Collecting and analyzing water quality samples is costly and typically requires significant effort compared to streamflow data, thus water quality data are typically collected at a low frequency. Regression models, identifying a relationship between streamflow and water quality data, are often used to estimate pollutant loads. A web-based tool using LOAD ESTimator (LOADEST) as a core engine with four modules was developed to provide user-friendly interfaces and input data collection via web access. The first module requests and receives streamflow and water quality data from the U.S. Geological Survey. The second module retrieves watershed area for computation of pollutant loads per unit area. The third module examines potential error of input datasets for LOADEST runs, and the last module computes estimated and allowable annual average pollutant loads and provides tabular and graphical LOADEST outputs. The web-based tool was applied to two watersheds in this study, one agriculturally-dominated and one urban-dominated. It was found that annual sediment load at the urban-dominant watershed exceeded the target load; therefore, the web-based tool identified correctly the watershed requiring best management practices to reduce pollutant loads.

Water quality samples are typically collected less frequently than flow since water quality sampling is costly. Load Estimator (LOADEST), provided by the United States Geological Survey, is used to predict water quality concentration (or load) on days when flow data are measured so that the water quality data are sufficient for annual pollutant load estimation. However, there is a need to identify water quality data requirements for accurate pollutant load estimation. Measured daily sediment data were collected from 211 streams. Estimated annual sediment loads from LOADEST and subsampled data were compared to the measured annual sediment loads (true load). The means of flow for calibration data were correlated to model behavior. A regression equation was developed to compute the required mean of flow in calibration data to best calibrate the LOADEST regression model coefficients. LOADEST runs were performed to investigate the correlation between the mean flow in calibration data and model behaviors as daily water quality data were subsampled. LOADEST calibration data used sediment concentration data for flows suggested by the regression equation. Using the mean flow calibrated by the regression equation reduced errors in annual sediment load estimation from −39.7% to −10.8% compared to using all available data.

Increases in imperviousness due to urbanization and industrialization increases stormwater runoff and nonpoint source pollution. Approaches reducing these hydrological impacts include low impact development (LID) methods. Various methods have been developed and applied to date, and an evaluation of stormwater runoff and a reduction of non-point source pollution has been conducted. The Storm Water Management Model (SWMM) is capable of simulating various LID approaches, although selecting and implementing a suitable method for a specific target area, when considering the cost of various low impact development approaches, requires significant time and effort. A software program called Storm Water Management Model–low impact development design program (SWMM-ING), that can be optimally applied to deal with the cost of low impact development methods, was developed in this study. For SWMM-ING, an optimization process was conducted for low impact development, which can reduce stormwater runoff by 10%, suspended solid by 15%, and total phosphorus by 15%. The spatial arrangement and the area of the permeable pavement, bioretention cells, infiltration trenches, and green roofs were determined. Because SWMM-ING has a user-friendly graphical interface, and the optimization process of the low impact development approach is simple and straightforward, it has the advantage of not requiring specialized knowledge.