The data-based mechanistic approach in hydrological modelling

The chapter will outline a data-based mechanistic (DBM) approach to modeling, forecasting, and control that often starts with the construction and evaluation of a simulation model that reflects the scientists' perception of the physical, chemical, and biological mechanisms that characterize the environmental system. However, these DBM studies are not simple exercises in simulation modeling, rather they constitute a critical evaluation of the model in both stochastic and response terms. Exploiting some of the tools of DBM modeling that are later applied to real data, this critical evaluation considers the simulation model as a natural extension of the thought processes and scientific speculation that reside in the mind of the model builder. And, by providing insight into the strengths and limitations of the simulation model, it provides a prelude to the exercises in DBM modeling from real data that becomes possible when data is available on the response of the environmental system to natural or anthropogenically-induced perturbations. It is a simple, parametrically efficient (parsimonious) model that is straightforward to identify and estimate using algorithms available in the CAPTAIN toolbox for Matlab. The model is computationally efficient, in contrast to recent rainfall–flow models that rely on numerical Bayesian methods; it explains the data reasonably well on both the estimation and validation sets, and it can also be interpreted in physically meaningful terms, taking into consideration both the rainfall and the snow-melt contributions to the flow.

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