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University of British Columbia

Faculty of Forestry

An integrated modelling approach for the An integrated modelling approach for the

assessment of forest growth and development in the assessment of forest growth and development in the

face of climate change:face of climate change: A case study in the western A case study in the western

boreal forestboreal forest

B. Seely, C. Welham, J.A. Blanco, and J.P. KimminsB. Seely, C. Welham, J.A. Blanco, and J.P. Kimmins

Department of Forest Sciences, University of British Columbia,

Vancouver, BC


Faculty of Forestry

Introduction:Introduction: Potential Climate Impacts on ForestsPotential Climate Impacts on Forests

•• Natural disturbance agentsNatural disturbance agents

•• Species distributionsSpecies distributions

•• Early stand development Early stand development

•• Growth ratesGrowth rates

•• Ecosystem carbon storageEcosystem carbon storage

Need for modeling toolsNeed for modeling tools

Scenario analysis of regional Scenario analysis of regional

climate change impactsclimate change impacts

•• Risk assessment at stand and Risk assessment at stand and

landscape scaleslandscape scales

•• Efficacy of mitigation strategiesEfficacy of mitigation strategies


Faculty of Forestry

Presentation OutlinePresentation Outline

Case Study: Case Study: Model development / calibration for application in Model development / calibration for application in

western boreal forests:western boreal forests: Oil sands reclamation in Oil sands reclamation in

Northern AlbertaNorthern Alberta

• Climate impacts in western boreal forests

• Risk factors in plantation establishment

• Brief overview of modeling tools & calibration work

• Example application: Factorial analysis of water stress in reclaimed

ecosystems

•• Ongoing work and developmentOngoing work and development


Faculty of Forestry

Case Study:Case Study: Oil Sands reclamationOil Sands reclamation

Reclamation Goal:Reclamation Goal: Establish a healthy, selfEstablish a healthy, self--sustaining forest ecosystemsustaining forest ecosystem

Basic Approach:Basic Approach:

••Design and construct a soil cover Design and construct a soil cover

to facilitate ecosystem processesto facilitate ecosystem processes

•• Establish desired vegetation Establish desired vegetation

communitycommunity

Risk Factors?Risk Factors?

•• 33--year risk assessment study funded by year risk assessment study funded by Cumulative Environmental Cumulative Environmental

Management AssociationManagement Association (CEMA) (2007(CEMA) (2007--2009)2009)


Faculty of Forestry

Risk factors:Risk factors: Climate impacts in Western Boreal ForestsClimate impacts in Western Boreal Forests

Climate change impacts on the productivity

and health of aspen (CIPHA) (Hogg et al.)

(http://cfs.nrcan.gc.ca/projects/150/1)

• Substantial aspen drought-related aspen

die-back throughout western boreal

forest

• Tree-ring analysis show historical growth

rates primarily influenced by growing

season precipitation

•Eddy-covariance analyses

•Psn: with drought & with spring temp

when drought not limiting

•Decomposition (respiration) with drought

Boreal Ecosystem Research and Monitoring

Sites (BERMS) (Barr et al., 2007, Global Chg. Bio.)


Faculty of Forestry

Risk Factors:Risk Factors: Temporal dynamics of risk in plantationsTemporal dynamics of risk in plantations

RiskRisk

High

Low

TimeTime

Moisture Moisture

DeficitDeficit

Nutrient Nutrient

DeficitDeficit

ResilienceResilience

High

Low

Ecosystem Ecosystem

ResilienceResilience

period of

canopy

closure


Faculty of Forestry

Oil Sands Reclamation Case Study:Oil Sands Reclamation Case Study: Project objectivesProject objectives

Primary ObjectivePrimary Objective

Expand our current forest growth model (FORECAST) to include

representation of climate impacts on forest growth and

development including hydrological interactions between

vegetation and soil covers (ForWaDy model).

