Why Ecologists Need Soil Physics, and Vice Versa
Dennis Baldocchi
Dept of Environmental Science, Policy and Management
University of California, Berkeley
Contributions from:
Siyan Ma, Jianwu Tang, Jorge Curiel-Yuste, Gretchen Miller, Xingyuan Chen
Kirkham Conference on Soil Physics
UC Davis
Feb, 2007
The Big Picture
• Soil Physics Drives Many of the Biological Processes in the soil that
are of interest to Ecologists
– Soil Temperature, Moisture, Trace Gas Diffusion
• Ecologists Have Many interesting Questions that relate to Mass and
Energy Transfer and Require Collaboration with Soil Physicists
– Soil Respiration, Evaporation, Decomposition, Trace Gas (N2O, CH4,
CO2) Production
Outline
• Temperature and Soil Respiration
• Photosynthesis vs Soil Respiration
• Soil Evaporation Measurements and
Modeling
• How Moisture Regulates Soil
Respiration & Evaporation
• Alternative/’novel’ Measurement
Methods
–
–
–
–
–
Better Experimental Design
Eddy Covariance, an alternative to Chambers
Soil CO2 probes & Fickian Diffusion
Improved Incubation Measurement Protocols
Improved Sapflow Sampling Protocols
Soil Respiration vs Soil Temperature, at one
depth, yields complicated functional
responses, hysteresis and scatter
Irvine and Law, GCB 2002
Janssens and Pilegaard, 2003 GCB
Soil Temperature Amplitude and Phase Angle Varies with Depth
Day 204, 2001, Vaira Grassland
40
Soil Temperature
35
2 cm
4 cm
8 cm
16 cm
32 cm
30
25
20
15
10
0
5
10
15
20
Time (hours)
It is critical to measure Soil Temperature
at Multiple Depths and with Logarithmic Spacing
Measure Soil Temperature at the Location of the Source
Rsoil at 8 cm (mol m-2 s-1)
4.5
2 cm
4 cm
8 cm
16 cm
32 cm
4.0
3.5
3.0
2.5
R(T )  R(Tr ) exp(
Ea T
)
RT
2.0
10
15
20
25
30
35
40
Tsoil (C)
Otherwise Artificial Hysteresis or Poor Correlations may be Observed
But Sometimes Hysteresis between Soil Respiration and Temperature is Real:
The Role of Photosynthesis and Phloem Transport
Tonzi Under trees
Tonzi Open areas
0.50
1.7
14:50h
0.45
12:50h
Under trees
DOY 211
Soil Respiration
1.6
12h
0.40
1.5
0.35
16h
10h
1.4
0.30
20h
1.3
0.25
6h
Fo=0.037e0.0525T, Q10=1.69, R2=0.95
0.20
6h
1.2
0.15
30
35
40
45
o
Soil tempreture ( C)
Tang, Baldocchi, Xu, GCB, 2005
50
24h
Fu=0.337e0.0479T, Q10=1.61, R2=0.80
1.1
25
30
35
o
Soil temperature ( C)
Continuous Soil Respiration with soil CO2 Sensors
Transmitters
Datalogger
7.5 cm
Probe (sensor)
2 cm
8 cm
Casing
16 cm
Theory/Equations
dC
F   Ds
dz
Da  Da 0 (T / 293.15)1.75 ( P /101.3)
Moldrup et al. 1999
Ds
2    Fcp
 ( )
Da

