Uncertainty analysis in flood hazard assessment: hydrological and hydraulic calibrationThis article is one of a selection of papers published in this Special Issue on Hydrotechnical Engineering.

Interest in the actual estimation of the uncertainty affecting flood hazard assessments is increasing within the scientific community and among decision makers. Several works may be found in the hydrological and hydraulic literature listing the sources of uncertainty affecting the estimation of extreme flood levels. Here, a well-assessed uncertainty treatment procedure is applied to carry out a complete flood hazard assessment study to encompass both the hydrological and hydraulic components. In particular, the focus is on modeling the sources of uncertainty via a direct (for discharge) or inverse (for roughness hydraulic coefficient) approach. The results illustrate the relative importance of the hydraulic and hydrological uncertainty sources on the final uncertainty. The solution of the inverse problem for the calibration of the roughness coefficient proves useful for several reasons, including the quantification of model error.

[1]  Erich J. Plate,et al.  Flood risk and flood management , 2002 .

[2]  Ellen Wohl,et al.  Uncertainty in flood estimates associated with roughness coefficient , 1998 .

[3]  Peggy A. Johnson Uncertainty of Hydraulic Parameters , 1996 .

[4]  Richard D. Hey,et al.  Flow Resistance in Gravel-Bed Rivers , 1979 .

[5]  Jan Kyselý,et al.  A Cautionary Note on the Use of Nonparametric Bootstrap for Estimating Uncertainties in Extreme-Value Models , 2008 .

[6]  Michel Lang,et al.  The flood probability distribution tail: how heavy is it? , 2008 .

[7]  M. S. Horritt,et al.  Stochastic Modelling of 1-D Shallow Water Flows over Uncertain Topography , 2002 .

[8]  B. Gnedenko Sur La Distribution Limite Du Terme Maximum D'Une Serie Aleatoire , 1943 .

[9]  Reuven Y. Rubinstein,et al.  Simulation and the Monte Carlo Method , 1981 .

[10]  André Robert,et al.  Boundary roughness in coarse-grained channels , 1990 .

[11]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice , 2002 .

[12]  Leonard M. Lye,et al.  a New Look at Flood Risk Determination , 1989 .

[13]  M. Kendall,et al.  The advanced theory of statistics , 1945 .

[14]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[15]  Laurens de Haan,et al.  On regular variation and its application to the weak convergence of sample extremes , 1973 .

[16]  Bernard Bobée,et al.  Towards operational guidelines for over-threshold modeling , 1999 .

[17]  B. Merz,et al.  Flood risk assessment and associated uncertainty , 2003 .

[18]  Stephen E. Darby,et al.  Effect of Riparian Vegetation on Flow Resistance and Flood Potential , 1999 .

[19]  F. Massey,et al.  Introduction to Statistical Analysis , 1970 .

[20]  Pietro BernardaraP. Bernardara,et al.  Uncertainties in 1D flood level modeling: Stochastic analysis of upstream discharge and friction parameter influence , 2008 .

[21]  R. Rosso,et al.  Uncertainty Assessment of Regionalized Flood Frequency Estimates , 2001 .

[22]  J. Hosking L‐Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics , 1990 .

[23]  Khaled H. Hamed,et al.  Flood Frequency Analysis , 1999 .

[24]  Bruno Merz,et al.  Separating natural and epistemic uncertainty in flood frequency analysis , 2005 .

[25]  M. Horritt A linearized approach to flow resistance uncertainty in a 2-D finite volume model of flood flow , 2006 .

[26]  Venkatesh Merwade,et al.  Uncertainty in Flood Inundation Mapping: Current Issues and Future Directions , 2008 .

[27]  R. Fisher,et al.  Limiting forms of the frequency distribution of the largest or smallest member of a sample , 1928, Mathematical Proceedings of the Cambridge Philosophical Society.

[28]  P. van Gelder,et al.  Distribution functions of extreme sea waves and river discharges , 2008 .

[29]  Gilbert Saporta,et al.  Probabilités, Analyse des données et statistique , 1991 .

[30]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

[31]  D. Cacuci Sensitivity theory for nonlinear systems. I. Nonlinear functional analysis approach , 1981 .

[32]  Dirk P. Kroese,et al.  Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.

[33]  Renzo Rosso,et al.  Statistics, Probability and Reliability for Civil and Environmental Engineers , 1997 .

[34]  A. Tarantola Inverse problem theory : methods for data fitting and model parameter estimation , 1987 .

[35]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[36]  G. Blöschl,et al.  Flood frequency regionalisation—spatial proximity vs. catchment attributes , 2005 .

[37]  R. Clarke,et al.  Uncertainty in the estimation of mean annual flood due to rating-curve indefinition , 1999 .

[38]  B. Hoadley Asymptotic Properties of Maximum Likelihood Estimators for the Independent Not Identically Distributed Case , 1971 .

[39]  Stefano Tarantola,et al.  Uncertainty in Industrial Practice , 2008 .

[40]  N. Gouta,et al.  A finite volume solver for 1D shallow‐water equations applied to an actual river , 2002 .

[41]  F. Pappenberger,et al.  Ignorance is bliss: Or seven reasons not to use uncertainty analysis , 2006 .

[42]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[43]  Alain Arneodo,et al.  Revisiting multifractality of high‐resolution temporal rainfall using a wavelet‐based formalism , 2005 .

[44]  E. Gaume,et al.  Study of the hydrological processes during the Avene river extraordinary flood (south of France): 6–7 October 1997 , 2003 .

[45]  G. Benito,et al.  Combined palaeoflood and rainfall-runoff assessment of mountain floods (Spanish Pyrenees) , 2001 .

[46]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[47]  Tarantola Stefano,et al.  Uncertainty in Industrial Practice - A Guide to Quantitative Uncertainty Management , 2008 .

[48]  E. de Rocquigny,et al.  Inverse probabilistic modelling of the sources of uncertainty: a non-parametric simulated-likelihood method with application to an industrial turbine vibration assessment , 2009 .