Critical Source Areas for Nonpoint Source Pollution and Their Spatial Relationship with Impervious Cover in Urban Watersheds
Article
Yahyavi Rahimi, A, Ebrahimian, A. (2026). Critical Source Areas for Nonpoint Source Pollution and Their Spatial Relationship with Impervious Cover in Urban Watersheds
.(6), 10.1061/JOEEDU.EEENG-8594
Yahyavi Rahimi, A, Ebrahimian, A. (2026). Critical Source Areas for Nonpoint Source Pollution and Their Spatial Relationship with Impervious Cover in Urban Watersheds
.(6), 10.1061/JOEEDU.EEENG-8594
Urbanization alters hydrologic processes and pollutant transport dynamics, making the identification of critical source areas (CSAs) - where high pollutant generation coincides with strong connectivity to downstream - essential for effective urban watershed management. This study presents an integrated framework to identify and assess CSAs in urban watersheds. Using Soil and Water Assessment Tool (SWAT) modeling results, CSAs were delineated by integrating the cumulative pollutant load (CPL) method and the Getis-Ord Gi∗ spatial statistic with a transport factor (TF). The spatial distribution of CSAs was evaluated for the first time by analyzing their relationship with two impervious cover indicators, total impervious area (TIA) and directly connected impervious area (DCIA), and their proximity to key watershed features (e.g., centroid and outlet). The framework applicability was demonstrated in an urban watershed in Saint Paul, Minnesota. Results indicated that a relatively small fraction of subbasins contributed disproportionately to total sediment loads, e.g., 26% of the watershed area generated 50% of the load. The CPL-TF approach provided broader coverage, identifying 60 (out of 1,169) subbasins as CSAs, whereas Gi*-TF highlighted five subbasins as statistically significant hot spots. The CSA subbasins exhibited the highest DCIA among all subbasins, which was not the case for TIA, indicating DCIA as a better indicator of urban imperviousness than TIA in relation to urban stormwater runoff quality. Proximity analysis indicated central-to-downstream clustering patterns for CSAs using CPL-TF, while Gi*-TF exhibited greater CSA variability. This study advances CSA understanding in urban watersheds, supporting strategic, watershed-wide implementation of best management practices for nonpoint source-pollution control.
