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