Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Uncertainty analysis of the rainfall runoff model LisFlood within the Generalized Likelihood Uncertainty Estimation (GLUE)
AU - Pappenberger, Florian
AU - Beven, Keith
AU - Roo, Ad De
AU - Thielen, Jutta
AU - Gouweleeuw, Ben
PY - 2004/1/1
Y1 - 2004/1/1
N2 - 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 (Mx 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.
AB - 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 (Mx 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.
KW - Flood
KW - GLUE
KW - LisFlood
KW - Meuse
KW - Rainfall runoff
KW - Uncertainty
U2 - 10.1080/15715124.2004.9635227
DO - 10.1080/15715124.2004.9635227
M3 - Journal article
AN - SCOPUS:85009578495
VL - 2
SP - 123
EP - 133
JO - International Journal of River Basin Management
JF - International Journal of River Basin Management
SN - 1571-5124
IS - 2
ER -