titel gap filling of co 2 fluxes of frequently cut grassland christof ammann agroscope art federal...
TRANSCRIPT
TitelTitel
Gap Filling of COGap Filling of CO22 Fluxes Fluxes
of Frequently Cut Grasslandof Frequently Cut Grassland
Christof Ammann
Agroscope ART Federal Research Station, Zürich
Gap Filling Comparison Workshop, Jena, 2006
Federal Research Station Agroscope Reckenholz-Tänikon ART
MotivationMotivation
Motivation and ContentsMotivation and Contents
Most gap-filling algorithms up to now have been optimized and evaluated based mainly on forest NEE datasets
Managed grassland sites (as other agricultural sites) can experience very rapid changes (discontinuities) in vegetation cover and soil conditions
Not only annual NEE but also management/event-related NEE (over few weeks/months) is of interest.
We applied a specific gap filling algorithm that should be able to reproduce fast changes and yield an adequate seasonal course of NEE.
CONTENT
Short description of Swiss grassland site (with specific problems)
Description of gap filling method
Performance of gap filling for examplary events
Site/RegionSite/Region
Measurement Site near OensingenMeasurement Site near Oensingen
2002 2004
2004 2008
Site plotsSite plots
Oensingen Site: Experimental PlotsOensingen Site: Experimental Plots
Intensively managed grassland• mineral fertilizer and manure
(ca. 200 kg/ha/y total N) • 4-5 cuts per year
Extensively managed grassland• no fertilizer • 3 cuts per year
Various crops (rotation)
Flux measurement systems
(1.2 m above
ground)-200
-150
-100
-50
0
50
100
150
200
-300 -250 -200 -150 -100 -50 0 50 100
local WE scale [m]
loca
l S
N s
cale
[m
]
Highway
0
N
annual distribution of wind directions
CO2 nightCO2 night
Low Wind Conditions during the NightLow Wind Conditions during the Night
intermittent/no turbulence
mostly identified with stationarity and/or integral turbulence criteria
windspeed ca. 1m above ground(Jun-Oct 2002)
0 1 2 3 4 5 6 7
wind speed [m/s]
freq
uenc
y of
occ
urre
nce
[rel
. uni
ts] daytime
nighttime
Data selectionData selection
Quality Control and Data CoverageQuality Control and Data Coverage
individual rejection rate data coverage
(combined) effect / rejection criterion
INT EXT INT EXT
power/data acquisition failure 11% 6% 89% 94%
(A) erroneous raw data 15% 13% 76% 82%
(B) integral turbulence (w /u*) 10% 14%
(C) flux stationarity 36% 37% 49% 47%
(D) footprint 35% 38% 32% 30%
ManagementManagement
Vegetation Development and Management EventsVegetation Development and Management Events
0
20
40
60
80
100
Jan 2002 Jan 2003 Jan 2004 Dez 2004
canopy height INT [cm]
canopy height EXT [cm]
-1
-0.5
0
0.5
1
1.5
2
2.5
Jan 2002 Jan 2003 Jan 2004
harvest export EXT [tC/ha]
harvest export INT [tC/ha]
manure import INT [tC/ha]
Gap Method 1Gap Method 1
Applied Gap-Filling MethodApplied Gap-Filling Method
Low data coverage (mostly short gaps: 1 hour...2 days)
Rapidly changing vegetation cover during the entire growing season
Highly adaptive gap-filling 3-day (5-day/7-day) moving window
Best use of available data non-linear regression functions:
NEE = R(Tsoil) – A(QPAR)
To keep the method simple and robust, only R10 and A2000 are fitted with the moving window
T0 and /A2000 are kept konstant (determined by an overall regression)
]t[AQ
2000Q
1
Q)Q(A
2000
PARPAR
PARPAR
0soil010
10soil TT
1
TT
1K309exp]t[R)T(R
Gap Method 2Gap Method 2
Applied Gap Filling MethodApplied Gap Filling Method
0
10
20
30
40
0 500 1000 1500 2000
photosynthetic photon flux densitiy QPAR [E m-2 s-1]C
O2
ass
imila
tion
flu
x A
[ m
ol m
-2 s
-1]
fitted function (Michaelis-Menten)
observed data
0
4
8
12
-5 0 5 10 15 20 25
soil Temperature Tsoil at -5cm [°C]
noc
turn
al C
O2
flux
[m
ol m
-2 s
-1]
measured data
fitted function (Lloyd&Taylor)
Respiration R(Tsoil) was only fitted to nighttime data.
