Abstract
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.
Highlights
1.1 Conceptual hydrological modelsCatchment-scale hydrological models are widely used to predict catchment behavior under natural and human-impacted conditions as well as to represent and improve our understanding of internal catchment functioning (e.g., Beven, 1989)
We focus on flexible frameworks intended for conceptual hydrological modeling
This hydrological response units (HRUs) model structure differs from model structure M4 (Sect. 3.1) in the following additional elements: (i) a “snow” reservoir, WR, which controls the partition of incoming precipitation between rainfall and snowfall based on temperature, (ii) a lag function between UR and fast” reservoir (FR), and (iii) a “slow” reservoir, SR, which acts in parallel to FR and is controlled by the same equations as FR but with different parameter values
Summary
Catchment-scale hydrological models are widely used to predict catchment behavior under natural and human-impacted conditions as well as to represent and improve our understanding of internal catchment functioning (e.g., Beven, 1989). An important class of catchment models are “processbased” models, which attempt to explicitly describe the cascade of processes transforming catchment inputs (e.g., precipitation) into outputs (e.g., streamflow) These models are an appealing choice due to their broad physical underpinnings as well as their ability to represent internal catchment processes and their potential for predicting catchment responses under changing environmental conditions. A common strategy for developing distributed conceptual models is to represent individual landscape elements using independent (non-interacting) lumped models and obtain total catchment outflow by aggregating the outflows from these individual models, potentially incorporating flow routing elements to represent routing delays This strategy is often referred to as “semi-distributed” modeling (e.g., Boyle et al, 2001) and typically employs discretization based on principles of “hydrological similarity” (e.g., Sivapalan et al, 1987); HRU-based discretization is common (e.g., Leavesley, 1984). For the purposes of this presentation, we consider semi-distributed modeling to be a special case of distributed modeling
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