| Title |
Regression-Based Estimation of Pollutant Loads from Direct Runoff and Baseflow |
| Authors |
박윤식(Youn Shik Park) ; 허용구(Younggu Her) |
| DOI |
https://doi.org/10.15681/KSWE.2025.41.3.151 |
| Keywords |
Baseflow; Direct runoff; LOADEST; Pollutant load |
| Abstract |
Because water quality data are more costly to collect and analyze than streamflow data, they are often unavailable for the full duration of streamflow records. As a result, pollutant loads are commonly estimated using statistical models such as the LOAD ESTimator (LOADEST). While LOADEST offers a convenient method for interpolating pollutant loads, recent studies have highlighted the need to evaluate its performance more rigorously. In this study, we assessed the performance of the LOADEST model by grouping water quality data and separating baseflow using a digital filter equation. The measured water quality data were divided into four groups, and each group was used for model calibration and validation. Additionally, baseflow and direct runoff were separated to estimate pollutant loads associated with each flow component. Daily pollutant loads were analyzed for five water quality parameters: BOD, COD, T-N, TOC, and T-P. Overall, the model met acceptable levels of performance across three evaluation criteria for both the calibration and validation phases. This approach enabled the monthly estimation of pollutant loads from baseflow and total streamflow. The baseflow contributions to total pollutant loads were estimated as follows: 17.30% for BOD, 25.82% for COD, 24.69% for T-N, 18.82% for TOC, and 20.51% for T-P. Notably, pollutant loads from baseflow were higher in spring and winter compared to summer and fall. |