Fcp: fraction silt and sand; : constant; :porosity;
: air-filled pore space
Validation with Chambers
2003
7
Coefficients:
b[0]: -0.115
b[1]: 0.943
r ²: 0.915
Fc: flux-gradient (mol m-2 s-1)
6
5
4
3
2
under tree
open
1
0
0
1
2
3
4
Fc: chamber (mol m-2 s-1)
Tang et al, 2005, Global Change Biology
5
6
7
Validation with Eddy Covariance
CO2 Efflux, Oak Savanna, 2003
2004
4
4
Eddy Covariance
Flux-Gradient
Fc (mol m-2 s-1)
3
3
2
2
1
1
0
0
-1
-1
150
200
250
Day
Baldocchi et al, 2006, JGR Biogeosciences
300
350
0
50
Day
Savanna: Ideal Model to Separate
Contributions from Roots and
Microbes
Under a Tree:
~Ra+Rh
Open Grassland:
~Rh (summer)
Soil CO2 is Greater Under Trees
2003-2004
Open Grassland
2003-2004
Savanna UnderStory
100000
100000
2 cm
8 cm
16 cm
CO2, Daily Average
CO2 (ppm)
2 cm
8 cm
16 cm
10000
1000
10000
1000
0
100
200
300
Day after Jan 1, 2003
Baldocchi et al, 2006, JGR Biogeosciences
400
500
0
100
200
300
Day after Jan 1, 2003
400
500
Interpreting Data by Modeling CO2 in Soil
CO2 Diffusion Model, t =768 hours
Millington and Quick Tortuosity
Soil Respiration, F = 3 mol m s
-2
-1
0.001
0.01
F=1
F3.0
F=5
0.1
Z (m)
Depth, m
0.01
0.1
soil moisture: 0.1 m 3 m -3
0.2
0.3
1
1
100
0
2000
4000
6000
8000
CO2, ppm
10000
12000
14000
16000
1000
10000
CO2 (ppm)
100000
Impact of Rain Pulse and Metabolism on Ecosystem respiration:
Fast and Slow Responses
6
understory
open grassland
Fc (mol m-2 s-1)
5
4
3
2
1
0
150
200
250
Day
Baldocchi et al, 2006, JGR Biogeosciences
300
350
Lags and Leads in Ps and Resp: Diurnal
June
7
Flux Density
soil respiration
canopy photosynthesis
6
0
-2
-4
-6
-8
-10
0
4
8
12
Time (hour)
Tang et al, Global Change Biology 2005.
16
20
24
Continuous Measurements Enable Use of Inverse
Fourier Transforms to Quantify Lag Times
July, Rsoil-Ps lag
0.0
lag correlation
-0.2
-0.4
-0.6
-0.8
-1.0
-30
-20
-10
0
lag (30 min)
Tang et al, Global Change Biology 2005.
10
20
30
Other Evidence that Soil Respiration Scales with GPP
Auto Chambers
4
2
y = 0.23x
r2 = 0.78, p<0.01
0
FCO2, B (µmol
Respm-2 s-1)
6
Understory Eddy Flux
5
10
15
20
Photo m-2 s-1)
GPPA (µmol
Figure 10. Nighttime understory CO2 flux below the
main canopy as a function of whole canopy GPP
Irvine et al 2005 Biogeochemistry
Misson et al. AgForMet. 2007
Soil Evaporation:
Chambers Perturb Solar Energy Input & Wind and
Turbulence, Humidity and Temperature Fields
Scalar Fluxes Diminish with Time, using Static Chambers, due
to C build-up and its negative Feedback on F
c F F (c)


 t z
h
Soil Chamber
Soil Chamber
0.0009
-1.75e-6
0.0008
-1.80e-6
-1.85e-6
Flux
[C]
0.0007
Chamber h: 0.1 m
Chamber h: 0.3 m
0.0006
-1.90e-6
Chamber h: 0.1 m
Chamber h: 0.3 m
0.0005
-1.95e-6
0.0004
-2.00e-6
-2.05e-6
0.0003
1
10
100
Time
1000
10000
0
200
400
600
800
1000
Time
Ability to measure dC/dt well is a function of chamber size and F
1200
Understory Eddy Flux Measurement System:
An Alternative Means of Measuring Soil Energy Fluxes: LE and H
Understory Latent Heat Exchange Can be a Large Fraction
of Total Evaporation:
2002
Oak Savanna
1.0
Efloor/Ecanopy
0.8
0.6
0.4
0.2
0.0
0
50
100
150
200
Day
Baldocchi et al. 2004 AgForMet
250
300
350
Reasonable Energy Balance Closure can be Achieved
300
H + E + G (W m-2)
250
slope = 1.22
int = 4.66
200
150
100
50
Jack pine
0
-50
-50
0
50
100
150
200
Rn Forest Floor (W m-2)
Baldocchi et al. 2000 AgForMet
250
300
Overstorey Latent Heat Exchange Partitioning:
Closed Oak Forest and Patchy Mature Pine Forest
-2
Energy Flux Density (W m )
125
Jack pine
Forest Floor
D144-259
100
Rn
E
75
H
50
G
25
0
-25
0
4
8
12
16
20
24
Time (hours)
-2
energy flux density (W m )
300
250
Ponderosa pine
forest floor
200
Rn
150
E
100
H
50
Gsoil
0
-50
0
4
8
12
Time (Hour)
Baldocchi et al. 2000 AgForMet
Figure 11
dienjkpp.spw
12/8/99
16
20
24
LE is a Non-Linear Function of Available Energy
70
jack pine forest
ponderosa pine forest
60
-2
E forest floor (W m )
temperate deciduous
forest
50
40
30
20
10
0
-10
-50
0
50
100
150
-2
Rn -G: forest floor (W m )
Baldocchi et al. 2000 AgForMet
200
Why Does Understory LE Max out at about 20-30 W m-2 in closed canopies?
50
40
30
E(t)
Consider Evaporation
into the Canopy Volume
and feedbacks with
vapor pressure deficit, D
s
A
s 
20

