Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale

作者：Rasmus Rumph Frederiksen, Søren Erik Larsen, Henrik Tornbjerg, Hans Thodsen, Brian Kronvang · 发表于：Water Resources Research · 年份：2025 · DOI：10.1029/2024wr039451 · 被引用次数：4 · 研究领域：Hydrological Forecasting Using AI、Air Quality Monitoring and Forecasting、Water Quality Monitoring Technologies

Abstract Understanding and quantifying total organic nitrogen (TON) concentrations in streams and their spatial variation is essential for accurately assessing their importance for total nitrogen (TN) loadings to coastal waters and the possible sources of TON in the landscape. Total organic nitrogen constitutes almost 20% of the TN riverine loadings to Danish coastal waters. We used environmental monitoring data from 390 stations across Denmark to calculate indirectly measured annual average TON concentrations using a wide range of predictor variables. We then trained a machine learning model to predict spatially distributed average annual TON concentrations in Danish streams, achieving a mean error of 0 mg L −1 and a root‐mean‐squared error of 0.20 mg L −1 . The mean annual predicted (measured) TON concentrations in Danish streams were 0.84 (0.70) mg L −1 , with a standard deviation of 0.36 (0.31) mg L −1 . The model is primarily driven by mean elevation and the percentages of agricultural land, tile‐drained areas, lakes and carbon‐enriched soils in the catchment. The developed model contributes to our understanding of the spatial variation in annual TON concentrations in streams at a national scale, supporting our understanding of processes driving nitrogen cycling.