An Infiltration Model of an Underground Rock Storage Bed Infiltration Bmp.
Infiltration best management practices (BMPs) are becoming an increasingly common measure to meet stormwater volume reduction regulations. One such BMP is an underground infiltration bed, which temporarily stores runoff that slowly seeps into the underlying soil. While these BMPs are extremely effective, especially in areas heavily developed and areas with tight space constraints, modeling the system to predict the anticipated infiltration can be difficult. A common method used to calculate infiltration is the Green and Ampt Equation (Veismann et al., 2003). This physically based approach uses hydraulic conductivity, suction and depth of the wetting front to calculate the rate of infiltration. The difficulty with modeling this approach is that soil properties vary from site to site and even within a site, and the infiltration rates are affected by temperature, moisture content, and depth of ponded water. As part of the research into the performance of underground infiltration BMPs, a model was developed that allows the user to adjust the factors in the Green and Ampt equation for both the temperature of the water and the moisture content of the soil. This allows the user to examine the BMP’s performance under a variety of field conditions providing a more realistic estimate of the infiltration. This model was verified using three years of data collected from the Porous Concrete Infiltration BMP at Villanova University (Bragga , 2005, Kwiakowski, 2004, Ladd, 2004, Traver et al., 2003 and Traver et al., 2005). The initial conditions for each modeled storm were used as input parameters for the model and the resulting relationship of ponded depth versus time was compared to the recorded data. For comparison, the standard Green and Ampt model was also run. The developed model showed up to a 99.53% improvement over the standard model in predicting the infiltration from the subsurface infiltration bed. Various sensitivity analyses were performed as well as statistical analyses of the results.
|Main Author:||LeBoon, Megan.|
|Other Authors:||Traver, Robert G.|