Hydrology

Nitrate pollution in streams is a significant environmental challenge, affecting water quality and ecosystem health. Hydrological connectivity, the way water moves through the landscape, governs nitrate transport and transformation, but measuring it across entire catchments remains a challenge. Spatial patterns of soil moisture can provide key insights into catchment-scale hydrological connectivity, yet they have not been widely integrated into nitrate prediction models. In this study, we developed a deep learning framework trained on daily streamflow data and SMAP-HydroBlocks, a high-resolution satellite-based soil moisture data set. We tested whether deep learning models can predict nitrate concentrations using spatial soil moisture patterns and applied Explainable AI (XAI) methods to identify which areas contribute most to the model predictions. Our findings reveal that soil moisture patterns significantly improve nitrate predictions, with near-stream areas having the highest predictive power. This study highlights the potential of AI-driven approaches to provide spatially detailed insights into nitrate transport, which could support better water quality management and help identify nutrient pollution hotspots at fine spatial scales.

The Pantanal, the world’s largest tropical wetland, dried unusually over 2000-2021, with climate variability and land clearing in its headwaters both proposed as causes. Land-cover maps, gauged discharge and records of rainfall and evaporative demand back to 1951 were combined in a water balance analysis for the Upper Paraguay River Basin, reaching three decades further back than the land-cover record. We found that the runoff change across two wet and two dry periods, 96% traced to climate and only 4% to land cover, while wetland area shrank about 30% after 1985. The recent dry period was hotter and more arid than the 1965-1975 drought and may worsen rather than revert to humid conditions.

Brazil holds 16% of the world’s freshwater, yet its water systems are still not fully understood. To understand how rainfall and rivers interact across the country, we studied the hydrologic signatures of 735 catchments. Through catchment classification we identified six major water behavior groups, mostly based on how wet or dry the regions are. In drier areas, climate drives streamflow; in wetter ones, soil and land features matter more. This helps map and predict when and where floods or droughts are likely to happen and it also guides smarter water storage and usage in vulnerable regions. Ultimately, this research supports better planning for a changing climate.

Soil water is a major uncertainty in crop models, and correcting it against measured soil moisture can reduce uncertainty on yield gain estimation. We tested 19 site-years of corn and soybean at five US Midwest sites, 2011 to 2019, and tested such corrections using in-situ sensors and satellite soil moisture, including SMAP-HydroBlocks, a 30-meter product not previously used in a crop model. Sensors improved yield estimates in 63% of site-years, mostly under water stress. The satellite data constrained the soil profile far more weakly yet still cut yield error by a median 17.2%.