2. Cumulative Impact Estimation For Landscape Scale Forest Planning Finn Krogstad & Peter Schiess Forest Engineering, U. Washington

3. OUTLINE PROBLEM: Evaluating Landscape Plans APPROACH: Hydrologic Modeling in GIS QUESTION: What is ‘Cumulative Impact’?

4. PLANNING IN TIME AND SPACE

5. DETAIL

6. COMPARING ALTERNATIVES ECONOMICS Timber Yield Road Costs ENVIRONMENT Sediment Water etc.

7. 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

8. HYDROLOGIC MODELING IN GIS Divide the problem into component modules of production, routing, and accumulation.

9. GRIDDING THE WORLD PROCESS LOCALLY All physical processes are local in nature. INTEGRATE GLOBALLY Impacts can accumulate across space, time and management activities.

10. CUMULATIVE IMPACT 1. Different sources For example, fine sediment: –roads –harvest –streambank –landslide

11. CUMULATIVE IMPACT 1. Different sources 2. Different reaches –‘a reach is a reach’ –reach sensitivity –habitat location –habitat quality

12. CUMULATIVE IMPACT 1. Different sources 2. Different reaches 3. Different years –catastrophic vs. chronic –species life history

13. 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

14. CUMULATIVE IMPACT 1. Different sources 2. Different reaches 3. Different years 4. Different inputs 5. Different species –salmon vs. elk vs. frogs

15. 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