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. Pawan.Thapa@iws.uni-stuttgart.de

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