Opportunity Information: Apply for F16AS00449
Apply for F16AS00449
- The Department of the Interior, Fish and Wildlife Service in the other (see text field entitled explanation of other category of funding activity for clarification) sector is offering a public funding opportunity titled "Wolverine Climate Change Snow Refugia Study" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.650.
- This funding opportunity was created on Sep 06, 2016.
- Applicants must submit their applications by Sep 12, 2016 This is a Notice of Intent to award a Single Source Cooperative Agreement to the Regents of the University of Colorado iaw DOI Policy 505 DM2.14 (4) Unique Qualifications.. (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 $150,000.00 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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Opportunity Summary:
The Wolverine Climate Change Snow Refugia Study is a U.S. Department of the Interior, Fish and Wildlife Service (FWS) discretionary funding opportunity (Funding Opportunity Number F16AS00449) intended to identify and map places that are likely to hold onto snow longest under climate change, sometimes called snow refugia, in areas important to wolverine habitat. The award is structured as a cooperative agreement and was issued as a single-source notice of intent to fund the Regents of the University of Colorado based on unique qualifications under DOI policy. The planned effort supports wolverine conservation by improving the understanding of how spring snowpack may persist or disappear across mountainous landscapes as temperatures warm, and by producing data products that can be used for habitat assessment and long-term planning. The maximum award amount listed is $150,000, with one expected award. The notice was created on September 6, 2016, with an original closing date of September 12, 2016.
The core of the work is high-resolution snow and hydrologic modeling led by the University of Colorado (CU). CU is tasked with running fine-scale simulations at roughly 250-meter spatial resolution using the Distributed Hydrology Soil Vegetation Model (DHSVM). Modeling is planned for two subregions of about 1,000 square kilometers each, or alternatively for a similar total modeled area distributed within the broader core region relevant to wolverine habitat. The exact locations of these subregions are to be selected in consultation with FWS (specifically Dr. Steve Torbit), which reflects the cooperative nature of the agreement and ensures the modeling targets the most management-relevant landscapes.
CU modeling includes several scenario runs meant to capture both historical conditions and plausible futures. First, a historical (observed) simulation is proposed for 2000 to 2015. A baseline simulation is also included and may be identical to the historical run depending on the climate downscaling approach used (the description mentions the delta method as a possibility). Future-facing simulations are then proposed for the mid-21st century, nominally spanning 2014 to 2070, under both high and low rates of climate change, with an optional medium-change scenario if time and resources allow. Beyond simply running the model, CU must evaluate how well the historical simulation matches observed snow patterns from MODIS satellite snow-cover products, and where appropriate, compare against ground-based observations such as SNOTEL snow measurements and streamgage data within the modeled subregions. This evaluation step is meant to strengthen confidence in the model outputs before they are used to infer refugia.
A major expected outcome from the CU component is an analysis that identifies where snow refugia do or do not persist under projected climate conditions, especially during periods critical to wolverine habitat needs. To support practical use by FWS and partners, CU is also expected to deliver digital map products, specifically gridded outputs of snow water equivalent and snow depth for key times of year. These spatial datasets are intended to be usable in GIS and other analytical tools, making them directly applicable to habitat mapping, research, and potential future decision support.
In parallel, a second major component focuses on analyzing historical snowpack variability using remote sensing, described under PSD. This task uses MODIS snow cover data to examine how spring snow cover has varied across years, with an emphasis on identifying areas that remain snow-covered more consistently despite swings in precipitation and temperature. The analysis is based on data availability for 2001 to 2015 and looks for resilience patterns during both high and low precipitation years and during warm versus cold years. The PSD team is expected to produce digital gridded datasets summarizing snow persistence, such as the percentage of years in which snow is retained in spring, and to flag any potential "analog years" that might resemble future climate conditions if such analogs can be identified from the historical record.
The opportunity also includes supporting and integrative tasks shared across CU and PSD. These include ongoing consultation with FWS throughout the project to help interpret model results, facilitate data access, and discuss additional analyses that may become relevant as findings emerge. The scope explicitly notes that this collaboration could include travel for meetings. The teams are also expected to prepare draft and final reports for FWS and to develop a manuscript suitable for submission to a peer-reviewed publication, indicating that the project aims to produce both management-ready products and scientifically defensible outputs. Because high-resolution modeling and satellite time series generate large files, the project also anticipates data storage needs, including potential hardware to store large datasets.
