Development of an integrated hydrologic model with high-resolution snow processes for water supply forecasting and forecast-based operations

Project ID: 22055
Principal Investigator: Drew Loney
Research Topic: Water Supply Forecasting
Funded Fiscal Years: 2022, 2023 and 2024
Keywords: None

Research Question

This effort proposes to implement and validate snow processes 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) Does refining the spatial resolution to more accurately describe slope and aspect that snow processes improve model accuracy?
2) Does refining spatial resolution improve overall hydrologic model accuracy by better capturing the interaction among physical processes?
3) Do more new snow process representations, such as the radiation-derived temperature index (RTI) method (Follum et al., 2018), better capture snow processes compared to current methods?

It is anticipated that the first two hypotheses will be confirmed by the proposed effort. The third hypothesis may vary by selected basin. However, it will be investigated specific to the two validation cases.

Need and Benefit

The proposed effort addresses the snowpack and model performance enhancement needs in the water supply/streamflow forecasting area. Snowpack modeling is addressed by expanding the algorithms available within the ADHydro engine and validating its performance as an integrated hydrologic model at several basins. The hydrologic modeling is addressed by evaluating ADHydro as a high spatial resolution, physics based hydrologic model capable of describing the interaction of multiple physical processes.

Contributing Partners

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Research Products

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Last Updated: 6/22/20