Mathematical model to identify nitrogen variability in large rivers
Article first published online: 7 JUN 2010
Copyright © 2010 John Wiley & Sons, Ltd.
River Research and Applications
Volume 27, Issue 10, pages 1216–1236, December 2011
How to Cite
Ani, E.-C., Hutchins, M., Kraslawski, A. and Agachi, P. S. (2011), Mathematical model to identify nitrogen variability in large rivers. River Res. Applic., 27: 1216–1236. doi: 10.1002/rra.1418
- Issue published online: 21 NOV 2011
- Article first published online: 7 JUN 2010
- Manuscript Accepted: 19 APR 2010
- Manuscript Revised: 16 FEB 2010
- Manuscript Received: 14 OCT 2009
- nutrient dynamics;
- pollutant transformation;
- pollutant transport modelling;
- river water quality;
- transport parameter model;
- dispersion coefficient;
- river Swale
The river Swale in Yorkshire, northern England has been the subject of many studies concerning water quality. This paper builds on existing data resources and previous 1D river water quality modelling applications at daily resolution (using QUESTOR) to provide a different perspective on understanding pollution, through simulation of the short-term dynamics of nutrient transport along the river. The two main objectives are (1) building, calibration and evaluation of a detailed mathematical model (Advection-Dispersion Model: ADModel), for nutrient transport under unsteady flow conditions and (2) the development of methods for estimating key parameters characterizing pollutant transport (velocity, dispersion coefficient and transformation rates) as functions of hydrological parameters and/or seasonality.
The study of ammonium and nitrate has highlighted temporal variability in processes, with maximum nitrification and denitrification rates during autumn. Results show that ADModel is able to predict the main trend of measured concentration with reasonable accuracy and accounts for temporal changes in water flow and pollutant load along the river. Prediction accuracy could be improved through more detailed modelling of transformation processes by taking into account the variability of factors for which existing data were insufficient to allow representation. For example, modelling indicates that interactions with bed sediment may provide an additional source of nutrients during high spring flows. Copyright © 2010 John Wiley & Sons, Ltd.