Record extension for short-gauged water quality parameters using a newly proposed robust version of the Line of Organic Correlation technique

Abstract

Abstract. In many situations the extension of hydrological or water quality time series at short-gauged stations is required. Ordinary least squares regression (OLS) of any hydrological or water quality variable is a traditional and commonly used record extension technique. However, OLS tends to underestimate the variance in the extended records, which leads to underestimation of high percentiles and overestimation of low percentiles, given that the data are normally distributed. The development of the line of organic correlation (LOC) technique is aimed at correcting this bias. On the other hand, the Kendall-Theil robust line (KTRL) method has been proposed as an analogue of OLS with the advantage of being robust in the presence of outliers. Given that water quality data are characterised by the presence of outliers, positive skewness and non-normal distribution of data, a robust record extension technique is more appropriate. In this paper, four record-extension techniques are described, and their properties are explored. These techniques are OLS, LOC, KTRL and a new technique proposed in this paper, the robust line of organic correlation technique (RLOC). RLOC includes the advantage of the LOC in reducing the bias in estimating the variance, but at the same time it is also robust in the presence of outliers. A Monte Carlo study and empirical experiment were conducted to examine the four techniques for the accuracy and precision of the estimate of statistical moments and over the full range of percentiles. Results of the Monte Carlo study showed that the OLS and KTRL techniques have serious deficiencies as record-extension techniques, while the LOC and RLOC techniques are nearly similar. However, RLOC outperforms OLS, KTRL and LOC when using real water quality records.

Potential controls of isoprene in the surface ocean

“…Regression equations were calculated as the Robust Line of Organic Correlation (RLOC), which is affected less than the LOC approach by outliers and points below the detection limit [ Khalil and Adamowksi , ]. Equations were determined for each data set internally (A22 for AMT 22, A23 for AMT 23, and Ac2 for ACCACIA 2) and also for all available data sets for that variable combined (ALL; may refer to all three cruises or only AMT data; Table ).…”

Statistical methods in water resources

“…All four variables were measured with error, and predictions for any one variable could be made from specific critical values of the other three. A robust version of LOC was proposed by Khalil and Adamowski (2012) which performed better than LOC itself for data with outliers, though not unless sample sizes were large. As simulations for the robust version of LOC used only mixtures of normal distributions, further evaluation on distributions more similar to hydrologic records (lognormal or gamma distributions) is needed before replacing LOC with its robust cousin.…”