GoalGoal

Provide a tool/methodology for assessing the risk of

plantation failure/poor performance with respect to

different Oil Sands reclamation strategies and climate

scenarios


Faculty of Forestry

FORECAST: FORECAST: OverviewOverview

•• Wide variety of stand types and management systemsWide variety of stand types and management systems

•• Uses a Uses a ““HybridHybrid”” simulation approach where historical growth data is used to simulation approach where historical growth data is used to

parameterize mechanistic modelparameterize mechanistic model

•• Growth presently limited by light and nutrients but adding climaGrowth presently limited by light and nutrients but adding climate change te change

capability capability

Potential Net PrimaryPotential Net Primary

ProductionProduction

(Potential growth)(Potential growth)

Actual Net PrimaryActual Net Primary


(Actual growth)(Actual growth)

LitterLitterCoarse woodyCoarse woody

debrisdebrisFine rootsFine roots

Nutrient poolNutrient pool

Nutrient cyclingNutrient cycling

Potential Net PrimaryPotential Net Primary


(Potential growth)(Potential growth)

Actual Net PrimaryActual Net Primary


(Actual growth)(Actual growth)

LitterLitterCoarse woodyCoarse woody

debrisdebrisFine rootsFine roots

Nutrient poolNutrient pool

Nutrient cyclingNutrient cycling

www.forestry.ubc.ca/ecomodels/

•• Ecologically based, standEcologically based, stand--level model for simulating the effects of level model for simulating the effects of

alternative management strategies on biophysical indicators of Salternative management strategies on biophysical indicators of SFMFM

ForWaDyForWaDy

Canopy

Transpiration

Canopy Interception

Rain

Humus layer

Outflow

Soil B

Soil A

Forest floorpercolation

Soil A percolation

Soil B percolation

Runoff

Snowpack

Throughfall

Minor Veg

Transpiration

Transpiration Deficit

Index

Litter layer

Infiltration

EvaporationEvaporation

Interflow

Canopy

TranspirationDemand

Snowthroughfall

Snow

Air tempmelt

Radiation

melt

Sublimation

Subsoil

drainage

TranspirationTranspiration

EvaporationEvaporation•Energy balance

approach

•Daily time step

•Water

competition and

stress

• Impacts of

vegetation on

hydrology

•Stand alone or

submodel


Faculty of Forestry

Water Stress

Index

Climate

Decomp

Multiplier

Daily Solar

Radiation

Daily Precip

Daily Air

Temperature

ForWaDy

ForWaDy

FORECAST

Nutrient release

NPP

Foliage, Roots,

Stem, etc.

Litterfall &

Humus

production

Development of FORECAST ClimateDevelopment of FORECAST Climate

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Water Stress Index

NP

P m

ultip

lier

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40

Mean Temp (Deg C)

NP

P m

ultip

lier

Model LinkageModel Linkage


Faculty of Forestry

Approach:Approach: Calibrating Ecosystem Response to ClimateCalibrating Ecosystem Response to Climate

Dendroclimatology:Dendroclimatology: A bioassay A bioassay

of climate impacts on tree growthof climate impacts on tree growth

Data sources:Data sources:

• The Boreal Ecosystem-

Atmosphere Study

(BOREAS) Sites

• CIPHA Study

• Local data from natural

and reclaimed sites

(Terry Macyk data)


Faculty of Forestry

Calibration: Calibration: Analysis of Local Analysis of Local DendroDendro DataData

Results:Results:

• Spring temperature: Weak positive correlation with RWI

• Mid growing season rainfall (Jun – Aug): Best correlation with RWI

• Species effect: Similar trends among species

0.0

0.4

0.8

1.2

1.6

1992 1994 1996 1998 2000 2002 2004 2006 2008

Year

RW

Index

Sw

At

Pj

SW site


Faculty of Forestry

JunJun--Aug CumulativeAug Cumulative

with thresholdwith threshold

Rainfall vs. RWIRainfall vs. RWI

Regression Analysis:

0.620.450.79Pine

0.380.400.35Aspen

0.270.300.23Spruce

MeanSCSWSpecies

SiteR2

SW spruce 200mm threshold

June-Aug Rainfall

y = 0.0031x + 0.5769

R2 = 0.2346

0.4

0.8

1.2

1.6

0 50 100 150 200 250

Rainfall (mm)

RW index

SC spruce 200mm threshold

June-Aug Rainfall

y = 0.0029x + 0.6036

R2 = 0.302

0.4

0.8

1.2

1.6

0 50 100 150 200 250

Rainfall (mm)

RW index

SC aspen 180mm threshold

June-Aug Rainfall

y = 0.0048x + 0.4281

R2 = 0.4039

0.4

0.8

1.2

1.6

0 50 100 150 200

Rainfall (mm)

RW index

SW aspen 180mm threshold

June-Aug Rainfall

y = 0.0039x + 0.4865

R2 = 0.3507

0.4

0.8

1.2

1.6

0 50 100 150 200

Rainfall (mm)

