A Community Dataset for Large-Scale River Nitrogen Modeling in the United States

Water quantity predictions have advanced rapidly, driven by ready-to-use benchmarks such as CAMELS (Catchment Attributes and Meteorology for Large-Sample Studies). In contrast, large-scale water quality predictions, especially for nutrients, lag behind due to a lack of comparable datasets. Existing water quality datasets face four major limitations: (1) underrepresentation of human-impacted systems, (2) absence of nutrient inputs, (3) incomplete watershed metadata, and (4) sparse monitoring coverage. To address these gaps, we developed IWAND-Nitrogen (Integrated Watershed Attributes and Nutrient Data for Nitrogen) for the contiguous United States. IWAND-Nitrogen integrates 574,767 nitrate records from 1,877 catchments (median 272 samples per gauge; IQR = 231–346) with at least 200 measurements each from 1980–2023, linked with 93 watershed attributes, eight nitrogen input forcings (both basin-averaged and gridded), and eleven climate forcings. Compared to existing benchmarks such as CAMELS-Chem, IWAND-Nitrogen complements prior efforts by extending spatial/temporal coverage and enhancing representation across anthropogenic gradients. IWAND-Nitrogen aims to serve as a nutrient community benchmark, advancing from model development to new insights from catchment to national scales.