README for Dataset of the project "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" 1. Project Overview 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. Key Words: Urban Water Resources; Flood Management; MS4; Runoff Monitoring; Environmental Planning 2. Author Information: Principal Investigator Contact Information: Name: Marccus D. Hendricks Institution: University of Maryland Email: mdh1@umd.edu Authors: Hendricks, M. D. Qianyao Si Priscila B. R. Alves Mitchell A. Pavao-Zuckerman Allen P. Davis Tara Burke Elizabeth M. Bonsignore Jason Baer Kaitlyn Peterson Jennifer Cotting Pierre Gaunaurd Tamara Clegg David Loshin Andrew Fellows Taylor Keen Gerrit-Jan Knaap 3. Date of data collection: 20210201-20220228 4. File Descriptions Data Files - outfall3_clean_20212-20222.csv: Cleaned sensor data for Outfall Site #3 during the studying period, including parameters such as flow depth, turbidity, pH, conductivity, etc. - outfall5_clean_20212-20222.csv: Cleaned sensor data for Outfall Site #5 during the studying period, covering the same parameters as Site 3. - outfall19_clean_20212-20222.csv: Cleaned sensor data for Outfall Site #19 during the studying period, covering the same parameters as Site 3 and 5. - norm flow rate.csv: A subset of the converted runoff and spatial data, focusing on selected rainfall event sizes, the associated runoff behavior (normalized peak flow, discharging and recession rate), and catchment characteristic data, including imperviousness%, slope, and BMP coverage. Code File - runoff_behavior.R: R script used to process the sensor data, including: - Conversion of flow depth to flow rate. - Statistical analysis (regression, correlation, etc.). - Visualization of stormwater data for further analysis. Spatial Files - outfall_locations.shp: Shapefile containing the location information of the three outfall sites in this study. - catchment_boundaries.shp: Shapefile containing the boundaries of the catchment areas for each outfall location. - Citation: The catchment boundary map was referenced from Si, Q., Brito, H. C., Alves, P. B. R., Pavao-Zuckerman, M. A., Rufino, I. A. A., & Hendricks, M. D. (2024). GIS-based spatial approaches to refining urban catchment delineation that integrate stormwater network infrastructure. Discover Water, 4(1), 24. https://doi.org/10.1007/s43832-024-00083-z 5. Methodology This 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. 6. How to Use the Files 1. Data files: Open any of the CSV files in 2021.09.0 version of RStudio analysis tool. The columns correspond to different stormwater parameters recorded over time. 2. Code: Use the runoff_behavior.R script to process the data. The script includes detailed comments explaining each step of the analysis, from data cleaning to statistical computations and generating plots. 3. Spatial files: The shapefiles can be viewed using GIS software such as ArcGIS pro or QGIS. These files provide geographical information about the locations where stormwater data was collected. 7. Funding source and grant number This work was supported by the University of Maryland Sustainability Fund 2019 under Grant Agreement number 7781160 8. Recommended citation for the data: Hendricks, M. D., Si, Q., Alves, P.B.R., Pavao-Zuckerman, M.A., Davis, A.P., Burke, T., Bonsignore, E.M., Baer, J., Peterson, K., Cotting, J., Gaunaurd, P., Clegg, T., Loshin, D., Fellows, A., Keen, T., Knaap, G.(2024). 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 [Dataset]. 9. Contact Information For questions or further information, please contact: - Marccus Hendricks [mdh1@umd.edu] - Qianyao Si [qysi@umd.edu]