Joint Monte Carlo and possibilistic simulation for flood damage assessment

A joint Monte Carlo and fuzzy possibilistic simulation (MC-FPS) approach was proposed for flood risk assessment. Monte Carlo simulation was used to evaluate parameter uncertainties associated with inundation modeling, and fuzzy vertex analysis was applied for promulgating human-induced uncertainty in flood damage estimation. A study case was selected to show how to apply the proposed method. The results indicate that the outputs from MC-FPS would present as fuzzy flood damage estimate and probabilistic-possibilistic damage contour maps. The stochastic uncertainty in the flood inundation model and fuzziness in the depth-damage functions derivation would cause similar levels of influence on the final flood damage estimate. Under the worst scenario (i.e. a combined probabilistic and possibilistic uncertainty), the estimated flood damage could be 2.4 times higher than that computed from conventional deterministic approach; considering only the pure stochastic effect, the flood loss would be 1.4 times higher. It was also indicated that uncertainty in the flood inundation modeling has a major influence on the standard deviation of the simulated damage, and that in the damage-depth function has more notable impact on the mean of the fitted distributions. Through applying MC-FPS, rich information could be derived under various α-cut levels and cumulative probabilities, and it forms an important basis for supporting rational decision making for flood risk management under complex uncertainties.

[1]  P. Bates,et al.  A simple raster-based model for flood inundation simulation , 2000 .

[2]  F. Pukelsheim.  The Three Sigma Rule , 1994 .

[3]  Paul D. Bates,et al.  Distributed Sensitivity Analysis of Flood Inundation Model Calibration , 2005 .

[4]  H. Moel,et al.  Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates , 2011 .

[5]  Honghai Qi,et al.  Simulation-based decision support system for flood damage assessment under uncertainty using remote sensing and census block information , 2011 .

[6]  Guohe Huang,et al.  Multistage scenario-based interval-stochastic programming for planning water resources allocation , 2009 .

[7]  J. Chatterton,et al.  The Benefits of Flood and Coastal Risk Management: A Handbook of Assessment Techniques , 2005 .

[8]  Bruno Merz,et al.  A Probabilistic Modelling System for Assessing Flood Risks , 2006 .

[9]  G. M. Zeng,et al.  Integrated Fuzzy-Stochastic Modeling of Petroleum Contamination in Subsurface , 2003 .

[10]  H. K. Nandalal,et al.  Flood risk analysis using fuzzy models , 2011 .

[11]  Keith Beven,et al.  Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data , 1998 .

[12]  Karen C. Rice,et al.  Evaluation of Bayesian Estimation of a Hidden Continuous-Time Markov Chain Model with Application to Threshold Violation in Water-Quality Indicators , 2012 .

[13]  Guohe Huang,et al.  An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution , 2008 .

[14]  Suphat Vongvisessomjai,et al.  Intangible Flood Damage Quantification , 2001 .

[15]  M. Aral,et al.  2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment , 2005 .

[16]  P. Bates,et al.  Predicting floodplain inundation: raster‐based modelling versus the finite‐element approach , 2001 .

[17]  Stefano Tarantola,et al.  Sensitivity Analysis as an Ingredient of Modeling , 2000 .

[18]  Guohe Huang,et al.  Characterizing Uncertainties Associated with Contaminant Transport Modeling through a Coupled Fuzzy-Stochastic Approach , 2009 .

[19]  I. Kelman,et al.  An overview of flood actions on buildings , 2004 .

[20]  B. Merz,et al.  Estimation uncertainty of direct monetary flood damage to buildings , 2004 .

[21]  Paul D. Bates,et al.  Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE , 2002 .

[22]  Keith Beven,et al.  Influence of uncertain boundary conditions and model structure on flood inundation predictions. , 2006 .

[23]  A. Saltelli,et al.  Sensitivity Anaysis as an Ingredient of Modeling , 2000 .

[24]  Zhongwei Liu,et al.  Using HSPF to Model the Hydrologic and Water Quality Impacts of Riparian Land-Use Change in a Small Watershed , 2011 .

[25]  D. I. Smith.  Flood damage estimation - A review of urban stage-damage curves and loss functions , 1994 .

[26]  B. Merz,et al.  Flood Risk Mapping At The Local Scale: Concepts and Challenges , 2007 .

[27]  Guohe Huang,et al.  Analysis of Solution Methods for Interval Linear Programming , 2011 .

[28]  Y. P. Li,et al.  Planning Regional Water Resources System Using an Interval Fuzzy Bi-Level Programming Method , 2010 .

[29]  Florian Pappenberger,et al.  Multi-method global sensitivity analysis of flood inundation models. , 2008 .

[30]  Guohe Huang,et al.  A fuzzy-Markov-chain-based analysis method for reservoir operation , 2012, Stochastic Environmental Research and Risk Assessment.

[31]  Menno Straatsma,et al.  The effect of uncertain floodplain roughness in hydrodynamic flood simulations , 2010 .

[32]  Suphat Vongvisessomjai,et al.  Estimation of flood damage cost for Bangkok , 1992 .

[33]  Viljoen,et al.  Loss functions for sugar-cane: Depth and duration of inundation as determinants of extent of flood damage , 2000 .

[34]  Matthew D. Wilson,et al.  Tracking the uncertainty in flood alerts driven by grand ensemble weather predictions , 2009 .

[35]  Slobodan P. Simonovic,et al.  A three‐dimensional fuzzy methodology for flood risk analysis , 2011 .

[36]  Keith Beven,et al.  Bayesian updating of flood inundation likelihoods conditioned on flood extent data , 2004 .