Post on 26-Jul-2020
Summer Course in Flux
Measurement and Advanced
Modeling
July 19-30 2010 Niwot Ridge, Colorado
Dea Doklestic
Basic Information
• 2 weeks
• 26 students
• 12+ lecturers
• Location: Mountain Research Station at
Niwot Ridge
• Elevation: 2895 meters
• 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, …
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)
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
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
Niwot Ridge, CO & Flagstaff, AZ
Exercise 3:
• For a single date plot a transect of 100km
in one direction (GPP)
• How does this vary with land cover?
Gross primary productivity typically highest in evergreen forests.
MYD17A2
MCD12Q1
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.
Image Subset
Making a filter
Filtering out all pixels with red DN value less than 146:
Total area affected by bark
beetle: 5.4%
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
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
Choose a year
year 2003
Choose wet and dry month
29 J
un
e -
29 J
uly
2 S
ept -
2 O
ct
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
NEE dry period assimilation
NEE wet period assimilation
All model outputs without data
assimilation
All model outputs with data
assimilation
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
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