FY 2025 104g Grants (General)

FY 2025 104g Grants (General) is sponsored by U.S. Geological Survey (USGS) (in cooperation with Iowa Water Center). This grant program focuses on model advancement and machine learning integration, specifically exploring methods to develop new hydrologic models and applying AI and machine learning techniques for water quality data harmonization and groundwater/base flow predictions.

2025 NEW FUNDING ANNOUNCEMENT - Fiscal Year 2025 104g Grants - Iowa Water Center Skip to content 2025 NEW FUNDING ANNOUNCEMENT – Fiscal Year 2025 104g Grants The Iowa Water Center is accepting proposals from investigators in the state of Iowa.

Projects may request up to $346,875 (AIS), $310,000 (General), or $309,000 (PFAS) in federal funds for projects up to a three year duration, with a 1:1 matching requirement of non-federal to federal funds. The deadline for submissions to the Iowa Water Center is September 11, 5:00 p. m.

Investigators should follow the Iowa Submission Guidelines linked at the bottom of this page and the Project Narrative requirements as outlined in each specific RFP. Emails with a Notification of Intent to submit are due to the Iowa Water Center on September 4th. Proposals are sought on the topic of economic valuation of information, as well as model advancement and machine integration.

Proposals are sought on the following specific areas of inquiry (levels of priority are not assigned and the order of listing does not indicate the level of priority): Priority 1: Economic value of Information of the USGS streamgage network and associated National Water Information System (NWIS). Applicants should review the National Hydrologic Warning Council 2006 report for a listing of benefits categories.

For this proposal, USGS is most interested in categories (1) through (3) in the 2006 report; categories (4) and (5) are of significant but lesser interest. USGS appreciates that proposals may seek a tradeoff between examining multiple benefit categories at a reduced geographic scale versus a limited number of benefits categories at a national scale.

While preference will be given to those proposals that provide a Nation-wide assessment, please consider the minimum geographic scope to be Hydrologic Unit Code 12. Please review the RFP for further details about what the economic valuation should include. Priority 2: Model Advancement and Machine Learning Integration.

Explore methods to develop new hydrologic models in large, regional areas or, where possible, at the national level to enhance understanding of water availability. Provide information on promising modeling approaches to inform science questions specific to a region.

Examples include: Machine Learning Techniques for Water Quality Data: Apply AI and machine learning methods to harmonize water quality data across different sources, improving integration and accessibility for hydrologic modeling.

Groundwater and Base Flow Predictions: Specifically apply machine learning techniques to predict transient groundwater levels or base flow to streams, enhancing the understanding of these critical hydrologic processes. Causal Machine Learning Exploration: Investigate the use of causal machine learning to evaluate current or ongoing studies impacted by non-causal modeling.

This approach should help quantify the extent of the problem associated with non-causal machine learning modeling and inform the development of more robust, process-based modeling frameworks. The challenges and opportunities that link aquatic invasive species and water resources are poorly understood, despite the real and growing effect of numerous aquatic invasive species on water quality, water quantity, and aquatic ecosystems.

Research is needed to better identify and understand these interactions and to guide management decisions that will help to improve invasive species management and thus reduce effects of invasive species on water resources and aquatic ecosystems at local, regional, and national scales.

Proposals are sought on the following specific areas of inquiry (levels of priority are not assigned, and the order of listing does not indicate the level of priority): Effects: Research that improves our understanding of the effects of aquatic invasive species on lakes, rivers, and associated tributaries in the upper Mississippi River basin, including changes to water quantity, water quality, and ecosystem dynamics.

Characteristics: Research that identifies physical, biological, and chemical characteristics of water bodies that infer resistance and resilience to the distribution, establishment, and effects of aquatic invasive species in the upper Mississippi River basin.

Research is needed to better understand these interactions to guide management decisions that will improve invasive species management and result in positive effects on aquatic ecosystems Management: Research on assessment of the detection, spread, and management of aquatic invasive species in the upper Mississippi River basin and the connections to human dimensions, both socially and economically.

Note that this does not include physical control of AIS. The challenges and opportunities of understanding the effects of PFAS substances on water resources are poorly understood, despite the real and growing effect of this group of man-made substances on water quality and the resultant exposure to humans, other organisms, and ecosystems.

Research is needed to better understand these interactions and guide management decisions that will improve water resources at the regional or national scale.

Proposals are sought on the following specific areas of inquiry: Media-specific methods: Enhanced methods for detection on specific media, with a clear indication of New or different compounds, New or different methodological approaches, Lower detection levels for specific media or compounds, especially with respect to EPA health guidelines for PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonate).

Media of interest include (in ranked order) (1) Tissues/plasma, (2) sediment, (3) air or interfaces, (4) water. Atmospheric sources: Improved understanding of atmospheric exchange in PFAS distribution and fate. This may include methods to determine transport of PFAS to the atmosphere and to subsequent receiving waters, such as a water method that determines “new” compounds based on their likelihood to occur in the atmosphere.

Processes oriented at molecular level: Process-oriented research of PFAS fate, transport, and effects, with emphasis on molecular-level understanding of PFAS precursor transformation, sorption dynamics, or mechanisms of bioaccumulation and(or) biological/ecological effects, or biodegradation of PFAS along source to receptor pathways and identification of mitigation methods and engage modeling and forecasting processes for prediction, prevention, and mitigation of environmental risk of exposure to PFAS in ecosystems and human population.

Iowa Submission Guidelines Access the full RFPs as follows: Click on this link and go to the “Related Documents” tab and download the Full Announcement for Grant G25AS00485. 104g PFAS (Per- and Polyfluoroalkyl Substances) Click on this link and go to the “Related Documents” tab and download the Full Announcement for Grant G25AS00484.

104g Aquatic Invasive Species (AIS) Click on this link and go to the “Related Documents” tab and download the Full Announcement for Grant G25A00483.