Reliability of Water Resources Quality Monitoring Program Data

Abstract

Data are a vital component of water resources management activities. The consequences of using poor quality data include faulty decisions, higher risk to the environment or human health, wasted resources and loss of credibility. Box-whisker plot, cations-anions balance and relative total dissolved solids (TDS) to electrical conductivity (EC) ratio were used to examine validity of water quality data. These techniques and reliability check applied on water quality data of Tigris river in 2013 to ensure that the data can be used for decision making in the management of water resources with a high level of confidence, improve the reliability of water quality assessments, and discovering some uncertainty. Box-whisker plot technique has been used to detect outliers and summarize data to show the centrality, spread and skewness of data along Tigris river monitoring stations. The results showed that about 5% of data classified as outliers. An analysis of data by using percent of error in cation-anion balance technique to check reliability of data showed that 21.6 % of data exceeded the permit level of error in ions balance. Relative TDS/EC ratio accuracy check shows that the data agree with the range 0.55 to 0.75 with exception in stations from T11 to T16B where the ratio slightly less than 0.55. Also the results showed that they are not entirely consistent with the nature and the characteristics of the river water quality especially at the first section about 400 km from T1 to T10 where the bicarbonate is the domain anion and final section about 300 km from T29 to T33 where sulphate is the domain anion. Analysis of
Data showed that the data results have serious measuring or sampling errors, which means that the resultant data quality is insufficient for drawing reliable conclusions about water quality and for supporting decision making with high level of confidence. Applying quality control and quality assurance procedures have been required to ensure validity and reliability of data.