Opportunity Information: Apply for W81EWF 24 SOI 0011

The Natural and Nature-Based Features (NNBFs) in Water Resources Planning opportunity (Funding Opportunity Number W81EWF 24 SOI 0011) is a research-focused cooperative agreement offered by the U.S. Army Engineer Research and Development Center (ERDC). It targets science and technology work that helps planners and engineers better integrate natural and engineered nature-based approaches into water resources infrastructure, with the goal of delivering combined economic, environmental, and social benefits. The central idea is to advance how NNBFs are planned, designed, and implemented by improving the modeling tools used to predict how these features perform and change over long time horizons.

The technical emphasis is on developing and applying a mix of process-based modeling and data-driven methods, including data science, machine learning (ML), and artificial intelligence (AI). ERDCs Ecohydrology Team at the Environmental Laboratory is specifically looking for approaches that can simulate how NNBFs evolve within their surrounding environmental context over decadal timescales and under a range of possible loading conditions (for example, changing hydrology, storms, sediment transport, sea level influences, or other stressors relevant to water resources systems). A key motivation is that conventional models often struggle to capture long-term, coupled physical and ecological dynamics at the level needed for practical design and management decisions, so hybrid approaches that combine physics-based understanding with ML/AI learning from data are of high interest.

A major component of the work is foundational synthesis: the project calls for data science efforts and structured literature review to identify, categorize, and compile what is already known about NNBF performance from past experiments, demonstrations, monitoring programs, and applied projects. That synthesized evidence base is intended to support parameterization and calibration of next-generation hybrid models, and to make it easier to compare NNBF types, settings, and outcomes in a consistent way. In practical terms, the effort is aimed at turning scattered results from the literature into usable inputs for engineering-oriented models and decision support.

The program objectives are organized around producing concise written products rather than long multi-year field campaigns. Specifically, the RampD objectives emphasize (1) literature reviews that describe the current state of research and practice, and (2) short reports that lay out methods, tasks, and deliverables for targeted subtopics. The four subtopic areas listed are: (a) the role of AI/ML in supporting NNBF research and design, (b) modeling feedbacks between physical processes and natural or ecological systems, (c) characterizing NNBF performance and associated risk-reduction benefits, and (d) engineered design of NNBFs. Each literature review and short report is expected to be under 10 pages and include supporting materials such as citations, background data, and code where relevant, signaling an emphasis on reproducibility and practical utility.

Eligibility is limited: applications are restricted to non-federal partners of the Colorado Plateau Cooperative Ecosystems Studies Unit (CESU). That limitation is important because otherwise capable institutions may not qualify unless they are part of that CESU network as an eligible non-federal partner. The award is expected to be a single cooperative agreement, with an anticipated award ceiling of $40,000 and one expected award. The original closing date listed is 2024-02-22, and the CFDA number associated with the opportunity is 12.630.

The notice also highlights compliance and disclosure expectations around current and pending support. Applicants are warned that disclosures made in the application can affect eligibility, and ERDC may request updated information both before award and throughout the period of performance; those updates can be reviewed and could lead to discontinuation of funding if issues arise. Finally, the announcement reiterates that religious organizations may compete on equal footing with secular organizations for federal financial assistance, consistent with Executive Order 13798.

  • The Engineer Research and Development Center in the science and technology and other research and development sector is offering a public funding opportunity titled "Natural and Nature-Based Features (NNBFs) in Water Resources Planning" 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 2023-12-22.
  • Applicants must submit their applications by 2024-02-22. (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 $40,000.00 in funding.
  • The number of recipients for this funding is limited to 1 candidate(s).
  • Eligible applicants include: Others.
Apply for W81EWF 24 SOI 0011

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FAQs: Natural and Nature-Based Features (NNBFs) in Water Resources Planning (W81EWF 24 SOI 0011)

What is this grant opportunity?

This is a research-focused cooperative agreement offered by the U.S. Army Engineer Research and Development Center (ERDC) titled "Natural and Nature-Based Features (NNBFs) in Water Resources Planning" (Funding Opportunity Number W81EWF 24 SOI 0011).

What is the overall purpose of the program?

The program aims to improve how planners and engineers integrate natural and engineered nature-based approaches into water resources infrastructure. The goal is to support combined economic, environmental, and social benefits by advancing how NNBFs are planned, designed, and implemented.

What is the main technical idea behind the work?

The central idea is to improve modeling tools used to predict how NNBFs perform and change over long time horizons. The focus is on better prediction of long-term evolution and performance so the results can support practical design and management decisions.

