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Institute of Hydraulic Engineering
Department of Hydrology and GeohydrologyProf. Dr. rer. nat. Dr.-Ing. András Bárdossy
Pfaffenwaldring 61, 70569 Stuttgart, Germany www.iws.uni-stuttgart.de
Addressing several issues like sedimentation, water quality, conservation measures,
environmental and geomorphologic studies etc, needs the prediction of erosion patterns
which, in turn, needs runoff source areas within the catchment. Several modeling
alternatives exist, all with certain potential and limitations. The use of a distributed rainfall-
runoff model is basis for identification of such areas. Such model, even in case of physically-
based, needs prior calibration of some or many parameters. The optimization and prediction
capability of those distributed models is being assessed based on their ability to correctly
predict lumped hydrograph at watershed outlet.
The presented work aims to show the unreasonable consequences that we have
encountered while calibrating and applying a distributed rainfall runoff model. The model
used was WaSiM-ETH, a physically based spatially distributed rainfall-runoff model. At first
to apply for events in a small agricultural catchment in central Belgium, its 11 parameters
were calibrated using Gauss-Marcquardt-Levenberg algorithm. As is the trend, the
calibration was done with objective function of minimizing prediction errors in the catchment
outlet. Very nice results were obtained with closely matching hydrographs and Nash-Sutcliffe
efficiency as high as 0.97 in calibration and 0.81 in validation. But when the modeled runoff
source areas within the catchment were investigated, a very much unrealistic patterns were
observed with almost all the runoff are coming from a small isolated patch in the catchment.
Further we calibrated the model using more accepted Schuffle Complex Evolution (SCE-UA)
algorithm and, in addition, sets of equally well performing parameter vector are estimated
based on Tukey’s half space depth function. They are applied to a bigger Rems catchment in
southern Germany where also we found that very good model performance were not
accompanied by the reasonable runoff patterns within the catchment.
Very good prediction of a distributed rainfall runoff model but for all wrong reasons
Thapa, P.K.; Bárdossy, A. [email protected]
Conclusions
� Well performing parameter sets may lead to good results with high model efficiency but these can be for all the wrong reasons
� Better hydrographs prediction by models do not guarantee better hydrology representation by them.
References
Bardossy A. & Singh S. (2008): Robust estimation of hydrological parameters. HESSD (in press)
Doherty J. PEST. (2002): Model-Independent Parameter Estimation,Watermark Numerical Computing, Australia, 2002.
Duan, Q., Sorooshian, S. and Gupta, V.K. (1994): Optimal use of the SCE-UA global optimization method for calibrating catchment models, J. Hydrol., 158, 265-284.
Oost KV, Govers G, Cerdan O, Thaure D, Rompaey AV, Steegena A, Nachtergaele J, Takken I & Poesen J. (2005): Spatially distributed data for erosion model calibration and validation: The Ganspoel and Kinderveld datasets. Catena 61, 105– 121.
Schulla J. & Jasper K. (1999, 2006, 2007): Model Description WASIM-ETH. Institut für Atmosphäre und Klima. ETH Zürich.
Some Results:
Court of Miracles, A scientific workshop, Paris, June 2008
Catchment Area: 111 ha
Mean annual precipitation: 740 mm
LU: farmland; scarce built-up areas
Soil: loess (Haplic Luvisols)
Ganspoel catchment (central Belgium)
3.39o
Slope
+3.17o
21.62o
Slope
41.8o
+6.4o
-6.4o
8.7o
Rems catchment(southern Germany)
Catchment Area: 580 sq. km
Mean annual precipitation: 900 mm
LU: agriculture; forest; built-up areas
Soil: light sandy on highs; loamy clay on lows
Study Area
Model used: Water balance & flow Simulation Model [WaSiM-ETH]
Parameters estimation: . Gauss-Marcquardt-Levenberg method [PEST package]
. Schuffle complex evolution algorithm [SCE-UA]
. Use of statistical depth function [Tukey‘s Half space depth]
The model is calibrated for an event in Ganspoel catchment and very well matching of hydrograph is obtained. But the spatial runoff within the catchment producing this
hydrograph is completly unrealistic
Gauge 1 [Schwäbisch Gmünd]
0
5
10
15
20
25
30
1/1/1993 2/20/1993 4/11/1993 5/31/1993 7/20/1993 9/8/1993 10/28/1993 12/17/1993
time [d]
Dis
ch
arg
e [
m3/s
]
Obs.
Sim
lin. NS: 0.90
log NS: 0.70
Gauge 2 [Haubersbronn]
0
2
4
6
8
10
12
14
1/1/1993 2/20/1993 4/11/1993 5/31/1993 7/20/1993 9/8/1993 10/28/1993 12/17/1993
time [d]
Dis
ch
arg
e [
m3/s
]
Obs.
Sim
lin. NS: 0.77
log NS: 0.66
Gauge 3 [Schorndorf]
0
20
40
60
80
100
120
1/1/1993 2/20/1993 4/11/1993 5/31/1993 7/20/1993 9/8/1993 10/28/1993 12/17/1993
time [d]
Dis
ch
arg
e [
m3/s
]
Obs.
Sim
lin. NS: 0.85
log NS: 0.60
Gauge 4 [Neustadt]
0
20
40
60
80
100
120
140
1/1/1993 2/20/1993 4/11/1993 5/31/1993 7/20/1993 9/8/1993 10/28/1993 12/17/1993
time [d]
Dis
charg
e [m
3/s
]
Obs.
Sim
lin. NS: 0.85
log NS: 0.77
The model is calibrated for each subcatchment individually in Rems catchment and
reasonably matching hydrographs are obtained. But the runoff patterns within the catchment is again unacceptable as they vary highly among subcatchments.
SCE UA parameters
The model is calibrated for identical parameter set for all subcatchments in Rems catchment using SCE-UA algorithm. 11 different parameter sets performing equally
good are also obtained using depth function. All of them produce resonable model performance and at least, uniform runoff patterns within catchment. But the total
surface runoff from the catchment is highly varying with different parameter sets.
Introduction:
Pest parametersSCEUA parameters
Parameter set 2