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.