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.

Nitrogen (N) management in silage corn on sandy soils requires balancing biomass production with nitrate loss. This study evaluated biomass yield, crop N removal, soil, leaf, and pore water N responses to seven N rates (0–470.8 kg N ha⁻¹) at Ona, Florida, in 2025. A randomized complete block design included four replications and 28 plots. Biomass and harvest whole-plant N were measured separately by plot; soil and leaf samples were pooled across four blocks, and pore water from two blocks was pooled by treatment before analysis. Soil nitrate-N (NO₃⁻-N) concentrations were generally greatest at 0–30 cm. Seasonal mean pore water NO₃⁻-N increased from 1.7 mg N L⁻¹ in the control to 46.0 mg N L⁻¹ at the highest N rate. The highest observed mean dry biomass yield was 19.5 Mg ha⁻¹ at 392.4 kg N ha⁻¹, while upper rate means showed limited additional response. Apparent crop N recovery was approximately 71% at 235.4 kg N ha⁻¹ and 36–38% at the two highest rates. Estimated NO₃⁻-N leaching was 9–222 kg N ha⁻¹ using 519 mm of measured drainage; an illustrative ±50% drainage scenario gave 111–333 kg N ha⁻¹ at the highest rate. These descriptive findings indicate increasing nitrate exposure at high N rates under this wet season’s conditions, but do not establish an economic optimum or a transferable N rate threshold.