can we forecast changes in the distribution and regeneration of conifers in the inland northwest?...
TRANSCRIPT
![Page 1: Can we forecast changes in the distribution and regeneration of conifers in the Inland Northwest? Zachary Holden-- USDA Forest Service, Missoula MT Solomon](https://reader035.vdocuments.us/reader035/viewer/2022062516/56649e625503460f94b5ddc9/html5/thumbnails/1.jpg)
Can we forecast changes in the distribution and regeneration of conifers in the Inland
Northwest?
Zachary Holden-- USDA Forest Service, Missoula MTSolomon Dobrowski -- University of Montana John Abatzoglou-- University of Idaho
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Presentation Overview
• Correlative models of adult vs. juvenile presence
• Incorporating future climate into fine-scale water balance estimates
• Potential shifts in future regeneration with future climate scenarios
• Future directions and needs
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USFS outplanting activities in Region 1
• Average acres treated: ~ 9,000-12,000• Cost per acre: $600-700• 6.3-8.5 million dollars annually
• Success rate:• 70% success by 3rd year
(cost estimates: Glenda Scott Region 1)
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Climate Change?
• Broad agreement from models and historical observations that things are getting warmer
• Extensive evidence from paleoclimate records that shifts in climate were accompanied by shifts in vegetation
• Widely assumed that tree distribution will shift to higher elevations
IPCC 2007
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Implications for forest management
• a better understanding of bioclimatic controls on species occurrence and growth could save time and money
• Improve outplanting success
• Anticipate optimum locations for planting with projected warming
• Develop tools for near and long term forecasting of where and when to plant??
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The cottage industry of modeling changes in species distributions with climate change
• It’s easy!
• Correlate species presence with climate variables
• Fit favorite model
• Project onto current/future climate space
• Things move around!
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The challenge in mountains:
• All of the physical drivers that govern where species occur vary with terrain
• Temperature, radiation, wind, humidity
• Finer spatial resolution than available data
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Larch basal area predicted from spring-fall Landsat change
Figure: Joe Touzel
Strong aspect dependenceLarch occurs primarily on North-facing slopes
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Penman-Montieth equation for evapotranspirationIntegrates climate and energy into mechanistic variables
ET =
Radiation Atmospheric Vapor Pressure (RH)
Aerodynamic resistance (Wind)
Temperature
Toward Improved understanding of climate-vegetation dynamics: Development of high spatial resolution
climatic water balance models
Each aspect of the Penman-Monteith model varies with terrain
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The climatic water balance
• PET = potential for a site to evaporate water
• AET = actual evaporation, given moisture. (Think productivity)
• Deficit= The difference.• PET – AET = deficit. • Unmet atmospheric
demand
Figure from Nathan Stephenson, USGS, AGU presentation 2011
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Water Balance Inputs (60m)
30 yr Monthly Average:
Min. TemperatureMax. TemperatureSolar RadiationPrecipitation
Wind Speed (windninja)
Soil water capacity
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30 year (1971-2000) Deficit
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Using high resolution solar radiation grids, we can capture some aspect-scale variation in water balance
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Modeling the influence of wind on evapotranspiration
• Selkirk mountains
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Modeling adult vs regeneration niche
Most SDM studies focus on presence of adults
Many age cohorts representing large range of climatic conditions
Presence of juveniles may better represent contemporary climate
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Ponderosa Pine adult niche
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Ponderosa Pine regeneration niche
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Douglas fir adult niche
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Douglas fir regeneration niche
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Ponderosa regeneration
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Modeling species occurrence using a water balance approach
• Presence/Absence data from FIA• GLM: PIPO ~ AET + poly(DEFICIT) + TMIN• Limiting factors (energy, water, temperature)
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Adult Regen
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Difference in adult and regenerationniche
Lower probability for regenerationat lower elevations
Suggests contraction of range for Regenerating PIPO compared withadult size class
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Projected monthly changes in climate 14 GCM average
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Wind and Radiation
More frequent persistent high pressure systems
Higher July-August temperatures
Decreased wind speeds
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Modeled change in water balance for 2030
> 15 cm increase in moisture deficit
Complex patterns of relative change in Moisture availability:
-Warmer temperatures = increased evapotranspiration
-Lower wind speeds = decreased evapotranspiration at exposed sites
-Changes in cloud cover influence relativeChange on shaded vs exposed slopes
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Sheltered leaward slope (historically low wind speed)
Ridgetop exposed to prevailing wind (historically high wind speed)
Global stilling mediates wind-driven patterns in evapotranspiration
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Ponderosa pine predicted regeneration: Adult vs 2030
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Douglas fircurrent regen future regen
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-41.8 % to -34.1 %
-34.1 % to -26.4 %
-26.4 % to -18.7 %
-18.7 % to -11.1 %
-11.1 % to -3.4 %
-3.4 % to 4.3 %
4.3 % to 12 %
12 % to 19.6 %
19.6 % to 27.3 %
27.3 % to 35 %
Trends in HCN2 Precipitation (% change in 25th %ile)
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1950 1960 1970 1980 1990 2000
20
03
00
40
05
00
60
07
00
80
0
BOISE R NR TWIN SPRINGS IDA
nn
ua
l Flo
w (
mm
)Middle Fork Boise – Trend in Water Yield Quantiles
75th %ile
50th %ile
25th %ileP.25=0.02
Luce and Holden, 2009
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25th %ile
a)
50th %ile
b)
75th %ile
c)
Mean
d)
-47.3 % to -38.7 %-38.7 % to -30.1 %-30.1 % to -21.5 %-21.5 % to -12.9 %
-12.9 % to -4.3 %-4.3 % to 4.3 %4.3 % to 12.9 %12.9 % to 21.5 %
Not Signif icant at 0.1 Signif icant at 0.1
25th – Dry Years 75th – Wet Years
50th Mean Luce and Holden, 2009
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Snotel-HCN station pairs < 20km apart
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Changing patterns in orographic enhancement (the distribution of precipitation with elevation)
La Nina (wet) years have relativelyHigher amounts of high elevation Precipitation
El Nino (dry) years have relativelyLess precipitation falling at high Elevations
Trends detected at low elevationStations may be amplified in mountains
Holden et al. (in prep)
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A simple empirical model of snow ablation dateUsing distributed temperature sensors
Captures physics of snow accumulation and melt
Earliest melt on Southwest-facing slopes (interaction betweeen radiation and temperature)
4 week delay on high elevation North slopes
R2 adj = 0.76
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climatic and biophysical variation in complex terrain
Tmin, Tmax, Rhmin, Rhmax modeled using PCA and networks of ibuttons
Lower maximum temperatures and Higher RH on north slopes
Delayed snowmelt timing on high Elevation north slopes
Lower minimum temperatures and higher RH in valley bottoms
Holden and Jolly (2011)
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Fine-scale heterogeneous changes in air temperature warming rates
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What we can expect in a warming world:
• Heterogeneous fine-scale changes in: - surface air temperature warming with aspect - changes in inversion patterns/cold air pooling - relative changes in snowmelt timing with aspect? - changes in distribution of precipitation?
Potential for complex fine scale changes in where/how species grow
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TOPOFIRE: An interactive web system for monitoring disturbance and climatic influences on soil and fuel moisture in complex terrain
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Acknowledgements
FundingWestern Wildland Threat Assessment CenterNASA (Frank Lindsey)
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questions