I am currently guest editing the second Annual Review issue for Hydrological Processes planned for early in 2010 which will have a number of contributions focused on preferential flows in catchments and the estimation of mean travel times or mean residence time distributions in catchments. Both of these pose interesting issues in respect of all three focus areas in the Catchment Change Network – flood generation, water quality and water scarcity. Particularly in the water quality area, the way that they are linked will have an impact over both short and longer time scales. Despite this importance, our understanding of both preferential flows and travel time distributions is, however, still limited and this got me on to thinking about developing that understanding through predictive models treated as hypotheses about how a catchment system function.
This has some implications about predicting the effects of change since we clearly cannot easily test hypotheses (or sets of modelling assumptions) about what might happen in a particular catchment of interest in the future, we more usually rely on testing hypotheses under current conditions and, given a degree of belief that we are getting the right results for the right reasons, explore the consequences for scenarios of future change. Increasing that degree of belief is the purpose of testing but there are two difficulties involved in this process. The first is that, as with classical statistical hypotheses testing, there is a possibility of making Type I (false positives, or incorrectly accepting a poor model) or Type II (false negatives, or incorrectly rejecting a good model), particular when there are observational errors in the data being used in testing. The second is that this process of predicting future change relies on a form of uniformitarianism principle; i.e. that a model that has survived current tests has the functionality required to predict the potentially different future conditions. In both cases, classical hypothesis testing will be limited by epistemic errors (see the previous entry of 27th September) in the observations and in our expectations about future processes.
That does not mean, however, that we should not try to test models as hypotheses, only that new ways of doing so might be required. We could, for example, explore the possibility of using real-world analogues for different scenarios of future conditions with (approximately) the right combinations of expected temperatures, land use and rainfalls to show that, if there are significant differences in processes the predictive model can represent them acceptably. The analogues would not, of course, be perfect (uniqueness of place suggests that calibrated parameter values would also necessarily reflect other factors) but this might increase the degree of belief in model predictions of future change rather more than relying on a model that has only been shown to reproduce historical conditions at the site of interest. As far as I know, no such study has been reported (although analogues have been used in other ways)…does any reader know of such a study?
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