On the Evaluation of Model Performance in Physical Geography

Following the lead of Haggett and Chorley (1967), Rayner (1974), Terjung (1976), Strahler (1980) and many others, physical geography has adopted a “model-based paradigm” and, as a result, the development and application of a wide variety of models is now commonplace within virtually every sub-field from geomorphology to bioclimatology. Within climatology, many models have a predominately deductive genesis while other models are collages of statistical and empirical reasoning and, in a few cases, “best-fit” functions are extracted from data with seemingly little regard for the safeguards of a deductive stance. Still other models combine the mathematics of probability theory with empirically derived probabilities to create stochastic simulation models, e.g., Markov or Monte Carlo models. These categories of models are, by no means, mutually exclusive (or exhaustive for that matter) and a number of recent models may be considered combinatorial in that they incorporate two or more of the above-mentioned strategies into a single model.