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1. Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

   https://doi.org/10.3390/w12113222  

   Saliba, Sami M.; Bowes, Benjamin D.; Adams, Stephen; Beling, Peter A.; Goodall, Jonathan L. (November 2020, Water)

   null (Ed.)

   Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC. 

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2. GIS-based spatial approaches to refining urban catchment delineation that integrate stormwater network infrastructure

   https://doi.org/10.1007/s43832-024-00083-z  

   Si, Qianyao; Brito, Higor C; Alves, Priscila_B R; Pavao-Zuckerman, Mitchell A; Rufino, Iana_A A; Hendricks, Marccus D (December 2024, Discover Water)

   Abstract Rapid urbanization and escalating climate change impacts have heightened stormwater-related concerns (e.g., pluvial flooding) in cities. Understanding catchment dynamics and characteristics, including precise catchment mapping, is essential to accurate surface water monitoring and management. Traditionally, topography is the primary data set used to model surface water flow dynamics in undisturbed natural landscapes. However, urban systems also contain stormwater drainage infrastructure, which can alter catchment boundaries and runoff behavior. Acknowledging both natural and built environmental influences, this study introduces three GIS-based approaches to enhance urban catchment mapping: (1) Modifying DEM elevations at inlet locations; (2) Adjusting DEM elevations along pipeline paths; (3) Applying the QGRASS plug-in to systematically incorporate infrastructure data. Our evaluation using the geographical Friedman test (p > 0.05) and Dice Similarity Coefficient (DSC = 0.80) confirms the statistical and spatial consistency among the studying methods. Coupled with onsite flow direction validation, these results support the feasibility and reliability of integrating elements of nature and built infrastructure in urban catchment mapping. The refined mapping approaches explored in this study offer improved and more accurate and efficient urban drainage catchment zoning, beyond using elevation and topographic data alone. Likewise, these methods bolster predictive stormwater management at catchment scales, ultimately strengthening urban stormwater and flooding resilience. 

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3. Ecohydrology of Green Stormwater Infrastructure in Shrinking Cities: A Two-Year Case Study of a Retrofitted Bioswale in Detroit, MI

   https://doi.org/10.3390/w14193064  

   Papuga, Shirley Anne; Seifert, Emily; Kopeck, Steven; Hwang, Kyotaek (October 2022, Water)

   Stormwater management is of great importance in large shrinking cities with aging and outdated infrastructure. Maintenance of vegetated areas, particularly referred to as green infrastructure, is often aimed at mitigating flooding and the urban heat island effect by stormwater storage and evaporative cooling, respectively. This approach has been applied in large cities as a cost-effective and eco-friendly solution. However, the ecohydrological processes and how the ecohydrology influences the function of green infrastructure and its potential to provide those ecosystem services are not well understood. In this study, continuous field measurements including air temperature, stomatal conductance, and phenocam images were taken in a 308 m2 bioswale retrofitted into a 4063 m2 parking lot on the Wayne State University campus in Detroit, Michigan over a two-year period. Our results suggest that plant characteristics such as water use efficiency impact the ecohydrological processes within bioswales and that retrofitted bioswales will need to be adapted over time to meet environmental demands to allow for full and sustained success. Therefore, projected shifts in precipitation regime change are expected to affect the performance of green infrastructure, and each bioswale needs to be developed and engineered to be able to adapt to changing rainfall patterns. 

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4. Understanding Spatiotemporal Patterns and Drivers of Urban Flooding Using Municipal Reports

   https://doi.org/10.1002/hyp.70028  

   DeSousa, Stacie; Bhaskar, Aditi S; Kelleher, Christa; Livneh, Ben (December 2024, Hydrological Processes)

   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. 

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5. Estimating Combined Effects of Climate Change and Land Cover Change on Water Regulation Services of Urban Wetlands in Valdivia, Chile

   https://doi.org/10.1029/2023EF003801  

   Sauer, J; Grimm, N B; Barbosa, O; Cook, E M; Mustafa, A; Kunkel, K; McPhearson, T; Ballinger, A (May 2024, Earth's Future)

   The relationship between cities and wetland cover varies across the globe, with some cities converting wetlands to low‐ and high‐density urban cover and others preserving, conserving, or restoring wetlands, or constructing new ones. However, the scientific literature lacks studies relating changes in systemic flood risk in an urban stormwater management systems to changes in wetland cover. Furthermore, whether and how such relationships are affected by changing storm intensity under climate change is unknown. We present a case study on the effects of changes in urban wetland extent and storm intensity on flooding in an urban drainage system in Valdivia, Chile, under several co‐produced future scenarios and historical trends of development. We used data derived from stakeholder workshops and historical landcover to determine four plausible scenarios of urban development, plus one business‐as‐usual scenario, in Valdivia through the year 2080. Additionally, we used historical precipitation data and downscaled climate data to estimate event rainfall from extreme storms in the year 2080. We found that system flood volume and time the system was flooded increased with increasing wetland loss and rainfall volume. Mean rate and hour of peak discharge were unaffected by wetland loss. Infiltration's relative role in reducing flooding diminished as wetland loss increased. Cities may still experience dangerous and/or unacceptable flooding even with extensive wetland coverage and will likely need to pair conservation with additional improvements in their stormwater management systems and contributing watersheds. 

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