SBIR/STTR Research Award

Developing an Automated Uncertainty Quantification Tool to Improve Watershed-Scale Predictions ofWater and Nutrient Cycling

Award identifier 7e9d5f158f768afac4f10716dd15ab564eeccb7455b0f26d04c25226998d212aAwarding agency Department of Energy

Award amount

$700,000

Program and phase

STTR · Phase II

Award year

2019

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Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
Finsterle, Stefan
Proposal award date
2019-05-28
Contract end date
2021-05-27
UEI
PRNYLNZFVW13
Solicitation number
DE-FOA-0001975
Topic code
01c

Research abstract

Managing the flow of water, nutrients, and contaminants in watersheds is vital to addressing pressing issues related to water scarcity, access to clean drinking water, energy production, resilience to natural and anthropogenic perturbations, and ecological restoration. Decisions about the management of watersheds critically depend on the accuracy with which the flow of water and chemicals through the watershed can be predicted by computer models. Prediction uncertainty can be reduced by matching the model to data, which are collected in the field at great expense. The contribution of watershed characterization data to reducing uncertainty of relevant model predictions can be evaluated in a so-called data-worth analysis, which provides transparent, quantitative metrics about a data set’s value for the support of relevant watershed management objectives. To achieve this goal, we are developing an innovative software package that implements the data- worth analysis approach for use with state-of-the-art watershed models. The purpose of the proposed data-worth analysis is to help decision-makers allocate resources for watershed characterization such that the uncertainty in model predictions can be significantly reduced, which leads to better, more effective management decisions. At the same time, watershed characterization costs can be reduced. The technical feasibility of performing a data-worth analysis for watershed models in the so- called Expert Mode has been demonstrated in the SBIR Phase I project. As part of this SBIR/STTR Phase II project and associated commercialization effort, we focus on developing the Automatic Mode capability, which will be a substantial competitive advantage, as it allows customers to perform data-worth analyses without the need to have detailed knowledge of the code’s underlying multitude of program options. We will demonstrate the usefulness of the developed computer program by applying it to a relevant watershed-management problem. We will also collect information about the needs and requirements from potential users – engineering consultancies, watershed managers, policy-makers, and stakeholders – so that the approach and computer program can be turned into a powerful, user-friendly, and marketable software product. The commercial sector benefits from consulting work that supports water management agencies, and from additional remediation and mitigation projects that become more viable as the planning and design phases are more cost-effective. Identifying data worth directly translates into earned and saved money for users of the toolset and ultimately the taxpayer. Thus, the proposed toolset and associated engineering services are marketable products for a significant portion of the private and public sector committed to managing our watersheds. Finally, developing a transparent monitoring system that conclusively answers potentially contentious questions about a hydrological ecosystem will help strengthen environmental justice.

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

2 SBIR/STTR records share this UEI (2018–2019). Reported award amounts total $850,000. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: Department of Energy

Agency intelligence

Department of Energy

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