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 |
Year: | 2010 |
Journal: | Hydrology and Earth System Sciences |
Volume (Issue): | 14(12) |
Pages: | 2383-2397 |
Article ID: | |
DOI: | 10.5194/hess-14-2383-2010 |
URL (non-DOI journals): | |
Model: | SWAT |
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. |
Calibration Summary: | |
Validation Summary: | |
General Comments: | |
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%. |
Language: | English |
Keywords: | |