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

Youn Park,Bernie Engel

doi:10.3390/w8090368

Youn Park, Bernie Engel

Open Access

https://doi.org/10.3390/w8090368

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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.

Highlights

Water quality samples are collected less frequently than flow, because water quality sampling implementations require significant labor and are costly to collect and analyze
mean of flow in calibration data (MFC) were correlated to annual sediment load estimates, Load Estimator (LOADEST) underestimated loads with small MFCs and overestimated loads with large MFCs (e.g., Figure 1)
The results indicate that a water quality dataset needs to consist of an appropriate portion of water from low and high flows rather than large numbers of water quality samples, because the water quality quality data from low and high flows rather than large numbers of water quality samples, because datasets for a small number of data but with appropriate MFC demonstrated smaller errors than the water quality datasets for a small number of data but with appropriate MFC demonstrated the water quality datasets with larger numbers of data (Table 5)

Summary

Introduction

Water quality samples are collected less frequently than flow, because water quality sampling implementations require significant labor and are costly to collect and analyze. Water quality samples are collected by various sampling strategies which are based on flow, time, or flow and time composited [1,2]. Water quality data samples may not be consecutive or associated with the range of flow data, and a straightforward annual load estimate (e.g., sum of daily loads) may not be possible. Water quality samples typically need to be estimated for days on which samples were not collected [6]. Regression models (rating curves) are used to predict water quality concentrations (or loads) on days when flow data are measured. Various ranges of water quality data sampling frequencies were used to

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