A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Uncertainty in flood estimation
The objective of this contribution is to form a clear picture of uncertainties we encounter in flood estimation, including both real-time flood forecasting and simulation for flood risk estimation. In simulation, we prefer the thesis of equifinality to obtain global optima. Many models producing acceptable simulations can be considered as multiple working hypotheses about the system process representations. Some of those hypotheses might later be confirmed or rejected, given additional data. In GLUE (Generalized Likelihood Uncertainty Estimation) the parameter sets are sampled randomly from physically reasonable ranges, often using uniform sampling where there is no strong information about prior expectations of parameter values. The parameter sets are then used to generate different realizations of the model outputs, which are then evaluated using some criteria (measures of likelihood) to provide a weight associated with each parameter set. Likelihood here is used in a much broader sense than in statistical inference. If some limits of effective observation error can be specified prior to running any simulations, models predicting outside of those limits can then be rejected as non-behavioural. Thus, any model evaluation of this type needs to take account of the multiple sources of model error more explicitly. This, however, is difficult for realistic cases. The procedure for the GLUE methodology is illustrated in examples. Usability for practical problems is suggested and future development is outlined.
Uncertainty in flood estimation
The objective of this contribution is to form a clear picture of uncertainties we encounter in flood estimation, including both real-time flood forecasting and simulation for flood risk estimation. In simulation, we prefer the thesis of equifinality to obtain global optima. Many models producing acceptable simulations can be considered as multiple working hypotheses about the system process representations. Some of those hypotheses might later be confirmed or rejected, given additional data. In GLUE (Generalized Likelihood Uncertainty Estimation) the parameter sets are sampled randomly from physically reasonable ranges, often using uniform sampling where there is no strong information about prior expectations of parameter values. The parameter sets are then used to generate different realizations of the model outputs, which are then evaluated using some criteria (measures of likelihood) to provide a weight associated with each parameter set. Likelihood here is used in a much broader sense than in statistical inference. If some limits of effective observation error can be specified prior to running any simulations, models predicting outside of those limits can then be rejected as non-behavioural. Thus, any model evaluation of this type needs to take account of the multiple sources of model error more explicitly. This, however, is difficult for realistic cases. The procedure for the GLUE methodology is illustrated in examples. Usability for practical problems is suggested and future development is outlined.
Uncertainty in flood estimation
Blazkova, S. (author) / Beven, K. (author)
Structure and Infrastructure Engineering ; 5 ; 325-332
2009-08-01
8 pages
Article (Journal)
Electronic Resource
Unknown
Floods , Uncertainty , GLUE , TOPMODEL
Uncertainty in flood estimation
| Online Contents | 2009
Uncertainty in flood estimation
Taylor & Francis Verlag | 2009
Uncertainty in flood estimation
Online Contents | 2009
Uncertainty in flood damage estimation
| Online Contents | 2008
Uncertainty in flood damage estimation
| Online Contents | 2008