Opportunity Information: Apply for BOR DO 17 N027
The Multi-Algorithm Reservoir Sedimentation Estimation opportunity is a research-focused cooperative agreement from the U.S. Department of the Interior, Bureau of Reclamation (BOR), aimed at improving how sediment accumulation is estimated in aging reservoirs across the western United States. These reservoirs are central to water supply storage and flood risk management, but many are approaching or operating beyond the period they were originally designed for, and one of the biggest unknowns is how quickly storage capacity is being lost to sedimentation. Because direct sediment monitoring and reservoir surveys can be expensive, infrequent, and logistically difficult, the core idea of this project is to use coordinated modeling to reconstruct past sediment loading, estimate current conditions, and project future sediment inflows, while clearly quantifying uncertainty.
Technically, the project emphasizes a multi-model strategy rather than relying on a single sediment model. The proposed framework combines five different sediment modeling approaches spanning empirical, conceptual, and process-based methods. A key requirement is that the models be meaningfully different from each other, so the effort is not just running variations of the same physics with different settings, but instead comparing independent representations of sediment generation and transport. This is intended to strengthen confidence in results and make uncertainty estimates more defensible. The modeling system is built around the 1/16-degree Variable Infiltration Capacity (VIC) model framework developed for BOR by Livneh et al. (2015), which provides consistent hydrologic forcing and structure for integrating sediment algorithms at a regional scale. In addition to comparing model types, the work also investigates scale effects, recognizing that sediment processes can behave differently depending on spatial resolution and watershed characteristics.
A major component of the work is rigorous evaluation, including calibration and validation of simulated sediment loading and discharge against available observations. This matters because even strong theoretical models can drift without careful tuning and testing, especially when applied across diverse western basins with different climates, soils, vegetation, topography, and land uses. Once evaluated, the framework is intended to be applied to reservoirs that have not been surveyed recently (or may never have been surveyed in detail), producing projected sedimentation trajectories and, importantly, probabilistic estimates of reservoir sediment "design life" rather than a single deterministic number. In practical terms, that means generating ranges and likelihoods for when sedimentation will materially reduce storage or affect operations, giving managers a clearer sense of risk.
The expected outcomes are both scientific and operational. Scientifically, the project aims to improve understanding of sedimentation processes across the western U.S. and clarify how different modeling assumptions influence results. Operationally, it aims to deliver uncertainty-aware sedimentation estimates that can help identify which reservoirs or watersheds face the greatest near- to mid-term sedimentation risk, supporting prioritization of monitoring, maintenance, or mitigation strategies (for example, targeted surveys, watershed erosion control, sediment bypassing, dredging feasibility studies, or revised operational planning).
From an administrative standpoint, this was a discretionary funding opportunity (Funding Opportunity Number BOR DO 17 N027) offered as a cooperative agreement under CFDA 15.560. Eligible applicants were public and state-controlled institutions of higher education. The opportunity was posted on April 27, 2017, had an original closing date of May 11, 2017, and anticipated a single award with a ceiling of $298,515.Apply for BOR DO 17 N027
- The Department of the Interior, Bureau of Reclamation in the science and technology and other research and development sector is offering a public funding opportunity titled "Multi-Algorithm Reservoir Sedimentation Estimation" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.560.
- This funding opportunity was created on Apr 27, 2017.
- Applicants must submit their applications by May 11, 2017. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $298,515.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Public and State controlled institutions of higher education.
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Frequently Asked Questions (FAQs)
What is the "Multi-Algorithm Reservoir Sedimentation Estimation" opportunity?
It is a research-focused cooperative agreement from the U.S. Department of the Interior, Bureau of Reclamation (BOR). The project is designed to improve how sediment accumulation is estimated in aging reservoirs across the western United States, especially where direct monitoring and reservoir surveys are limited, expensive, or infrequent.
Which federal agency is offering this grant opportunity?
The opportunity is offered by the U.S. Department of the Interior, Bureau of Reclamation (BOR).
What type of funding mechanism is used?
It is offered as a cooperative agreement.
What is the Funding Opportunity Number (FON)?
The Funding Opportunity Number is BOR DO 17 N027.
What CFDA number is associated with this opportunity?
The opportunity is listed under CFDA 15.560.
Who was eligible to apply?
Eligible applicants were public and state-controlled institutions of higher education.
When was the opportunity posted?
The opportunity was posted on April 27, 2017.
What was the original closing date?
The original closing date was May 11, 2017.
How many awards were anticipated?
The announcement anticipated a single award.
What was the maximum funding amount (ceiling)?
The award ceiling was $298,515.
