Hydrological field data from a modeller's perspective: part 2: process-based evaluation of model hypotheses

The current generation of hydrological models has been widely criticized for their inability to adequately simulate hydrological processes. In this study, we evaluate competing model representations of hydrological processes with respect to their capability to simulate observed processes in the Mahurangi River basin in Northland, New Zealand. In the first part of this two-part series, the precipitation, soil moisture, and flow data in the Mahurangi were used to estimate the dominant hydrological processes and explore several options for their suitable mathematical representation. In this paper, diagnostic tests are applied to gain several insights for model selection. The analysis highlights dominant hydrological processes (e.g. the importance of vertical drainage and baseflow compared to sub-surface stormflow), provides guidance for the choice of modelling approaches (e.g. implicitly representing sub-grid heterogeneity in soils), and helps infer appropriate values for model parameters. The approach used in this paper demonstrates the benefits of flexible model structures in the context of hypothesis testing, in particular, supporting a more systematic exploration of current ambiguities in hydrological process representation. The challenge for the hydrological community is to make better use of the available data, not only to estimate parameter values but also to diagnostically identify more scientifically defensible model structures