SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Ensemble modelling of nitrogen fluxes: Data fusion for a Swedish meso-scale catchment 
Authors:Exbrayat, J.-F., N.R. Viney, J. Seibert, S. Wrede, H.-G. Frede and L. Breuer 
Journal:Hydrology and Earth System Sciences 
Volume (Issue):14(12) 
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URL (non-DOI journals): 
Broad Application Category:hydrologic and pollutant 
Primary Application Category:model and/or data comparison 
Secondary Application Category:nitrogen cycling/loss and transport 
Watershed Description:281 km^2 Vattholma and 699 km^2 Savja Rivers, which are tributaries of the 2,000 km^2 Fyris River (which flows into Lake Ekoln, in a northern part of Lake Malaren which drains into the Baltic Sea) located in east central Sweden. 
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Abstract:Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a self developed tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden. Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure. In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which resulted in the RMSE decreasing by up to 90%.