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Department of Biological Sciences


Nitrate pollution of water bodies is a critical issue in many parts of the world because of its negative effects on aquatic ecosystem and human health. Effective management of pollution, such as the continuous or instantaneous release from point-sources, requires an understanding – with high spatial and temporal resolution – of how nitrate is dispersed and cycled within rivers. Nitrate sensing data show promise for this purpose, but their integration into numerical models is scarce; thus, questions remain regarding the necessary spatial grid size and temporal resolution required to resolve sensor readings. In this study, we developed an unsteady two-dimensional model to simulate nitrate transport, dispersal, and cycling along a 33-km stretch of the Kansas River (USA), following a strategic release of nitrogen from a decommissioned fertilizer plant. To validate modeled estimates of dispersion and uptake, we integrated 15-minute nitrate and temperature data from two aquatic sensors, one located proximal to the fertilizer release point and a second further downstream after complete lateral mixing. Model results at the site near to the contamination (0.4 km) were highly sensitive to river grid size and turbulent mixing, but insensitive to uptake. Results at the site far downstream of the contamination (31 km) were unaffected by grid size or mixing parameterization but were very sensitive to selection of uptake rate. High-frequency sensors allowed us to resolve diel variability in nitrate signals, which we incorporated into the model to improve performance and model realism. The 33-km study reach assimilated 14% of the total nitrate load in the river, or approximately half of what was contributed by the fertilizer release, during the two-month study period. Regarding nitrate cycling, modeled Cdiel/Cmax ranged from 0.04 to 0.11 whereas sensor observations showed much higher Cdiel/Cmax values of 0.11 to 0.25. Disagreements between data observations and model simulations in cycling are hypothesized to exist due to potential breakdown of the first-order rate kinetics. Together, our study shows the potential of combining numerical models and high-frequency data for a better understanding of the physical and biogeochemical processes that control nitrate dynamics in aquatic environments.

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© 2024 The Author(s). Published by Elsevier Ltd. Publisher’s version of record:

Publication Title

Advances in Water Resources

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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