Taylor & Francis Group
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Propagation of radar rainfall uncertainties into urban pluvial flood modeling during the North American monsoon

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journal contribution
posted on 2021-09-22, 12:20 authored by Annika Hjelmstad, Ashish Shrestha, Margaret Garcia, Giuseppe Mascaro

Pluvial flooding in urban regions is a natural hazard that has been rarely investigated. Here, we evaluate the utility of three radar (Stage IV, Multi-Radar Multi-Sensor or MRMS, and gauge-corrected MRMS or GCMRM) quantitative precipitation estimates (QPEs) and the Storm Water Management Model (SWMM) hydrologic–hydraulic model to simulate pluvial flooding during the North American monsoon in Phoenix. We focus on an urban catchment of 2.38 km2 and, for four storms, we simulate a set of flooding metrics using the original QPEs and an ensemble of 100 QPEs characterizing radar uncertainty through a statistical error model. We find that Stage IV QPEs are the most accurate, while MRMS QPEs are positively biased and their utility to simulate flooding increases with the gauge correction done for GCMRMS. For all radar products, simulated flood metrics have lower uncertainty than QPEs as a result of rainfall–runoff transformation. By relying on extensive precipitation and basin datasets, this work provides useful insights for urban flood predictions.


This work was supported by the National Science Foundation (NSF) Award “SCC: Community-Based Automated Information for Urban Flooding” (Award 1831475), and by the UCAR COMET Award “Assessing the Accuracy of Multi-Radar/Multi-Sensor (MRMS) Precipitation Estimates in the Phoenix Metropolitan Area to Support Flash Flood Warning Operations” (Award SUBAWD001574).