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Measurement uncertainties are inherent to assessment of biological indices of water bodies. The effect of these uncertainties on the probability of misclassification of ecological status is the subject of this paper. Four Monte-Carlo (M-C) models were applied to simulate the occurrence of random errors in the measurements of metrics corresponding to four biological elements of surface waters: macrophytes, phytoplankton, phytobenthos, and benthic macroinvertebrates. Long series of error-prone measurement values of these metrics, generated by M-C models, were used to identify cases in which values of any of the four biological indices lay outside of the “true” water body class, i.e., outside the class assigned from the actual physical measurements. Fraction of such cases in the M-C generated series was used to estimate the probability of misclassification. The method is particularly useful for estimating the probability of misclassification of the ecological status of surface water bodies in the case of short sequences of measurements of biological indices. The results of the Monte-Carlo simulations show a relatively high sensitivity of this probability to measurement errors of the river macrophyte index (MIR) and high robustness to measurement errors of the benthic macroinvertebrate index (MMI). The proposed method of using Monte-Carlo models to estimate the probability of misclassification has significant potential for assessing the uncertainty of water body status reported to the EC by the EU member countries according to WFD. The method can be readily applied also in risk assessment of water management decisions before adopting the status dependent corrective actions.

This article addresses the issue of estimating Pom—the probability of misclassifying the chemical status confidence of a water body status assessment. The main concerns of the authors were chemical quality elements with concentrations in water bodies which are close to or even smaller than the limit of quantification (LOQ). Their values must be set to half of this limit to calculate the mean value. This procedure leads to very low standard deviation values and unrealistic values of Pom for chemical indicators. In turn, this may lead to the false conclusion that not only is the chemical status good but also that this status assessment is perfect. Therefore, for a more reliable calculation of Pom, the authors suggested a modified calculation in which the value of half the LOQ for calculating the mean value was kept, but zero as the concentration value for the standard deviation calculation was adopted. The proposed modification has been applied to the Hierarchical Approach procedure for Pom estimation of the chemical status of Polish rivers and lakes. The crucial finding is that current chemical status assessments may be incorrect in the case of approximately 25% of river water bodies and 30% of lake water bodies categorised as good, and 20% of both types of water bodies classified as below good.

Within the context of assessing status of water bodies in EU countries, the Water Framework Directive (WFD) has introduced notions of confidence, precision and probability of misclassification (PoM). Although defined by WFD in a rather vague manner, the three measures of uncertainty have become compulsory elements of the reporting process. The EC requires all classifications of European water bodies to be accompanied by estimates of these uncertainty measures. The article describes the Hierarchical Approach introduced to assess PoM of riverine water bodies' status using uncertain water quality monitoring data. The approach stems from the observation that uncertainty of higher level classifications (e.g. assessment of water body status) depends on uncertainties of lower order assessments, (e.g. assessments of chemical status and ecological status of that water body). Specifically, the Hierarchical Approach describes how uncertainties intrinsic for water quality measurements propagate through the stages of water body status classification. To assess PoM of a water body, twodimensional probability distributions are used sequentially. At every stage, they are derived by combining one-dimensional probabilities of committing statistical errors of the II-nd type when classifying corresponding elements of the lower stage. For instance, to assess PoM of status of some water body two onedimensional PoMs of its chemical and biological status are used. The proposed method of assessing PoM is also shortly discussed within the context of risks involved in water management decisions based on misclassified water bodies.