Abstract. This study documents the development of a semi-distributed hydrological
model aimed at reflecting the dominant controls on observed streamflow
spatial variability. The process is presented through the case study of the
Thur catchment (Switzerland, 1702 km2), an alpine and pre-alpine
catchment where streamflow (measured at 10 subcatchments) has different
spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on
streamflow spatial variability and build a model that reflects them, we
follow a two-stage approach. In a first stage, we identify the main
climatic or landscape properties that control the spatial variability of
streamflow signatures. This stage is based on correlation analysis,
complemented by expert judgement to identify the most plausible cause–effect
relationships. In a second stage, the results of the previous analysis are
used to develop a set of model experiments aimed at determining an
appropriate model representation of the Thur catchment. These experiments
confirm that only a hydrological model that accounts for the heterogeneity
of precipitation, snow-related processes, and landscape features such as
geology produces hydrographs that have signatures similar to the observed
ones. This model provides consistent results in space–time validation,
which is promising for predictions in ungauged basins. The presented
methodology for model building can be transferred to other case studies,
since the data used in this work (meteorological variables, streamflow,
morphology, and geology maps) are available in numerous regions around the
globe.
Abstract. Catchment-scale hydrological models are widely used to represent and improve
our understanding of hydrological processes and to support operational water
resource management. Conceptual models, which approximate catchment
dynamics using relatively simple storage and routing elements, offer an
attractive compromise in terms of predictive accuracy, computational
demands, and amenability to interpretation. This paper introduces
SuperflexPy, an open-source Python framework implementing the SUPERFLEX
principles (Fenicia et al., 2011) for building conceptual
hydrological models from generic components, with a high degree of control
over all aspects of model specification. SuperflexPy can be used to build
models of a wide range of spatial complexity, ranging from simple lumped
models (e.g., a reservoir) to spatially distributed configurations (e.g., nested sub-catchments), with the ability to customize all individual model
components. SuperflexPy is a Python package, enabling modelers to exploit
the full potential of the framework without the need for separate software
installations and making it easier to use and interface with existing
Python code for model deployment. This paper presents the general
architecture of SuperflexPy, discusses the software design and
implementation choices, and illustrates its usage to build conceptual models
of varying degrees of complexity. The illustration includes the usage of
existing SuperflexPy model elements, as well as their extension to implement
new functionality. Comprehensive documentation is available online and
provided as a Supplement to this paper. SuperflexPy is available
as open-source code and can be used by the hydrological community to
investigate improved process representations for model comparison and for
operational work.
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