Conceptual models are among the most frequently used type of models in watershed modelling studies, due to their low computational requirements and ease of interpretation. Model selection requires the comparison of model alternatives, which is complicated when the models present in the literature differ in many uncontrolled aspects, such as conceptualization, implementation, and source code availability. To overcome this limitation, several model-building frameworks have been introduced in the last decade, which facilitate model comparisons by enabling different model alternatives within the same software and numerical architecture. Building on the decennial experience with the development and usage of Superflex, a flexible modeling framework for conceptual model building, so far implemented in FORTRAN language and not available as open source, we propose SuperflexPy, an open source Python framework for building conceptual hydrological models. Compared to other existing models or flexible frameworks, SuperflexPy is designed to be extremely easy to modify or extend, allowing scientists to build models that reflect their processes understanding; thanks to its object-oriented architecture and its complete integration with the Python programming environment, SuperflexPy can be seen as a high-level programming language for constructing hydrological models that are extremely flexible both in terms of elements configuration (i.e. how the elements are connected into a structure) and spatial organization. By design, SuperflexPy is not limited to water quantity but can be extended to simulate transport processes (water quality). In this presentation, we will illustrate the principles behind the design of the framework and showcase some applications.
The development of semi-distributed hydrological models that reflect the dominant processes controlling streamflow spatial variability is a challenging task. In small, well-instrumented headwater catchments the model can be built taking advantage of knowledge derived from extensive fieldwork activities; that is, however, not possible in much larger catchments where, usually, these models are actually needed. To address this problem, we propose a new methodology where we analyze the correlations between hydrological signatures, catchments characteristics, and climatic indices to get insights about the hydrological functioning of the catchment and to guide the decisions involved in the development of a semi-distributed model. The methodology is tested in the Thur catchment (Switzerland, 1702 km2); in a first stage we show how to identify catchment characteristics and climatic indices that control streamflow variability; in a second stage, we use these findings to develop a set of model experiments aimed at determining an appropriate model representation for the catchment. Results show that only models that account for the influencing factors indicated by the correlation analysis are able to represent correctly the observed streamflow signatures, confirming our understanding of the processes happening in the catchment.