Introduction
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, i.e. 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.
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.