Use of spatially distributed water table observations to constrain uncertainty in a rainfall–runoff model

Use of spatially distributed water table observations to constrain uncertainty in a... The Generalised Likelihood Uncertainty Estimation (GLUE) methodology is used to investigate how distributed water table observations modify simulation and parameter uncertainty for the hydrological model TOPMODEL, applied to the Sæternbekken Minifelt catchment in Norway. Errors in simulating observed flows, continuously-logged borehole water levels and more extensive, spatially distributed water table depths are combined using Bayes' equation within a `likelihood measure' L . It is shown how the distributions of L for the TOPMODEL parameters change as the different types of observed data are considered. These distributions are also used to construct corresponding simulation uncertainty bounds for flows, borehole water levels, and water table depths within the spatially-extensive piezometer network. Qualitatively wide uncertainty bounds for water table simulations are thought to be consistent with the simplified nature of the distributed model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Water Resources Elsevier

Use of spatially distributed water table observations to constrain uncertainty in a rainfall–runoff model

Advances in Water Resources, Volume 22 (4) – Oct 20, 1998

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Publisher
Elsevier
Copyright
Copyright © 1998 Elsevier Science Ltd
ISSN
0309-1708
eISSN
1872-9657
D.O.I.
10.1016/S0309-1708(98)00020-7
Publisher site
See Article on Publisher Site

Abstract

The Generalised Likelihood Uncertainty Estimation (GLUE) methodology is used to investigate how distributed water table observations modify simulation and parameter uncertainty for the hydrological model TOPMODEL, applied to the Sæternbekken Minifelt catchment in Norway. Errors in simulating observed flows, continuously-logged borehole water levels and more extensive, spatially distributed water table depths are combined using Bayes' equation within a `likelihood measure' L . It is shown how the distributions of L for the TOPMODEL parameters change as the different types of observed data are considered. These distributions are also used to construct corresponding simulation uncertainty bounds for flows, borehole water levels, and water table depths within the spatially-extensive piezometer network. Qualitatively wide uncertainty bounds for water table simulations are thought to be consistent with the simplified nature of the distributed model.

Journal

Advances in Water ResourcesElsevier

Published: Oct 20, 1998

References

  • Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach
    Freer, J.; Beven, K.; Ambroise, B.
  • Automatic calibration of conceptual rainfall–runoff models: the question of parameter observability and uniqueness
    Sorooshian, S.; Gupta, V.K.

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