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Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements in artificial intelligence and the availability of large, high-quality datasets. This review explores the current state of ML applications in hydrology, emphasizing the utilization of extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, and GRACE. These datasets provide critical data for modeling various hydrological parameters, including streamflow, precipitation, groundwater levels, and flood frequency, particularly in data-scarce regions. We discuss the type of ML methods used in hydrology and significant successes achieved through those ML models, highlighting their enhanced predictive accuracy and the integration of diverse data sources. The review also addresses the challenges inherent in hydrological ML applications, such as data heterogeneity, spatial and temporal inconsistencies, issues regarding downscaling the LSH, and the need for incorporating human activities. In addition to discussing the limitations, this article highlights the benefits of utilizing high-resolution datasets compared to traditional ones. Additionally, we examine the emerging trends and future directions, including the integration of real-time data and the quantification of uncertainties to improve model reliability. We also place a strong emphasis on incorporating citizen science and the IoT for data collection in hydrology. By synthesizing the latest research, this paper aims to guide future efforts in leveraging large datasets and ML techniques to advance hydrological science and enhance water resource management practices. [ABSTRACT FROM AUTHOR]

With a burgeoning world population that is expected to reach 10 billion by 2050, 30 % more than today, there is an urgent need to harness available water resources to support regions across the world. This study introduces a new method to identify, prioritize, and select areas for pine basal area reduction to maximize water yields in pine forests along the Northern Gulf of Mexico, USA. The method, demonstrated in the Apalachicola Region of Northwest Florida, an area covered by dense vegetation and pine plantation forests, has experienced freshwater loss due to increased upstream water demand, climate change, and past forest management practices. Potential initial water-yield gains were: 1) 469 m3 d−1 if all pine basal areas were reduced from current to a maximum of 18 m2 ha−1, and 53,400 m3 d−1 if pine basal areas were reduced from current to a maximum of 7 m2 ha−1 for the Apalachicola Region. The method identifies watersheds mainly along the Apalachicola and other rivers and near the Gulf coast that have the greatest potential to increase water yields. Increasing forest water yields translates to increased freshwater availability and improved forest and soil health, water quality, and ecosystem function, services, and resilience, as well as socioeconomic outcomes for communities and people who rely on ecotourism and fisheries for their livelihoods. This method will empower forest managers to focus scarce resources in targeted areas to maximize water-resource benefits per resource investment. Although demonstrated in the Apalachicola Region, the method is easily transferable throughout other pine forests of the Northern Gulf Coast Region. This scientifically sound method is repeatable, scalable, and easily upgraded and adapted as newer, higher resolution datasets become available and relationships between forest metrics, evapotranspiration, and water yields are improved. [ABSTRACT FROM AUTHOR]