Climate Change and Forest
Adaptation, Mitigation and
Monitoring
Data and Analysis Tool
Needs for California
Tim Robards
Forest Biometrician
Fire and Resources Assessment Program
California Department of Forestry and Fire Protection
Discussion Points
Adaptation
„ Mitigation
„ Monitoring
„ Summary of Needs
„
Adaptation
„
„
Vast Uncertainty
Crosses Multiple Disciplines
• Forests, water, economics, fire,
sequestration, public health, energy
„
Climate Models
• GCMs and downscaling
• Complex regional climate (coastal fog)
• Temp, precip, atmospheric carbon
• Deterministic or stochastic projections
Adaptation continued
„
Forest Growth Models
• Empirical planning tools
• Process-based research tools
• Vegetation/Climate predictors
„
Information Needs
• Minimize emissions
• Accurate yield streams
• Interactions to nutrient and water cycles
• Industry and regional impacts
Adaptation: FIA Help
„
Data Base for Growth Modeling
• Repeated measures of dbh and height
• Climate data overlays (i.e. PRISM)
• Increment cores in future measures for
annual or seasonal growth
„
Data Base for Mortality Modeling
• Repeated measures
• Increment cores to characterize decline
„
Validation Data
Plots Not Averaged
|
Trees Clustered
1
(-0.5)
BAF 40
0.96
(2.3)
Uneven-aged
0.9
(1.9)
Not Highest Density
0.9
(5.8)
0.037
(21.0)
1
(14.3)
Figure 16. Decision tree for CRYPTOS variable-probability plots without mortality and deterministic projections. If condition is true follow
to the left. Resulting numbers are the proportion of unbiased results given the conditions and standardized residual (%) in parenthesis.
Equivalence test using 25% acceptance threshold and alpha of 0.05.
Mitigation
„
Meeting California 2020
and 2050 Targets
•
•
•
„
„
„
Carbon pools
Change estimates
Inventory independent of
project-level accounting
Allometric Data Needs
Accurate and Timely
Industry Information
Protocol Baseline
Characterization
Mitigation: FIA Help
„
„
„
Independent Source for Flux
Estimates
Calibration and Ancillary Data for
Remote Sensing Approaches
Baseline Characterization for Forest
Management Protocols
• Scale vs. accuracy
• Forest products pool
Monitoring
„
„
Species Shifts
Contraction or Expansion of Ranges
•
•
•
„
„
„
“Natural”
Afforestation
Disturbance driven
Forest Health
Productivity Changes
Potentially Influential Factors
• Topography, land use, silviculture, cultural
treatments, fragmentation, soils, gene pools
Monitoring: FIA Help
„
Species: Abundance and Diversity
• Overstory and understory
• How far?
„
Definition of Forestland
• Potential
• Abandonment
„
Forest Health Metrics
•
•
•
Early warning
Influential Factors
Urban Forests
Monitoring: FIA Help cont.
„
Productivity
• Climate, topography and soil factors
• Other factors such as management
„
Tissue Samples
• Genetic and tree rings (chemical and
growth)
• Collection and storage
„
Partnerships
• Monitoring and Adaptation Research
Summary of Needs
„
„
„
„
„
„
Data for Model Building and
Validation
Flux Estimates
Baseline Characterization
Monitoring of Forest Condition
Monitoring of Industry Condition
Urban Forest Expansion
Thank You
Lake Tahoe
CARBON
ME