Deliverables are clearly staged with near-term drafts and data releases followed by final reporting. For CU, the deliverables include a draft report summarizing preliminary DHSVM modeling results for one subregion by mid-December 2016, along with digital gridded snowpack data for that same subregion on a similar timeline. Additional digital gridded data products described in the modeling task are due in January 2017, with final report chapters covering the DHSVM modeling delivered at the end of the project. For PSD, deliverables include a mid-December 2016 draft report on the historical variability analysis, digital gridded products for the first region of interest (identified as the Crown of the Continent) by mid-December 2016, additional gridded data for the Colorado region by January 2017, and final report chapters that cover the historical analysis and provide a synthesis of results at the end of the project.
Overall, this grant opportunity is designed to combine physically based hydrologic modeling with satellite-derived historical snow analysis to pinpoint where spring snow is most likely to persist now and under climate change, producing mapped datasets and reports that can directly inform wolverine habitat understanding and conservation planning.
Frequently Asked Questions (FAQs)
What is the Wolverine Climate Change Snow Refugia Study?
The Wolverine Climate Change Snow Refugia Study is a U.S. Department of the Interior, Fish and Wildlife Service (FWS) discretionary funding effort focused on identifying and mapping places that are likely to retain snow the longest as the climate warms. These long-lasting snow areas are often referred to as "snow refugia," and the work is targeted to landscapes that are important to wolverine habitat.
What is the Funding Opportunity Number (FON) for this grant?
The Funding Opportunity Number is F16AS00449.
Which federal agency is offering this funding opportunity?
The opportunity is offered by the U.S. Department of the Interior, Fish and Wildlife Service (FWS).
What type of award is being used?
The award is structured as a cooperative agreement, which indicates substantial involvement and coordination with FWS during project implementation (for example, consultation on study locations and interpretation of results).
Is this an open competitive grant opportunity?
No. The notice indicates it was issued as a single-source notice of intent to fund the Regents of the University of Colorado based on unique qualifications under DOI policy.
Who is the intended recipient of the award?
The intended recipient is the Regents of the University of Colorado.
What is the maximum award amount?
The maximum award amount listed is $150,000.
How many awards are expected?
One award is expected.
When was the notice created and when did it close?
The notice was created on September 6, 2016, and the original closing date was September 12, 2016.
What problem is the project trying to solve for wolverine conservation?
The project supports wolverine conservation by improving understanding of how spring snowpack may persist or disappear across mountainous landscapes as temperatures warm. It also produces mapped data products that can be used in habitat assessment and long-term planning in areas relevant to wolverine habitat.
What does "snow refugia" mean in this project?
In this context, snow refugia are locations that are likely to hold onto snow the longest under climate change. The study aims to pinpoint where such refugia persist (or do not persist) under projected climate conditions.
What are the main technical components of the project?
The scope combines two major approaches: (1) high-resolution snow and hydrologic modeling led by the University of Colorado (CU) and (2) analysis of historical snowpack variability using MODIS satellite snow cover data (described under PSD). The work is integrated through consultation and joint reporting to FWS.
What model is CU using for the snow and hydrologic simulations?
CU is using the Distributed Hydrology Soil Vegetation Model (DHSVM).
What is the planned spatial resolution of the modeling?
The modeling is planned at roughly 250-meter spatial resolution.
How large are the modeled areas?
Modeling is planned for two subregions of about 1,000 square kilometers each, or alternatively for a similar total modeled area distributed within the broader core region relevant to wolverine habitat.
How will the specific modeling subregions be selected?
The exact locations are to be selected in consultation with FWS, specifically with Dr. Steve Torbit. This reflects the cooperative nature of the agreement and is intended to focus the work on the most management-relevant landscapes.
What time period is covered by the historical (observed) simulation?
The historical (observed) simulation is proposed for 2000 to 2015.
