A Geographic Information Systems (GIS) Approach for Estimating Runoff Characteristics for Erosion and Sediment Control Practices in the Southeastern United States

Abstract

Soil discharged from construction sites to nearby waterbodies have a negative impact on water quality and the aquatic ecosystem. Therefore, it is necessary to develop a stormwater pollution prevention plan (SWPPP) in accordance with the National Pollutant Discharge Elimination System (NPDES) Construction General Permit (CGP) requirements. The SWPPP dictates the erosion and sediment control practices to employ on a construction site to minimize the amount of soil leaving the site and entering a waterbody. Runoff characteristics (i.e.,, peak flow rate, total runoff volume, rainfall intensity, etc.) are required when selecting appropriate erosion and sediment control practices. Various methods (e.g. Rational Method, Hydrograph method and etc.) have been developed to estimate peak flow rate from a watershed. For this study, the Hydrograph method is selected to estimate the peak flow rate from a 1 acre typical highway median drainage basin in Southeastern U.S. The prediction models of runoff characteristic (i.e., peak flow rate, 30/60/90 minute average flow rates, and the 24 hour total runoff volume) are developed for the entire Southeastern U.S. using PondpackTM, ArcGISTM and ExcelTM. After collecting weighted curve number (CNW) and rainfall depth (P) for a 2-yr, 24-hr storm event data for the study area, designers can input collected data into prediction models and calculate project specific runoff characteristics for projects under consideration in the Southeastern U.S.. The prediction models can assist designers to calculate runoff characteristics from a typical highway median drainage basin or develop site-specific prediction models of runoff characteristics with specified procedures introduced later. The prediction models of Southeastern U.S. are proved to be effective when applying on the state of Alabama, therefore, the prediction models for entire Southeastern U.S. can be also used to predict runoff characteristics from individual States located in Southeastern U.S. In addition, by comparing two different rainfall databases (TP-40 and Atlas 14) and the prediction models generated based upon them (the prediction models for TP-40 are cited from Perez). It can be concluded that Atlas 14 rainfall database is more accurate than TP-40 and the data collected from Atlas 14 is under a raster file which can imported directly into GIS, therefore, Atlas 14 is better than TP-40 in this study.