Hendricks, Marccus D.Si, QianyaoAlves, Priscila B. R.Pavao-Zuckerman, Mitchell A.Davis, Allen P.Burke, TaraBonsignore, Elizabeth M.Baer, JasonPeterson, KaitlynCotting, JenniferGaunaurd, PierreClegg, TamaraLoshin, DavidFellow, AndrewKeen, TaylorKnaap, Gerrit-JanThis dataset was generated by IoT sensors deployed at three outfall locations, collecting real-time stormwater data, including quality parameters such as turbidity and conductivity, and quantity parameters like flow depth and velocity. The data was processed using R scripts to convert onsite measurements into usable formats for analysis.This dataset is part of the research project titled “A Smart, Connected, and Sustainable Campus Community: Using the Internet of Things (IoT) and low-cost sensors to improve stormwater management at UMD/Greater College Park”. We use an Internet of Things (IoT) framework along with low-cost sensors to monitor and improve stormwater management on the University of Maryland Campus. This project provides real-time data that can inform both short term responses and longer-term adaptations to stormwater surface runoff. New buildings, the Purple Line, and other developments on the UMD campus will potentially increase the amount of impervious cover and thus increases the amount of surface runoff. Furthermore, as a result of climate change, the region is expected to experience more frequent and intense rainfall events over shorter periods of time. These two factors have implications for higher quantities of water on campus, pooling water, and potential localized flooding. Stormwater issues can affect the movement of people, goods and services, campus infrastructure, and students as they walk across campus exposing their belongings, and particularly their feet to wetter conditions. As part of more sustainable development, communities and campuses across the world, are beginning to plan for adaptations within the built campus environment to mitigate both larger scale stormwater issues as well as more practical everyday concerns, including wet pathways, and to meet and evaluate the effectiveness of stormwater permitting requirements. The research objectives for this project are fourfold: (1) Install low-cost stormwater sensors that measure water levels at a number of locations across campus that include high pedestrian traffic areas and major campus arterials; (2) Develop an online database for campus water levels; (3) Train students to install and read the stormwater sensors, manage the data platform, interpret the data (4) Use the data to write adaptation plans and designs to better manage stormwater on campus and, perhaps subsequently, downstream from campus. The dataset contains clean stormwater quality and quantity measurements collected from three different sites, along with processed data that describe runoff behavior during selected rainfall events and corresponding catchment characteristics (imperviousness, slope). The spatial data files provide location information for the outfall locations and the corresponding catchment boundaries. The R code provided includes data processing, statistical analysis, and visualization steps.Urban Water Resources; Flood Management; MS4; Runoff Monitoring; Environmental PlanningDataset