Effects of Surface Area and Inflow on the Performance of Stormwater Best Management Practices with Uncertainty Analysis. (1st September 2013)

• Record Type: Journal Article
• Authors: Park, Daeryong; Roesner, Larry A.
• Is Part Of: Water environment research. Volume 85:Number 9(2013)
• Page Start: 782
• Page End: 792
• Publication Date: 2013-09-01
• Subjects: stormwater -- detention basins -- retention ponds -- International Stormwater BMP Database -- k‐C* model -- best management practice (BMP) surface area -- inflow -- uncertainty analysis -- Latin hypercube sampling; Water quality management -- Periodicals; Water -- Purification -- Periodicals; Water -- Pollution -- Periodicals; Water -- Pollution; Water -- Purification; Water quality management; Sewage; Water Pollution; Periodicals; Electronic journals; Periodicals; 628.16
• DOI: 10.2175/106143013X13736496908825
• Languages: English
• ISSNs: 1061-4303

Abstract

ABSTRACT: : The performance of stormwater best management practices (BMPs) is affected by BMP geometric and hydrologic factors. The objective of this study was to investigate the effect of BMP surface area and inflow on BMP performance using the k‐C * model with uncertainty analysis. Observed total suspended solids (TSS) from detention basins and retention ponds data sets in the International Stormwater BMP Database were used to build and evaluate the model. Detention basins are regarded as dry ponds because they do not always have water, whereas retention ponds have a permanent pool and are considered wet ponds. In this study, Latin hypercube sampling (LHS) was applied to consider uncertainty in both influent event mean concentration (EMC), C in, and the areal removal constant, k . The latter was estimated from the hydraulic loading rate, q, through use of a power function relationship. Results show that effluent EMC, C out, decreased as inflow decreased and as BMP surface area increased in both detention basins and retention ponds. However, the change in C out, depending on inflow and BMP surface area for detention basins, differed from the change in C out for retention ponds. Specifically, C in was more dominantly associated with the performance of the k‐C * model of detention basins than were BMP surface area and inflow. For retention ponds, however, results suggest that BMP surface area and inflow both influenced changes in C out as well as C in . These results suggest that sensitive factors in the performance of the k‐C * model are limited to C in for detention basins, whereas BMP surface area, inflow, and C in are important for retention ponds.