Improving Diameter Growth Prediction of Douglasfir in Eastern Washington State, U.S.A. by Incorporat

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Improving Diameter Growth Prediction of
Douglas-fir in Eastern Washington State,U.S.A.
by Incorporating Precipitation and Temperature

• Andrew D. Hill, Ph.D.
• University of Washington
• College of Forest Resources

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Rationale for Study

• Older methods need improvement
• Old assumptions no longer valid
• Forest Service said we should
• Gave us money
• Gave us data
• Older research said we could

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Literature

• Douglass (1909, 1919)
• Coile (1936)
• Diller (1935)
• Schumacher and Meyer (1937)

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• Holdaway (1990)
• Peterson and Peterson (1994)
• Peterson and Heath (1990/91)
• Wensel and Turnblom (1998)
• Yeh and Wensel (1999)
• Stage et al. (1999)
• Lopez-Sereno et al. (2005)

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Primary Objective

• Add weather or climate to a known growth model
• Is it possible?
• If so, is it desirable?

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Definition of Climate and Weather

• Climate Defined as a 30-year average
• Weather In my work was defined in five-year
  increments

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Wanted to use existing model

• That is widely used
• That provided a starting point
• That followed a more mathematically honest form
  than a log-linear model
• Chose ORGANON
• Exponential function
• Wont let trees grow smaller
• Used to determine WAs DFC rules

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ORGANON Function
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ORGANON Variables

• ?D 5-year change in diameter
• SI site index
• SBAL basal area larger
• SBA stand basal area
• D diameter
• CR crown ratio
• I data the location of the stand within the
  forests
• ai the coefficients estimated for Xis

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Tree data

• Forest Inventory and Analysis Data
• Five-year increment
• Eastern Washington
• Between lat 45.849o and 48.927o and long 117.096o
  and 121.941o
• Both mixed and pure Douglas-fir stands
• 7 stands measured in 1993 and 1998
• 10 stands measured in 1994 and 1999
• 3 stand measured in 1995 and 2000
• 28 stands measured in 1996 and 2001

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Stand Statistics
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Tree Statistics
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Douglas-fir Statistics
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Weather and Climate

• From Climate Source, Inc.
• Parameter-elevation Regressions on Independent
  Slopes Model (PRISM)
• Creates data for NOAA
• 2km by 2km grid of monthly total precipitation
  and average temperature for whole of WA.
• January 1950 through December 2002
• Used to create other variables used in modeling
  the effects of weather and climate

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Table 4. Basic weather variable description. D
denotes dormant season (November through April),
G denotes growing season (May through October), W
denotes total seasonal (dormant or growing)
precipitation (mm), X denotes average seasonal
temperature (oC), T denotes temperature sums, and
P denote precipitation sums.
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Dataset Creation

• Located each of the 48 stands in the proper cell
  on the 2km by 2km weather grid.
• Generated the weather and climate variables
  needed.
• 1019 Douglas-fir. Of these, 994 were suitable for
  our project.

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Bootstrapped datasets

• Created 1500 random samples of n 994
• With replacement
• Better estimate of the variance for the
  coefficients of the models generated.
• Small sample size
• Allowed for more robust estimates of the
  parameters
• Used these datasets for analysis.

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Three studies

• Add weather over the measurement interval to the
  model and see if it improves the model.
• Add climate and weather and various deviations
  from the average and see if that improves the
  model
• Use the Parameter Prediction Method in
  conjunction with weather and climate to improve
  the model

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First Study

• IMPROVING MODELED PREDICTIONS
• OF SHORT-TERM DOUGLAS-FIR DIAMETER GROWTH
• IN EASTERN WASHINGTON, U.S.A.,
• BY INCORPORATING LOCAL WEATHER INFORMATION

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Revised ORGANON model

• Did not have SI for 30 of the stands
• Did have Mean Annual Increment at Culmination
  (ft3/ac2/yr). This is a function of SI. Used this
  instead of SI. (See Van Clay 1994).

