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

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0018473
Agency Tracking Number: 243706
Amount: $700,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: 01c
Solicitation Number: DE-FOA-0001975
Timeline
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-05-28
Award End Date (Contract End Date): 2021-05-27
Small Business Information
315 Vassar Avenue
Kensington, CA 94708-1105
United States
DUNS: 080441499
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Stefan Finsterle
 (510) 516-2506
 stefan@finsterle-geoconsulting.com
Business Contact
 Stefan Finsterle
Phone: (510) 516-2506
Email: stefan@finsterle-geoconsulting.com
Research Institution
 Lawrence Berkeley National Laboratory
 Bhavna Arora
 
1 Cyclotron Road
Berkeley, CA 94720-8099
United States

 (510) 486-2163
 Federally funded R&D center (FFRDC)
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.

* Information listed above is at the time of submission. *

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