Summer Course in Flux Measurement and …...Dea Doklestic, Zulia Sanchez and Marjolein De Weirdt...
Transcript of Summer Course in Flux Measurement and …...Dea Doklestic, Zulia Sanchez and Marjolein De Weirdt...
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Summer Course in Flux
Measurement and Advanced
Modeling
July 19-30 2010 Niwot Ridge, Colorado
Dea Doklestic
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Basic Information
• 2 weeks
• 26 students
• 12+ lecturers
• Location: Mountain Research Station at
Niwot Ridge
• Elevation: 2895 meters
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• Topics covered: Leaf-Level Gas Exchange, Theory and
measurement of canopy fluxes, Eddy Flux
Instrumentation, Ecosystem modeling, Satellite
observations and estimates of fluxes, Introduction to
Bayesian tools, Uncertainty in flux measurements, Data
Assimilation…
• Lecturers: Dave Moore, Ray Leuning, Paul Stoy, Tristan
Quaife, John Zobitz, Kiona Ogle, Marcy Litvak, Larry
Jacobsen, …
• Hands-on exercises: Work with LiCor 6400, Calculation
of eddy fluxes, AmeriFlux Site (field trip), Process
modelling using OpenBugs, A walk in the woods, …
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Project 1: Satellite Observations and
Estimates of Fluxes
Objectives:
• Hands-on experience of using MODIS data for carbon
studies
• To provide a simple tool for interfacing with MODIS Web
Service that you can use in your research
• The MODIS Web Service at ORNL:
http://daac.ornl.gov/MODIS/MODIS-
menu/modis_webservice.html
• Information on MODIS land products:
• https://lpdaac.usgs.gov/lpdaac/products/modis_products_t
able
(Lecture and lab by Tristan Quaife)
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Exercise 1:
• Get one year of MODIS fPAR and GPP
data
• Apply appropriate Quality Analysis to the
data
• Plot both variables together
• Estimate the annual GPP and mean fPAR
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Exercise 2:
• Get all years of GPP and fPAR data (for
Niwot Ridge)
• Plot the estimated annual GPP and mean
fPAR
• Repeat this for another site of your choice
• Compare results
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Niwot Ridge, CO & Flagstaff, AZ
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Exercise 3:
• For a single date plot a transect of 100km
in one direction (GPP)
• How does this vary with land cover?
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Gross primary productivity typically highest in evergreen forests.
MYD17A2
MCD12Q1
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Project 2: A walk in the woods
An estimate of how much bark beetle damage
there has been in an area of the local landscape.
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Image Subset
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Making a filter
Filtering out all pixels with red DN value less than 146:
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Total area affected by bark
beetle: 5.4%
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Project 3: Playing with SIPNET Ecosystem Model
(Student Presentations)
• We coupled SIPNET (Simple Photosynthesis
EvapoTranspiration), a simplified model of
ecosystem function, with a data-assimilation system
to estimate parameters leading to model predictions
most closely matching the net CO2 and H2O fluxes
measured by eddy covariance in a high-elevation,
subalpine forest ecosystem.
Moore et al., 2008: Estimating transpiration and the sensitivity of carbon uptake to water
availability in a subalpine forest using a simple ecosystem process model informed
by measured net CO2 and H2O fluxes
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Data assimilation for a wet
month and a dry month
Summer Course in Flux Measurement and Advanced Modeling
July 30, 2010
Dea Doklestic, Zulia Sanchez and Marjolein De Weirdt
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Choose a year
year 2003
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Choose wet and dry month
29 J
un
e -
29 J
uly
2 S
ept -
2 O
ct
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Approach
• Step 1: Subset input file so it shows only
the dry / wet month
• Step 2: Estimate parameters for both
cases
• Step 3: Forward run SIPNET model using
the estimated parameters
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NEE dry period assimilation
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NEE wet period assimilation
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All model outputs without data
assimilation
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All model outputs with data
assimilation
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soilWFracInit 0.5 0.385884 0.724857
aMax 8.3 1.91237 2.677668
baseFolRespFrac 0.1 0.219455 0.07345
psnTmin 2 -7.742925 -7.961604
psnTOpt 24 14.674133 15.7536
vegRespQ10 2 1.408566 1.898913
frozenSoilThreshold 0 -3.622455 -4.161335
dVpdSlope 0.05 0.053095 0.173967
halfSatPar 17 5.144229 6.815386
leafAllocation 0.22 0.253422 0.854318
baseVegResp 0.006 0.005918 0.014385
baseSoil Resp 0.06 0.01895 0.014385
soilRespQ10 2 3.322231 4.792508
waterRemoveFrac 0.088 0.144246 0.14959
wueConst 10.9 5.972129 5.018063
soilWHC 12 27.2648 7.503008
rdConst 36.5 1300.9476 202.49921
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Conclusions
• Wet vs. Dry:
– Leaf C increases in the wet month and
decreases in the dry month
– NPP and GPP – greater amplitude of diurnal
oscillation in the dry month
– NEE – virtually no difference between the two
cases
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