dE
dE (t ) dD

dt
dD dt
10
0
0
s
t
s
 E (t ) 
A  exp(  )[ E (0) 
A]
s 

s 
1
2
3
t/
4
5
Periodic and Coherent Eddies Sweep through the Canopy
Frequently, and Prevent Equilibrium Conditions from Being
Reached
Timescale for Evaporation under a Forest
100000
16.5
10000
16

T (C)
15.5
1000
15
Gs = 0.001 m s-1
Gs = 0.01
Gs = 0.1
14.5
100
14
8300
8400
8500
8600
8700
8800
8900
9000
10
0.0001
0.001
t, 10 Hz
0.01
0.1
1
10
-1
Ga (m s )

h ( s     (Gav / Gs ))
Gav ( s   )
Timescale for Equilibrium Evaporation (~1000s) >> Turbulence Timescales (~200s)
ESPM 228 Adv Topics Micromet &
Biomet
100
Modeling Soil Evaporation
Rn  (1   ) Rg   L   Ts4   E  H  G
FCO2
LE
H
Albedo
Gsoil
Below Canopy Energy Fluxes enable Us to
Test Model Calculations of Soil Energy
Exchange
300
Rnet (W m-2)
250
Ponderosa Pine
Forest Floor
D187-205, 1996
200
150
measured
calculated
100
50
0
-50
E (W m-2)
75
50
25
0
-25
200
-2
H (W m )
150
100
50
G (W m-2)
0
150
125
100
75
50
25
0
-25
-50
-75
0
4
Figure 15
enbmod.spw
12/8/99: lai eff=1.8, zlitter=0.08
Baldocchi et al. 2000 AgForMet
8
12
Time (hours)
16
20
24
Lessons Learned:
1. Convective/Buoyant Transport Has a Major Impact on Understory
Aerodynamic Resistances
H   aC p
Ta  Tsfc
z 2
(ln( ))
z0

Ra 
(
1


)
k 2u
Ra
5 gz (Ts  Ta )

Ta u2
Daamen and Simmons Model (1996)
Ignoring Impact of Thermal Stratification
Produces Errors in H AND Rn, LE, & G
Ra=f(stability)
Ra: neutral
300
250
200
150
100
50
0
-50
70
60
50
40
30
20
10
0
-2
H (W m )
-2
E (W m )
-2
Rn (W m )
Ponderos Pine
Forest Floor
150
125
100
75
50
25
0
-2
G (W m )
200
150
100
50
0
-50
0
4
8
12
Time (hours)
Figure 16
enmodstb.spw
12/8/99
Baldocchi et al. 2000 AgForMet
16
20
24
Sandy Soils Contain More Organic Content than May be Visible
Metolius, OR, sand and Ponderosa Pine
depth (m)
0.01
0.1
1
0.0
0.1
0.2
0.3
0.4
2
-1
Thermal Diffusivity (mm s )
0.5
0.6
Litter Depth affects Thermal Diffusivity and Energy Fluxes
Litter depth, 0.01 m
litter depth, 0.02 m
Ponderos Pine
Forest Floor
-2
H (W m )
-2
E (W m )
-2
Rn (W m )
litter depth, 0.05 m
300
250
200
150
100
50
0
-50
60
50
40
30
20
10
0
150
125
100
75
50
25
0
-2
G (W m )
150
100
50
0
-50
0
4
8
12
Time (hours)
Figure 17
enmodlit.spw
12/8/99
Baldocchi et al. 2000 AgForMet
16
20
24
Use Appropriate and Root-Weighted Soil Moisture,
Not Arithmetic Average
z
 
