Improved urban runoff prediction using high-resolution land-use, imperviousness, and stormwater infrastructure data applied to a process-based ecohydrological model
Modeling large-scale hydrological impacts brought about by site-level green and gray stormwater remediation actions is difficult because urbanized areas are extremely complex dynamic landscapes that include engineered features that, by design, expedite urban runoff to streams, rivers and other water bodies to reduce urban flooding during storm events. Many urban communities use heavily engineered gray infrastructure to achieve that goal, along with more recent additions of green infrastructure such as rain gardens, bioswales, riparian corridors, etc. Therefore, successfully characterizing those design details and associated management practices, interactions, and impacts requires a detailed understanding of how fine and course-scale hydrologic processes and routing are altered and managed in urban watersheds. To enhance hydrologic modeling capabilities of urban watersheds, we implemented a number of improvements to an existing ecohydrology model called Visualizing Ecosystem Land Management Assessments (VELMA), including the addition of spatially explicit engineered features that impact urban hydrology (i.e., impervious surfaces, curbed roadways, stormwater routing) and improvements to the computational representations of plant evapotranspiration and soil matrix water movement dynamics within natural systems. We demonstrate improved capabilities for modeling within complex urbanized watersheds by simulating stream runoff within the Longfellow Creek watershed, City of Seattle, Washington, United States with and without these added urban watershed characteristics. The results demonstrate that the newly improved VELMA model allows for more accurate modeling of hydrology within urban watersheds. Being a fate and transport ecohydrology model, the improved hydrologic flow enhances VELMA’s current capacity for modeling nutrient, contaminant, and thermal loadings.