Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment

Abstract

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.

SuperflexPy 1.2.0: an open source Python framework for building, testing and improving conceptual hydrological models

“…Alternatively, distributed configurations can be used when the interest is in modeling hydrological behavior at individual landscape sections (e.g., sub-catchments). In such distributed setups, the catchment is subdivided into spatial elements such as sub-catchments (e.g., Feyen et al, 2008;Lerat et al, 2012), Hydrological Response Units (HRUs) (e.g., Arnold et al, 1998;Fenicia et al, 2016 Molin et al, 2020b), or grids (e.g., Samaniego et al, 2010). A common strategy for developing distributed 100 conceptual models is to represent individual landscape elements using independent (non-interacting) lumped models, and then obtain total catchment outflow by aggregating the outflows from these individual models, potentially incorporating flow routing elements to represent routing delays.…”

“…The development of the proposed framework capitalizes on the authors' collective experience in using the earlier implementations of SUPERFLEX in a series of empirical case studies over the last decade, ranging from lumped model implementations (e.g., Kavetski and Fenicia, 2011;Fenicia et al, 2014), to distributed setups (e.g. Fenicia et al, 2016;Dal Molin et al, 2020b), interpretation in the context of 195 https://doi.org/10.5194/gmd-2020-409 Preprint. Discussion started: 16 December 2020 c Author(s) 2020.…”

“…• Level 4 is needed only in models that generate predictions at interior points (e.g. Fenicia et al, 2016;Dal Molin et al, 2020b).…”

“…The 420 example follows the procedure illustrated in Sect. 2.2, creating the more realistic model M02, developed in Dal Molin et al (2020b) to provide streamflow predictions at 10 sub-catchments of the Thur catchment in Switzerland, see Figure 7a. Each sub-catchment receives its own forcing (precipitation, potential evapotranspiration, and temperature).…”

Remote sensing-aided large-scale rainfall-runoff modelling in the humid tropics

“…Opportunities exist in form of including additional variables to streamflow for model calibration and validation, providing more realistic internal hydrological partitioning (Dal Molin et al, 2020;Rakovec et al, 2016;Xiong and Zeng, 2019). The latter comes at the expense of increased computational cost (Arheimer et al, 2020).…”