Wijayarathne, D., Keshavarz, K., Stadnyk, T., Pietroniro, A., Clark, M., and Knoben, W.
HYPE model workflow – a “bottom-up” approach to community large-domain hydrological modelling
Wijayarathne, D., Keshavarz, K., Stadnyk, T., Pietroniro, A., Clark, M., and Knoben, W. (2023). HYPE model workflow – a “bottom-up” approach to community large-domain hydrological modelling, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9545, https://doi.org/10.5194/egusphere-egu23-9545
Large-domain hydrological modelling is vital to understand and predict water resources under a changing climate. Here we summarize our efforts to develop a model configuration workflow for the Hydrological Predictions for the Environment (HYPE) model as a proof-of-concept of a “bottom-up” approach to community large-scale hydrological modelling. The initiative of Community Workflows to Advance Reproducibility in Hydrologic Modeling (CWARHM, Knoben et al. 2022) provides a blueprint of a hydrological modelling workflow, separating the model-agnostic and model-specific pre-processing tasks. We extend the CWARHM blueprint to establish an open-source and automated HYPE workflow by adding processing codes to generate geospatial fabric, climate forcing, and parametrization.
Our primary contribution is to generalize and automate the HYPE workflow to improve the reproducibility of hydrologic experiments. In this research, numerous global geographic, physiographic, and climatic datasets, covering various spatiotemporal scales are used to develop a geospatial fabric and climate forcing for the HYPE model, using the Bow River watershed in Alberta, Canada as a test case. The geographic and physiographic data are obtained through the “gistool” (https://github.com/kasra-keshavarz/gistool
), while climate forcing is obtained using the “datatool” (https://github.com/kasra-keshavarz/datatool
). Independent of the data source, these tools provide physiographic attributes and meteorological time series as catchment averaged quantities, enabling semi-distributed hydrological modelling with HYPE. The preliminary analysis shows that the HYPE workflow has successfully separated the model-agnostic and model-specific parts of the model workflow. It substantially reduces manual work in preparing model geospatial fabric and input datasets, saving more time for hydrological analysis. This workflow will support developing probabilistic streamflow using different input datasets and will be upgraded to create a HYPE model instantiation for the entire North American domain.