Cumulative Impact Estimation For Landscape
Scale Forest Planning
Finn Krogstad & Peter SchiessForest Engineering, U. Washington
OUTLINE
PROBLEM: Evaluating Landscape Plans
APPROACH: Hydrologic Modeling in GIS
QUESTION:What is ‘Cumulative Impact’?
PLANNING IN TIME AND SPACE
DETAIL
COMPARING ALTERNATIVES
ECONOMICS
• Timber Yield
• Road Costs
ENVIRONMENT
• Sediment
• Water
• etc.
MODELING OBJECTIVES
Input - entire landscape plans
Output - cumulative impacts ratio
Simplicity - for general users
Transparency - assumptions & processes
Modularity - alternate models
Consistency - watershed analysis
Compatibility - ArcInfo GIS system
Robustness - relative rather than absolute
HYDROLOGIC MODELING IN GIS
Divide the problem into component modules of production, routing, and accumulation.
coarsesediment
finesediment
peakflows
LWDshade
cumulative impact
stream sensitivity/resource vulnerability
stands/roadstopography
soils
harvestingconstruction
fire/blowdown
Events Maps
masswasting
surfaceerosion
rain-on-snow/road interception
GRIDDING THE WORLD
PROCESS LOCALLY
All physical processes are local in nature.
INTEGRATE GLOBALLY
Impacts can accumulate across space, time and management activities.
CUMULATIVE IMPACT
1. Different sources
For example, fine sediment:– roads– harvest– streambank– landslide
CUMULATIVE IMPACT
1. Different sources
2. Different reaches– ‘a reach is a reach’– reach sensitivity– habitat location– habitat quality
CUMULATIVE IMPACT
1. Different sources
2. Different reaches
3. Different years– catastrophic vs. chronic– species life history
CUMULATIVE IMPACT
1. Different sources
2. Different reaches
3. Different years
4. Different inputs
production
delivery
accumulation
sensitivity
vulnerability
Stream shade
production
delivery
accumulation
sensitivity
vulnerability
Wood
production
delivery
accumulation
sensitivity
vulnerability
Fine sed
production
delivery
accumulation
sensitivity
vulnerability
Peakflow
production
delivery
accumulation
sensitivity
vulnerability
Coarse sed
Cumulative Impact
CUMULATIVE IMPACT
1. Different sources
2. Different reaches
3. Different years
4. Different inputs
5. Different species– salmon vs. elk vs. frogs
CONCLUSIONS
This approach goes beyond the accuracy of current models and data, but it should be pursued because:
1. Even flawed predictions provide insight
2. Improve models with observations
3. Crude predictions vs. broad regulations
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