A statistical approach for the rationalization of water quality indicators in surface water quality monitoring networks

Summary Despite several decades of operation and the increasing importance of water quality monitoring (WQM) networks, authorities still rely on subjective or semi-subjective decision processes to identify the optimal combination of water quality variables to measure. For this purpose, a statistical approach is developed for assessment and selection of the optimal combination of water quality variables. The proposed approach overcomes deficiencies in the conventional correlation–regression approach used to assess and eventually reduce the number of water quality variables in WQM networks. For the reduction of water quality variables, criteria developed from record-augmentation procedures are integrated with correlation analysis and cluster analysis to identify highly associated water quality variables. This step is followed by the application of an information performance index to systematically identify the optimal combination of variables to be continuously measured and those to be discontinued. The linear regression and maintenance of variance (MOVE) record-extension techniques are employed to reconstitute information about discontinued variables. The proposed approach is applied for rationalization of the water quality variables in the Nile Delta surface WQM network in Egypt. Results indicate that the proposed approach represents a useful decision support tool for the optimized selection of water quality variables. The MOVE record-extension technique is shown to result in better performance than regression for the estimation of discontinued variables.