View
1.315
Download
2
Category
Preview:
DESCRIPTION
This two-year project creates about 50 jobs to work on the watershed-level prioritization of fuel treatments in Oregon, Washington, New Mexico and Arizona.
Citation preview
ARRA INTEGRATED FUELS PRIORITIZATION PROJECT
IMAP User Group Presentation
February 17, 2010
Janine Salwasser, INR project coordinator
Project intent
To create/retain ~ 50 jobs
To support watershed-level prioritization of fuel treatments by estimating restoration potential and understanding treatment effects throughout Oregon, Washington, New Mexico and Arizona by December 2011
Targeted users
Primary users are: planners, analysts, and land managers at regional, state, and watershed levels
Secondary users are: scientists, policymakers, and large-area land owners
Our intent is that the tools, data, and information from this project can be used well beyond its two-year scope.
Questions to be addressed
What are the conditions and trends of forest fuels?
What are the conditions and trends of key habitats?
Will management activities have economic benefits?
Which watersheds should managers prioritize for restoration to reduce critical fuels, improve wildlife habitats, generate revenues, and provide community benefits?
How may climate changes impact watershed prioritizations?
Deliverables
Science Delivery Modules
Jimmy Kagan
Knowledge Discovery Modules
Brenda McComb
Spatial Data - Jimmy Kagan (lead) Joe Bernert
(coordinator)
Wildlife Habitat – Anita Morzillo
Fire & Fuel Characterizations – Jessica Halofsky
Fuel Treatment Finances – Xiaoping Zhou
ARRA Integrated Fuels Prioritization Project OverviewOversight
TeamHemstrom(lead), Barbour, Gaines,
Tesch, Kagan, McComb, Triepke,
Weisz
Project Coordinator
Janine Salwasser
Optimized Decision Support System- Michael Wing
EMDS System – Sean Gordon
IMAP User Group OR/WAWashington Office
contacts group NM/AZ
advisors
Project Advisors:
VDDT Modeling – Josh Halofsky (lead) Emilie Grossmann
(coordinator)
Project intent: to create/retain jobs and support watershed-level prioritization of fuel treatments by estimating restoration potential and understanding treatment effects throughout Oregon, Washington, New Mexico and Arizona by December 2011.
2/15/09
Usi
ng
Exis
ting K
now
ledg
e
Develo
pin
g N
ew
K
now
ledge
Data and models: new statewide spatial data, new VDDT models, Knowledge Discovery module outputs
Integrated analysis: maps, tables, graphs, and other decision support system outputsReports and publications: General Technical Reports, white papers, journal articles, theses and
dissertations Web tools: Decision support tools, project website, data/models/documents archive
Community Economics – Claire Montgomery
Climate Change – Jessica Halofsky
Fire Probabilities & Climate Change – Rebecca KennedyWatershed – Gordie Reeves
New analyses, maps, reports, data for current and future conditions based on different management scenarios:
• Wildlife habitats across OR, WA, AZ, NM
• Fire and fuel characteristics across OR, WA, AZ, NM
• Fuel treatment financials across OR, WA, AZ, NM
• Community economics across OR, WA
• Climate change in OR, AZ prototype areas
• Aquatic conditions in OR, WA mixed ownership watersheds
Project productsScience Delivery Module
ProductsSpatial data sets for OR, WA , AZ, NM:
• Potential Natural Vegetation (PAG)
• Current Vegetation (GNN)
• Standardized Management/Ownership Allocations
New state & transition models (VDDT):
• Western Washington forest lands
• AZ and NM non-federal forest lands
• OR, WA, AZ, NM shrublands and grasslands
2/15/09
Usi
ng
Exis
ting K
now
ledg
e
Develo
pin
g N
ew
K
now
ledge
Knowledge Discovery Module Products
• Integrated analysis (maps, tables, graphs): EMDS outputs, integrated and optimized decision support system outputs, NetMap outputs
• Reports and publications: General Technical Reports, white papers, journal articles, theses and dissertations
• Web tools: Accessible decision support tools, project website, data/models/document archive
Project prototype areas
Apache-Sitgreaves Natl. ForestModules: wildlife habitat,
fire &fuels, treatment finances
Central Washington Landscape AreaModules: wildlife habitat, community economics,fire &fuels, treatment finances, watersheds
Central Oregon Landscape AreaModules: climate change, fire probabilities & climate change, community economics
Examples of VDDT outputs
Average available biomass by ownership in selected watersheds
Comparing the percentage of different structures across the entire landscape under A) current management, and B) no-management other than fire suppression.
Example of EMDS output
Improved Wildlife HabitatsCommunity Economics(+) Treatment RevenuesFuel ReductionsIncreased Water Supply
Priority watersheds for fuel treatments
Start-up
Model developme
nt
Run models
Connect interpretation
s
Integrate findings
Final reporting
Agreements in Place
Prototype Data
Rev. 2/9/10
1st Q 2nd Q 3rd Q 4th Q 1st Q 2nd Q 3rd Q 4th Q2010 2011
Knowledge Discovery Module Methods Finished & Tested
Knowledge Discovery Module Results Finished
Decision Support Modules Results Finished – Integrated Analyses
Project Reporting Finished
Scenario Results Available
VDDT Models Ready
All Spatial Data Ready
Expected project outcomes
Inform watershed restoration decision-making
Support Forest Plan revisions Support statewide
assessments and bioregional plans
Promote other collaborative landscape assessments
Demonstrate value of annual vegetation updates
Create capacity to address priority public agency issues over large landscapes, such as Evaluating future water supply Quantifying climate change Supporting carbon markets and
biomass opportunities Identifying rural economic
development opportunities Demonstrate value of a
collaborative Center for Landscape Analysis
Short-term Long-term
Role of project advisors
Advise on project design and development, for example Scenarios Geographies for reporting results Feedback on module products
Advise on need and use of decision-support products Identify project end-users Help us to investigate how decision makers and
analysts integrate new information into their work and decisions
Recommended