Objectives - Compare effects of climate and land use on fluxes within the same climate zone and among the mesic and semi-arid regions - Combine multi-scale observations (satellite, flux sites, inventories, tall towers) in neural networks to determine how current climate, land-use and land cover influence processes - Modify CLM to reduce uncertainties in simulated effects of land use and land cover on biogeochemical and biophysical processes (crops, poplar) - Investigate future climate variability, and effects of changes in land use and land cover on terrestrial processes

1. Carbon cycle dynamics in Oregon and Western US
Beverly Law
Oregon State University

2. Carbon cycle dynamics within Oregon’s urban-suburban-forested-
agricultural landscapes Part 1: Current Land-Use/Land-Cover
PI: B.E. Law, CoIs: C. Still, T. Hilker, A. Schmidt
Objective
• Multi-scale observations and
neural networks to determine
how current climate and
LC/LUC influence ecosystem
processes
Approach
• Establish flux sites in major
crops and poplar
• Compare seasonal and annual
fluxes among cover types
Project # OREZFES-867

3. Flux Measurements in Crops, Poplar for Comparisons and Modeling

4. Annual Fluxes – Forests, Crops, Poplar
0
100
200
300
400
500
600
2008 2009 2010
NEP (g C m-2 yr-1)
Mature Douglas-fir Mature Ponderosa Pine
0
100
200
300
400
500
600
700
800
900
winter wheat tall fescue poplar
NEP (gC m-2 yr-1)
Willamette Valley
Biocycle Farm

5. Annual Carbon Budget for Oregon
Initial Estimates
Source
Sector
Fossil Fuels Forest NECB Crop NECB
TgCO2yr-1
-30
-20
-10
0
10
20
30
40
Land-based carbon sink
is ~30% of the equivalent
of Oregon’s FFE
(Inventories, Flux sites, Ancillary Plots, Satellite Land Cover & Fire Emissions)

6. Carbon cycle dynamics within Oregon’s urban-suburban-forested-
agricultural landscapes: Part 2 Future Climate & Land-Use/Land-Cover
PI: B.E. Law, CoIs: C. Still, T. Hilker, A. Schmidt (Oregon State University), Collaborator: T. Hudiburg (UI)
Objective
• Investigate future climate
variability and effects of land
cover and land use changes on
terrestrial processes
Approach
• Reduce uncertainties in CLM
projections
• Simulate future climate effects on
ecosystem processes
• Simulate thinning of vulnerable
forests, LUC non-forage crop to
poplar
Project # OREZFES-868
Model underestimated NEP in high biomass forests
L: Difference between prior and posteriori NEP
R: Current non-forage grass crops

7. Land Cover Willamette Valley
Vegetation Type / Crops
Alfalfa
Apples
Barley
Barren
Blueberries
Broccoli
Buckwheat
Cabbage
Caneberries
Cauliflower
Cherries
Christmas Trees
Clover/Wildflowers
Corn
Cucumbers
Dbl Crop WinWht/Corn
Deciduous Forest
Developed/High Intensity
Developed/Low Intensity
Developed/Med Intensity
Developed/Open Space
Dry Beans
Evergreen Forest
Fallow/Idle Cropland
Flaxseed
Garlic
Grapes
Grass/Pasture
Greens
Herbaceous Wetlands
Herbs
Hops
Mint
Misc Vegs & Fruits
Mixed Forest
Mustard
Oats
Onions
Open Water
Other Crops
Other Hay/Non Alfalfa
Other Tree Crops
Peaches
Pears
Peas
Peppers
Perennial Ice/Snow
Plums
Potatoes
Pumpkins
Radishes
Rape Seed
Rye
Shrubland
Sod/Grass Seed
Sorghum
Spring Wheat
Squash
Strawberries
Sugarbeets
Sunflower
Sweet Corn
Triticale
Turnips
Vetch
Walnuts
Winter Wheat
Woody Wetlands

8. Conversion from Coal to Bioenergy – Oregon
~3 Tg torrefied biomass per year needed to
run 518 MW power plant at base load
Optimize for minimizing impacts on forests,
sustainable supply

9. Future C Stocks and Emissions – Oregon
In progress:
NECB and C stocks in forests post-thinning
NECB and C stocks on agr land if convert
150K ha non-forage grass to poplar (100%
conversion unlikely).
Maximum potential supply of biomass to
electric facility, and uncertainties
Refine Life Cycle Assessment of emissions
from land post-harvest, transport,
torrefaction, pelletization, fossil fuel
displacement
Assessment of effects of LUC to poplar on
carbon and water cycle

10. Forest Die-off, Climate Change, and Human Intervention in Western
North America
PI: P. Mote, Co-lead PI: B.E. Law (OSU) Co-Is: A. Plantinga (UC-SB), J. Hicke (UI)
Objectives
• Improve ability to predict mortality
• Map vulnerability of forests to mortality
under present and future climate
• Assess & reduce uncertainty in forecast
Approach
• CLM: Drought- and beetle-related
mortality
• Economic model to optimize thinning of
vulnerable forests
• Life Cycle Assessment
Project #:OREW-2013-00628
(Berner et al. in rev)
NASA fellowship

11. Forest Biomass Mortality – Western US (2002-2012)
Negative water balance is the dominant
driver of mortality in the W US
Dry ecoregions were exposed to below-
average water availability for longer duration
(Berner & Law 2015)
Water
availability
Biomass
density

12. (Berner et al. BGD 2016)
Water Availability Mean Over Western US (1985-2014)

13. Mean Magnitude (kg * m-1 s-1)
60 70 80 90 100 110 120 130
LocationofMaximumMoistureTransport(latitudeN)
36
38
40
42
44
46
11 Yr Mean
Perturbed Physics
Default Physics setting
1 Standard Deviation
Reanalysis Datasets
Simulated Moisture Transport over NE Pacific
Aim: Parameterize global model to
bring correct amount of moisture into
western boundary of regional model
Default: Location of the jet is too far
N and not enough moisture
Several parameter sets improve jet
location and moisture transport