Mathematical model to identify nitrogen variability in large rivers

• Published in: River research and applications Vol. 27; no. 10; pp. 1216 - 1236
• Main Authors: Ani, E.‐C, Hutchins, M, Kraslawski, A, Agachi, P. S
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.12.2011; Wiley
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; autumn; Biological and medical sciences; denitrification; dispersion coefficient; England; Fresh water ecosystems; Freshwater; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Mathematical models; Methods and techniques (sampling, tagging, trapping, modelling...); Modelling; nitrification; nitrogen; nutrient dynamics; nutrient transport; Nutrients; pollutant transformation; pollutant transport modelling; pollution load; prediction; QUESTOR; river Swale; river water; river water quality; Rivers; sediments; spring; Synecology; temporal variation; Transformations; transient flow; Transport; transport parameter model; Water quality; England; nutrient dynamics; river Swale; Transformation; transport parameter model; Rivers; Nitrogen; Modeling; Dispersion; pollutant transport modelling: river water quality; dispersion coefficient; Pollutant; Dynamics; Water quality; Nutrient; Stream; Mathematical model; Transport; pollutant transformation; River water; QUESTOR
• ISSN: 1535-1459; 1535-1467; 1535-1467
• DOI: 10.1002/rra.1418

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.