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

Radiation Atmospheric Vapor Pressure (RH)

Aerodynamic resistance (Wind)

Temperature

Each variable in the Penman-Monteith model varies with terrain position

• 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

Scaling Climate in Mountainous Terrain

Holden and Jolly (2011)

• 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

Scaling Climate in Mountainous Terrain

Holden and Jolly (2011)

• Snow melt timing

• Earliest melt on southwest facing slopes

• 1 month delay high elevation north slopes

Scaling Climate in Mountainous Terrain

Holden and Jolly 2011

• 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

Scaling Climate in Mountainous Terrain

Holden and Jolly (2011)

• 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

Scaling Climate in Mountainous Terrain

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

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

STATSGO raw SSURGO

Soil depth and physical properties make up the “bucket” that

stores water making it available for plants

Deeper soils in valley

bottoms

Deeper soils on North-

facing slopes

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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

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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

Daily cloud/shade corrected radiation (1979-present) Covariate in maximum temperature 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

Soil moisture as covariate for Tmax model

• Daily FASST runs generated each day at 240m

• Soil moisture (0-10 cm)

Gridded daily FASST soil moisture

Maximum daily temperature

Empirical model with physical basis:

Tmax = reanalysis lapse + radiation *

FASST soil moisture + MODIS VCF

Tmax: captures interaction between

Surface moisture and insolation

Tmax: captures differences in north and

South slope temperatures

Cold air drainage potential (CAD-P)

• Difference between free air temperature and observed surface temperature

• Modeled as a function of topography

21

Holden et al. 2015

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Cold Air Drainage potential model Difference between free air temperature (NARR) and sensor observations

Modeled as a function of terrain covariates

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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)

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High resolution daily air temperature models for the US Northern Rockies

Tmin = reanalysis lapse + CAD * pressure + humidity

+ MODIS VCF

Minimum temperature

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TOPOFIRE weather obs database

TOPOFIRE technical progress: weather obs. database

27

Daily evapotranspiration and soil moisture (1979-present)

Daily Penman-Monteith evapotranspiration

-250 m resolution, including solar radiation

-Strong aspect differences/drier south facing slopes

28

Daily snow accumulation and melt model (1979-present)

29

Daily Minimum/Maximum air temperature (2000-present)

30

Daily average dewpoint temperature (2000-present)

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Spatially complete maps of soil properties (gSSURGO)

Imputation of missing SSURGO data using terrain and satellite data

Deeper soils on

Shaded slopes

shallow soils on

steep slopes

Deeper soils in areas

of local accumulation

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

Empirical modeling of LWP

LWP = f(VWC + SOLAR + VPD + species*VWC + species*VPD)

Generalized linear model

VWC = volumetric water content VPD = vapor pressure deficit

Boosted regression tree fit

r= 0.98

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

questions