SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Comparative predictions of discharge from an artificial catchment (Chicken Creek) using sparse data 
Authors:Hollander, H.M., T. Blume, H. Bormann, W. Buytaert, G.B. Chirico, J.-E. Exbrayat, D. Gustafsson, H. Hotzel, P. Kraft, C. Stamm, S. Stoll, G. Bloschl and H. Fluhler 
Journal:Hydrology and Earth System Sciences 
Article ID: 
URL (non-DOI journals):http://edoc.gfz-potsdam.de/gfz/get/14291/0/06ba6f0d8028563e614d6d52d2ca453b/14291.pdf 
Broad Application Category:hydrologic only 
Primary Application Category:model comparison 
Secondary Application Category:hydrologic assessment 
Watershed Description:6 ha Chicken Creek artificial watershed in open mining pit in Lusatia, Germany 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Ten conceptually different models in predicting discharge from the artificial Chicken Creek catchment in North-East Germany were used for this study. Soil texture and topography data were given to the modellers, but discharge data was withheld. We compare the predictions with the measurements from the 6 ha catchment and discuss the conceptualization and parameterization of the models. The predictions vary in a wide range, e.g. with the predicted actual evapotranspiration ranging from 88 to 579 mm/y and the discharge from 19 to 346 mm/y. The predicted components of the hydrological cycle deviated systematically from the observations, which were not known to the modellers. Discharge was mainly predicted as subsurface discharge with little direct runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. The actual evapotranspiration (AET) and the ratio between actual and potential ET was systematically overestimated by nine of the ten models. None of the model simulations came even close to the observed water balance for the entire 3-year study period. The comparison indicates that the personal judgement of the modellers was a major source of the differences between the model results. The most important parameters to be presumed were the soil parameters and the initial soilwater content while plant parameterization had, in this particular case of sparse vegetation, only a minor influence on the results.