Daytime assimilation was calculated as NEE–R(Tsoil)
Overall fit of A(QPAR) was made with selected dataset
(canopy height > 20 cm)
Gap ResultGap Result
Normalized Assimilation and RespirationNormalized Assimilation and Respiration
... resulting from the gap filling procedure
0
2
4
6
8
Jan 2002 Jan 2003 Jan 2004 Jan 2005 Jan 2006
R10
[
mol
m-2
s-1]
INT
EXT
(b)
0
20
40
60
Jan 2002 Jan 2003 Jan 2004 Jan 2005 Jan 2006
A20
00 [ m
ol m
-2 s
-1]
INT
EXT
NEE topoNEE topo
Seasonal and Diurnal COSeasonal and Diurnal CO22 Exchange (INT 2002 - 2004) Exchange (INT 2002 - 2004)
CO2 flux[mol m-2 s-1]
cumul NEE
Cumulative NEE for Different Years and ManagementCumulative NEE for Different Years and Management
-7
-6
-5
-4
-3
-2
-1
0
1
0 50 100 150 200 250 300 350
julian day
cum
ulat
ive
NE
E [
tC/h
a]
2002
2003
2004
2005
INT
-7
-6
-5
-4
-3
-2
-1
0
1
0 50 100 150 200 250 300 350
julian day
2002
2003
2004
EXT
Examp: CutExamp: Cut
Example of Gap-Filled Time Series: Cutting EventExample of Gap-Filled Time Series: Cutting Event
-30
-20
-10
0
10
09.10.03 11.10.03 13.10.03 15.10.03 17.10.03
CO
2 flu
x [u
mol
m-2
s-1]
gap-filled data
measured datacut
Examp: WinterExamp: Winter
Example of Gap-Filled Time Series: FreezingExample of Gap-Filled Time Series: Freezing
-15
-10
-5
0
5
10
15
16. Dez 02 21. Dez 02 26. Dez 02 31. Dez 02 05. Jan 03 10. Jan 03
CO
2 fl
ux
[um
ol/m
2/s
] ; s
oil
Te
mp
. [°C
]
CO2 flux data
soil resp. (param.)
CO2 flux (param.)
soil Temp. (-5cm)
0
2
4
6
05/2003 06/2003 07/2003 08/2003 09/2003 10/2003 11/2003
norm
. re
spir
atio
n R
10 [
umo
l m-2
s-1
]
R_SWC_1
Respiration during Summer 2003Respiration during Summer 2003
0
5
10
15
-5 0 5 10 15 20 25 30
soil temperature (-5cm) [°C]
noct
urna
l res
pira
tion
[mm
ol m
-2 s-1
]
R_SWC_2
Nocturnal Respiration and Soil MoistureNocturnal Respiration and Soil Moisture
10
20
30
40
05/2003 06/2003 07/2003 08/2003 09/2003 10/2003
soil
wat
er c
onte
nt [
vol.%
] -5 cm
-10 cm
-30 cm
0
2
4
6
05/2003 06/2003 07/2003 08/2003 09/2003 10/2003 11/2003
norm
. re
spir
atio
n R
10 [
umo
l m-2
s-1
]
R_SWC_3
Nocturnal Respiration and Soil MoistureNocturnal Respiration and Soil Moisture
10
20
30
40
05/2003 06/2003 07/2003 08/2003 09/2003 10/2003
soil
wa
ter
cont
en
t [vo
l.%]
0
10
20
30
40
50
60
rain
fall
[mm
d-1
]
-5 cm
-10 cm
-30 cm
rain
0
2
4
6
05/2003 06/2003 07/2003 08/2003 09/2003 10/2003 11/2003
norm
. re
spir
atio
n R
10 [
umo
l m-2
s-1
]
ConclusionsConclusions
ConclusionsConclusions
The applied gap filling method is relatively simple and well suited for rapidly changing conditions and a low data coverage (with short gaps)
Larger gaps can be filled by interpolation of R10 and A2000 or by using
default values (long-term means).
Potential improvement: Time dependent fit for all functional parameters (partly with larger window size?)
Further activities: Comparison with other methods (test performance on discontinuities)
END
Thank You!
Gap Method 3
observed flux NEE[t] (with gaps)
daytime fluxnighttime flux R[t]
assimilation A[t]
3..7-day moving average filter for R10[t]
complete time series NEE[t] (Eq.2)
A[t]R[t]
R10[t] A2000[t]
3..7-day moving average filter for A2000[t]
time-independent fitof param. T0
time-independent fitof ratio /A2000 for
hc>20cm
C-budg avgC-budg avg
Carbon Budget for Intensive and Extensive Management Carbon Budget for Intensive and Extensive Management (2002-2004)(2002-2004)
C/t
CO2
HarvestManure
-6
-4
-2
0
2
4
6
carb
on
exc
han
ge
[tC
ha-1
y-1
]
intensive field
extensive field
NEE + Hexport - Mimport = - NBP
(a)