Transcript Influence of Land Cover Heterogeneity, Land

Influence of Land Cover
Heterogeneity, Land-Use Change
and Management on the
Regional Carbon Cycle
in the Upper Midwest USA
American Geophysical Union Fall 2005 Meeting
B44B-05
Ankur R Desai, Kenneth J Davis: The Pennsylvania State University
Paul R Moorcroft: Harvard University
Paul V Bolstad: University of Minnesota
Complex Regions: 1+1≠2
• Observational data scaling and ecosystem modeling of landatmosphere carbon dioxide flux relying solely on dominant
cover types is difficult in regions with complex land cover
arising from topography, land management and other biotic
& abiotic interactions (e.g., light environment, soil type)
Disturbance and Land Cover
• Past and current land
use and forest harvest
leaves its imprint on
modern day land cover
on the order of 100 yrs
or longer in forested
regions
• This imprint has the
potential to alter landatmosphere carbon
exchange magnitudes
and patterns
• The upper-Midwest was
heavily clear-cut in the
late 19th / early 20th
century
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1800
1820
1840
1860
1880
Primary
1900
Secondary
1920
1940
Agricultural
1960
1980
2000
The Upper Midwest
• Today, the region is a complex, actively managed, heavily
forested region with extensive wetland cover
• The densely-instrumented landscape is ideal for testing the
roles of disturbance, management and scaling on the
regional carbon cycle
A Very Tall Tower
• Park Falls, WI - WLEF
– Regionally representative
CO2 fluxes at 30-396 m
– Tower shows small source
(positive NEE) of CO2 to
atmosphere, in contrast to
stand-scale towers in
region
– Larger ecosystem
respiration (ER), similar
gross production (GPP) to
stand-scale towers
– Are we undersampling
certain stand ages
(young/old) or ecosystems
(e.g., wetlands)?
Stand Scale Observations
• 12+ stand scale eddy covariance towers in region
– No significant difference in meteorology among sites
– Stand age/cover lead to significant differences in flux
– Coherent interannual variability in NEE and GPP
ChEAS Summer 2003 Observed Fluxes
700
600
500
NEP (g C m-2)
400
300
200
100
0
-100
Hardwood
Red Pine
Young
Jack Pine
Intermediate
Wetland
Mature
Old
Pine Barren
Multi-tower Aggregation Scaling
• Stand-scale towers were scaled to regional flux, based on:
– LandSat 30m land cover type
– Forest Inventory Analysis stand age
ChEAS % Land Cover 40-km radius of tall tow er
50%
45%
40%
35%
% cover
30%
25%
20%
15%
10%
5%
0%
Hardw ood
Re d Pine
Young
Jack Pine
We tland
Inte rm e diate
Pine Barre n
Othe r
(Ag/Urban/Wate r)
M ature
Old
– Two equation parameter optimization
• Sites are assumed to observe same climate – mostly true
Regional Flux Comparisons
WLEF region bottom-up comparisons Jun-Aug 2003
800
700
600
gC m
-2
500
400
300
200
100
0
NEE * -1
Tall-tower
ER
Footprint weighted decomposition
GEP
Multi-tower aggregation
• Multi-tower aggregated fluxes (NEP, ER, GPP) for summer
2003 (blue) has smaller ER and larger GPP than tall tower
(red) (Desai et al, in press)
• However when tall tower fluxes were decomposed &
downscaled using footprint models (Wang et al., submitted)
and regionally aggregated by land cover density (green) –
upscaling and downscaling agree better
Biogeochemical models
• We can further investigate regional flux with models
• Biome-scale biogeochemical models treat each “cell” as a
single plant functional type (or fractions of a few) and 1-2
canopy layers with grid-average values of biomass/fluxes
– Age can only be modeled by following a cell with time as
it builds and loses biomass
Park Falls, WI
7.5
17.5
15.0
-1
GPP (gC m d )
2.5
-2
-2
-1
NEE (gC m d )
5.0
0.0
-2.5
-5.0
12.5
10.0
7.5
5.0
2.5
-7.5
0.0
0
60
120
180
240
300
360
0
60
120
180
Julian Day
Julian Day
Biome-BGC with DBF
Biome-BGC with Mixed Forest
Tower
240
300
360
Dynamic Ecosystem Models
• On the other side of the spectrum are “gap” models that
simulate the growth and fate of every plant with explicit
interaction among them
– Computationally expensive
– Difficult to parameterize
– Can be complicated to scale
• Instead, we apply a height-and-age structured gap model to
