Post-wildfire forecasting improvements using non-Newtonian flow processes with a high-resolution, integrated hydrologic model

Project ID: 22056
Principal Investigator: Drew Loney
Research Topic: Water Operation Models and Decision Support Systems
Funded Fiscal Years: 2022 and 2023
Keywords: None

Research Question

This effort proposes to implement and validate post-wildfire overland flow process representations at the fine spatial resolutions necessary to accurately resolve its behavior and its interaction with other hydrologic processes. The effort will address three core questions:

1) Do non-Newtonian overland flow representations improve stream volume and timing forecasts compared to Newtonian approaches?
2) Does refining spatial resolution improve overall hydrologic model accuracy by better capturing the interaction among physical processes during post-burn conditions?
3) Can spatially distributed process representations be leveraged to adapt models to post-wildfire conditions and understand hydrologic uncertainties?

It is anticipated that all three questions will be confirmed by the proposed effort. However, the extent of the improvement from combining the non-Newtonian flow processes with the high spatial resolution model will ultimately govern the demonstrated skill improvement.

Need and Benefit

The proposed effort addresses the tool development for long term planning and uncertain hydrologic conditions within the water operations models and decision support systems need. The proposed improvement of the overland flow methodology addresses the post-wildfire physical process changes. The proposed model construction tools simplify model deployment to enable continuous updates as basins recover to accelerate model deployment and save Reclamation resources during model development.

Contributing Partners

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Research Products

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Last Updated: 6/22/20