### Abstract

Original language | Undefined |
---|---|

Title of host publication | Flood risk management in Europe: Innovation in policy and practice |

Editors | Selina Begum, Marcel J.F. Stive, Jim W. Hall |

Place of Publication | Dordrecht, The Netherlands |

Publisher | Springer |

Pages | 293-310 |

Number of pages | 534 |

ISBN (Print) | 978-1-4020-4199-0 |

Publication status | Published - 2007 |

### Publication series

Name | Advances in natural and technological hazards research series |
---|---|

Publisher | Springer |

Number | 25 |

ISSN (Print) | 0921-030X |

ISSN (Electronic) | 1573-0840 |

### Keywords

- IR-61518
- METIS-214469

### Cite this

*Flood risk management in Europe: Innovation in policy and practice*(pp. 293-310). (Advances in natural and technological hazards research series; No. 25). Dordrecht, The Netherlands: Springer.

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*Flood risk management in Europe: Innovation in policy and practice.*Advances in natural and technological hazards research series, no. 25, Springer, Dordrecht, The Netherlands, pp. 293-310.

**Propagation of discharge uncertainty in a flood damage model for the Meuse river.** / Xu, YuePing; Booij, Martijn J.; Mynett, Arthur.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Propagation of discharge uncertainty in a flood damage model for the Meuse river

AU - Xu, YuePing

AU - Booij, Martijn J.

AU - Mynett, Arthur

PY - 2007

Y1 - 2007

N2 - Uncertainty analysis plays an important role in the decision- making process. It can give decision makers a complete idea of how different measures will affect the whole river system. Thus it helps decision makers to make a better choice among measures in a more systematic manner. In case of flood damage reduction projects, uncertainty analysis helps to evaluate the main decision criterion – expected annual damage. The aim of this paper is to investigate the propagation of discharge uncertainty, which is one of the main uncertainty sources in a damage model, into expected annual damage. The discharge uncertainty considered here includes model uncertainty (choice of different probability distributions) and sampling errors due to finite gauge record lengths. The calculated uncertainty in the discharge varies between 17 percent for a return period of 5 year and 30 percent for a return period of 1250 year. A first order method is used here to explore the role of discharge uncertainty in the expected annual damage model. The results from the damage model indicate that both model uncertainty and sampling errors are important, with the latter being somewhat more important. The Log-Pearson Type 3 gives a much smaller uncertainty range of the expected annual damage than the other three distribution models used. The uncertainty is aggravated when propagated into the damage results. The uncertainty in the damage reduces a great amount when the sample size increases to n=80. The results derived from the first order method in fact give two bounds of uncertainty, which is an overestimate in this case.

AB - Uncertainty analysis plays an important role in the decision- making process. It can give decision makers a complete idea of how different measures will affect the whole river system. Thus it helps decision makers to make a better choice among measures in a more systematic manner. In case of flood damage reduction projects, uncertainty analysis helps to evaluate the main decision criterion – expected annual damage. The aim of this paper is to investigate the propagation of discharge uncertainty, which is one of the main uncertainty sources in a damage model, into expected annual damage. The discharge uncertainty considered here includes model uncertainty (choice of different probability distributions) and sampling errors due to finite gauge record lengths. The calculated uncertainty in the discharge varies between 17 percent for a return period of 5 year and 30 percent for a return period of 1250 year. A first order method is used here to explore the role of discharge uncertainty in the expected annual damage model. The results from the damage model indicate that both model uncertainty and sampling errors are important, with the latter being somewhat more important. The Log-Pearson Type 3 gives a much smaller uncertainty range of the expected annual damage than the other three distribution models used. The uncertainty is aggravated when propagated into the damage results. The uncertainty in the damage reduces a great amount when the sample size increases to n=80. The results derived from the first order method in fact give two bounds of uncertainty, which is an overestimate in this case.

KW - IR-61518

KW - METIS-214469

M3 - Chapter

SN - 978-1-4020-4199-0

T3 - Advances in natural and technological hazards research series

SP - 293

EP - 310

BT - Flood risk management in Europe: Innovation in policy and practice

A2 - Begum, Selina

A2 - Stive, Marcel J.F.

A2 - Hall, Jim W.

PB - Springer

CY - Dordrecht, The Netherlands

ER -