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2014 ASHS Annual Conference

Using Scaled Sensor Networks to Estimate Green Roof Stormwater Runoff

Monday, July 28, 2014: 4:30 PM
Salon 7 (Rosen Plaza Hotel)
Olyssa Starry, Portland State University, Portland, OR
Whitney Griffin, University of Maryland, College Park, MD
Bruk E. Belayneh, Plant Science and Landscape Architecture, University of Maryland, College Park, MD
David Kohanbash, Carnegie Mellon University, Pittsburgh, PA
John D. Lea-Cox, University of Maryland, College Park, MD
Green roofs are increasingly recognized as an effective strategy for mitigating storm water runoff. However, in order to effectively quantify storm water retention and efficiency of green roofs at any scale, we need to be able to resolve two important issues:  (1) Monitoring of green roofs is both resource-intensive and very expensive, especially in retrofit situations and; (2) Green roofs have both physical and biotic components, both of which change over time.  We have installed two cellular-enabled (Em50G) sensor networks on two commercial roofs in MD and TX to quantify soil moisture and environmental variables for assessing green roof efficiency and validating a storm water retention model.  This model estimates runoff based on actual measured rainfall and evapotranspiration using simple water balance approach, whereby precipitation (P) is set to equal evapotranspiration (ET) plus runoff (R) minus any change in storage (P=ET+R-deltaS). Environmental (air temperature, relative humidity, total radiation, wind speed and direction) are collected every 1 minute and the average logged every 5 minutes; soil temperature and moisture data (Echo-TM, Decagon Devices, Inc.) are collected on a 15-minute basis along drainage transects on each roof.  Data are transmitted via EM50G nodes every six hours to a cloud server (Decagon Devices, Inc.); data are downloaded and imported into Sensorweb software on a computer in College Park MD.  Sensorweb allows a secure data-sharing platform for all collaborators in the project (MD, TX, OR and PA) over the internet using a browser interface.  Results from two case study installations are provided.  The first case study takes place on a 20,000 square foot retrofit extensive green roof in Washington, DC. The second case study takes place on a 30,000 square foot installation in Houston TX.   Preliminary findings from the roof in Texas indicate that previous irrigation practices maintained a frequently saturated substrate and generated potentially unnecessary runoff. Changes in irrigation revealed that dry down (from approximately 25% to less than 10% VWC) might last as long as a week for the weather conditions observed in April 2014.  Future work will investigate how to optimize green roof performance through precision irrigation.   These examples illustrate how we can cost-effectively monitor relatively remote locations in real-time using these sensor networks, and provide the data for analysis among research groups anywhere in the world.