AbstractStatistical methodolgies appropriate to certain interesting study areas of environmental systems are presented. Three approaches are described which are useful for:ithe pooling of the multi‐source information coming from a number of stations, i. e., the simultaneous formalization of real and/or simulated dta together with the quantification of expert knowledge using a globally informative probability density function.iithe prediction of the class to which an environmental variable belongs among several pre‐established classes. Two non‐parametric discriminant approaches are presented; both were derived from projection pursuit techniques.iiithe modelling of the time evolution of dichotomic environmental variables; the modelling is focused on statistical inference methods, such as estimation of the probabilities of state transitions and of the stay in a particular
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