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1. Adaptation of SWAT Watershed Model for Stormwater Management in Urban Catchments: Case Study in Austin, Texas

   https://doi.org/10.3390/w15091770  

   Glick, Roger; Jeong, Jaehak; Srinivasan, Raghavan; Arnold, Jeffrey G.; Her, Younggu (May 2023, Water)

   Computer simulation models are a useful tool in planning, enabling reliable yet affordable what-if scenario analysis. Many simulation models have been proposed and used for urban planning and management. Still, there are a few modeling options available for the purpose of evaluating the effects of various stormwater control measures (SCM), including LID (low-impact development) controls (green roof, rain garden, porous pavement, rainwater harvesting), upland off-line controls (sedimentation, filtration, retention–irrigation) and online controls (detention, wet pond). We explored the utility and potential of the Soil and Water Assessment Tool (SWAT) as a modeling tool for urban stormwater planning and management. This study demonstrates how the hydrologic modeling strategies of SWAT and recent enhancements could help to develop efficient measures for solving urban stormwater issues. The case studies presented in this paper focus on urban watersheds in the City of Austin (COA), TX, where rapid urbanization and population growth have put pressure on the urban stormwater system. Using the enhanced SWAT, COA developed a framework to assess the impacts on erosion, flooding, and aquatic life due to changes in runoff characteristics associated with land use changes. Five catchments in Austin were modeled to test the validity of the SWAT enhancements and the analytical framework. These case studies demonstrate the efficacy of using SWAT and the COA framework to evaluate the impacts of changes in hydrology and the effects of different regulatory schemes. 

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2. Integrating the Spatial Configurations of Green and Gray Infrastructure in Urban Stormwater Networks

   https://doi.org/10.1029/2023WR034796  

   Chen, Xiating; Davitt‐Liu, Indigo; Erickson, Andrew J.; Feng, Xue (December 2023, Water Resources Research)

   Abstract Green infrastructure (GI) practices improve stormwater quality and reduce urban flooding, but as urban hydrology is highly controlled by its associated gray infrastructure (e.g., stormwater pipe network), GI's watershed‐scale performance depends on its siting within its associated watershed. Although many stormwater practitioners have begun considering GI's spatial configuration within a larger watershed, few approaches allow for flexible scenario exploration, which can untangle GI's interaction with gray infrastructure network and assess its effects on watershed hydrology. To address the gap in integrated gray‐green infrastructure planning, we used an exploratory model to examine gray‐green infrastructure performance using synthetic stormwater networks with varying degrees of flow path meandering, informed by analysis on stormwater networks from the Minneapolis‐St. Paul Metropolitan Area, MN, USA. Superimposed with different coverage and placements of GI (e.g., bioretention cells), these gray‐green stormwater networks are then subjected to different rainfall intensities within Environmental Protection Agency's Storm Water Management Model to simulate their hydrological benefits (e.g., peak flow reduction, flood reduction). Although only limited choices of green and gray infrastructure were explored, the results show that the gray infrastructure's spatial configuration can introduce tradeoffs between increased peak flow and increased flooding, and further interacts with GI coverage and placement to reduce peak flow and flooding at low rainfall intensity. However, as rainfall intensifies, GI ceases to reduce peak flow. For integrated gray‐green infrastructure planning, our results suggest that physical constraints of the stormwater networks and the range of rainfall intensities must be considered when implementing GI. 

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3. Watershed studies at the Hubbard Brook Experimental Forest: Building on a long legacy of research with new approaches and sources of data

   https://doi.org/10.1002/hyp.14016  

   Campbell, John L.; Rustad, Lindsey E.; Bailey, Scott W.; Bernhardt, Emily S.; Driscoll, Charles T.; Green, Mark B.; Groffman, Peter M.; Lovett, Gary M.; McDowell, William H.; McGuire, Kevin J.; et al (January 2021, Hydrological Processes)