What is the baseline simulation and how does it relate to the historical run?
A baseline simulation is included and may be identical to the historical run, depending on the climate downscaling approach used. The description notes the delta method as a possible approach, which influences whether the baseline and historical runs are the same.
What future climate simulations are planned?
Future-facing simulations are proposed for the mid-21st century, nominally spanning 2014 to 2070, under both high and low rates of climate change. A medium-change scenario is described as optional if time and resources allow.
How will the model results be evaluated for realism?
CU is expected to evaluate how well the historical simulation matches observed snow patterns from MODIS satellite snow-cover products. Where appropriate, CU may also compare results against ground-based observations such as SNOTEL snow measurements and streamgage data within the modeled subregions.
Why is model evaluation against MODIS, SNOTEL, and streamgage data important here?
The evaluation step is intended to strengthen confidence in the model outputs before they are used to infer snow refugia locations and persistence under projected climate conditions.
What kinds of mapped or digital data products will be delivered?
Expected digital map products include gridded outputs of snow water equivalent and snow depth for key times of year. These datasets are intended to be usable in GIS and other analytical tools for habitat mapping, research, and planning.
What is the PSD component of the project?
The PSD task focuses on analyzing historical snowpack variability using remote sensing. It uses MODIS snow cover data to examine how spring snow cover has varied across years and to identify areas that remain snow-covered more consistently despite variation in precipitation and temperature.
What time span does the MODIS-based historical analysis cover?
The analysis is based on data availability for 2001 to 2015.
What does the MODIS variability analysis look for?
It emphasizes resilience patterns, such as locations that retain spring snow more consistently during high versus low precipitation years and during warm versus cold years.
What outputs are expected from the PSD historical snow analysis?
PSD is expected to produce digital gridded datasets summarizing snow persistence, such as the percentage of years in which snow is retained in spring. The team is also expected to flag potential "analog years" that might resemble future climate conditions, if such analogs can be identified from the historical record.
What are "analog years" in this project?
"Analog years" refer to historical years that may resemble future climate conditions. The project notes that these may be flagged if they can be identified based on the historical record used in the MODIS analysis.
How are CU and PSD expected to work with FWS during the project?
The scope includes ongoing consultation with FWS to help interpret model results, facilitate data access, and discuss additional analyses that may become relevant as findings emerge. Travel for meetings is explicitly noted as a possibility as part of this collaboration.
What written deliverables are expected?
The teams are expected to prepare draft and final reports for FWS. The scope also calls for development of a manuscript suitable for submission to a peer-reviewed publication, indicating an intent to produce both management-ready and scientifically defensible outputs.
Are there any data management or storage considerations mentioned?
Yes. The project anticipates that high-resolution modeling and satellite time series will generate large files, so data storage needs are expected, including potential hardware to store large datasets.
What are the CU deliverables and due dates described?
CU deliverables include a draft report summarizing preliminary DHSVM modeling results for one subregion by mid-December 2016, along with digital gridded snowpack data for that same subregion around that timeline. Additional digital gridded data products described in the modeling task are due in January 2017. Final report chapters covering the DHSVM modeling are to be delivered at the end of the project.
What are the PSD deliverables and due dates described?
PSD deliverables include a mid-December 2016 draft report on the historical variability analysis, digital gridded products for the first region of interest (identified as the Crown of the Continent) by mid-December 2016, and additional gridded data for the Colorado region by January 2017. Final report chapters covering the historical analysis and a synthesis of results are due at the end of the project.
Which regions are explicitly mentioned for PSD gridded products?
The Crown of the Continent is identified as the first region of interest, and a Colorado region is also explicitly mentioned for additional gridded data products.
What is the practical end use of the gridded snow datasets?
The datasets are intended to be directly usable in GIS and other analytical tools for habitat assessment, research, mapping, and long-term planning related to wolverine habitat and conservation.
What does the overall study aim to produce when the components are combined?
Overall, the study is designed to combine physically based hydrologic modeling with satellite-derived historical snow analysis to pinpoint where spring snow is most likely to persist now and under climate change, and to deliver mapped datasets and reports that can inform wolverine habitat understanding and conservation planning.
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