NONPARAMETRIC APPROACHES TO ENVIRONMENTAL IMPACT ASSESSMENT¹

Abstract

The interesting developments in non‐parainetric testing and estimation methods presented in the upcoming sequence of nine papers are evaluated, compared, and put into proper perspective. Because a deterioration in water quality constitutes a direct threat to human health, it is of utmost importance to have flexible non‐parametric methods available for detecting and describing trends in water quality time series. A distinct advantage of nonparametric tests is that they are usually very effective when applied to “messy” environmental data which may, for example, contain many missing observations and not be normally distributed. By applying their enhanced approaches for nonparametric methods to water quality time series, as well as employing well designed simulation experiments, the authors of the papers clearly demonstrate the efficacy of utilizing nonparametric tests in environmental impact assessment.

Statistical estimation and interpretation of trends in water quality time series

“…A simulation study of the test, when seasonal observations are assumed to be correlated, is presented by Hirsch and Slack [1984]. The test has been applied to water quality series by several authors [e.g., Smith et al, 1982;Hipel, 1988]. The test is further extended to m dependent seasonal observations by Zetterqvist [1988].…”

Trend, seasonality, cycle, and irregular fluctuations in primary productivity at Lake Tahoe, California-Nevada, USA

“…As a result, many techniques have been developed to assess whether or not a significant change is underway and what its magnitude might be (Hipel, 1988). It is not possible, however, to identify change as a trend based solely on the statistical significance of an increase or decrease.…”

Water Quality Monitoring and Network Design

“…The use of most of these techniques is shown by data analyses, often including regression techniques and standard error criteria in estimation of parameters that relate to the information conveyed by data (HipeI, 1988;Hirsch, 1988;Lettenmaier, 1988;Tirsch and Male, 1984;. For example, Gupta (1982) uses the Modified Langbein Method, which is essentially based on regression techniques, to maximize information gain via optimum data lengths determined for primary and secondary gaging stations.…”