Top-down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale

The data-based mechanistic (DBM) approach to modelling has developed as a stochastic, 'top-down' response to the problems associated with the deterministic, 'bottom-up' approach. As such, it can be compared with the deterministic, top-down modelling methods that have been attracting attention recently in the hydrological literature. Using catchment-scale rainfall-flow modelling as an example, this paper compares the inductive DBM approach with its hypothetico-deductive, deterministic alternative and shows how they can be used to identify and estimate low-order, nonlinear models of the rainfall-flow dynamics in the River Hodder catchment of northwest England based on a limited set of rainfall-flow data.