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SIMULATING THE IMPACT OF AREA BURNED ON GOALS FOR SUSTAINABLE FOREST MANAGEMENT Jimmie Chew, RMRS Christine Stalling, RMRS Barry Bollenbacher, Region One.

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Presentation on theme: "SIMULATING THE IMPACT OF AREA BURNED ON GOALS FOR SUSTAINABLE FOREST MANAGEMENT Jimmie Chew, RMRS Christine Stalling, RMRS Barry Bollenbacher, Region One."— Presentation transcript:

1 SIMULATING THE IMPACT OF AREA BURNED ON GOALS FOR SUSTAINABLE FOREST MANAGEMENT Jimmie Chew, RMRS Christine Stalling, RMRS Barry Bollenbacher, Region One Original work presented at

2 OBJECTIVES: ORIGINAL: Display an approach to examine assumptions for the level of hectares that will be burned by wildfire over a planning horizon.

3 OBJECTIVES: An approach to help quantify the level of resources and the desired future conditions, that can be set as realistic goals for sustainable management. CURRENT: An approach to examine the concept of sustainability for a number of resources. An approach that can also provide input; levels of constraints, goals, and desired future conditions that can be used within other models. ( SPECTRUM, MAGIS )

4 An approach that is spatially explicit and incorporates the occurrence of disturbance processes. The following slides help to stress the need to include these two components.

5 Number of plantations Hectares of fire within plantations Total hectares of production lands with fire 2,04421,531243,135 Northern Rocky Mountains- Forest Service Totals On a total of 3,520,779 hectares of land allocated to the production of forest products, the following has burned in wildfires from 2000 - 2003

6 Custer National Forest – Sioux Ranger District

7 Loss of 90 percent of forest stands from the two fires

8 Recorded hectares of wildfire for the Bitterroot National Forest 1870 2000

9 1870 2000 Recorded hectares of wildfire for the Bitterroot National Forest 19501990 The period of 50s through 90s is being referred to as an unusual cool and moist period. Do we use the disturbance process behavior associated with this period as the basis in future planning?

10 1870 2000 Recorded hectares of wildfire for the Bitterroot National Forest 2000 + Or do we plan using behavior that may be associated with cycles of drought?

11 Approach Apply a spatially explicit, stochastic, landscape level simulation model using different assumptions on the frequency of drought cycles and the probability of extreme fire behavior. Compare differences in: - vegetation inventories, harvest and economic benefits on lands allocated for timber production - hectares of insect and disease activity - fire suppression costs by level of treatments - potential watershed impact - hectares burned within drainages - potential for old growth vegetation conditions - hectares of stand replacing fire within a wildland urban interface May not what this part? Above just an example of “indicators”

12 The model: Chew, Stalling, and Moeller 2004. Integrating Knowledge for Simulating Vegetation Change at Landscape Scales. West. J. Appl.For. 19(1)

13 Simulation label timeRegional climate Probability of Extreme fire Treatment level Simulations used in this analysis No change Six different types of simulations Three levels Three levels Two levels

14 Simulation label timeRegional climate Probability of Extreme fire Treatment level For other analyses can drop No change ? different types of simulations Three levels ? levels Two levels

15 Simulation label timeRegional climate Probability of Extreme fire Treatment level Or add / change No change ? different types of simulations Three levels ? levels Two levels Increased insect disease ? Alternatives ?

16 Long term Sustained Yield of Forest Products Water Quality Biological Diversity – Old Growth Protection of Structures For the original work we utilized SIMPPLLE output to look at the following indicators of sustainability: This should serve as an example of how SIMPPLLE output could potentially be utilized to address a number of indicators

17 Long term sustained yield of Forest Products from lands managed for timber production 87,080 hectares

18 Size classTotal Bitterroot hectares (638,194) Managed for products hectares (87,080) Seedling/sapling 58,68218,309 Pole224,13022,708 Medium287,19141,395 Large 48,248 2,862 Very-large 19,943 1,806 Current forest inventory:

19 Acres of large and very-large size classes available for harvest at a rate of 1 percent per year while accommodating other resource values Average yield of 57 cubic meters per hectare Assumptions made for quantifying potential harvest levels on lands managed for timber products:

20 The resulting inventory as impacted by disturbances could be the basis for input into SPECTRUM, or without using another model a spreadsheet approach linking volumes (yield tables) to the inventory could be used for deriving timber volumes. Assumptions made for quantifying potential harvest levels on lands managed for timber products:

21 Non declining potential harvest levels based available inventories from the simulations (difference based on whatever changes one wants in the simulations instead of what is shown in the below legend)

22 Treatments consist of underburning, thinning and underburning and regeneration harvest. Yearly treatments for two levels (treatment levels can be those that represent a range of alternatives, investments, etc.)

