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|Title:||Evaluation of the Difference of Eight Model Applications to Assess Diffuse Annual Nutrient Losses from Agricultural Land|
|Authors:||SCHOUMANS O.; SILGRAM M.; WALVOORT D.; GROENENDIJK P.; BOURAOUI Faycal; ANDERSEN H.; LO PORTO A.; REISSER H.; LE GALL G.; ANTHONY S.; ARHEIMER B.; JOHNSSON H.; PANAGOPOULOS Y.; MIMIKOU M.; ZWEYNERT U.; BEHRENDT H.; BARR A.|
|Citation:||JOURNAL OF ENVIRONMENTAL MONITORING vol. 11 no. 3 p. 540-553|
|Publisher:||ROYAL SOC CHEMISTRY|
|Type:||Articles in periodicals and books|
|Abstract:||The capability of eight nutrient models to predict annual nutrient losses (nitrogen and phosphorus) at catchment scale have been studied in the EUROHARP project. The methodologies involved in these models differ profoundly in their complexity, level of process representation and data requirements. This evaluation is focused on model performance in three core catchments: the Vansjø-Hobøl (Norway), the Ouse (Yorkshire, UK) and the Enza (Italy). These three different model applications have been evaluated by comparing calculated annual nutrient loads (total N or nitrate and total P), based on observed flow and total nitrogen or nitrate and total phosphorus concentrations, and the annual nutrient loads that were simulated by the eight nutrient models. Four statistics have been applied for this purpose: the root mean squared error (RMSE), the mean absolute error (MAE), the mean error (ME), and Nash¿Sutcliffe¿s model efficiency (NS). The results show that all model approaches can predict the calculated annual discharges. Depending on the observed statistics (RMSE, MAE, ME and NS) the scores of the model application differed, therefore no overall ¿best model¿ could be identified. Although the water and nutrient loads from (sub)catchments can be predicted, the modelled pathways of nutrients within agricultural land and the nutrient losses to surface waters from agricultural land vary among the catchments and among those model approaches which are able to make this distinction.|
|JRC Institute:||Institute for Environment and Sustainability|
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