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Urban stormwater management is increasingly a challenge due to land use change, aging infrastructure, and climate‐driven precipitation variability. Likewise, maintaining regulatory compliance for stormwater permits is becoming more difficult. This study develops and deploys stormwater sensors using an Internet of Things‐based monitoring framework on the University of Maryland campus, a spatially compact but land use diverse testbed, designed to support both compliance and adaptive planning. Across three campus outfalls for stormwater quantity and quality data collection, the study investigates how hyperlocal precipitation and catchment characteristics affect stormwater flow and identifies key patterns in stormwater flow and quality through continuous monitoring. Findings reveal correlations between runoff behaviors and catchment characteristics (i.e., imperviousness) and highlight site‐specific associations between runoff flow and water quality indicators (pH, turbidity, conductivity, and dissolved oxygen). These associations can be leveraged as indicators of flood and pollution risk for management and planning purposes. This study also explores the role of campus stakeholders in guiding a “smart” system design, deployment, and big data use and outlines adaptive and preventive strategies for mitigating field deployment challenges and optimizing system performance that is a practical, compliance‐oriented model for smart stormwater monitoring in complex urban settings at various scales. 

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.