Optimizing Nitrogen Management in Food and Energy Production and Environmental Protection: 2nd International Nitrogen Conference 2001View this Special Issue
Research Article | Open Access
Mona Radwan, Alaa El-Sadek, Patrick Willems, Jan Feyen, Jean Berlamont, "Modeling of Nitrogen in River Water Using a Detailed and a Simplified Model", The Scientific World Journal, vol. 1, Article ID 207295, 7 pages, 2001. https://doi.org/10.1100/tsw.2001.351
Modeling of Nitrogen in River Water Using a Detailed and a Simplified Model
To model catchment surface water quantity and quality, different model types are available. They vary from detailed physically based models to simplified conceptual and empirical models. The most appropriate model type for a certain application depends on the project objectives and the data availability. The detailed models are very useful for short-term simulations of representative events. They cannot be used for long-term statistical information or as a management tool. For those purposes, more simplified (conceptual or meta-) models must be used. In this study, nitrogen dynamics are modeled in a river in Flanders. Nitrogen sources from agricultural leaching and domestic point sources are considered. Based on this input, concentrations of ammonium (NH4-N) and nitrate (NO3-N) in the river water are modeled in MIKE 11 by taking into consideration advection and dispersion and the most important biological and chemical processes. Model calibration was done on the basis of available measured water quality data. To this detailed model, a more simplified model was calibrated with the objective to more easily yield long-term simulation results that can be used in a statistical analysis. The results show that the conceptual simplified model is 1800 times faster than the MIKE 11 model. Moreover the two models have almost the same accuracy. The detailed models are recommended for short-term simulations unless there are enough data for model input and model parameters. The conceptual simplified model is recommended for long-term simulations.