Improving Data Anomaly Detection and Forecasting in the US Army Corps of Engineers Reservoir Sedimentation Information (RSI) Database | W81EWF 23 SOI 0007

Opportunity Information: Apply for W81EWF 23 SOI 0007

The Department of Defense, through the U.S. Army Engineer Research and Development Center (ERDC), is seeking a single cooperative agreement award to improve data quality controls and forecasting capability within the U.S. Army Corps of Engineers Reservoir Sedimentation Information (RSI) database. The opportunity focuses on expanding anomaly detection and prediction methods for reservoir sedimentation surveys and then demonstrating whether the resulting models can credibly forecast future storage capacity loss. The work is positioned as applied research and development that supports planning and decision-making by helping USACE anticipate the economic, environmental, operational, and social consequences of reservoir sedimentation.

The first base task expands the underlying dataset used for analysis. Earlier work only included USACE and U.S. Bureau of Reclamation (USBR) reservoirs that had three or more sedimentation surveys recorded in RSI. Under this award, the analysis is broadened to include every USACE and USBR reservoir in RSI with at least two surveys, adding 133 reservoirs immediately and likely more as new data are incorporated during summer 2023 updates. This expansion is meant to strengthen the RSI system's data quality control by extending preliminary survey flagging, autonomous anomaly detection, and prediction model development across a much larger portion of the inventory. To do that, the performer will need to quantify reservoir pool elevations (needed for consistent capacity analysis) and compute vertical datum correction factors for these additional reservoirs so that comparisons across surveys are technically sound. The notice also highlights an interagency coordination element: USBR is developing its own separate database tailored to its needs, with plans to eventually connect the two systems for smoother updates. In the near term, the project is expected to require coordination with a USBR collaborator to access the most current USBR survey information.

The second base task is about proving forecasting feasibility in a way that USACE can trust and use. The core idea is to compare reservoir capacities forecasted by statistical and machine learning methods against capacities estimated through more traditional extrapolation of known sedimentation rates. That head-to-head comparison is intended to validate the proof of concept for predicting reservoir sedimentation and storage loss using ordinary least squares (OLS) approaches and machine learning predictive models. If the early work demonstrates sufficient progress and strong results in early FY24, the government may exercise an option for additional work in FY25, signaling that the base tasks are essentially a gate for a broader operational capability.

The optional task shifts from research validation to an implementable toolset that analysts can use in routine workflows. The option calls for an automated ArcGIS Pro Add-In that can estimate capacity loss for a reservoir from the dam construction date to the present and then use the forecasting outcomes from Base Task 2 to project future losses. Users would provide a minimal set of inputs, specifically dam location, construction date, and original volume capacity, while the remaining model parameters would be derived through automated GIS analysis. The toolset is expected to include a water-supply-focused visualization component and a simple stoplight-style ranking to categorize reservoirs based on selected RSI database fields, making it easier to triage systems that may be at higher risk or require attention. Finally, the tool is expected to be built in a way that supports integration with the RIMORPHIS geomorphic database, and the team would collaborate with USACE and other federal partners to determine a permanent, public-facing hosting arrangement for the database or related outputs.

Administratively, this is a discretionary science and technology R and D opportunity issued under Funding Opportunity Number W81EWF 23 SOI 0007 and CFDA 12.630. It is structured as a cooperative agreement, indicating substantial involvement or collaboration with the government during performance. The agency anticipates one award with an award ceiling of $60,000. The opportunity was posted May 26, 2023, with an original closing date of July 26, 2023, and eligibility is listed broadly as "Others" with additional details referenced in the full eligibility text.

• The Department of Defense, Engineer Research and Development Center in the science and technology and other research and development sector is offering a public funding opportunity titled "Improving Data Anomaly Detection and Forecasting in the US Army Corps of Engineers Reservoir Sedimentation Information (RSI) Database" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.630.
• This funding opportunity was created on May 26, 2023.
• Applicants must submit their applications by Jul 26, 2023. (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 $60,000.00 in funding.
• The number of recipients for this funding is limited to 1 candidate(s).
• Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).

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