Snowpack Modeling to Support Water Supply Forecasting

This project generates the long term, historical, physically based snowpack modeling data set to enhance existing National Water and Climate Center (NWCC) water supply forecasting methods by providing additional spatial context of the snowpack state that is not capture. The proposed project has two components: creation of the historical gridded data set and demonstration of near real-time capabilities. While a similar contract currently exists, this work is for snowpack modeling for additional areas and represents completely new work not covered by the current contract.

The first component is long-term historical snow state modeling (including a minimum of SWE, cold content, surface water input), encompassing water year (WY) 1980 through WY 2025 for approximately 12 model domains at 5000 square mile (approx.) each to be specified through a collaborative effort with NWCC hydrologists. Modeling domains will encompass different hydroclimatic and snowpack regimes in the mountainous Western United States where snowmelt provides most of the available surface water. High resolution gridded modeled snow water equivalent (SWE) data will then be aggregated and summarized by subbasin and elevation bands within the modeling domain. The summarized information will be passed to NWCC hydrologists for evaluation in the existing operational water supply forecasting system to determine viability of these input variables for improving water supply forecast skill.

The second component is the near real-time running of the same modeling systems and framework to support operational water supply forecasting. Thus, any product derived from gridded snow data must also be capable of supporting real-time modeling, which is defined here as the capability of delivering model results from the previous day by 8:00 am Pacific Time (24 hours latency) for all basins of interest within the modeling domains.

Project Requirements:

1. Begin work by October 1, 2025.


2. Employ a physically based snowpack model capable of high resolution (
   
3. Use high resolution (
   
4. Use lidar derived bare earth elevation data to initiate the snowpack model if available. Comprehensive lidar-derived vegetation parameterization should also be included within the model if available. This includes characterization of large fire scars, if applicable, as terrain, vegetation, and disturbance at fine scales are an important control on snowpack accumulation and evolution.


5. Assimilate remote sensing data into the model including snow depth, albedo, and snow-covered area as available. Provide data assimilation reports of the changes and results.


6. Implement spatial and temporal bias correction methods for model forcing variables with a focus on precipitation data. The need for bias-corrected input fields should be based on comparison to existing weather and snow monitoring station measurements, including the SNOTEL network. Bias correction methods must be capable of being deployed in near real-time to be applicable for operational water supply forecasting.


7. Deliver a report quantifying model performance / error in snowpack state variables (to include depth, density, and SWE) based on comparisons between model results and snow measurements from observation stations (including the SNOTEL network) across all model runs for each modeling domain.


8. Deliver daily gridded snowpack state and flux fields, and any derived snowpack summary products for WY 1980 to WY 2025 from the physically-based snowpack model.


9. Deliver daily gridded bias-corrected forcing fields for WY 1980 to WY 2025 and a summary report of the bias correction methods and comparisons to observations (including observations from the SNOTEL network).


10. Deliver documentation detailing model operation, input data sources, and calibration / validation results.


11. Demonstrate the capability of near real-time modeling to support operational streamflow forecasting by providing model output and derived data products at the specified latency. This includes: 1) near real-time daily SWE grids, summarized by subbasin and elevation bands; 2) analysis reports on a semimonthly basis (1st and 16th day of the month) from December 1 through July 16 detailing distributed snowpack state variables (SWE, cold content and surface water input) how they have changed since the previous report.


12. Deliver summary data in a format to facilitate integration and testing within the existing NWCC water supply forecasting system and architecture.

Project Timeline, Deliverable and Invoice Schedule:

Timelines are for delivery from project start which is targeted to be October 1, 2025. An accelerated delivery schedule is preferred.

January 1, 2026: Complete modeling for a single basin for the entire period of record (WY 1980 to WY 2025) with applied forcing data bias adjustments and snowpack data assimilation as possible. Deliver gridded, daily model results and summarized data products to NWCC. Invoice opportunity for work completed (15% contract total).

February 1, 2026: Demonstrate real-time modeling capability for initial model domain. Invoice opportunity for near real-time modeling capability (10% contract total).

July 1, 2026: Complete optimized/debiased model runs for half of the remaining model domains for the entire historical period of record (WY 1980 to WY 2025). Invoice opportunity for work completed (25% contract total).

January 1, 2027: Complete optimized/debiased model runs for all remaining model domains for the entire historical period of record (WY 1980 to WY 2025). Final bias correction, data assimilation and model performance reports complete. Invoice opportunity for work completed (30% contract total).

Water Year 2027: Initiate real-time modeling capability for all model domains for first available full water year.

Provide near real-time daily SWE grids, subbasin and elevation band summaries. Invoice for work completed (20% contract total).