Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale
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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR039451 |
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| author | Rasmus R. Frederiksen Søren E. Larsen Henrik Tornbjerg Hans Thodsen Brian Kronvang |
| author_facet | Rasmus R. Frederiksen Søren E. Larsen Henrik Tornbjerg Hans Thodsen Brian Kronvang |
| author_sort | Rasmus R. Frederiksen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3a46326acb504d9ca39a1792c4bd9bee |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-3a46326acb504d9ca39a1792c4bd9bee2025-08-20T02:44:24ZengWileyWater Resources Research0043-13971944-79732025-06-01616n/an/a10.1029/2024WR039451Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National ScaleRasmus R. Frederiksen0Søren E. Larsen1Henrik Tornbjerg2Hans Thodsen3Brian Kronvang4Department of Ecoscience Aarhus University Aarhus DenmarkDepartment of Ecoscience Aarhus University Aarhus DenmarkDepartment of Ecoscience Aarhus University Aarhus DenmarkDepartment of Ecoscience Aarhus University Aarhus DenmarkDepartment of Ecoscience Aarhus University Aarhus DenmarkAbstract 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.https://doi.org/10.1029/2024WR039451machine learningorganic nitrogenTONSHAPstream water qualityxgboost |
| spellingShingle | Rasmus R. Frederiksen Søren E. Larsen Henrik Tornbjerg Hans Thodsen Brian Kronvang Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale Water Resources Research machine learning organic nitrogen TON SHAP stream water quality xgboost |
| title | Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale |
| title_full | Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale |
| title_fullStr | Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale |
| title_full_unstemmed | Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale |
| title_short | Modeling Annual Total Organic Nitrogen Concentrations in Streams Using Machine Learning at National Scale |
| title_sort | modeling annual total organic nitrogen concentrations in streams using machine learning at national scale |
| topic | machine learning organic nitrogen TON SHAP stream water quality xgboost |
| url | https://doi.org/10.1029/2024WR039451 |
| work_keys_str_mv | AT rasmusrfrederiksen modelingannualtotalorganicnitrogenconcentrationsinstreamsusingmachinelearningatnationalscale AT sørenelarsen modelingannualtotalorganicnitrogenconcentrationsinstreamsusingmachinelearningatnationalscale AT henriktornbjerg modelingannualtotalorganicnitrogenconcentrationsinstreamsusingmachinelearningatnationalscale AT hansthodsen modelingannualtotalorganicnitrogenconcentrationsinstreamsusingmachinelearningatnationalscale AT briankronvang modelingannualtotalorganicnitrogenconcentrationsinstreamsusingmachinelearningatnationalscale |