A Generalized Automated Framework for Urban Runoff Modeling and Its Application at a Citywide Landscape

Abstract

This research presents a fully automated framework for runoff estimation, applied to Philadelphia, Pennsylvania, a major urban area. Trends in global urbanization are exacerbating stormwater runoff, making it an increasingly critical challenge in urban areas. Understanding the fine-scale spatial distribution of local flooding is difficult due to the complexity of the urban landscape and lack of measured data, but it is critical for urban management and development. A one-meter resolution Digital Elevation Model (DEM) was used in conjunction with a model developed by using ArcGIS Pro software to create urban micro-subbasins. The DEM was manipulated to account for roof drainage and stormwater infrastructure, such as inlets. The generated micro-subbasins paired with 24-h storm data with a 10-year return period taken from the National Resources Conservation Service (NRCS) for the Philadelphia area was used to estimate runoff. One-meter land-cover and land-use data were used to estimate pervious and impervious areas and the runoff coefficients for each subbasin. Peak runoff discharge and runoff depth for each subbasin were then estimated by the rational and modified rational methods and the NRCS method. The inundation depths from the NRCS method and the modified rational method models were compared and used to generate percent agreement, maximum, and average of inundation maps of Philadelphia. The outcome of this research provides a clear picture of the spatial likelihood of experiencing negative effects of excessive precipitation, useful for stormwater management agencies, city managers, and citizen.

Multi-variable assessment of green stormwater infrastructure planning across a city landscape: Incorporating social, environmental, built-environment, and maintenance vulnerabilities

“…In addition to flooding areas, this study also found that vulnerability to both flooding and excessive heat is unevenly distributed throughout the city. Areas within the northern portion of the city were found to have lower levels of inundation which corresponded with higher amounts of green stormwater infrastructure installations in that area (Hosseiny et al, 2020).…”

“…The flood extent model used in this study comes from recent research that predicted the peak runoff discharge from a 10-year, 24-h design storm simulated for the city of Philadelphia (Hosseiny et al, 2020). Runoff estimates were found using the Rational method (Hosseiny et al, 2020). These values were averaged per block group and were then standardized from 0 to 1 for the purpose of this study.…”

Quantifying Urban Flood Extent Using Satellite Imagery and Random Forest: A Case Study in Southeastern Pennsylvania

“…The World Bank estimated in 2022 that 1.81 billion people, or 23% of the world's population, are at risk of intense oods (Rentschler et al, 2022). Floods cause many types of harm (de Bruijn et al, 2019;Rosser et al, 2017), including economic losses (Pinos & Quesada-Román, 2022), damages to private homes/assets, damages to public infrastructure (Go et al, 2020), involuntary displacement, impacts on mental health (Markhvida et al, 2020), and disruptions to daily life and tra c ow (Hosseiny et al, 2020). In the most dangerous circumstances, oods can lead to loss of life (Go et al, 2020), taking 146 lives in 2021 in the U.S. (US Department of Commerce, 2021).…”

Call for a Dynamic Approach to GSI Maintenance

“…This framework requires data and analysis tools to make connections between a site's maintenance needs and the contributing factors. Geographic information system (GIS) tools are emerging for spatial decision-making techniques to determine risk level in urban stormwater networks due to social, environmental, and technical factors (Shariat et al 2019) and for statistical techniques to analyze urban flood risk at the city scale (Hosseiny et al 2020), which could aid this framework.…”