Spatio-Temporal Variability in
Topoclimate Inferred from Land
Surface Temperature Data
(and its relevance for mapping climate refugia)
Solomon Dobrowski, Jared Oyler, Brady Allred
College of Forestry and Conservation
University of Montana
Outline
§  Motivations
§  Topoclimate
§  Methods
§  Results
§  Conclusions
Motivation
§  Refugia should have topoclimatic heterogeneity,
environmental stability, and accessibility (Keppel et al. 2015)
Topoclimate
Large-­‐scale Atmospheric Circula0on +
Topographic posi0on Slope/Aspect Land Surface Proper0es =
Topoclimate Topoclimate
§  “The climate of a place” or “local climate” (Thornthwaite, 1953)
§  “…the climate of terrain of several km2…[topoclimate is] strongly interrelated with
relief and surface properties such as aspect, slope angle, surface albedo and
roughness” (Littmann, 2008).
§  Topoclimates (10m to 1 km) are the effects of aspect, slope, relative elevation,
and surrounding terrain on solar exposure, wind, and cold air drainage (Ackerly et
al., 2010).
§  “…the climate experienced by an organism in situ…the sum of regional advective
influences and local terrain influences…spatial estimates of climate as it varies
with topographic position in the landscape” (Dobrowski, 2011)
Motivation
§  Refugia should have topoclimatic heterogeneity,
environmental stability, and accessibility (Keppel et al. 2015)
§  How can we map topoclimatic diversity?
§  Does high terrain variability = topoclimatic diversity?
§  Why LST?
Motivation
§  LST is the radiometric temperature of the ground or canopy
surface
§  LST spatial patterns are sensitive to air temperature, land
surface properties such as land cover, albedo, soil moisture,
and their interaction with atmospheric conditions
§  LST is a biophysical parameter that differs from air
temperature, but the two variables are physically related and
can be strongly correlated
Methods
LST ~
Geographic
Position
(X,Y,Z)
+
Land Cover
(NDVI, snow)
+
Topoclimatic Drivers
(SRAD, TPI, CTI, HLI)
Methods (data)
§  LST (land surface temperature) - MODIS Aqua MYD11A2 8-day 1km product. 10-year (2003-2012) climatological LST means for each
month for day and night (Oyler et al. 2015).
§  X,Y,Z – longitude, latitude, elevation
Geographic Position
§  NDVI - MODIS Terra MOD13A3 monthly composite 1-km product
monthly time
varying
§  Snow - MODIS Terra MOD10A2 8-day 500-m snow cover product.
Land Cover
§
§
§
§
SRAD – monthly average clear sky shortwave radiation
TPI – topographic position index
CTI – topographic wetness index (also known as TWI or TCI)
HLI – Heat load index
Topoclimate Drivers
Methods
LST1 ~
Geographic
Position
(X,Y,Z)
LST2 ~
Geographic
Position
(X,Y,Z)
+
Land Cover
(NDVI, snow)
LST3 ~
Geographic
Position
(X,Y,Z)
+
Land Cover
(NDVI, snow)
+
Topoclimatic Drivers
(SRAD, TPI, CTI, HLI)
Methods (study area)
or
linear predict
Methods (modeling)
lon
LST3 ~
Geographic
Position
(X,Y,Z)
+
Land Cover
(NDVI, snow)
+
lat
Topoclimatic Drivers
(SRAD, TPI, CTI, HLI)
or
linear predict
v
ele
lat
v
ele
Used GAM
Fit separate models for each month
n=5000
s(x,y,z) interaction term
s(term,2) for other predictors
lon
or
linear predict
-
-
-
-
-
Results (LST
1.0
night)
0.8
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0.4
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lst~(x,y,z)
lst~(x,y,z)+snow+ndvi
lst~(x,y,z)+snow+ndvi+srad+hli+cti+tpi
0.0
% deviance explained
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2
4
6
month
8
10
12
Results (LST
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lst~(x,y,z)
lst~(x,y,z)+snow+ndvi
lst~(x,y,z)+snow+ndvi+srad+hli+cti+tpi
0.0
% deviance explained
1.0
night)
2
4
6
month
8
10
12
Results
“ OK, this looks cool but
are these temperature
differences real?”
“ Do these reflect actual
air temperature
differences?”
Methods
Temp1 ~
Geographic
Position
(X,Y,Z)
Temp2 ~
Geographic
Position
(X,Y,Z)
+
Land Cover
(NDVI, snow)
Temp3 ~
Geographic
Position
(X,Y,Z)
+
Land Cover
(NDVI, snow)
+
Topoclimatic Drivers
(SRAD, TPI, CTI, HLI)
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Middle Rockies
Tmin vs LSTnight
n ~ 300 met
stations
model differences − station air temp (degree C)
−3
−1
Results
0.92
−3
1 2 3
jan
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model differences − station air temp (degree C)
West Cascades
Tmin vs LSTnight
n ~ 100 met
stations
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−1
Results
feb
1 2 3
1 2 3
jan
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3
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−1
model differences − LST (degree C)
1
Conclusions
§  Work in progress
§  Topoclimatic effects are time varying and site specific
§  LST data seems promising
§  Challenges – cloudy areas?
§  Retrieval of other biophysical variables?
Acknowledgements
§  The Nature Conservancy (Joe Fargione, Brad McRae)
§  Wilburforce Foundation
§  NSF
§  RMRS