There are several deficiencies in the statistical approaches proposed in the literature for the assessment and redesign of surface water-quality-monitoring locations. These deficiencies vary from one approach to another, but generally include: (i) ignoring the attributes of the basin being monitored; (ii) handling multivariate water quality data sequentially rather than simultaneously; (iii) focusing mainly on locations to be discontinued; and (iv) ignoring the reconstitution of information at discontinued locations. In this paper, a methodology that overcomes these deficiencies is proposed. In the proposed methodology, the basin being monitored is divided into sub-basins, and a hybrid-cluster analysis is employed to identify groups of sub-basins with similar attributes. A stratified optimum sampling strategy is then employed to identify the optimum number of monitoring locations at each of the sub-basin groups. An aggregate information index is employed to identify the optimal combination of locations to be discontinued. The proposed approach is applied for the assessment and redesign of the Nile Delta drainage water quality monitoring locations in Egypt. Results indicate that the proposed methodology allows the identification of (i) the optimal combination of locations to be discontinued, (ii) the locations to be continuously measured and (iii) the sub-basins where monitoring locations should be added. To reconstitute information about the water quality variables at discontinued locations, regression, artificial neural network (ANN) and maintenance of variance extension (MOVE) techniques are employed. The MOVE record extension technique is shown to result in a better performance than regression or ANN for the estimation of information about water quality variables at discontinued locations.

Hydrological data in general and water quality (WQ) data in particular frequently suffer from missing records and/or short-gauged monitoring/sampling sites. Many statistical regression techniques are employed to substitute missing values or to extend records at short-gauged sites, such as the Kendall-Theil robust line (KTRL), its modified version (KTRL2), ordinary least squares regression (OLS), four MOVE techniques, and the robust line of organic correlation (RLOC). In this study, in aspiring to achieve better accuracy and precision, the À Trous-Haar wavelet transform (WT) was adopted as a data denoising preprocessing step prior to applying record extension techniques. An empirical study was performed using real WQ data, from the National WQ monitoring network in the Nile Delta in Egypt, to evaluate the performance of these eight record-extension techniques with and without the WT data preprocessing step. Evaluations included the accuracy and precision of the techniques when used for the restoration of WQ missing values and for the extension of the WQ short-gauged variable. The results indicated that for the restoration of missing values, the KTRL and WT-KTRL outperformed other techniques. However, for the extension of short-gauged variables, WT-KTRL2, WT-MOVE3, and WT-MOVE4 techniques showed more accurate and precise results compared with both other techniques and their counterparts without the WT.