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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 As stormwater control measures (SCMs) capture surface runoff from impervious areas, a shift in the water balance and flow regime components may emerge in urban watersheds, but the amount of SCM treatment needed to detectably shift these components may vary. We used the Soil and Water Assessment Tool (SWAT) hydrologic model to assess the sensitivity of 16 hydrologic metrics as an increasingly dense rain garden SCM network was applied across the West Creek watershed, near Cleveland, Ohio (USA). As the area treated by SCMs increased, annual baseflow increases matched decreases in surface runoff, while water yield and evapotranspiration changes remained small. The stream's peak response to rainfall decreased with SCM implementation across storm sizes, ranging from the threshold rainfall depth (4.8 mm) to values higher than the design storm of a single rain garden (19 mm). SCM networks draining >20% of directly connected impervious area (DCIA) significantly decreased the magnitude of discharges with a return period of less than 1 year, the percentage of time above mean flow, and flashiness. Recession slopes and annual 1‐ and 7‐day low flows exhibited a slight response that fell within uncertainty limits of the model. Water balance and rainfall response metrics exhibited the greatest sensitivity to different intensities of stormwater management, while infrequent high and low flows were resistant to detectable change even at high levels of SCM treatment when model uncertainty was included. 

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. 

Clay tiles and tracer particles were deployed in Mill Creek in Cleveland, OH to investigate how biofilm and streambed materials respond to high flow events. Ten cross-sectional transects were established evenly across a 100-meter reach where cinderblocks with 16 unglazed clay tiles were buried in the streambed near the deepest part of the channel to promote biofilm growth. Particles of sizes corresponding to the 50th, 75th, and 90th percentile of the substrate size classes at each transect were painted and numbered for use as tracer particles. Samples from the tiles were collected after each high-flow event and measured their biomass using chlorophyll a (chla) and ash-free dry mass (AFDM). Movement of tracer particles (yes/no) was recorded to estimate how much of the streambed moved. 

Water quality sensors were placed in 3 urban streams in Cleveland, OH and 4 urban streams in Denver, CO to estimate stream metabolism and assess response to high flow events. MiniDOT (dissolved oxygen and temperature) and Onset (specific conductance) sensors were placed mid-channel near USGS gages. Light was measured as global horizontal irradiance (GHI) and supplied by SolCast. Data collection was part of the NSF STORMS project (PI Jefferson, co-PIs Costello, Bhaskar, Turner). Specific conductance, dissolved oxygen, and light were measured every 10 minutes. Sensors were removed during winter months to avoid damage. Datasets were cleaned to remove values when sensors were out of water, buried, and removed for maintenance/calibration. 

This dataset contains turbidity data and storm event characters of three urban watersheds in Cuyahoga County, Ohio. Turbidity data were collected at a frequency of 10 minutes using in-situ Cyclop-7 turbidimeters designed by Turner Designs and integrated with a Cyclops-7 logger by Precision Measurement Engineering, Inc. Data were collected for three years from September 2018 to 2021. Turbidity data is harmonized with instantaneous discharge data from USGS stream gages. Event characteristics contains runoff, precipitation and antecedent characteristics. The data support the findings of the study titled "Urbanization and Suspended Sediment Transport Dynamics: A Comparative Study of Watersheds with Varying Degree of Urbanization using Concentration-Discharge Hysteresis". 

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. 

This dataset contains stream bed material size distributions for 39 stream reaches along 12 streams in and near Cuyahoga County, Ohio. All data were collected using the Wolman pebble count technique (Wolman, 1954), in either transect or zig-zag forms (Bunte and Abt, 2001). Data were collected between 2016 and 2023 over the course of several projects. 

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.