Hydrology 2018, 5, x; doi: FOR PEER REVIEW www.mdpi.com/journal/hydrology Article 1 An Urban Flash Flooding Alert Tool for Megacities – 2 Application for Manhattan Borough, New York City, 3 U.S.A 4 Rafea Al-Suhili 1 , Cheila Cullen 1,2 * and Reza Khanbilvardi 3 5 1 Civil Engineering Department, NOAA-Crest Center at the City College of New York, NY 10031, USA; 6 ralsuhili@ccny.cuny.edu 7 2 Chemistry, Earth & Environmental Sciences Department, Bronx Community College, New York, NY 8 10479, USA; ccullen@gradcenter.cuny.edu 9 3 Civil Engineering Department, NOAA-Crest Center at the City College of New York, NY 10031, USA; 10 khanbilvardi@ccny.cuny.edu 11 * Correspondence: ccullen@gradcenter.cuny.edu; Tel.: +1-718-289-5569 12 Received: date; Accepted: date; Published: date 13 14 Abstract: Climate change and the world trends on urbanization are driving attention to the urban 15 flooding phenomenon due to its increasing frequency. Available physical models for simulating 16 flooding events such as SWIM, MIKE Urban-II and others of their kind, require the capability of 17 accurately mapping or simulating the underground storm drainage system. Sadly, this capability is 18 usually not available for old or large, the so-called mega-cities, such as New York City. Other model 19 types in the semi-physical category, like Cellular Automata (CA) requires the incorporation of very 20 fine resolution data. These types of data in turn, demand massive computer power and time for 21 analysis. In the sense of available rainfall forecasting systems, they may provide a way to determine 22 the amount of rain during extreme rainfall events, but they do not provide insight regarding the 23 geographical area expected to be flooded. This work proposes to expedite the alert system process by 24 introducing an urban flooding alert tool that couples a rainfall-runoff model with a flood-level map 25 database. First, for any forecasted extreme rainfall event, the rainfall-runoff model estimates the 26 expected runoff volume at different times during the storm interval. Then, using Python and ArcGIS 27 10.4, the area of study, Manhattan borough, New York City, USA, is divided into 140 sub-catchments. 28 Starting at the lowest topographic value and with different level increments, several flood-level maps 29 at each location are developed. Subsequently, both systems are coupled so at each user’s query, the 30 appropriate map with the exact geographical flooding is selected. This tool presents the potential to 31 facilitate the urban flooding management process as geographical flooded areas would be known 32 before the storm. 33 Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the 34 article; yet reasonably common within the subject discipline.) 35 36 37 38