Conventional multivariate statistical methods have been used for decades to calculate
environmental indicators. These methods generally work fine if they are used in a situation where the
method can be tailored to the data. But there is some skepticism that the methods might fail in the
context of skewed data distributions or spatial auto-correlation. Further, the indicators developed by
statistical approaches can not be used to compare different regions or time periods that had not been
covered by the input data. The aim of the paper is to demonstrate some of the shortcomings and to
identify how the Malmquist index might be used as an alternative. The paper presents the results of
an exhaustive review in the field of environment, hydrology and water quality which identified the most
commonly used approaches. Then principal component analysis as representative of these
approaches and the Malmquist index are challenged with simulated time series data to demonstrate
the failure of statistical methods in two of the most common problems faced in construction of a water
quality index.