Large-sample based evaluation of the spatial resolution discretization of the wflow_sbm model for the CAMELS dataset

In this study, we investigate the effect of spatial resolution discretization at 3km, 1km, and 200m by evaluating the streamflow estimation of the model. A hypothesis driven approach is used to investigate why changes in states and fluxes are taking place at different spatial resolutions and how they relate to model performance. These changes are evaluated in the context of landscape and climate characteristics as well as hydrological signatures. Answering the research question: can landscape, climate and hydrological characteristics dictate appropriate spatial modelling resolution a priori?

We use a spatially distributed wflow_sbm model (Imhoff et al., 2020, code: https://zenodo.org/record/4291730) together with the CAMELS dataset (Addor et al., 2017), covering the Continental United States. The wflow_sbm model is chosen due to flexibility in the spatial resolution of the watershed discretization while maintaining run time performance suitable for large-sample studies. The flexibility in spatial resolution is achieved by the use of point-scale (pedo)transfer functions (PTFs) with upscaling rules to global datasets to ensure flux matching across scales (Imhoff et al., 2020; Samaniego et al., 2010, 2017). The model relies on open datasets for parameter estimation and requires minimal calibration efforts as it is most sensitive to two model parameters, rooting depth and horizontal conductivity .

This study is carried out within the eWaterCycle framework; allowing for a FAIR by design research setup that is scalable in terms of case study areas and hydrological models.