Uncertainty analysis of the rainfall runoff model LisFlood within the Generalized Likelihood Uncertainty Estimation (GLUE)

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Abstract
The uncertainty of the GIS based rainfall runoff model LisFlood has been investigated within the Generalized Likelihood Uncertainty Estimation (GLUE) framework. Multipliers for the saturated and unsaturated hydraulic conductivity, the porosity of the upper and lower soil layer, channel and overland flow roughness and the maximum percolation from upper to lower storages have been sampled within a Monte Carlo analysis from a uniform random distribution. With each parameter set the model has been computed with input for the 1995 flood event of the river Meuse situated in France, Belgium and The Netherlands. Eight gauging stations have been used for model evaluation by the Multicomponent Mapping (M x ) method. All parameters demonstrate equifinality and no parameter set could be classified as behavioural for all the evaluation datasets. However, the results of the prediction of uncertainty percentiles on the flow are very satisfactory and encouraging. The model did further show the capability to predict the uncertainty for estimating the exceedence of threshold levels, which can be used in flood warning decision making and river basin management.