Using Geophysics and Terrestrial LiDAR to Assess Stormwater Parameters in Vacant Lots in Philadelphia

Abstract

Managing stormwater volume and quality has become an important issue in urban hydrology. Impervious cover associated with urbanization increases surface runoff volumes and degrades the water quality of urban streams and rivers. Cities with combined stormwater and sewer lines such as Philadelphia, have been tasked with decreasing runoff volumes to help reduce combined sewer overflows and improve the water quality of local waterways. The Philadelphia Water Department uses the Environmental Protection Agency’s Storm Water Management Model (SWMM) to predict runoff and evaluate if proposed stormwater infrastructure will reduce overflows. This study focused on the hydrogeological properties of grassy areas on and near Temple University’s main campus in north Philadelphia. The dataset includes terrestrial LiDAR, ground penetrating radar, soil moisture sensor, surface compaction, and double ring and mini disk infiltrometer measurements. These data were used to establish what controls infiltration rates in the area and also provide input parameters for a SWMM model. A terrestrial LiDAR scan of the Berks St. site, a grassy vacant lot located just east of Temple’s campus was used to generate a high-resolution digital elevation model. This elevation model was used to calculate the depression storage parameter, partition subcatchments in the SWMM model, and calculate a topographic wetness index (TWI). The TWI is a microtopography-based predictor of where runoff will collect and infiltrate. The TWI assumes a homogeneous infiltration rate and that runoff is routed by topography. This TWI was compared with soil moisture sensor measurements to determine if the microtopographic index could predict the majority of change in soil moisture at the field site. To determine if accounting for buried debris helped strengthen the TWI, GPR was used to map the extent and depth of subsurface objects. The results indicate that the TWI and GPR data could not predict where runoff would accumulate and then infiltrate because the TWI’s assumptions were not met. Measurements made with a double ring infiltrometer indicate that infiltration rates at the site were both high and heterogeneous (40 to 1060 mm/hr), allowing precipitation to infiltrate into the subsurface rather than become runoff, minimizing the influence of microtopography. Co-located surface compaction and double ring infiltrometer measurements at sites on and nearby Temple’s campus showed a negative correlation between surface compaction and infiltration rate (R2 = 0.67). Compacted areas on campus had lower infiltration rates and exhibited depression storage and runoff during rain events. Less compacted areas off campus had higher infiltration rates and exhibited no depression storage or runoff. The results of this study showed variance in surface compaction caused grassy areas around Temple’s campus respond differently to rain events. The results not only provided field-based parameter values for a SWMM model, but shows that compaction’s influence on infiltration should be considered when constructing a SWMM model. Runoff volumes in SWMM may be underestimated if compacted grassy areas are modeled with high infiltration rates.