Climate Refugia: the physical hydrologic and
disturbance basis
Zachary Holden - US Forest Service Region 1, Missoula MT
Marco P. Maneta – University of Montana Dept. Geosciences
Alan Swanson – University of Montana Dept. of Geography
John Abatzoglou –University of Idaho
Jason Forthofer – US Forest Service, Missoula MT
Solomon Dobrowski – University of Montana Dept. of Forestry
Allen Warren – University of Montana
Anna Klene – University of Monana Dept. of Geography
Vegetation management in Complex Terrain
Fine-scale gradients drive large variation in vegetation and fuel dynamics
We lack fundamental tools and data needed to make informed
Decisions about:
What to plant where
How fast it will grow
How it will burn
Penman-Montieth equation for evapotranspiration
Integrates climate and energy into mechanistic variables
Temperature
Radiation
Atmospheric Vapor Pressure (RH)
Aerodynamic resistance (Wind)
Each variable in the Penman-Monteith model varies with terrain
Scaling Climate in Mountainous Terrain
• Mountains create steep biophysical gradients
• Every energy input to available moisture varies at fine scale in complex
terrain
•
•
•
•
•
Radiation
Minimum temperature
Max. temperature
atmospheric humidity
Wind speed
Holden and Jolly (2011)
Scaling Climate in Mountainous Terrain
• Mountains create steep biophysical gradients
• Every energy input to available moisture varies at fine scale in complex
terrain
•
•
•
•
•
Radiation
Minimum temperature
Max. temperature
atmospheric humidity
Wind speed
Holden and Jolly (2011)
Scaling Climate in Mountainous Terrain
• Mountains create steep biophysical gradients
• Every energy input to available moisture varies at fine scale in complex
terrain
•
•
•
•
•
Radiation
Min. temperature
Max. temperature
atmospheric humidity
Wind speed
Holden and Jolly (2011)
Scaling Climate in Mountainous Terrain
• Mountains create steep biophysical gradients
• Every energy input to available moisture varies at fine scale in complex
terrain
•
•
•
•
•
Radiation
Min. temperature
Max. temperature
atmospheric humidity
Wind speed
Holden and Jolly (2011)
Topographic variation in windspeed
Slower wind speeds in valley bottoms
Higher wind speeds on ridge tops
Large effect on ET
WindNinja Simulation for August 13, 2013
Soil water holding capacity
Soil depth and physical properties make up the “bucket” that
stores water making it available for plants
STATSGO
STATSGO data has complete US
coverage – But it’s thought to poorly
characterize soil variability
SSURGO higher quality but large areas
Of missing data in western US
raw SSURGO
Deeper soils in valley
bottoms
Deeper soils on Northfacing slopes
Massive microclimate sampling with low-cost sensor networks
2000 sites in N. Rockies and Canada (2010-2013)
300 sites in WA/OR/CA in 2013-2014
| 12
Development of high resolution daily gridded air temperature data with
distributed sensor networks For the US Northern Rockies
240 meter daily air temperature grids
1979-2013 daily Tmin and Tmax
Holden et al. (in press)
13
Cold air drainage potential (CAD-P)
• Difference between
free air temperature
and observed surface
temperature
• Modeled as a function
of topography
14
Cold Air Drainage potential model
Difference between free air temperature (NARR) and sensor observations
Modeled as a function of terrain covariates
15
Daily cloud/shade corrected radiation
Wind speed modeling with WindNinja
• Historical daily wind climatology (240 meters)
• Daily 1979-2013 simulations on 1 degree tiles
Missoula, MT
1981-2010 Mean daily August Wind Speed
Spatially complete maps of soil properties (gSSURGO)
Imputation of missing SSURGO data using terrain and satellite data
Deeper soils in areas
of local accumulation
Deeper soils on
Shaded slopes
shallow soils on
steep slopes
| 18
Vegetation and climate refugia
• Vegetation mediates near-surface climate (radiation, air
temperature, skin temperature, interception, wind speed)
• Fires and disturbance accelerate climate-driven shifts in
vegetation
• What factors will mediate the effects or ‘severity’ of wildfires
as warming continues?
2009 Kootenai Creek Fire, Montana
MTBS fires (1984-2014)
Modeling potential for low severity (non-stand
replacing) wildfire effects
Spatially independent sample of pixels from 535 Northern Rockies wildfires
Pixels classified as low severity or “other”
• linear model
• Low Severity = GLM(DNBR ~ poly(solar) + poly(CAD) + wind +
soil + ndvi*soil + ndvi*solar + deficit anomaly)
Model fits
GLM
Random Forests
Gradient Boosting
AUC
0.70
0.71
0.72
Partial effects of model terms
Interaction between soil depth and pre-fire
vegetation
• Probability of low
severity:
Deep soil mediates
severity where pre-fire
vegetation density is
high
Highest with low prefire greenness and
shallow soils
Lower severity on sparsely
vegetated south facing
slopes
Lower severity on shaded
but densely vegetated
north facing (shaded)
slopes
Missoula, MT
Valleys and north slopes are unique environments
• high cold air drainage potential, low wind speeds, shading
from sun and deeper soils
Historical wildfire data suggest that fine-scale variation in
energy and moisture mediate the effects of wildfires when
they occur
Revising the fire triangle
terrain
weather
Veg/fuels
A revised fire triangle
weather
topoclimate
Veg/fuels
terrain
TOPOFIRE: a system for mapping terrain influences on
climate for improved wildfire decision support
Topofire.dbs.umt.edu
TOPOFIRE datasets
•
•
•
•
•
•
•
•
CONUS Daily 240 meter (1979-present):
Solar radiation
Minimum temperature
Maximum temperature
Dewpoint
Vapor Pressure Deficit
Snow Water Equivalent
Soil Moisture
Fuel Moisture
Thank you
Extra slides in case of questions
Minimum temperature
High resolution daily air temperature models for the US Northern Rockies
Tmin = reanalysis lapse + CAD * pressure + humidity
+ MODIS VCF
| 35
Maximum temperature
02/10/2015
Empirical model with physical basis:
Tmax = reanalysis lapse + radiation *
FASST soil moisture + MODIS VCF
Tmax: captures differences in north and
South slope temperatures
Tmax: captures interaction between
Surface moisture and insolation
| 36
1981-2010
2035-2065
difference
More low severity fire in
areas with high CAD
potential
Small differences with wind
speed
No apparent differences with
Soil depth???
Lower severity on southfacing slopes??