As the earth's population grows, on a planet that is shrinking due to improved transportation and communication systems, there is a need for concerted efforts in the environmental sciences. In the 1990s, it has become apparent that the appropriate organism to study is the earth itself, that local processes are subcomponents of regional processes that in turn are components of global processes. Variability is inherent at all scales and is often one of the most vexing parts of an environmental study. The traditional scientific method of controlling variability in the presence of responses and explanatory variables is statistical experimental design. There one has the opportunity to perform experiments over and over while incorporating the three principles of blocking, randomization, and replication.
Environmental statistics is concerned with the study of variability in the environmental sciences.
From a classical point of view, every environmental study and every datum within a study is unique and incomparable to any other datum. However, from a statisical-modeling point of view, environmental processes have structure and are characterizable through interpretable parameters. The traditional statistical goals of parameter estimation are augmented or replaced with the more basic goals of process prediction, for which the tools of Bayesian statistics play a fundamental role. Oftentimes, these processes exhibit important spatio-temporal variability that needs to be characterized, in conjunction with purported dependencies on explanatory variables. Finally, prediction of processes under study are usually for small areas at given points in time.