An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA

Abstract

Urban flooding is a frequent problem affecting cities all over the world. The problem is more significant now that the climate is changing and urbanization trends are increasing. Various, physical hydrological models such as the Environmental Protection Agency Storm Water Management Model (EPA SWMM), MIKE URBAN-II and others, have been developed to simulate flooding events in cities. However, they require high accuracy mapping and a simulation of the underground storm drainage system. Sadly, this capability is usually not available for older or larger so-called megacities. Other hydrological model types are classified in the semi-physical category, like Cellular Automata (CA), require the incorporation of very fine resolution data. These types of data, in turn, demand massive computer power and time for analysis. Furthermore, available forecasting systems provide a way to determine total rainfall during extreme events, but they do not tell us what areas will be flooded. This work introduces an urban flooding tool that couples a rainfall-runoff model with a flood map database to expedite the alert process and estimate flooded areas. A 0.30-m Lidar Digital Elevation Model (DEM) of the study area (in this case Manhattan, New York City) is divided into 140 sub-basins. Several flood maps for each sub-basin are generated and organized into a database. For any forecasted extreme rainfall event, the rainfall-runoff model predicts the expected runoff volume at different times during the storm interval. The system rapidly searches for the corresponding flood map that delineates the expected flood area. The sensitivity analysis of parameters in the model show that the effect of storm inlet flow head is approximately linear while the effects of the threshold infiltration rate, the number of storm inlets, and the storm inlet flow reduction factor are non-linear. The reduction factor variation is found to exhibit a high non-linearity variation, hence requiring further detailed investigation.

Comprehensive Flood Risk Assessment for Wastewater Treatment Plants under Extreme Storm Events: A Case Study for New York City, United States

“…This study uses GIS and NYC's digital elevation model (DEM) (1 m resolution) to create 889 sub-basins for NYC. The study conducted by Al-Suhili et al showed that increasing the number of sub-basins in an urbanized area can improve the accuracy of flood inundation [32]. This study also adopted this method and completed the division of sub-basins through GIS.…”

Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica

“…For islands like Jamaica, where steep topography abides the production of heavy rainfall, large amounts of precipitation occur over high elevations that increase as wind speed increases [17]. Furthermore, it is expected that global climate change will impact regional precipitation patterns and thus influence the recurrence of associated risks [21][22][23]. Moreover, due to limited natural and human resources, topography, population size, and economy, small Caribbean islands like Jamaica have been identified as most vulnerable to climate change and extremes [15,24].…”

Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis

“…In the last decades, large efforts have been focused on the modelling of urban floods, with different aims such as hazard mapping, risk communication, probabilistic approaches, design and management of green/grey risk mitigation and climate adaptation solutions (Al-Suhili et al, 2019;Bates et al, 2010;Costabile, Costanzo, De Lorenzo, et al, 2021;Di Baldassarre et al, 2010;Hammond et al, 2015;Nguyen et al, 2006). Many approaches and models exist that are able to tackle those goals with different degrees of detail, ranging from empirical/conceptual black-box models (e.g., Chen et al, 2009;Samela et al, 2020) to rigorous approaches solving mono-, two-or threedimensional problems (Bulti & Abebe, 2020;Costabile, Costanzo, De Lorenzo, & Macchione, 2020;Di Baldassarre et al, 2010;Henonin et al, 2013;Martins et al, 2017;Martins et al, 2018;Taskinen & Bruen, 2007).…”