Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.2166/nh.2026.074 |
Lizenz  |
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| Titel (primär) | Mechanistic prediction of dissolved oxygen concentrations in surface water considering the influence of salinity and temperature |
| Autor | Farias, L.; Martínez-López, N.; García, M.R.; Rode, M.
; Schäfer, R.B.; Yen Le, T.T. |
| Quelle | Hydrology Research |
| Erscheinungsjahr | 2026 |
| Department | ASAM |
| Seite von | nh2026074 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 |
| Keywords | freshwaters; global changes; multiple stressors; primary production; process-based modelling; water quality |
| Abstract | Dissolved oxygen (DO) is critical to aquatic life, and its concentration is one of the most important indicators of water quality. Therefore, reliable estimates of DO concentrations are essential. DO dynamics arise from interacting physical and biogeochemical processes that are strongly modulated by temperature and salinity; here, we developed a process-based model to predict DO concentrations that explicitly incorporates both drivers. Primary producers were accounted for as three functional groups, i.e., phytoplankton, periphyton, and macrophytes. The local sensitivity analysis indicated that the reaeration coefficient was the most important variable affecting the DO dynamics. We calibrated and validated the model using field measurements from a large central German stream for the period of January 2014 to December 2015 and January 2016 to December 2018, respectively. The DO concentration was well predicted (Nash–Sutcliffe efficiency above 0.75). The model also reflected seasonal patterns of phytoplankton concentrations. However, the surge during summer periods in 2017 and 2018 was not captured by the model. Calibration relied solely on DO and phytoplankton observations. Periphyton and macrophytes were included as additional process-based state variables; however, corresponding field data were unavailable for calibration or validation. Further subdivision of algal groups may improve model performance. |
| Farias, L., Martínez-López, N., García, M.R., Rode, M., Schäfer, R.B., Yen Le, T.T. (2026): Mechanistic prediction of dissolved oxygen concentrations in surface water considering the influence of salinity and temperature Hydrol. Res. , nh2026074 10.2166/nh.2026.074 |
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