What problem is this project trying to address?
Many reservoirs in the western United States are approaching or operating beyond their original design period. A major uncertainty for reservoir operators is the rate at which storage capacity is being lost to sedimentation. Because sediment monitoring and reservoir surveys can be costly and logistically challenging, this project focuses on reconstructing past sediment loading, estimating current conditions, and projecting future sediment inflows using coordinated modeling, while explicitly quantifying uncertainty.
Why is reservoir sedimentation such an important issue for BOR reservoirs?
The reservoirs are central to water supply storage and flood risk management. Sedimentation reduces storage capacity over time, which can materially affect reservoir operations and long-term performance.
What is the core technical approach proposed in this project?
The project uses a coordinated, multi-model modeling approach to estimate sediment loading and reservoir sedimentation over time. It is intended to reconstruct historical sediment inputs, estimate present conditions, and project future sediment inflows, all while providing clear uncertainty quantification.
What does "multi-algorithm" or "multi-model strategy" mean in this context?
Instead of relying on a single sediment model, the framework combines five different sediment modeling approaches spanning empirical, conceptual, and process-based methods. The intent is to compare independent representations of sediment generation and transport to strengthen confidence in results and make uncertainty estimates more defensible.
How different do the five sediment modeling approaches need to be?
A key requirement is that the models be meaningfully different from one another. The goal is not to run minor variations of the same physics with different parameter settings, but to compare genuinely independent approaches to representing sediment processes.
What hydrologic modeling framework is used as the foundation for integrating sediment algorithms?
The modeling system is built around the 1/16-degree Variable Infiltration Capacity (VIC) model framework developed for BOR by Livneh et al. (2015). It provides consistent hydrologic forcing and a common structure for integrating sediment algorithms at a regional scale.
What does the 1/16-degree VIC configuration imply for this work?
The project is organized around a regional-scale hydrologic framework at 1/16-degree resolution, which supports consistent application across western basins and provides a shared foundation for comparing different sediment algorithms.
Does the project consider how model results change with scale?
Yes. The work investigates scale effects, recognizing that sediment processes can behave differently depending on spatial resolution and watershed characteristics.
How will the models be evaluated to ensure credibility?
A major component is rigorous evaluation, including calibration and validation of simulated sediment loading and discharge against available observations. This is emphasized because even theoretically strong models can drift without careful tuning and testing, especially when applied across diverse western basins.
What kinds of basin differences make calibration and validation especially important?
The western United States includes basins with different climates, soils, vegetation, topography, and land uses. These differences can strongly influence sediment generation and transport, making testing against observations important for defensible results.
How will this project help reservoirs that have limited survey data?
After model evaluation, the framework is intended to be applied to reservoirs that have not been surveyed recently, or may never have been surveyed in detail. The goal is to produce sedimentation trajectories and risk-informed estimates even where traditional survey data are sparse.
What is meant by projecting "sedimentation trajectories"?
It refers to generating estimates of how sediment inflows and accumulation may evolve over time, including reconstruction of past loading, estimates of current conditions, and projections of future sediment inflows.
What is meant by "probabilistic" estimates of reservoir sediment design life?
Rather than producing a single deterministic date or number, the project aims to produce ranges and likelihoods for when sedimentation will materially reduce storage or affect operations. This provides a clearer understanding of risk and uncertainty for decision-making.
What does "design life" mean in this opportunity?
In the context provided, it refers to the period over which a reservoir can meet performance expectations before sedimentation materially reduces storage capacity or affects operations. The project emphasizes estimating this in a probabilistic, uncertainty-aware way.
What kinds of practical management decisions could this work support?
The results are intended to help identify which reservoirs or watersheds face the greatest near- to mid-term sedimentation risk. That can support prioritization of monitoring, maintenance, or mitigation strategies such as targeted surveys, watershed erosion control, sediment bypassing, dredging feasibility studies, or revised operational planning.
What are the expected scientific outcomes?
The project aims to improve understanding of sedimentation processes across the western U.S. and clarify how different modeling assumptions influence results.
What are the expected operational outcomes?
The project aims to deliver uncertainty-aware sedimentation estimates to support risk-informed reservoir management, including identifying higher-risk reservoirs and helping prioritize monitoring and mitigation actions.
Is the project limited to the western United States?
Yes. The opportunity is focused on aging reservoirs across the western United States.
Why not rely only on direct sediment monitoring and reservoir surveys?
The opportunity notes that direct sediment monitoring and reservoir surveys can be expensive, infrequent, and logistically difficult. The project approach is intended to complement or help guide such efforts by using coordinated modeling and uncertainty quantification.
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