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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. 

Assessing the uncertainty associated with projections of climate change impacts on hydrological processes can be challenging due to multiple sources of uncertainties within and between climate and hydrological models. Here we compare the effects of parameter uncertainty in a hydrological model to inter-model spread from climate projections on hydrological projections of urban streamflow in response to climate change. Four hourly climate model outputs from the RCP8.5 scenario were used as inputs to a distributed hydrologic model (SWMM) calibrated using a Bayesian approach to summarize uncertainty intervals for both model parameters and streamflow predictions. Continuous simulation of 100 years of streamflow generated 90 % prediction intervals for selected exceedance probabilities and flood frequencies prediction intervals from single climate models were compared to the inter climate model spread resulting from a single calibration of the SWMM model. There will be an increase in future flows with exceedance probabilities of 0.5 %-50 % and 2-year floods for all climate projections and all 21st century periods, for the modeled Ohio (USA) watershed. Floods with return periods of ≥ 5 years increase relative to the historical from mid-century (2046–2070) for most climate projections and parameter sets. Across the four climate models, the 90th percentile increase in flows and floods ranges from 17-108 % and 11–63 % respectively. Using multiple calibration parameter sets and climate projections helped capture the most likely hydrologic outcomes, as well as upper and lower bounds of future predictions. For this watershed, hydrological model parameter uncertainty was large relative to inter climate model spread, for near term moderate to high flows and for many flood frequencies. The uncertainty quantification and comparison approach developed here may be helpful in decision-making and design of engineering infrastructure in urban watersheds. 

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. 

Green stormwater infrastructure (GSI) is increasingly used to reduce stormwater input to the subsurface stormwater network. This work investigated how GSI interacts with surface runoff and stormwater structures to affect the spatial extent and distribution of roadway flooding and subsequent effects on the performance of the traffic system using a dual-drainage model. The model simulated roadway flooding using PCSWMM (Personal Computer Stormwater Management Model) in Harvard Gulch, Denver, Colorado, and was then used in a microscopic traffic simulation using the Simulation of Urban Mobility Model (SUMO). We examined the effect of converting between 1% and 5% of directly connected impervious area (DCIA) to bioretention GSI on roadway flooding. The results showed that even for 1% of DCIA converted to GSI, the extent and mean depth of roadway flooding was reduced. Increasing GSI conversion further reduced roadway flooding depth and extent, although with diminishing returns per additional percentage of DCIA converted to GSI. Reduced roadway flooding led to increased average vehicle speeds and decreased percentage of roads impacted by flooding and total travel time. We found diminishing returns in the roadway flooding reduction per additional percentage of DCIA converted to GSI. Future work will be conducted to reduce the main limitations of insufficient data for model validation. Detailed dual-drainage modeling has the potential to better predict what GSI strategies will mitigate roadway flooding. 

Abstract The paper examines relationships between stormwater control measure (SCM) priorities and environmental value orientations among stormwater managers in Cleveland, Ohio and Denver, Colorado, metro regions with contrasting environmental conditions and policy contexts. While studies show that governance explains differences in broad SCM priorities, less is known about what motivates individual “street level bureaucrats” who influence decisions at the project level. Drawing from cognitive social science perspectives, this study surveyed stormwater professionals (n = 185) about primary and co‐benefit SCM priorities and environmental value orientation. Results revealed different primary SCM priorities by region: Cleveland and Denver respondents prioritized quantity and quality goals, respectively, reflecting regional context. Co‐benefit priorities correlated to two environmental value orientation clusters — “Traditional Technocrats” with relatively anthropocentric orientations and “Champions” with relatively ecocentric orientations — who were equally abundant in both regions. Findings suggest that environmental value orientation influences co‐benefit priorities, which may have implications for project level articulation of policy. 

Abstract Decades of research has concluded that the percent of impervious surface cover in a watershed is strongly linked to negative impacts on urban stream health. Recently, there has been a push by municipalities to offset these effects by installing structural stormwater control measures (SCMs), which are landscape features designed to retain and reduce runoff to mitigate the effects of urbanisation on event hydrology. The goal of this study is to build generalisable relationships between the level of SCM implementation in urban watersheds and resulting changes to hydrology. A literature review of 185 peer‐reviewed studies of watershed‐scale SCM implementation across the globe was used to identify 52 modelling studies suitable for a meta‐analysis to build statistical relationships between SCM implementation and hydrologic change. Hydrologic change is quantified as the percent reduction in storm event runoff volume and peak flow between a watershed with SCMs relative to a (near) identical control watershed without SCMs. Results show that for each additional 1% of SCM‐mitigated impervious area in a watershed, there is an additional 0.43% reduction in runoff and a 0.60% reduction in peak flow. Values of SCM implementation required to produce a change in water quantity metrics were identified at varying levels of probability. For example, there is a 90% probability (high confidence) of at least a 1% reduction in peak flow with mitigation of 33% of impervious surfaces. However, as the reduction target increases or mitigated impervious surface decreases, the probability of reaching the reduction target also decreases. These relationships can be used by managers to plan SCM implementation at the watershed scale.