Transcript Reconstruction of Inundation and Greenhouse Gas Emissions from

Reconstruction of Inundation and Greenhouse
Gas Emissions from Siberian Wetlands over the
Last 60 Years
T.J. Bohn1, E. Podest2, R. Schroeder2, K.C. McDonald2, L. C.
Bowling3, and D.P. Lettenmaier1
1Dept.
of Civil and Environmental Engineering, University of
Washington, Seattle, WA, USA
2JPL-NASA, Pasadena, CA, USA,
3Purdue University, West Lafayette, IN, USA
Steve Burges Retirement Symposium
University of Washington
Seattle, WA, 2010-Mar-24
Western Siberian Wetlands
West Siberian
Lowlands
Wetlands:
Largest natural global source of CH4
30% of world’s
wetlands are in N.
Eurasia
High latitudes
experiencing
pronounced
climate change
(Gorham, 1991)
Response to future
climate change
uncertain
Climate Factors
CO2 non-linear
Relationships
Temperature
(via metabolic rates)
CO2
CH4
Water table depth not uniform
NPP
across landscape
- heterogeneous
Living Biomass
Acrotelm
Temperature
(via evaporation)
Aerobic Rh
Water Table
Precipitation
Catotelm
Note: currently not considering export of DOC from soils
Anaerobic Rh
Modeling Framework
• VIC hydrology model
– Large, “flat” grid cells (e.g.
100x100 km)
– On hourly time step,
simulate:
•
•
•
•
•
Soil T profile
Water table depth ZWT
NPP
Soil Respiration
Other hydrologic variables…
• Link to CH4/CO2
emissions model (Walter &
Heimann 2000)
How to represent spatial heterogeneity of water table depth?
Distributed Water Table
DEM (e.g. GTOPO30
or SRTM)
Summarize for One
100km VIC grid cell
Topographic Wetness
Index к(x,y)
Topographic Wetness Index CDF
кmax
кi
кmin
Resolution = 1 km
Water Table Depth Zwt(t,x,y)
Cumulative
Area Fraction
0
1
Soil Storage Capacity CDF(mm) = f(кi)
Smax
VIC Soil
Moisture (t)
Water Table
0
Cumulative
Area Fraction
0
1
Saturated
Soil
High-Resolution Lake Observations
Need multi-temporal observations
Remote Sensing products:
(courtesy JPL Carbon and Water Cycles Group (E.
Podest, R. Schroeder, and K. McDonald))
• LANDSAT open water
classifications
– 30m
– Repeat cycle: decadal
– 1987-2007
• PALSAR open water,
inundation, and saturated soil
– 30m
– Repeat cycle: sporadic
– 2006-2007
• AMSR inundation
– 25km
– Repeat cycle: 10 days
– 2006-2007
1 km LANDSAT
30m
LandcoverOpen
Classification
Water Class.
(Bartalev et al4.1%
1989-08-28:
1999-09-16:
2006-07-01:
1.1%
2.4%
2003):
Open
0.1%
Water
Open Water
Study Domain:
W. Siberia
Close
correspondence
between:
•wetness index
distribution and
Wetness Index from
GTOPO-30 and SRTM3
Yenisei R.
Ural Mtns
•observed
inundation of
wetlands from
satellite
observations
GLWD Wetland delineation
(Lehner and Doll, 2004)
Ob’ R.
Chaya/Bakchar/
Iksa Basin
Vasuygan Wetlands
Chaya/Bakchar/
Iksa Basin
Close correspondence
between wetness index
and wetland vegetation
4 focus areas (see
next slide)
100km Grid Cells
Comparison with PALSAR
•Spatial distribution of inundation compares favorably with remote sensing
•This offers a method to calibrate model soil parameters
Observed Inundated
Fraction (PALSAR
Classification)
ROI 1
Simulated Inundated
Fraction (at optimal Zwt)
Observed Inundated
Fraction (PALSAR
Classification)
ROI 3
2006-06-09
2006-05-28
ROI 2
ROI 4
2007-07-06
2007-07-18
Approx.
30 km
Simulated Inundated
Fraction (at optimal Zwt)
How do resulting emissions differ between uniform
water table and distributed water table?
Experiment:
• Calibrate methane model to match
in situ emissions at a point
(Bakchar site, Friborg et al, 2003)
• Distributed case: calibrate
distributed model water table depth
to match observed inundation
• Uniform case: select water table
timeseries from single point in the
landscape having same long-term
average methane emissions as the
entire grid cell in the distributed
case; apply this water table to
entire grid cell
CH4
Water Table
Inundated Area
(matching remote sensing)
Interannual Variability, 1948-2007
Possible trend in temperature,
also in CH4
Uniform Water Table:
Shallower than average of
distributed case
But never reaches surface; no
inundation
Resulting CH4 has higher
variability than for distributed
case
Distributed case is buffered by
high- and low-emitting regions
Impact on trends?
Distrib Water Table
Uniform Water Table
Net Greenhouse Warming Potential
CH4 makes up small part of C budget, but large contribution to greenhouse warming
potential
On 100-year timescale, GHWP(CH4) = approx. 23 * GHWP(CO2)
NPP and RhCO2
approximately cancel
Net GHWP
essentially follows
GHWP(CH4)
Uniform water table:
•CH4 has larger
interannual variability
•So does net GHWP
•Impact on trend
assessment?
NPP
RhCO2 - NPP
RhCO2
NET GHWP
RhCH4
Conclusions
• Advantages of distributed water table:
– Facilitates comparison with satellite
measurements and point measurements
– More realistic representation of hydrologic
and carbon processes
• Spatial distribution of water table has large
effect on estimates of greenhouse gas
emissions and their trends
Thank You
This work was carried out at the University of
Washington and the Jet Propulsion Laboratory
under contract from the National Aeronautics
and Space Administration.
This work was funded by NASA grant
NNX08AH97G.
Calibration – Bakchar Bog, 1999
Soil T
ZWT (water table depth)
CH4
(Bohn et al., 2007)
VBM = VIC-BETHY-Methane