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Base Model

• D represents tree dbh
• LGD denotes ln(D 1), where ln denotes natural
  logarithm
• LGSI denotes ln(SI 4.5)
• BALT denotes
• LGCR denotes
• ai s represent equation coefficients to be fit
  with least squares regression

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• Where LGMAIC denotes ln(MAIC), and all other
  variables are as before

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Base Model Statistics

• R2 0.30886
• BIAS 0.00412
• BIAS 0.27674
• STD Resid 0.36261
• SSE 11.10882

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Models with weather added

• Added weather to base model

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Models with weather added

• Used above models with base model fixed
• Refit with all parameters allowed to vary

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Models with Base fixed

• M2
• M3
• M4

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Models fitted simultaneously
M5
M6
M7
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Model Comparison
Table 5. Fit statistics of the models.
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Discussion

• Used the base model to test against
• Found any added weather improved the prediction
• At worst 7
• Only one added variable
• Literature says in arid regions dormant season
  precipitation is the driving variable in
  year-to-year ring growth

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Discussion

• At best 15
• More complicated model
• Harder to interpret why it works
• Best fit statistics save bias

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Conclusions from First Study

• We can improve a model by adding weather.
• We can make simple changes that have a
  significant impact.
• More complex models give a better fit.
• The trade-off between better fit and more complex
  model may not be desirable.

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Second Study

• USING LOCAL SHORT-TERM WEATHER AND
• LONG-TERM CLIMATE INFORMATION TO
• IMPROVE PERIODIC DIAMETER GROWTH PREDICTION
• FOR DOUGLAS-FIR GROWING IN PURE AND MIXED
• STANDS IN EASTERN WASHINGTON, U.S.A.

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Added weather, climate, and deviations from base
model

• See handout for calculation of variables used.
• Attached these variables to the fixed base model
  presented above.
• Solved for best fit of added variables
• Compared fit statistics

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Models developed
(1)
(2)
(3)
(4)
(5)
(6)
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Results
Table 6. Parameter estimates for Models 2-6 and
their standard errors.
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Table 7. Fit statistics pertaining to Models 1-6.
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Table 8. Ranks of each model, 1 through 6, by fit
statistics.
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Conclusions from Second Study

• Weather works better than climate at predicting
  diameter change.
• Deviations work too, but not quite as well.
• Climate improves the model, but not as well as
  weather or deviations from weather.

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Third Study

• CAN THE PARAMETER PREDICTION METHOD
• IMPROVE DIAMETER PREDICTION WHEN USED
• TO INCORPORATE WEATHER AND CLIMATE IN
• AN EXISTING MODEL?

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Parameter Prediction Method

• Three-step method
• Fit base model plot by plot
• Examine relationship between weather and climate
  variables and the coefficients of the base model
  fits to each plot.
• Use these relationships in a new equation that
  incorporates the exogenous information into the
  base model, simultaneously fitting all first and
  second step parameters.

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Models Developed
(1)
(2)
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Compared these to Best Models in Studies One and
Two
(3)
(4)
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Results
Table 9. Fit statistics for Eq. 1-4.
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Conclusions from Third Study

• PPM does work to improve prediction change in
  diameter over a five-year increment in
  Douglas-fir.
• PPM does not a provide a significant improvement
  in model fit over other methods.
• May not be worth the extra effort it takes to use
  three steps, where one seems to do as well.

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Contributions of the Study

• Prediction of Five-year diameter increment of
  Douglas-fir in Eastern Washington can be improved
  by incorporating Precipitation and Temperature.
• Site-specific weather is most helpful
• The model used climate in the initial modeling,
  rather than as an adjustment post hoc.

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Contributions of the Study

• Model should correct to conditions without the
  need for recalibration
• Easily replicated climate data is available and
  inexpensive
• Easily transportable to other locations
• Could help predict the impacts of weather cycles

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Limitations

• Only a small sample
• Only one dataset
• Only one species
• Limited geographic region
• Limited climatic variation
• Only a five-year interval

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General Conclusions

• Different ways of using weather produce similar
  results, which gives us confidence that the
  results are valid.
• It is possible to use weather to improve diameter
  increment models.
• Climate was not useful in this case.

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Future Research

• Larger geographic area
• More variation in type of weather experienced
• In this study it was generally drier and cooler
  than average.
• More species
• Expand to include height growth
• Expand to use with mortality models

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Questions?