   Abstract The Hubbard Brook Experimental Forest (HBEF) was established in 1955 by the U.S. Department of Agriculture, Forest Service out of concerns about the effects of logging increasing flooding and erosion. To address this issue, within the HBEF hydrological and micrometeorological monitoring was initiated in small watersheds designated for harvesting experiments. The Hubbard Brook Ecosystem Study (HBES) originated in 1963, with the idea of using the small watershed approach to study element fluxes and cycling and the response of forest ecosystems to disturbances, such as forest management practices and air pollution. Early evidence of acid rain was documented at the HBEF and research by scientists at the site helped shape acid rain mitigation policies. New lines of investigation at the HBEF have built on the long legacy of watershed research resulting in a shift from comparing inputs and outputs and quantifying pools and fluxes to a more mechanistic understanding of ecosystem processes within watersheds. For example, hydropedological studies have shed light on linkages between hydrologic flow paths and soil development that provide valuable perspective for managing forests and understanding stream water quality. New high frequency in situ stream chemistry sensors are providing insights about extreme events and diurnal patterns that were indiscernible with traditional weekly sampling. Additionally, tools are being developed for visual and auditory data exploration and discovery by a broad audience. Given the unprecedented environmental change that is occurring, data from the small watersheds at the HBEF are more relevant now than ever and will continue to serve as a basis for sound environmental decision‐making. 

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4. Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality

   https://doi.org/10.1039/D1EW00582K  

   Bowes, Benjamin D.; Wang, Cheng; Ercan, Mehmet B.; Culver, Teresa B.; Beling, Peter A.; Goodall, Jonathan L. (March 2022, Environmental Science: Water Research & Technology)

   Real-time control of stormwater systems can reduce flooding and improve water quality. Current industry real-time control strategies use simple rules based on water quantity parameters at a local scale. However, system-level control methods that also incorporate observations of water quality could provide improved control and performance. Therefore, the objective of this research is to evaluate the impact of local and system-level control approaches on flooding and sediment-related water quality in a stormwater system within the flood-prone coastal city of Norfolk, Virginia, USA. Deep reinforcement learning (RL), an emerging machine learning technique, is used to learn system-level control policies that attempt to balance flood mitigation and treatment of sediment. RL is compared to the conventional stormwater system and two methods of local-scale rule-based control: (i) industry standard predictive rule-based control with a fixed detention time and (ii) rules based on water quality observations. For the studied system, both methods of rule-based control improved water quality compared to the passive system, but increased total system flooding due to uncoordinated releases of stormwater. An RL agent learned controls that maintained target pond levels while reducing total system flooding by 4% compared to the passive system. When pre-trained from the RL agent that learned to reduce flooding, another RL agent was able to learn to decrease TSS export by an average of 52% compared to the passive system and with an average of 5% less flooding than the rule-based control methods. As the complexity of stormwater RTC implementations grows and climate change continues, system-level control approaches such as the RL used here will be needed to help mitigate flooding and protect water quality. 

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5. Co-Design of Engineered Hyporheic Zones to Improve In-Stream Stormwater Treatment and Facilitate Regulatory Approval

   https://doi.org/10.3390/w11122543  

   Herzog, Skuyler P.; Eisenstein, William A.; Halpin, Brittnee N.; Portmann, Andrea C.; Fitzgerald, Nicole J.; Ward, Adam S.; Higgins, Christopher P.; McCray, John E. (December 2019, Water)

   Green infrastructure is an increasingly popular approach to mitigate widespread degradation of urban waters from stormwater pollution. However, many stormwater best management practices (BMPs) have inconsistent water quality performance and are limited to on-site, land-based deployments. To address basin-wide pollutant loads still reaching urban streams, hyporheic zone engineering has been proposed as an in-stream treatment strategy. Recognizing that regulator and practitioner perspectives are essential for innovation in the water sector, we interviewed U.S. water management professionals about the perceived risks, opportunities, and knowledge gaps related to in-stream stormwater treatment. We used engineered hyporheic zones as a case study to understand interviewee perspectives on an emerging class of in-stream treatment technologies. Interviews revealed that many considerations for in-stream stormwater treatment are common to land-based BMPs, but in-stream BMPs have additional unique design and siting requirements. Here, we synthesize practitioner goals, their recommendations on in-stream BMP design, and open research questions related to in-stream BMPs. Many interviewees suggested pairing engineered hyporheic zones with other BMPs in a treatment train to improve in-stream treatment, while simultaneously reducing risk and cost. We discuss how treatment trains and other strategies might also help overcome regulatory hurdles for innovative stormwater treatment. 

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