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ABSTRACT Urban flooding is an increasing threat to cities and resident well‐being. The Federal Emergency Management Agency (FEMA) typically reports losses attributed to flooding which result from a stream overtopping its banks, discounting impacts of higher frequency, lower impact flooding that occurs when precipitation intensity exceeds the capacity of a drainage system. Despite its importance, the drivers of street flooding can often be difficult to identify, given street flooding data scarcity and the multitude of storm, built environment, and social factors involved. To address this knowledge gap, this study uses 922 street flooding reports to the city in Denver, Colorado, USA from 2000 to 2019 in coordination with rain gauge network data and Census tract information to improve understanding of spatiotemporal drivers of urban flooding. An initial threshold analysis using rainfall intensity to predict street flooding had performance close to random chance, which led us to investigate other drivers. A logistic regression describing the probability of a storm leading to a flood report showed the strongest predictors of urban flooding were, in descending order, maximum 5‐min rainfall intensity, population density, storm depth, storm duration, median tract income, and stormwater pipe density. The logistic regression also showed that rainfall intensity and population density are nearly as important in determining the likelihood of a flood report incidence. In addition, topographic wetness index values at locations of flooding reports were higher than randomly selected points. A linear regression predicting the number of reports per area identified percent impervious as the single most important predictor. Our methodologies can be used to better inform urban flood awareness, response, and mitigation and are applicable to any city with flood reports and spatial precipitation data. 

Abstract Since the 1987 Clean Water Act Section 319 amendment, the US Government has required and funded the development of nonpoint source pollution programs with about $5 billion dollars. Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 US cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman’s correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g. impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the Environmental Protection Agency tended to include filters more frequently in their SCM inventories. Future work can build on the SCM assemblages used in this study and their explanatory variables to better understand the differences and drivers of differences in SCM effectiveness across cities, improve watershed modeling, and investigate city- and watershed-scale impacts of SCM assemblages.