Use of pollutant load regression models with various sampling frequencies for annual load estimation

Abstract

Water quality data are collected by various sampling frequencies, and the data may not be collected at a high frequency nor over the range of streamflow conditions. Therefore, regression models are used to estimate pollutant data for days on which water quality data were not measured. Pollutant load regression models were evaluated with six sampling frequencies for daily nitrogen, phosphorus, and sediment data. Annual pollutant load estimates exhibited various behaviors by sampling frequency and also by the regression model used. Several distinct sampling frequency features were observed in the study. The first was that more frequent sampling did not necessarily lead to more accurate and precise annual pollutant load estimates. The second was that use of water quality data collected from storm events improved both accuracy and precision in annual pollutant load estimates for all water quality parameters. The third was that the pollutant regression model automatically selected by LOADEST did not necessarily lead to more accurate and precise annual pollutant load estimates. The fourth was that pollutant regression models displayed different behaviors for different water quality parameters in annual pollutant load estimation. © 2014 by the authors; licensee MDPI, Basel, Switzerland.