Progress modeling topographic variation in temperature and moisture for inland Northwest forest management Zachary Holden - US Forest Service Region 1, Missoula MT Alan Swanson University of Montana Dept. of Geography David Affleck University of Montana (Forestry) Solomon Dobrowski University of Montana (Forestry) Marco Maneta University of Montana (Geosciences)
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
What spatial resolution is needed to capture climatic/biophysical variation in complex terrain? 1 meter resolution gridded indices Sample of 10,000 pixels Semivariogram analysis
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 position
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 Snow melt timing Earliest melt on southwest facing slopes 1 month delay high elevation north slopes 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 Minimum 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 raw SSURGO 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
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 14
Temperature/humidity models for CONUS NOAA Climate Forecast System Reanalysis (CFS) global hourly data from 1979-present + forecasts 42 Pressure levels (geopotential heights) 0.5 degree resolution 16
Daily cloud/shade corrected radiation (1979-present) Covariate in maximum temperature model
Soil moisture as covariate for Tmax model - Fast All Season Soil Strength model (FASST) - Model run at ~ 15,000 points - interpolated to 250 m grid - Daily 0-10 cm soil moisture grids created for 1979-2013
Gridded daily FASST soil moisture Daily FASST runs generated each day at 240m Soil moisture (0-10 cm)
Maximum daily temperature 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
Cold air drainage potential (CAD-P) Difference between free air temperature and observed surface temperature Modeled as a function of topography Holden et al. 2015 21
Cold Air Drainage potential model Difference between free air temperature (NARR) and sensor observations Modeled as a function of terrain covariates 22
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. (2015) 23
Minimum temperature High resolution daily air temperature models for the US Northern Rockies Tmin = reanalysis lapse + CAD * pressure + humidity + MODIS VCF 24
TOPOFIRE technical progress: weather obs. database TOPOFIRE weather obs database 25
Daily evapotranspiration and soil moisture (1979-present) Daily Penman-Monteith evapotranspiration -250 m resolution, including solar radiation -Strong aspect differences/drier south facing slopes 27
Daily snow accumulation and melt model (1979-present) 28
Daily Minimum/Maximum air temperature (2000-present) 29
Daily average dewpoint temperature (2000-present) 30
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 31
Modeling biophysical controls on plant stress and productivity with water and energy balance models ECH2O ecohydrology model Spatially distributed model Surface/subsurface flow Excellent snow model
Priest River Experimental Forest, Idaho Sites on North and South facing slopes Full weather station at each site Small, medium and mature stand ~ 700 leaf water potential measurements (2003-2005)
Empirical modeling of Leaf Water Potential (Psi) LWP depends on supply (soil moisture) and demand (vapor pressure deficit) Under high demand (high temperature, low RH) trees close stomates/minimize water loss Low soil moistures increase resistance; more difficult to move water from soil to atmosphere Species-specific responses
Generalized linear model LWP = f(vwc + SOLAR + VPD + species*vwc + species*vpd) Empirical modeling of LWP VWC = volumetric water content VPD = vapor pressure deficit
Spatial modeling LWP with ech2o All terrain-varying physical processes Are accounted for using TOPOFIRE data Tmin (cold air drainage) Solar insolation Wind speed (windninja) Soil properties (gssurgo) Tmax (corrected for insolation effects)
Soil moisture modeling with ech2o
ECH2O coupled with 3P-G Models carbon assimilation in roots and above ground Tracks stand age and LAI Potentially powerful tool for understanding site productivity Climatic influences on tree growth, stress, mortality
summary Rapid progress developing topographically resolved temperature/humdity gridded data Preliminary PNW datasets should be completed by August 2016 Coupling these datasets with hydrologic models could be useful for characterizing physical controls on tree occurrence/growth
TOPOFIRE: a system for mapping terrain influences on climate for improved wildfire decision support Topofire.dbs.umt.edu
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