Next Article in Journal
Implications of Extracellular Polymeric Substance Matrices of Microbial Habitats Associated with Coastal Aquaculture Systems
Previous Article in Journal
A New Hybrid Forecasting Approach Applied to Hydrological Data: A Case Study on Precipitation in Northwestern China
Article Menu

Export Article

Open AccessArticle
Water 2016, 8(9), 368;

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

Department of Rural Construction Engineering, Kongju National University, 54 Daehak-ro, Yesan-gun 32439, Chuncheongnam-do, Korea
Department of Agricultural and Biological Engineering, Purdue University, 225 South University St., West Lafayette, IN 47907-2093, USA
Author to whom correspondence should be addressed.
Academic Editor: Richard Skeffington
Received: 2 May 2016 / Revised: 12 August 2016 / Accepted: 22 August 2016 / Published: 26 August 2016
View Full-Text   |   Download PDF [2979 KB, uploaded 26 August 2016]   |  


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. View Full-Text
Keywords: annual sediment load; LOADEST; regression model; water quality sampling annual sediment load; LOADEST; regression model; water quality sampling

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Park, Y.S.; Engel, B.A. Identifying the Correlation between Water Quality Data and LOADEST Model Behavior in Annual Sediment Load Estimations. Water 2016, 8, 368.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top