Articles | Volume 26, issue 22
https://doi.org/10.5194/hess-26-5817-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5817-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Nov 2022
Research article |
| 17 Nov 2022
| 17 Nov 2022
How do inorganic nitrogen processing pathways change quantitatively at daily, seasonal, and multiannual scales in a large agricultural stream?
Department of Aquatic Ecosystem Analysis, Helmholtz Centre for Environmental Research – UFZ, Brueckstrasse 3a, 39114 Magdeburg, Germany
Chair of Hydrology and River Basin Management, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
Dietrich Borchardt
Department of Aquatic Ecosystem Analysis, Helmholtz Centre for Environmental Research – UFZ, Brueckstrasse 3a, 39114 Magdeburg, Germany
Michael Rode
Department of Aquatic Ecosystem Analysis, Helmholtz Centre for Environmental Research – UFZ, Brueckstrasse 3a, 39114 Magdeburg, Germany
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Strasse 24–25, 14476 Potsdam-Golm, Germany
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Cited
16 citations as recorded by crossref.
- Dynamic contribution by aquatic metabolism to carbon dioxide emissions from a low‐land mixed land‐use catchment H. Conroy et al. https://doi.org/10.1002/lno.70531
- Exploring the Potential of Mathematical Self-Purification Models Used for Evaluating Water Quality in Rivers F. García-Avila et al. https://doi.org/10.3390/earth6040131
- Development of The Navigator: A Lagrangian sensing system to characterize surface freshwater ecosystems A. Khandelwal et al. https://doi.org/10.1016/j.watres.2023.120577
- Summer drought enhances diurnal amplitude of CO2 in two German rivers of different size P. Leng et al. https://doi.org/10.1016/j.watres.2024.122870
- Stream restoration can reduce nitrate levels in agricultural landscapes X. Zhou et al. https://doi.org/10.1016/j.scitotenv.2023.164911
- Spatiotemporal contributions of hydrological components to streamflow and nitrogen load in Gyeongancheon Watershed, South Korea J. Park et al. https://doi.org/10.1007/s13201-026-02960-5
- Winter post-droughts amplify extreme nitrate concentrations in German rivers F. Saavedra et al. https://doi.org/10.1088/1748-9326/ad19ed
- Dynamic integration of water quality simulation into Pywr A. Slaughter et al. https://doi.org/10.1016/j.envsoft.2026.106965
- Multivariate assessment of riverine water quality in the Ouémé Delta (Benin): ecological suitability and adaptive monitoring design M. Djihouessi et al. https://doi.org/10.1007/s10661-026-15300-6
- Mechanistic prediction of dissolved oxygen concentrations in surface water considering the influence of salinity and temperature L. Farias et al. https://doi.org/10.2166/nh.2026.074
- Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest M. Arhab & J. Huang https://doi.org/10.3390/s23136057
- Revealing nitrate uptake and dispersion dynamics using high-frequency sensors and two-dimensional modeling in a large river system A. Zarnaghsh et al. https://doi.org/10.1016/j.advwatres.2024.104693
- Changes in water quality and ecosystem processes at extreme summer low flow of 2018 with high-frequency sensors J. Huang et al. https://doi.org/10.5194/hess-29-5835-2025
- System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science L. Schmidt et al. https://doi.org/10.1016/j.envsoft.2023.105809
- Mapping the spatial heterogeneity of nitrogen species in road dust through integrated multi-source remote sensing and interpretable machine learning M. Ashraf et al. https://doi.org/10.1016/j.envres.2026.124867
- Field-scale simulation of runoff and water quality in a paddy field: A study of novel RWQSPad model D. Lin et al. https://doi.org/10.1016/j.ecolmodel.2025.111042
16 citations as recorded by crossref.
- Dynamic contribution by aquatic metabolism to carbon dioxide emissions from a low‐land mixed land‐use catchment H. Conroy et al. https://doi.org/10.1002/lno.70531
- Exploring the Potential of Mathematical Self-Purification Models Used for Evaluating Water Quality in Rivers F. García-Avila et al. https://doi.org/10.3390/earth6040131
- Development of The Navigator: A Lagrangian sensing system to characterize surface freshwater ecosystems A. Khandelwal et al. https://doi.org/10.1016/j.watres.2023.120577
- Summer drought enhances diurnal amplitude of CO2 in two German rivers of different size P. Leng et al. https://doi.org/10.1016/j.watres.2024.122870
- Stream restoration can reduce nitrate levels in agricultural landscapes X. Zhou et al. https://doi.org/10.1016/j.scitotenv.2023.164911
- Spatiotemporal contributions of hydrological components to streamflow and nitrogen load in Gyeongancheon Watershed, South Korea J. Park et al. https://doi.org/10.1007/s13201-026-02960-5
- Winter post-droughts amplify extreme nitrate concentrations in German rivers F. Saavedra et al. https://doi.org/10.1088/1748-9326/ad19ed
- Dynamic integration of water quality simulation into Pywr A. Slaughter et al. https://doi.org/10.1016/j.envsoft.2026.106965
- Multivariate assessment of riverine water quality in the Ouémé Delta (Benin): ecological suitability and adaptive monitoring design M. Djihouessi et al. https://doi.org/10.1007/s10661-026-15300-6
- Mechanistic prediction of dissolved oxygen concentrations in surface water considering the influence of salinity and temperature L. Farias et al. https://doi.org/10.2166/nh.2026.074
- Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest M. Arhab & J. Huang https://doi.org/10.3390/s23136057
- Revealing nitrate uptake and dispersion dynamics using high-frequency sensors and two-dimensional modeling in a large river system A. Zarnaghsh et al. https://doi.org/10.1016/j.advwatres.2024.104693
- Changes in water quality and ecosystem processes at extreme summer low flow of 2018 with high-frequency sensors J. Huang et al. https://doi.org/10.5194/hess-29-5835-2025
- System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science L. Schmidt et al. https://doi.org/10.1016/j.envsoft.2023.105809
- Mapping the spatial heterogeneity of nitrogen species in road dust through integrated multi-source remote sensing and interpretable machine learning M. Ashraf et al. https://doi.org/10.1016/j.envres.2026.124867
- Field-scale simulation of runoff and water quality in a paddy field: A study of novel RWQSPad model D. Lin et al. https://doi.org/10.1016/j.ecolmodel.2025.111042
Saved (final revised paper)
Latest update: 11 Nov 2026
Short summary
In this study, we set up a water quality model using a 5-year paired high-frequency water quality dataset from a large agricultural stream. The simulations were compared with the 15 min interval measurements and showed very good fits. Based on these, we quantified the N uptake pathway rates and efficiencies at daily, seasonal, and yearly scales. This study offers an overarching understanding of N processing in large agricultural streams across different temporal scales.
In this study, we set up a water quality model using a 5-year paired high-frequency water...
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