Abstract
Pluvial floods are the most frequent natural hazard impacting urban cities because of extreme rainfall intensity within short duration. Owing to the complex interaction between rainfall, drainage systems and overland flow, pluvial flood warning poses a challenge for many metropolises. Although physical-based flood inundation models could identify inundated locations, hydrodynamic modeling is limited in terms of computational costs and sophisticated calibration. Thus, herein, a quick pluvial flood warning system using rainfall thresholds for central Taipei is developed. A tabu search algorithm is implemented with hydrological-analysis-based initial boundary conditions to optimize rainfall thresholds. Furthermore, a cross test is adopted to evaluate the effect of each rainfall event on rainfall threshold optimization. Urban sewer flood is simulated via hydrodynamic modeling with calibration using crowdsourced data. The locations and time of occurrence of pluvial floods can be obtained to increase the quality of observed data that dominate the accuracy of pluvial flood warning when using rainfall thresholds. The optimization process is a tabu search based on flood reports and observed data for six flood-prone districts in central Taipei. The results show that optimum rainfall thresholds can be efficiently determined through tabu search and the accuracy of the issued flood warnings can be significantly improved.
Highlights
Pluvial flooding can be a serious problem in cities with a densely populated urban area
Heavy rain 0614 in 2015 had the highest cumulative rainfall of all the events (105 mm/h; 188 mm/3 h); the values of the 3-h optimal rainfall threshold (86 mm) produced by the optimization without that event are the smallest among the other rainfall thresholds in the Zhongzheng district. These results show that the values of optimal rainfall thresholds (ORTs) would become small because small cumulative rainfalls from flood events limit the ability of tabu search (TS) to find appropriate ORTs
The improvement of warning time is from 1 to 3 h ahead and some flood events can be altered after adjusting the ORTs
Summary
Pluvial flooding can be a serious problem in cities with a densely populated urban area. In order to improve the flood warnings via rainfall thresholds, the optimal simulated by the SOBEK model, which is calibrated and validated with observed data. SOBEK is an integrated modeling framework for river, estuary and storm sewer eyewitness observations can be retrieved by searching and sorting keywords such as flooding water location, systems, which is capable of simulating hydrodynamics of flood inundation phenomena. The start time of floods is determined by Recently, exploiting naturaldivided disasterrainfall data has garnered a ORTs, great deal the hydrodynamic modeling crowdsourced based on completely events. The social media content based on eyewitness observations can be retrieved by searching and sorting keywords such as flooding location, time and water depth [43,44] This information source should be explored and implemented in a more effective way which improves the yield even more warning time
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