Towards more credible models in catchment hydrology to enhance hydrological process understanding: Preface

Catchment modelling has undergone tremendous developments during the past decades. In the 1970s, the focus was on simulation of catchment runoff with process descriptions and data inputs being lumped to the catchment scale. Later developments included spatially distributed models allowing data inputs and hydrological processes to be simulated at model grid scale, that is, much finer than catchment scale. These models were able to explicitly simulate various processes such as soil moisture, evapotranspiration, groundwater and surface runoff. With the advancements in remote sensing technology and availability of high-resolution data, increased attention has in recent years been given to enhancing the capability of catchment models to reproduce spatial patterns and in this way improve our understanding of hydrological processes and the physical realism of catchment models. This development process has involved a wide spectrum of different aspects in the modelling process, reaching from an improved understanding of uncertainties in data, model parameters and model structures to new protocols for good modelling practices in water management. Recognizing the important role of biodiversity and social aspects, hydrologists are now extending the scope of their models to capture the interactions between water, biota and human social systems.<br/><br/>This special issue (SI) of hydrological processes is the result of an open call for abstracts announced in October 2020. The SI comprises a collection of 14 papers authored and co-authored by 77 scientists from 37 research institutions in 16 countries. Based on the key focus for each of the papers we have grouped them into five thematic topics: (i) review papers; (ii) papers developing and testing new process descriptions; (iii) papers focusing on how model calibration can improve process descriptions; (iv) papers exploring how the use of multiple model structures can improve model performance and process descriptions; and (v) papers focusing on modelling uncertainties. The grouping of the papers into the five topics should be considered as indicative only, because all papers address more than one of the five themes. The key findings in the papers of this Special Issue are summarized in the following five topic sections.