EGU22-8451
https://doi.org/10.5194/egusphere-egu22-8451
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Advancing the science and practice of community hydrologic modeling: Development of open-source models, methods, and datasets to enable process-based hydrologic prediction across North America (and beyond)

Martyn Clark1, Louise Arnal1, Andrew Bennett2, Dave Casson1, Shervan Gharari1, Janine Hay1, Jim Freer1, Wouter Knoben1, Hongli Liu1, Naoki Mizukami3, Bart Nijssen2, Simon Papalexiou4, Raymond Spiteri1, Guoqiang Tang1, Ashley Van Beusekom1, and Andy Wood3
Martyn Clark et al.
  • 1University of Saskatchewan Coldwater Laboratory, Canmore, Canada (martyn.clark@usask.ca)
  • 2University of Washington
  • 3NCAR
  • 4University of Calgary

Many hydrologic modelling groups face similar challenges, with untapped opportunities to share code and concepts across different model development groups. An active community of practice is emerging, where the focus is not so much on developing a community hydrologic model, but more on advancing the science and practice of community hydrologic modeling. This presentation will summarize our recent efforts to develop open-source models, methods, and datasets to enable process-based hydrologic prediction across North America (and beyond). The contributions include (1) developing ensemble meteorological datasets for North America and the globe; (2) developing modular approaches to hydrologic modeling through a hierarchal approach that separates different model sub-domains (vegetation, snow, soil, groundwater) and separates the physical representations from the numerical solution; (3) implementing third-party numerical solvers (sundials) to improve the robustness and efficiency of the numerical solutions; (4) developing agile parallelization methods capable of handling heterogeneous computing loads and bottlenecks in the downstream reaches of large river networks; (5) implementing flexible model configuration toolbox to accelerate the implementation of large-domain hydrologic models; (6) advancing methods for river lake routing, including development of integrated river-lake hydrography datasets and development of large-domain reservoir management models; (7) advancing methods for large-domain parameter estimation; (8) advancing methods for ensemble data assimilation; and (9) advancing methods for probabilistic hydrologic prediction on time scales from seconds to seasons. We will discuss some of the major challenges encountered and the high-priority research that is necessary to advance capabilities in large-domain hydrologic prediction.

How to cite: Clark, M., Arnal, L., Bennett, A., Casson, D., Gharari, S., Hay, J., Freer, J., Knoben, W., Liu, H., Mizukami, N., Nijssen, B., Papalexiou, S., Spiteri, R., Tang, G., Van Beusekom, A., and Wood, A.: Advancing the science and practice of community hydrologic modeling: Development of open-source models, methods, and datasets to enable process-based hydrologic prediction across North America (and beyond), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8451, https://doi.org/10.5194/egusphere-egu22-8451, 2022.