RW index

SW pine 160mm threshold

June-Aug Rainfall

y = 0.0083x + 0.1216

R2 = 0.7871

0.4

0.8

1.2

1.6

0 50 100 150

Rainfall (mm)

RW index

SC pine 160mm threshold

June-Aug Rainfall

y = 0.0034x + 0.5505

R2 = 0.4524

0.4

0.8

1.2

1.6

0 50 100 150

Rainfall (mm)

RW index

Spruce 200+ mm

Aspen 180 mm

Pine 160 mm

Best fit threshold

Calibration: Calibration: Analysis of Local Analysis of Local DendroDendro DataData


Faculty of Forestry

lowhighMinor veg. comp.3

dry normalClimate2

southnorthAspect1

40 cm peat over sand20 cm peat over sandCover type

15 years5 yearsStand age

spruce aspen mixedwoodpineVegetation type

OptionsVariable

1. Assumes a 15 degree slope.

2. Using two consecutive years of climate data from average and dry years.

3. Degree of competition from minor vegetation

Use ForWaDy model to examine potential water stress

under the following reclamation conditions

A total of 48 runsA total of 48 runs

Year 1 Work: Year 1 Work: ForWaDyForWaDy factorial analysisfactorial analysis


Faculty of Forestry

ForWaDy Factorial Analysis: ForWaDy Factorial Analysis: Results

Growing Season Transpiration Deficit Index (GS TDI)

GS TDI = ∑ Tree Transpiration Actual

∑ Tree Transpiration Demand

Where:

Tree Transpiration Actual = fn (available soil water,

root depth, veg competition)

Tree Transpiration Demand = fn (leaf area, radiation

load, canopy resistance)

High Med Low

Dry Normal N Aspect S Aspect Mean Dry Normal N Aspect S Aspect Mean

20 cm 0.44 0.15 0.23 0.36 0.30 0.50 0.23 0.29 0.44 0.36

40 cm 0.41 0.19 0.23 0.38 0.30 0.47 0.24 0.28 0.44 0.36

low veg 0.33 0.09 0.16 0.26 0.21 0.39 0.11 0.19 0.31 0.25

high veg 0.51 0.24 0.30 0.44 0.37 0.54 0.28 0.33 0.48 0.41

Mean 0.42 0.17 0.23 0.36 0.47 0.22 0.27 0.41

Pine Spruce Aspen

GS TDI

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Climate Minor veg

comp

Aspect Cover

depth

Factor

Pine

Spruce-Aspen


Faculty of Forestry

ForWaDy Factorial Analysis: ForWaDy Factorial Analysis: Results

Total Evapotranspiration (Total ET)

Total ET = Tree trans + veg trans + surface

evap

High Med Low

Dry Normal N Aspect S Aspect Mean Dry Normal N Aspect S Aspect Mean

20 cm 272 335 279 328 304 277 343 289 331 310

40 cm 287 342 291 338 315 294 353 303 343 323

low veg 262 310 258 314 286 267 322 271 319 295

high veg 278 346 293 332 312 281 349 298 332 315

Mean 275 333 280 328 280 342 290 331

Pine Spruce Aspen

0

10

20

30

40

50

60

70

Climate Minor veg

comp

Aspect Cover

depth

Factor

Pine

Spruce-Aspen

Total ET


Faculty of Forestry

ForWaDy Factorial Analysis: ForWaDy Factorial Analysis: Summary

• Estimate of frequency of ‘dry’ years will be essential for risk

assessment

• Aspect can have substantial effect on water stress, plant

appropriate species

• Minor vegetation can significantly increase water stress in

young trees

• Increasing cover depth is likely to be of limited value for

reducing tree water stress*

* more important where subsoil is not a suitable rooting

substrate


Faculty of Forestry

Ongoing work:Ongoing work:

•• Calibrate climateCalibrate climate--driven peat and litter decomposition driven peat and litter decomposition

functionsfunctions

•• Complete Complete FORECAST ClimateFORECAST Climate model development

•• Model testing / RefinementModel testing / Refinement

•• Scenario analysisScenario analysis

Oil sandsOil sands

•• 33--year project to develop year project to develop FORECAST climateFORECAST climate for interior BC for interior BC

applications (begins April 2008)applications (begins April 2008)

BC Forest Science ProgramBC Forest Science Program


Faculty of Forestry

Ongoing work: (continued)Ongoing work: (continued)

