Knowledge-theoretic models in hydrology

The rate of progress in quantitative modelling since the 1950s has been such that application of sophisticated computer models to a wide range of geoscientific problems is now routine. It is generally held that by making such models more physically (physics) based, their explanatory power and predictive reliability are enhanced. This formulation, a model-theoretic approach, assumes accurate knowledge of the properties, states and relationships between all of the objects that are known to matter within the system of interest but, simultaneously, an incomplete understanding of the totality that this knowledge creates. In hydrological modelling, this translates into a severe dependence upon the data models that are needed to make a hydrological model work. The opposite extreme is a model-data approach in which measurements become the basis of generic relationships. Even in the most heavily data-derived cases (eg, neural network forecasting of river flows) these data models can be shown implicitly to have a theoretical content. Thus, both model-theoretic and model-data approaches sit within a general class of modelling, best labelled as ‘data-theoretic’. Here, we illustrate this point and advocate an approach that is knowledge-theoretic rather than data-theoretic, to capture the much richer sources of knowledge available to the modeller. These sources include third-party reports, personal recollections and diaries, old photographs and press articles, opinions, etc, which are, by convention, either excluded from analysis, or simply added into descriptions of model results at the point of dissemination and consultation of model findings. We conclude by noting that this approach to hydrological modelling fits into current thinking that the process by which publics engage with knowledge must be moved upstream. Here, the production of scientific knowledge comes to include not just scientists and specialists, but also those people for whom model predictions make a material difference.