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Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems
K. Beven, J. Freer, B. Hankin, K. Schulz (2000)
The use of generalised likelihood measures for uncertainty estimation in high order models of environmental systems
A. Kemp, V. Barnett, K. Turkman (1995)
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Q. Duan, S. Sorooshian, V. Gupta (1992)
Effective and efficient global optimization for conceptual rainfall‐runoff modelsWater Resources Research, 28
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Evaluation of predictive uncertainty in non-linear hydrological models using a Bayesian approach
P. Gineste, C. Puech, P. Mérot (1998)
Radar remote sensing of the source areas from the Coët‐Dan catchmentHydrological Processes, 12
Beven Beven
On uniqueness of place and process representations in hydrologyHydrology and Earth Systems Science, 4
K. Beven (1997)
TOPMODEL : a critique.Hydrological Processes, 11
J. Piñol, K. Beven, J. Freer (1997)
MODELLING THE HYDROLOGICAL RESPONSE OF MEDITERRANEAN CATCHMENTS, PRADES, CATALONIA. THE USE OF DISTRIBUTED MODELS AS AIDS TO HYPOTHESIS FORMULATIONHydrological Processes, 11
J. Fisher, K. Beven (1996)
Modelling of streamflow at Slapton Wood using TOPMODEL within an uncertainty estimation framework.
K. Beven (1993)
Prophecy, reality and uncertainty in distributed hydrological modellingAdvances in Water Resources, 16
(1999)
Subsurface runoff generation on a slope
J. Refsgaard (1997)
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(2001)
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Hydrograph modelling strategies
R. Lamb, K. Beven (1997)
Using interactive recession curve analysis to specify a general catchment storage modelHydrology and Earth System Sciences, 1
J. Freer, K. Beven, B. Ambroise (1996)
Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE ApproachWater Resources Research, 32
G. Kuczera (1983)
Improved parameter inference in catchment models: 2. Combining different kinds of hydrologic data and testing their compatibilityWater Resources Research, 19
M. Kirkby, K. Beven (1979)
A physically based, variable contributing area model of basin hydrology, 24
Sarka Blazkov, K. Beven (1997)
Flood frequency prediction for data limited catchments in the Czech Republic using a stochastic rainfall model and TOPMODELJournal of Hydrology, 195
David Cameron, K. Beven, J. Tawn, S. Blazková, P. Naden (1999)
Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty)Journal of Hydrology, 219
(1996)
Bilan Model
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Fitting Methods for Conceptual Catchment ModelsJournal of Hydraulic Engineering, 97
(2001)
The effect of deforestation on a hydrological balance of a small catchment
R. Lamba, K. Bevenb, S. Myrabøc (1998)
Use of spatially distributed water table observations to constrain uncertainty in a rainfall – runoff model
C. Brun (1990)
Mapping saturated areas with a helicopter-borne C-bandWater Resources Research, 26
K. Beven, A. Binley (1992)
The future of distributed models: model calibration and uncertainty prediction.Hydrological Processes, 6
J. Nash, J. Sutcliffe (1970)
River flow forecasting through conceptual models part I — A discussion of principles☆Journal of Hydrology, 10
P. Quinn, K. Beven, R. Lamb (1995)
The in(a/tan/β) index:how to calculate it and how to use it within the topmodel frameworkHydrological Processes, 9
In press b. On uniqueness of place and process representations in hydrology
C. Brun, R. Bernard, D. Vidal-Madjar, C. Gascuel-Odoux, P. Mérot, J. Duchesne, H. Nicolas (1990)
Mapping saturated areas with a helicopter-borne C band scatterometerWater Resources Research, 26
S. Franks, P. Gineste, K. Beven, P. Mérot (1998)
On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration processWater Resources Research, 34
This study uses field observations of the extent of saturated area over limited areas of the small Uhlirska catchment (1·87 km2) in the Czech Republic in calibrating the parameters of a version of TOPMODEL. The field information is used within the GLUE methodology, which involves evaluating many different randomly chosen parameter sets within the chosen model structure. The different parameter sets are evaluated on performance in both discharge prediction and prediction of the observed saturated areas using appropriate likelihood measures. The results show that the saturated area information results in a strong constraint of the transmissivity parameter of the model, but that the other parameters show good fits across most of the range over which they are sampled. Quite different posterior distributions for the transmissivity parameter are found for the two different years of data used in conditioning the model. The effect on the prediction bounds for stream discharges is much less, perhaps because the transmissivity parameter combines with different values of the other parameters in the two years to capture the dominant modes of discharge response of the catchment. Copyright © 2002 John Wiley & Sons, Ltd.
Hydrological Processes – Wiley
Published: Feb 15, 2002
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