Hydrology relates to many complex challenges due to climate variability, limited resources, and especially, increased demands on sustainable management of water and soil. Conventional approaches often cannot respond to the integrated complexity and continuous change inherent in the water system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing the most important facets of hydrological research, including soil and land surface modeling, streamflow, groundwater forecasting, water quality assessment, and remote sensing applications in water resources. In soil and land modeling, AI techniques could further enhance accuracy in soil texture analysis, moisture estimation, and erosion prediction for better land management. Advanced AI models could also be used as a tool to forecast streamflow and groundwater levels, therefore providing valuable lead times for flood preparedness and water resource planning in transboundary basins. In water quality, AI-driven methods improve contamination risk assessment, enable the detection of anomalies, and track pollutants to assist in water treatment processes and regulatory practices. AI techniques combined with remote sensing open new perspectives on monitoring water resources at a spatial scale, from flood forecasting to groundwater storage variations. This paper’s synthesis emphasizes AI’s immense potential in hydrology; it also covers the latest advances and future prospects of the field to ensure sustainable water and soil management.

The SWATDRAIN model was developed by incorporating the subsurface flow model, DRAINMOD, into a watershed scale surface flow model, SWAT (Soil and Water Assessment tool), to simulate the hydrology and water quality of agricultural watersheds. The model is capable of simulating hydrology under different agricultural management and climate scenarios. As an application of the SWATDRAIN model, the impact of climate change on surface/subsurface flow was evaluated in the Canagagigue Creek watershed in southern Ontario, Canada. Using the assumption that there has been no change in land cover and land management, the model was applied to simulate annual, seasonal, and monthly changes in surface and subsurface flows at the outlet of the watershed under current and future climate conditions. The climate scenario under consideration in this study for 2015–2044 was derived from CGCM2 (Canadian Global Circulation Model 2), with A2 scenario for future climatic simulation. The SWATDRAIN model’s ability to predict the impacts of future climate change scenarios in agricultural watersheds due to monthly NSE (Nash Sutcliffe Efficiency), PBIAS (Percent Bias), and RSR (Root Mean Square Error) values of 0.74, 3.67, and 0.37, respectively, for the validation phase. The results showed that general climate change effects more spring and winter hydrology than summer hydrology. The results show that the annual flow is expected to increase in future, which will lead to an increase in the sediment loads in the stream.