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International Journal of River Basin Management Volume 6, 2008 - Issue 2
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Research Papers

Data‐driven approaches for estimating uncertainty in rainfall‐runoff modelling

Durga Lal Shrestha Institute for Water Education, UNESCO‐IHE, Delft, The Netherlands
&
Dimitri P. Solomatine Institute for Water Education, UNESCO‐IHE, Delft, The Netherlands; Water Resources Section, Delft University of Technology, The Netherlands
Pages 109-122 | Received 07 May 2007, Accepted 18 Dec 2007, Published online: 23 Aug 2010
 
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Abstract

This paper presents application of the method for assessment of predictive uncertainty of a rainfall‐runoff model using data‐driven modelling techniques. Historical model errors which are mismatch between observed and simulated values of runoff by rainfall‐runoff models are assumed to be indicators of total model uncertainty. Uncertainty is represented by the model prediction interval. Different data‐driven techniques used to determine the interval are compared. The method is tested to estimate uncertainty of runoff simulations by conceptual rainfall‐runoff model with application to Brue catchment in the United Kingdom; it is also compared with other uncertainty estimation methods.

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