Identifying the Correlation between Water Quality Data and LOADEST Model Behavior in Annual Sediment Load Estimations

Abstract

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.

Pollutant Flux Estimation of the Lijiang River Based on an Improved Prediction-Correction Method

“…Although the pollution flux is a product of river discharge and water pollutant concentration, here, the fluxes of COD, NH 3 -N, and TP mainly depended on the variation of river discharge during the study period, whereas the influence of water pollutant concentration was weak. These results indicate that there is a significant correlation between pollution flux and river discharge, which is consistent with the results of previous studies (Park and Engel, 2016;Kim et al, 2018). The correlation between pollutant flux and concentration largely depends on the hydrological conditions; in rivers with stable discharge, it is higher than in those with large flow fluctuations (Li and Guo, 2017).…”

Spatiotemporal features of pollutant loads in the Yan River Basin, a typical loess hilly and gully watershed in the Chinese Loess Plateau

“…The Load Estimator (LOADEST) is an empirical statistical model, developed by the United States Geological Survey (USGS) and can estimate the long-term pollutant loads using the streamflow and pollutant concentration regression relationships (Sharifi et al 2017). The setting of the model requires at least 12 continuous flows and concentration data (Park and Engel 2016). Considering the different diffusion modes of pollutants and the adaptability of pollutant loads to river dynamics, 11 regression equations are available to users in the LOADEST.…”