Who is the sponsor and which ERDC group is highlighted?

The sponsor is ERDC, and the opportunity specifically highlights ERDC's Ecohydrology Team at the Environmental Laboratory as looking for approaches aligned with the described modeling and synthesis needs.

Is this opportunity more about fieldwork or analysis and writing?

Based on the stated objectives, it is more oriented toward foundational synthesis, literature review, and concise written products (plus supporting materials such as citations, background data, and code where relevant) rather than long, multi-year field campaigns.

What kinds of methods does ERDC want to see in this work?

The technical emphasis is on a mix of process-based modeling and data-driven methods, including data science, machine learning (ML), and artificial intelligence (AI), especially where hybrid approaches combine physics-based understanding with ML/AI learning from data.

What types of modeling challenges is this opportunity trying to address?

The notice points to the limitations of conventional models in capturing long-term, coupled physical and ecological dynamics at a level needed for practical design and management decisions. The opportunity is looking for approaches that better represent those coupled dynamics over decadal timescales.

Over what time horizon should NNBF evolution be simulated?

The opportunity emphasizes simulating how NNBFs evolve over decadal timescales and across a range of possible loading conditions.

What kinds of environmental conditions or stressors are mentioned?

Examples of loading conditions and stressors include changing hydrology, storms, sediment transport, sea level influences, and other stressors relevant to water resources systems.

What is meant by "hybrid" modeling in this context?

In this opportunity, hybrid modeling refers to combining physics-based (process-based) understanding with data-driven learning (such as ML/AI) so the model can better capture complex, coupled physical and ecological dynamics and improve long-term performance prediction.

What is the "foundational synthesis" component?

A major component is data science and structured literature review to identify, categorize, and compile existing knowledge about NNBF performance from prior experiments, demonstrations, monitoring programs, and applied projects.

Why is the literature synthesis important to the modeling goals?

The synthesized evidence base is intended to support parameterization and calibration of next-generation hybrid models, and to make it easier to compare NNBF types, settings, and outcomes consistently. The practical aim is to turn scattered literature results into usable inputs for engineering-oriented models and decision support.

What are the program objectives and expected deliverables?

The RampD objectives emphasize (1) literature reviews describing the current state of research and practice, and (2) short reports that lay out methods, tasks, and deliverables for targeted subtopics.

What subtopics are listed for the literature reviews and short reports?

The four subtopic areas listed are: (a) the role of AI/ML in supporting NNBF research and design, (b) modeling feedbacks between physical processes and natural or ecological systems, (c) characterizing NNBF performance and associated risk-reduction benefits, and (d) engineered design of NNBFs.

How long should each literature review and short report be?

Each literature review and short report is expected to be under 10 pages.

Are applicants expected to include anything beyond the written narrative?

Yes. Each product is expected to include supporting materials such as citations, background data, and code where relevant, reflecting an emphasis on reproducibility and practical utility.

Who is eligible to apply?

Eligibility is limited to non-federal partners of the Colorado Plateau Cooperative Ecosystems Studies Unit (CESU).

What does the eligibility limitation imply for otherwise qualified organizations?

The limitation is significant because institutions that are not eligible non-federal partners of the Colorado Plateau CESU would not qualify to apply under the terms described.

What type of award is expected?

The notice indicates a single cooperative agreement is expected.

How many awards are anticipated?

One award is expected.

What is the maximum funding amount mentioned?

The anticipated award ceiling is $40,000.

What is the closing date listed in the notice?

The original closing date listed is 2024-02-22.

What CFDA number is associated with this opportunity?

The CFDA number associated with the opportunity is 12.630.

Are there compliance or disclosure requirements related to other funding?

Yes. The notice highlights compliance and disclosure expectations around current and pending support. Disclosures in the application can affect eligibility, and ERDC may request updated information before award and throughout the period of performance.

Can updated disclosures affect funding after an award is made?

Yes. The notice states that updated information can be reviewed and could lead to discontinuation of funding if issues arise.

Can religious organizations apply?

Yes. The announcement reiterates that religious organizations may compete on equal footing with secular organizations for federal financial assistance, consistent with Executive Order 13798.

What kinds of results is ERDC trying to make easier for engineers and planners to use?

The effort is aimed at producing usable inputs for engineering-oriented models and decision support by organizing and synthesizing NNBF performance evidence in a consistent, comparable way across NNBF types, settings, and outcomes.

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