A comprehensive intercomparison study between a lumped and a fully distributed hydrological model across a set of 50 catchments in the United Kingdom

Abstract

AbstractThe choice of hydrological model, especially on the catchment scale, is never an easy one. This of course is dependent on the problem under consideration and the end user requirement. The continued development in data acquisition and its availability has taken us in the direction of more complex model development and application. Yet, it remains critical to identify a model that is not only parsimonious in parametric space but also provides good results. With the above motivation the purpose of this study is to compare the performance of a widely used lumped conceptual hydrological model (GR4J) and a well‐established fully distributed mesoscale hydrological model (mHM). Model performance is measured by four goodness of fit indicators (GOFIs) as well as five hydrological signatures that examine the capability of the models applied to reproduce different components of the flow. To undertake this comparison, the two models are applied to a set of fifty catchments that are part of the UK benchmark network (UKBN2), supplemented by additional catchments, not so pristine in nature, to incorporate a wider range of catchments. The results, quantified by the GOFIs and hydrological signatures, show that the lumped model performs as well as the fully distributed model and in multiple cases better than the more complex model. However, model results across the set of fifty catchments, examined here, are highly correlated. Model performance is also examined on seasonal basis and a slight deterioration in the model performance whilst simulating the summer flows is observed. Based on the comprehensive comparison undertaken here, we conclude that the applied lumped model might be the model of choice if the problem at hand is an investigation of rainfall‐runoff at the catchment outlet, without the need to consider the spatiotemporal variation of various hydrological states and fluxes.