23 Locations of accumulated treatments first 100 years – current level of treatments Treatments are applied spatially within SIMPPLLE, priorities can be set for areas, vegetation conditions, and disturbance process probabilities

24 Decade average simulated hectares of fire over a 300 year planning period. Two levels of treatments May or may not have different assumptions about disturbance processes Quantify the impact mgt can have on disturbance processes

25 Simulated insect and disease activity – total hectares over the 300 year planning period Includes root disease, mountain pine beetle, western spruce budworm, With treatments

26 Assumptions in Economic Analysis Analysis based on today’s dollars Costs were not discounted No expected change in technology A more detailed analysis could be linked to the SIMPPLLE output

27 *Direct income effects specific to sawmills are the calculated income dollars based on timber volumes entering the system. *Direct economic effects for each combination of climate, extreme fire probability, and treatment variables

28 *Indirect/induced effects are dollars generated as a function of an operating sawmill such as building maintenance. *Indirect/induced economic effects for each combination of climate, extreme fire probability, and treatment variables

29 Decade average hectares of fire Percent decrease in fire Direct benefit in dollars Percent change Indirect benefit in dollars Percent change 2000+ Cycles – 10% 93,3501,471,6591,517,207 2000+ Cycles –10% Current 83,995- 101,788,346+221,843,695+22 2000+ Cycles – 10% Increased 69,186-261,918,746+231,978,131+30 Comparison of direct and indirect benefits at decade 20

30 Treatment costs are only for the burning and thinning over the whole forest. Benefits from harvest volume are only from the land managed for timber production. Total benefits in dollars Treatment costs in dollars Difference in decade benefits 2000+ Cycles – 10% 2,988,866 2000+ Cycles – 10% current 3,632,0622,045,960634,196 2000+ Cycles – 10% increased 3,896,90016,556,300908,034 Increases in benefits from volumes harvest on suitable lands over no treatments does not equal or exceed the treatment costs.

31 Decade average for simulated fire suppression costs over the 300 year planning period by level of treatments.

32 Simulated fire suppression costs of no treatments and two levels of treatments

33 Fire suppression costs of two levels of treatments In between years of extreme fire conditions, increased treatments tend to lower fire suppression costs

34 Fire suppression costs of two levels of treatments In years of extreme fire increased treatments do not always lower fire suppression costs

35 Potential for Watershed Damage Percent of decades from the 300 year simulations where the percent of watersheds in stand replacing fire is greater than 10 percent

36 2000+ for regional climate in cycles – no treatments Number of decades where stand replacing fire is greater than 10 percent of drainage Potential for Watershed Damage

37 2000+ for regional climate in cycles – current level of treatments Number of decades where stand replacing fire is greater than 10 percent of drainage Potential for Watershed Damage

38 2000+ for regional climate in cycles – increased level of treatments Number of decades where stand replacing fire is greater than 10 percent of drainage Potential for Watershed Damage

39 Biological Diversity – potential old growth Percent of total landscape in size-classes that are potential old growth

40 no treatments Can be displayed by watersheds Number of decades where potential old growth is greater than 7 percent of drainage Biological Diversity – potential old growth

41 Current level of treatments Number of decades where potential old growth is greater than 7 percent of drainage Biological Diversity – potential old growth

42 Increased level of treatments Number of decades where potential old growth is greater than 7 percent of drainage Biological Diversity – potential old growth

43 Hectares that have a probability of stand replacing fire greater than zero within the wildland urban interface in the Bitterroot Face portion of the landscape Protection of structures:

44 Additional analysis needed: -Additional spatial fitting of fuel treatments with SIMPPLLE is needed. -Remake the simulations letting the system schedule harvest on suitable lands by watersheds (add scheduling constraints by watershed). -Test the assumption of 10 % level of harvest per decade. -Do we include the non-market values for resources other than forest products? -Do we try to take into account the impacts and costs of the infrastructure that goes with each treatment level? -Do we include looking at the use of wildland fire as a treatment option? For any other analysis using SIMPPLLE to address sustainability of resources the following items may apply - depends on the specifics of the analysis objectives.


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