   z  dP  z 
0
z
 dP  z 
0
Root Distribution
0.0
Depth
0.5
1.0
=0.98
0.90
1.5
2.0
0.0
0.2
0.4
0.6
0.8
Cumulative Distribution
1.0
1.2
Use of Root-Weighted Soil Moisture Enables a ‘Universal’
relationship between normalized Evaporation and Soil
Moisture to be Observed
Soil Moisture, arithmetic average
Soil Moisture, root-weighted
Chen et al, WRR in press.
Combining Root-Weighted Soil Moisture and Water Retention
Produces a Functional Relation between E and Water Potential
Vaira Soil
1.0
0
predawn water potential
soil water potential
0.8
E/Eeq
Water Potential, MPa
-10
-20
a: -0.0178
b: -2.0129
0.6
0.4
-30
0.2
-40
0.00
0.05
0.10
0.15
0.20
0.25
Volumetric Soil Moisture (cm3 cm-3)
0.30
0.35
0.0
-5
-4
-3
-2
-1
soil water potential (MPa)
Water Retention Curve Provides a Good Transfer
Function with Pre-Dawn Water Potential
Baldocchi et al. 2004 AgForMet
0
Impact of Rain Pulses on Soil Respiration:
Rains Pulse do not have Equal Impacts
Reco
30
4
10
0.3
0.3
0.2
0.2
0.1
0.1
v (cm cm )
2
-3
20
0.0
0.0
270
285
300
315
DOY
Xu, Baldocchi Agri For Meteorol , 2004
v (cm cm )
40
-3
6
50
3
PPT
8
PPT (mm d-1)
60
3
Reco (g C m-2d-1)
10
Quantifying the impact of rain pulses on respiration:
Assessing the Decay Time constant via soil
evaporation
10
8
Reco (gC m-2d-1)
2
(, Max/e)
1
0
0 2 4 6 8 10 12 14
4
2
Time constant (d)
3
6
10
d214 2003
understory
8
Understory
b0=1.43
b1=0.097
r2=0.95
6
4
2
0
0
0
-5
0
5
10
15
20
10
20
30
Grassland
b0=0.88
b1=0.07
r ²=0.98
40
50
60
Amount of the rain (mm)
Day after rain (d)
Xu, Baldocchi, Tang, 2004
Global Biogeochem Cycles
Reco  b0  b1 exp(
t

