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B. Ambroise, J. Freer, K. Beven (1996)
APPLICATION OF A GENERALIZED TOPMODEL TO THE SMALL RINGELBACH CATCHMENT, VOSGES, FRANCEWater Resources Research, 32
Q. Duan, S. Sorooshian, V. Gupta (1992)
Effective and efficient global optimization for conceptual rainfall‐runoff modelsWater Resources Research, 28
P. Whitehead, G. Hornberger (1984)
Modelling algal behaviour in the river thamesWater Research, 18
J. Blackie, C. Eeles (1985)
Lumped catchment models
B. Ambroise, K. Beven, J. Freer (1996)
Toward a generalization of the TOPMODEL concepts:Topographic indices of hydrological similarityWater Resources Research, 32
Lawrence Joseph, P. Lee (1989)
Bayesian Statistics: An IntroductionThe American Statistician, 47
R. Spear, T. Grieb, Nong Shang (1994)
Parameter uncertainty and interaction in complex environmental modelsWater Resources Research, 30
R. Spear (1980)
Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysisWater Research, 14
K. Beven (1989)
Changing ideas in hydrology — The case of physically-based modelsJournal of Hydrology, 105
K. Beven (1993)
Prophecy, reality and uncertainty in distributed hydrological modellingAdvances in Water Resources, 16
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
G. Hornberger, K. Beven, B. Cosby, D. Sappington (1985)
Shenandoah Watershed Study: Calibration of a Topography‐Based, Variable Contributing Area Hydrological Model to a Small Forested CatchmentWater Resources Research, 21
G. Stephenson, R. Freeze (1974)
Mathematical simulation of subsurface flow contributions to snowmelt runoff, Reynolds Creek Watershed, IdahoWater Resources Research, 10
R. Grayson, I. Moore, T. McMahon (1992)
Physically based hydrologic modeling: 2. Is the concept realistic?Water Resources Research, 28
M. Kirkby, K. Beven (1979)
A physically based, variable contributing area model of basin hydrology, 24
This paper addresses the problem of evaluating the predictive uncertainty of TOPMODEL using the Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Ringelbach research catchment in the Vosges, France. The wide range of parameter sets giving acceptable simulations is demonstrated, and uncertainty bands are presented based on different likelihood measures. It is shown how the distributions of predicted discharges are non‐Gaussian and vary in shape through time and with discharge. Updating of the likelihood weights using Bayes equation is demonstrated after each year of record and it is shown how the additional data can be evaluated in terms of the way they constrain the uncertainty bands.
Water Resources Research – Wiley
Published: Jul 1, 1996
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