Topoclimatic Influences on Climatic Water Balance in
Complex Terrain: Implications for modeling Species
Occurrence, Regeneration and Productivity
Zachary A. Holden, Solomon Dobrowski and John Abatzoglou
Scaling Climate in Mountainous Terrain
• Mountains create steep biophysical gradients
• Radiation
• Temperature lapse rates
• atmospheric humidity
• snowmelt timing
• Wind speed
• These factors will influence:
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Seed germination
Regeneration
Species redistributions
Site-specific Leaf area and productivity
Vegetation in Complex Terrain
Solar Radiation is King
• Topographic shading/variable sun angles with
• Time of year influence air temp, soil temperature, snowmelt,
humidity….
Scale and Resolution
GCM/RCM data typically downscaled to 10-15 km resolution
PRISM data produced at 800meters – 4km
Many forest management decision require data at the resolution of terrain
- Seed stock selection; growth; shifts in species occurrence
6-8 deg. C variation
Within a 1 km2 window
In complex terrain
Correlation Matrix
10 RAWS
Bitterroot NF
TMAX
Strong Regional Coherence
in Daytime temperatures
Solar heating during the
day leads to well mixed air
decreasing temperatures
with elevation (lapse rates)
Maximum Daily Temperature at 10
Bitterroot NF RAWS
Nighttime Decoupling from Free Atmosphere
Correlation Matrix
10 RAWS stations on
The Bitterroot NF
Circled Stations are
At mid-slope or
valley bottom locations
Minimum RH
Maximum RH
August 12th-21th: Daytime
Stable High Pressure system
sets up inversion
Nighttime lapse rates in Big Creek, Bitterroot NF
Average Difference Between Valley
bottom sensor and sensors 200
meters above it is 5 Degrees C.
400-500 meter deep mid-slope
thermal bands
Some nights show full inversions:
Temperatures warmer at 8000 feet
than at 3500 feet in the valley
bottom
Bitterroot Valley, August 7th, 2009
Prevailing wind from the west
Wind Speed Variation in
Complex terrain
Wind speed and direction
varies with topographic
exposure relative to
prevailing wind direction
Downscaling topographic variation in wind speed
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Windninja model (Jason Forthofer; Firelab)
Daily wind simulations (June-October; 1979-2010)
~ 700,000 daily simulations averaged to monthly avg.
Higher wind speeds on ridges/west-facing mountain fronts; lower in valleys
Toward Improved understanding of climate-vegetation
dynamics: Development of high spatial resolution
climatic water balance models
Penman-Montieth equation for evapotranspiration
Integrates climate and energy into mechanistic variables
Temperature
Radiation
ET =
Atmospheric Vapor Pressure (RH)
∆ 𝑅𝑛 −G +𝜌𝑎 𝑐𝑝 𝑒𝑠 −𝑒𝑎 /𝑟𝑎
𝑟
∆+𝛾 1+ 𝑠
𝑟𝑎
Aerodynamic resistance (Wind)
Each aspect of the Penman-Monteith model varies with terrain
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
Water Balance Inputs (60m)
30 yr Monthly Average:
Min. Temperature
Max. Temperature
Solar Radiation
Precipitation
Wind Speed (windninja)
Soil water capacity
30 year (1971-2000) Deficit
Using high resolution solar radiation grids, we can
capture aspect-scale variation in water balance
Modeling the influence of wind on
evapotranspiration
• Selkirk mountains
Topographic complexity and
implications for influences of wind on
climatic water balance
Modeling species occurrence using a
water balance approach
• Presence/Absence data from FIA
• GLM: PIAL ~ AET + DEFICIT + TMIN
• Limiting factors (energy, water, temperature)
PIAL niche space in 5
genetic seed zones
defined by Mahalovich
Differentiation in where
species occur on the
spectrum of
evapotranspiration and
deficit by zone
Comparison of Adult vs. 0-1 inch (regenerating) size classes
Evidence for shifting
The regeneration niche of
Whitebark?
Regeneration shift toward
higher energy (AET) and
higher deficits?
Some places are seeing
DOWNHILL shifts in
regeneration?!
Interesting….Mechanisms?
Visualizing Species niches on the
landscape
PIEN probability
PIPO probability
Fine-scale variation with aspect
PIEN
PIPO
Putting Whitebark in
the context of other
species Niches
Linking Water Balance to Productivity
Discrete Return LiDAR
Clear Creek Drainage,
Nez Perce NF
80,000 acres
LiDAR metrics at 20m resolution
Modeling stand level forest metrics
with LiDAR
• 62 Fixed radius plots
• Empirical models fit
• using LiDAR height
metrics as as
independent
variables
Clear Creek Drainage
Nez Perce NF
Basal Area and
Crown Bulk Density
modeled from LiDAR
data
Clear patterns of
productivity relative
to summer deficit
Low elevation south
slopes limited by
water
High elevation north
slopes limited by
energy
A topoclimatic monitoring program for the
Northwest US
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Many partners in
the USFS, USGS,
and area
universities
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2000 sensors
distributed from
2009-2011.
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400 RH sensors
added in 2010
What can we learn from inexpensive sensors?
Thermoworks Logtag ($22) Thermochron ibutton ($35)
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How much does
topography influence
surface air
temperatures?
• Space-time models of
surface air temperature
variation that account for
variable lapse rates, cold
air drainage and aspect
A gill-style radiation shield for measuring air
temperatures in forested environments
$2.00 material costs
12 minute
construction time
Minimal bias under
Forest canopy
August 2010 Boise Basin
Daily Tmax
There is huge variation
In lapse rates from day
To day.
Also dynamic adjust.
In how much north vs. south
Slopes vary depending on
Humidity and sky conditions
PRISM derived water balance vs. with microsensors
Valley bottoms much wetter when we account for CAD
Wind-exposed slopes much drier
Discussion
• Climatic water balance provides a physical
basis for addressing climate-terrain
interactions and relationships with vegetation
• Allows us to address climate-vegetation
relationships in a mechanistic way, while still
accounting for topography
• Obviates the need for using gradient models
and empirical indices
Future work in progress:
• Use distributed sensor networks to downscale
General Circulation Model predictions for
temperature and relative humidity
• Develop species occurrence and regeneration
models under a range of future climate scenarios
• Integrate climatic water balance models with LiDAR
data to develop LiDAR-based structure models
applicable to a range of forest types
Next Steps…integrating high resolution
microclimate data into water balance
Predicted Minimum temperature, Big Creek, July 24th