)
Forming a Bridge between Soil Physics and Ecology:
Refining Sampling and Analytical Measurements Protocols
Continuous Flow Incubation System
Intact soil core of known
volume and density to assess
water potential
Re-Designing Incubation Studies
• Use Closed path IRGA
– Data log CO2 continuously with precise time stamp to better
compute flux from dC/dt at time ‘zero’.
– Avoid/ exclude P and C perturbation when closing lid
• Use soil samples with constrained volume
– If you know bulk density and gravimetric water content, you can
compute soil water potential from water release curve
• Expose treatment to Temperature range at each time
treatment, a la Fang and Moncreif.
– Reduces artifact of incubating soils at different temperatures and
thereby burning off different amounts of the soil pool
– Remember F = [C]/t
– Because T will be transient sense temperature at several places
in the soil core.
[CO2] (ppm)
25.0
24.8
24.6
24.4
24.2
24.0
[CO2] =SR*x + a
98
445
97
440
435
96
[CO2] ppm
o
Pressure (Kpa) Temperature ( C)
Flux Experimental Data
95
430
425
420
415
450
440
430
420
410
400
410
405
140
150
160
170
SECONDS
0
50
100
150
200
250
TIME (SECONDS)
Curiel et al. 2008 GCB
300
350
400
180
190
200
-2
-1
Soil respiration (mol m s )
Sample of Results from Curiel-Yuste et al, 2008 GCB
6
a
b
4
2
2
Q10 = 3.1 (0.2); R = 0.8; p = 0.001
2
Q10 = 1.8 (0.1); R = 0.7; p = 0.012
2
Q10 = 1.6 (0.05); R = 0.7; p = 0.001
Q10 = 2.3 (0.1); R = 0.7; p = 0.002
2
Q10 = 2.11 (0.2); R = 0.7; p = 0.001
2
0
2
Q10 = 1.7 (0.2); R = 0.8; p = 0.01
0
5
10
15
20
o
25
30
5
-2
-1
Soil respiration (mol m s )
6
10
15
20
25
o
Soil temperature ( C)
Soil temperature ( C)
c
d
4
2
0
R2 = 0.86; p-value<0.001
10
20
30
40
Soil moisture (% vol)
50
R2 = 0.96; p-value<0.001
10
20
30
40
Soil moisture (% vol)
50
Use Distributed Soil Measurements and Tree Information to ‘Site’ Representative
Sapflow Stations
Data of Gretchen Miller and Xingyuan Chen
Soil Maps
Data of Gretchen Miller and Xingyuan Chen
Use Cluster Analysis to Determine where to Sample Sap Flow
Data of Gretchen Miller and Xingyuan Chen
Results of Clustering Analysis (DBH and Simulated Soil Properties Method)
Slope
(%)
Sand
(%)
Number of
Trees
1
117
33
●
168.
54
●
1.43
○
47.3
2
○
2
50
45
●
168.
11
○
2.27
●
47.8
8
●
3
52
29
●
169.
06
●
2.47
●
49.6
3
●
4
151
20
○
168.
79
●
1.6
○
47.7
5
○
5
21
16
○
168.
57
●
2.05
●
47.1
2
○
6
59
26
○
166.
85
○
2.66
●
48.6
5
●
79
11
○
168.
29
●
1.61
○
47.6
5
○
9
66
●
168.
86
●
1.9
●
47.4
4
○
7
8
Diameter
(cm)
Elevation
(m)
Cluster
Number
● = above average, ○ = below average
Data of Gretchen Miller and Xingyuan Chen
Summary
•
Temperature and Soil Respiration
– Vertical Gradients, Lags and Phase Shift
– Hysteresis, a need to match depth of production with temperature
•
Photosynthesis and Soil Respiration
– Photosynthesis Controls Soil Respiration
– But, Lags occur between Soil Respiration and Photosynthesis
•
Soil Evaporation & Moisture
– Turbulence Sweeps and Ejections Regulate Soil ET
– Modeling Soil Energy Exchange requires information on Convection
– Spatial scaling of Soil Moisture
• Pre-dawn water potential and root weighted soil moisture
– Soil Moisture and ET
• Soil Respiration & Rain
– Stimulation of Respiration by Rain
• Alternative/’novel’ Measurement Methods
–
–
–
–
–
Better Experimental Design for Soil Respiration
Understory Eddy Covariance, an alternative to Chambers
Soil CO2 probes & Fickian Diffusion
Improved Incubation Protocols
Improved Sapflow Sampling Protocols
3.0
Vaira grassland, 2002
Verhoef method
2 to 16 cm
thermal diffusivity (W m-1 K-1)
2.5
2.0
Lz  z O
 M
P
2N
ln( A / A Q

1.5
2
2
1
1
1.0
0.5
0.0
0.0
0.1
0.2
0.3
0.4
volumetric soil moisture @ 10 cm (cm3 cm-3)
0.5
2
Below Canopy Fluxes and Canopy Structure and Function
0.30
0.25
QE,soil/QE
0.20
0.15
0.10
0.05
0.00
0
20
40
60
80
100
120
LAI * Vcmax
140
160
180
200
Evaporation and Soil Moisture Deficits
Grassland
D70-300, 2001
Oak Savanna, 2002
D105-270
1.0
1.25
0.8
1.00
E/Eeq
E/Eeq
summer rain
0.75
0.6
0.50
0.4
0.25
0.2
0.00
0.00
0.05
0.10
0.15
0.20
0.25
0.30
weighted by roots (cm cm )
3
Baldocchi et al, 2004
AgForMet
-3
0.35
0.40
0.0
0.00
0.05
0.10
0.15
0.20
0.25
weighted by roots(cm cm )
3
-3
0.30