Numerical Modeling of Flash Flood Risk Mitigation and Operational Warning in Urban Areas

Abstract

This paper aims to demonstrate the research-to-application and operational use of numerical hydrologic and hydraulic modeling to (a) quantify potential flash flood risks in small urban communities with high spatial resolution; (b) assess the effectiveness of possible flood mitigation measures appropriate for such communities; and (c) construct an effective operational urban flash flood warning system. The analysis is exemplified through case studies pertaining to a small community with dense housing and steep terrain in Tegucigalpa, Honduras, through numerical simulations with a customized self-contained hydrologic and hydraulic modeling software. Issues associated with limited data and the corresponding modeling are discussed. In order to simulate the extreme scenarios, 24-h design storms with return periods from 1 to 100 years with distinctive temporal and spatial distributions were constructed using both daily and hourly precipitation for each month of the rainy season (May–October). Four flood mitigation plans were examined based on natural channel revegetation and the installation of gabion dams with detention basins. Due to limitations arising from the housing layout and budgets, a feasible plan to implement both measures in selected regions, instead of all regions, is recommended as one of the top candidates from a cost-to-performance ratio perspective. Numerical modeling, customized for the conditions of the case study, is proven to be an effective and robust tool to evaluate urban flood risks and to assess the performance of mitigation measures. The transition from hydrologic and hydraulic modeling to an effective urban flash warning operational system is demonstrated by the regional Urban Flash Flood Warning System (UFFWS) implemented in Istanbul, Turkey. With quality-controlled remotely sensed precipitation observations and forecast data, the system generates forcing in the hydrologic and hydraulic modeling network to generate both historical and forecast flow to assist forecasters in evaluating urban flash flood risks.