Assessment of sewer flooding model based on ensemble quantitative precipitation forecast

Summary Short duration rainfall intensity in Taiwan has increased in recent years, which results in street runoff exceeding the design capacity of storm sewer systems and causing inundation in urban areas. If potential inundation areas could be forecasted in advance and warnings message disseminated in time, additional reaction time for local disaster mitigation units and residents should be able to reduce inundation damage. In general, meteorological–hydrological ensemble forecast systems require moderately long lead times. The time-consuming modeling process is usually less amenable to the needs of real-time flood warnings. Therefore, the main goal of this study is to establish an inundation evaluation system suitable for all metropolitan areas in Taiwan in conjunction with the quantitative precipitation forecast technology developed by the Taiwan Typhoon and Flood Research Institute, which can be used for inundation forecast 24 h before the arrival of typhoons. In this study, information for the design capacity of storm sewer throughout Taiwan was collected. Two methods are proposed to evaluate the inundations: (a) evaluation based on the criterion of sewer capacity (CSC), and (b) evaluation based on the percentage of ensemble members (PEM). In addition, the probability of inundation is classified into four levels (high, medium, low, and no inundation). To verify the accuracy of the proposed system, Typhoon Megi and Typhoon Nanmadol were used as test cases. Four verification indices were adopted to evaluate the probability of inundation for metropolitan areas during typhoons. The inundation evaluation results basically match the observed data on flooding, which demonstrate that this flood evaluation system has an effective grasp on the probability of inundation for storm sewer systems.

[1]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[2]  Ashish Sharma,et al.  An application of artificial neural networks for rainfall forecasting , 2001 .

[3]  K. Emanuel.  Increasing destructiveness of tropical cyclones over the past 30 years , 2005, Nature.

[4]  S. F. Zevin.  A probabilistic approach to flash flood forecasting , 1986 .

[5]  Jean-Dominique Creutin,et al.  Evaluation of a simplified dynamical rainfall forecasting model from rain events simulated using a meteorological model , 1999 .

[6]  E. Grimit,et al.  Initial Results of a Mesoscale Short-Range Ensemble Forecasting System over the Pacific Northwest , 2002 .

[7]  Ming-Jen Yang,et al.  A Modeling Study of Typhoon Toraji (2001): Physical Parameterization Sensitivity and Topographic Effect , 2005 .

[8]  D. W. Reed.  A review of British flood forecasting practice , 1984 .

[9]  M. Borga,et al.  Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins , 2008 .

[10]  T. J. Chang,et al.  An integrated inundation model for highly developed urban areas. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[11]  Jutta Thielen,et al.  The european flood alert system EFAS - Part 2: statistical skill assessment of probabilistic and deterministic operational forecasts. , 2008 .

[12]  J. Dudhia.  A Nonhydrostatic Version of the Penn State–NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front , 1993 .

[13]  J. Salas,et al.  Forecasting of short-term rainfall using ARMA models , 1993 .

[14]  Shien Tsung Chen,et al.  Comparison of grey and phase-space rainfall forecasting models using a fuzzy decision method / Comparaison grâce à une méthode de décision floue des modèles gris et d’espace des phases pour la prévision de pluie , 2004 .

[15]  Yu,et al.  52. Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model , 2005 .

[16]  T. Mcnelley,et al.  Temperature dependence of , 1993, Metallurgical and Materials Transactions A.

[17]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[18]  Pao-Shan Yu,et al.  Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles Floues , 2005 .

[19]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[20]  Jean-Michel Tanguy,et al.  Réorganisation de l’annonce des crues en France , 2005 .

[21]  Albert S. Chen,et al.  Establishing the Database of Inundation Potential in Taiwan , 2006 .

[22]  Brian A. Colle,et al.  MM5 precipitation verification over the pacific northwest during the 1997-99 cool seasons , 2000 .

[23]  D. Havens.  Flash Flood Warning , 2000 .

[24]  Konstantine P. Georgakakos,et al.  Analytical results for operational flash flood guidance , 2006 .

[25]  Jen-Ping Chen,et al.  Temperature dependence of global precipitation extremes , 2009 .

[26]  J. Thielen,et al.  The European Flood Alert System – Part 1: Concept and development , 2008 .

[27]  Steven E. Koch,et al.  The Impact of Different WRF Model Physical Parameterizations and Their Interactions on Warm Season MCS Rainfall , 2005 .

[28]  Eric Gaume,et al.  Application of a distributed hydrological model to the design of a road inundation warning system for flash flood prone areas , 2010 .

[29]  J. Cunge,et al.  Practical aspects of computational river hydraulics , 1980 .

[30]  Waldemar Rebizant,et al.  Application of Artificial Neural Networks , 2011 .