WatershedWatershed--scale analysisscale analysis

•• Extrapolation of standExtrapolation of stand--level work to watersheds using Local Landscape level work to watersheds using Local Landscape

Ecosystem Management Simulator (LLEMS)Ecosystem Management Simulator (LLEMS)

•• RasterRaster--based extension of based extension of FORECAST ClimateFORECAST Climate


Faculty of Forestry

Energy Balance Approach: Energy Balance Approach: SW Radiation interception SW Radiation interception

through vertical profile through vertical profile (canopy, US (canopy, US vegveg, and forest floor), and forest floor)

1. Light is intercepted as a function of Canopy LAI and ground v1. Light is intercepted as a function of Canopy LAI and ground vegetation cover egetation cover

with seasonal adjustmentswith seasonal adjustments

3. Net radiation (3. Net radiation (RnRn) load for a given layer is determined using an estimated ) load for a given layer is determined using an estimated

surface albedo surface albedo

0

20

40

60

80

100

0 2 4 6 8LAI

Interception (%SW Rad)

0

0.2

0.4

0.6

0.8

1

J F M A M J J A S O N D

LAI Correction Factor

Con

Dec

Us

2. Simulation of SW radiation interception through a vertical pr2. Simulation of SW radiation interception through a vertical profileofile

Can LAI = 2Can LAI = 2

SW Rad Interception by Vertical Layer

0

5

10

15

20

25

30MJ*106 m

-2 day-1

Total

Can

Us

FF



Faculty of Forestry

Calculating EnergyCalculating Energy--limited PET for each layerlimited PET for each layer

wherewhere::

-- PETPET (layer) = Potential Evapotranspiration for (layer) = Potential Evapotranspiration for

a given layer (mm H20 daya given layer (mm H20 day--11))

-- RnRn (layer) = Net intercepted radiation for a (layer) = Net intercepted radiation for a

given layer (adjusted for surface albedo)given layer (adjusted for surface albedo)

-- LL = latent heat of vaporization= latent heat of vaporization

-- = coefficients weakly correlated = coefficients weakly correlated

with air temperaturewith air temperature

-- αα = experimentally determined surface = experimentally determined surface

resistance coefficientresistance coefficient

sss+s+γγ

PETPET (layer)(layer) = = ααsss+s+γγ

* * RnRn (layer)(layer) **11LL

Based on modified PriestlyBased on modified Priestly--Taylor EquationTaylor Equation

0.330.85Mixed conifer

canopy (dry)

0.131.1Broadleaf

canopy (dry)

0.440.7Pine canopy Pine canopy

(dry)(dry)

01.26Wet surface

RCan =

1- (αααααααα / 1.26)

ααααααααSurface Surface

TypeType

Experimentally determined valuesExperimentally determined values


Faculty of Forestry

ForWaDy: ForWaDy: Simulation results for cumulative AET for D3Simulation results for cumulative AET for D3

0

50

100

150

200

250

300

350

400

Cumulative AET (mm)

Bowen AET 2001 Model AET 2001

Bowen AET 2002 Model AET 2002


20 cm peat20 cm peat

80 cm till80 cm till


Faculty of Forestry

ForWaDy Factorial AnalysisForWaDy Factorial Analysis

Effect of slope and aspect on radiation load

North Facing slope (%) South Facing slope (%)

Daily gross incoming SW radiation

0

10

20

30

40

0 100 200 300 400

Julian Day

0

N-7.5

N-15

N-30

N-45

0

10

20

30

40

0 100 200 300 400

Julian Day

0

S-7.5

S-15

S-30

S-45

Slope Annual rad difference N v. S

7.5% 19%

15% 34%

30% 59%


Faculty of Forestry

ForWaDy Factorial Analysis: ForWaDy Factorial Analysis: What is a normal and a dry year?

Local variation in precipitation data: Mildred Lake vs. Ft McMurray Airport

Growing season precipitation

Total annual precipitation

Location GS (mm) Total (mm)

Mildred Lake 185.3 409.8

Ft. McMurray 214.1 405.2

Means (1994-2006)

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

1994 1996 1998 2000 2002 2004 2006

Year

Growing Season Whole year

no difference

Mildred Lake

higher

Ft McMurray

higher

0

50

100

150

200

250

300

350

400

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Growing Season Precip (mm)

Mildred Lake

Fort McMurray

0

100

200

300

400

500

600

700

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Total Annual Precip (mm)

Mildred Lake

Fort McMurray

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