the region that uses concepts of statistical mechanics and
ensemble averaging to simulate the dynamics of the meanmoment ensemble of gaps
– Moorcroft et al, 2001, Ecological Monographs
– Grid cell consists of multiple patches of different ages
– Patches also segregated by disturbance type
– Patches contains multiple cohorts of size and plant type
– Patch age affects light availability
The Ecosystem Demography Model
• Farquhar leaf-level
photosynthesis with soil
water/N limitations and
simple canopy light
extinction
• Mean-moment differential
equations for cohort density,
active plant/root tissue size,
non-active plant biomass,
and patch CWD, fast,
structural, slow and passive
soil C and water pools
• Boundary conditions
controlled by reproduction,
mortality, disturbance and
phenology
Source:
Moorcroft
al.,
2001
Source:
Hurtt etet
al.,
2002
Model Data Assimilation
• Region divided by into soil/topographic sub-sites:
– mesic upland (N hardwoods/hemlock), xeric upland (N
pines/ash-oak), lowland (shrub and forested wetlands)
• Constrained parameters
– USFS FIA: mortality, reproduction, harvest
– Chamber fluxes: component respiration rates, VcMax
– Biometric: site allometry, specific leaf area, C:N
• Input variables
– Meteorology: tower and NCDC air temperature, soil
temperature, PAR, CO2, humidity, precipitation
– Land use/cover: Public land survey for presettlement
vegetation (Schulte, 2002), Hurtt et al. land use change
• Time steps
– Hourly biogeochemistry, adaptive (days-month) growth
and allocation, monthly ecosystem dynamics
Model Setup
• Model sub-sites (mesic, xeric, wet, water/ag/barren)
summed by % landcover in 65-km radius around tall-tower
– Water, agricultural and barren lands are assumed to have
0 NEE, ER, GEP
• Four model scenarios:
– Full run (red)
– No anthropogenic disturbance (blue)
– Pre-industrial CO2 with anthropogenic disturb. (light red)
– No CO2 increase or anthropogenic disturbance (cyan)
• Each scenario and sub-site run from 1800-2004
– Forest tent caterpillar infestation in 2001 included
• Results compared to tall tower (LEF/black), footprint
decomposed and aggregated (LEF*) and stand-scaled tower
based upscaled fluxes
• Caveat: results are very preliminary at this point
Results: Land Cover in 2004
Results: Mean Fluxes 1997-2004
Results: Comparison to Tall Tower
Results: Comparison to Tower Scaling
Jun-Aug 2003
LEF = tall tower flux
LEF* = downscaled
regionally-integrated flux
Towers = multi-tower
upscaling
Model = ED Full Run
Results: Impact of Stand Age
Implications
• Model results fall within range observed by tall tower and by
footprint-based downscaling and stand scale upscaling
– More observation sites needed in wetlands and young
forests – currently underway
• Carbon fertilization significantly enhances net uptake today
in response to logging 100 yrs ago
– May be artifact from lack of CO2 downregulation
• Modeled ecosystem respiration is lower than observed at tall
tower, but larger than stand scale aggregated towers
– Young sites (esp. wetlands) have large ER:GPP ratio,
leading to positive NEE – role of disturbance residue
– Carbon sink strength declines in mature and old sites
except for xeric sites which continue to strengthen
– Mesic sites: age of max. ER precedes age of max. GPP
• Sampling of dominant cover types (mature mixed forest)
cannot solely explain regional carbon flux
Future Work
• Uncertainty analysis of model output
• Continued investigation of
– Climate-carbon flux coupling as a function of stand type
– Observation network density needed for scaling
– Minimum resolution required for spatial data
– Scaling to larger and smaller regions
– Role of local vs. global parameterizations
– Carbon fertilization effects
– Forest product lifecycle, net biome productivity
• Incorporation of wetland dynamics and biogeochemistry
• Comparison to atmospheric tracer based regional carbon
flux observations: see next two talks – B44B-06 (Davis) /
B44B-07 (Uliasz)
• Incorporation of new biometric and flux data: see talk after
that – B44B-08 (Bolstad)
• Running model into the future with IPCC scenarios