. From adapting novel machine learning techniques to assessing water balance components, the research in this collection addresses the critical challenges that are faced by watersheds worldwide. Featuring innovative approaches and practical applications, this Special Issue is an invaluable resource for researchers, practitioners, and policy-makers who are dedicated to advancing hydrological science and fostering sustainable water management solutions.
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