SBIR/STTR Research Award

Machine learning and ocean variables for improved predictions of water availability in the US

Award identifier 987f78c88af0f28b52ed70480543201172af4d229a20d592244ee8b66330f61fAwarding agency Department of Commerce · National Oceanic and Atmospheric Administration

Award amount

$649,935

Program and phase

SBIR · Phase II

Award year

2024

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Award details

Awarding agency
Department of Commerce
Branch
National Oceanic and Atmospheric Administration
Recipient company
SALIENT PREDICTIONS INC
Proposal award date
2024-09-01
Contract end date
2026-08-31
UEI
LSE3NRMW8L25
Solicitation number
NOAA-OAR-TPO-2024-2008239
Topic code
9.4

Research abstract

Salient proposes to develop technology for accurate subseasonal to seasonal (S2S) water availability forecasts so that communities may manage water resources in preparation for hazardous events like floods and droughts. Hydrological models face challenges related to data inputs, as climate change has been presenting weather patterns atypical from historical data. Without quality data inputs, the accuracy of the output from hydrological models suffers. To overcome these challenges, Salient proposes to use improved S2S forecasts as the weather input to Variable Infiltration Capacity (VIC) hydrological forecasts, thus providing improved water availability forecasts on the S2S timeframe. Salient’s base S2S forecast technology uses machine learning and various oceanic, atmospheric, and land-based variables to make improved predictions of weather up to a year in advance. In Phase I, Salient developed a predictive hydrologic model for soil moisture and river flow by combining its weather forecasts with the VIC hydrologic model for the Central Valley of California. Phase II will address challenges related to 1) Downscaling Salient’s forecasts for utilization as an input to the VIC model for improved soil moisture predictions, and 2) Improving scalability to make the deployment and execution of VIC hydrological models for the continental United States feasible. Successful completion of the proposed work will create a more efficient, scalable, and accurate version of the Phase I model and improve S2S forecast variables so that distributed hydrological models can more accurately forecast at the 2–52-week horizon for a variety of commercial applications. Many markets depend on water availability to thrive, including agriculture, energy, supply chain and logistics, disaster risk management, and the carbon market. At local scales, decision-makers depend on models like VIC to make strategic decisions based on hydrological responses to resource use. At present, the current VIC model does not account for human impacts from climate change, which directly affect water availability. Salient’s S2S weather insights (currently available) and water insights (in development via this funding) help enterprise customers optimize performance, mitigate risk, improve resiliency, and plan initiatives up to a year in advance. Combining Salient’s current technology with VIC will result in more useful hydrological models and water availability forecasts.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

3 SBIR/STTR records share this UEI (2023–2024). Reported award amounts total $1,099,793. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Commerce, National Science Foundation

Agency intelligence

Department of Commerce

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