LOADEST를 이용한 연간 부유사 오염부하량 추정을 위한 수질자료 검토

Since water quality sampling implementation requires significant labors and is costly to collect and to analyze samples, water quality samples are collected less frequently than flow. LOADEST is used to predict water quality concentration (or load) on days when flow data are measured so that the water quality is to be sufficient for annual pollutant load estimation. However, there is a need to identify the requirement of water quality data. Annual sediment load estimates from 211 locations were analyzed, and it was found that the mean of flow in calibration data were correlated to model behaviors. Also, a regression was developed to compute the required mean of flow in calibration data to calibrate the regression model coefficients. LOADEST runs were performed to investigate the correlation between the mean flow in calibration data and the model behaviors, as daily water quality data were subsampled. It was found that annual load estimates using calibration data of the required mean flow in calibration data by the regression have showed small error to the measured annual load. Moreover, a lot of water quality data by extensive sampling strategies did not always lead to the annual load estimates of small error.