Modeling Approaches for Understanding and Predicting Soil Carbon Sequestration: Field to Landscape to Region RC Izaurralde (JGCRI), K Paustian (CSU), JD Atwood (USDA), J Brenner (USDA), R Conant (CSU), M Easter (CSU), S Ogle (CSU), S Potter (TAMU), AM Thomson (JGCRI), JR Williams (TAMU) Third Annual Conference on Carbon Sequestration 4-6 May 2004 – Alexandria, VA
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Modeling Approaches for Understanding and Predicting Soil Carbon Sequestration:
Field to Landscape to Region
RC Izaurralde (JGCRI), K Paustian (CSU), JD Atwood (USDA), J Brenner (USDA), R Conant (CSU), M Easter (CSU), S Ogle (CSU), S Potter (TAMU), AM Thomson (JGCRI), JR Williams (TAMU)
Third Annual Conference on Carbon Sequestration4-6 May 2004 – Alexandria, VA
BackgroundSoil carbon sequestration (SCS) has significant potential to attenuate the increase of atmospheric CO2• Global: 0.4 – 0.8 Pg C y-1; 50 – 100 y (IPCC 1996)• USA: 0.08 – 0.21 Pg C y-1; 30 y (Lal et al. 1999)
Near-term SCS potential in croplands• Global: 0.12 Pg C y-1 by 2010; 0.26 Pg C y-1 by 2040 (Sampson et
al. 2000)Current estimates of SCS• USA: 0.021 Pg C y-1 during 1982 – 1997 (Eve et al. 2001)
The deployment of SCS practices will require robust methods for monitoring soil carbon changesSimulation models with ability to simulate soil carbon and nitrogen dynamics can play major role in monitoring SCS at field, landscape and regional scales
Detecting and scalingchanges in soil carbon
Detecting soil C changes• Difficult on short time scales• Amount changing small
compared to total CMethods for detecting and projecting soil C changes (Post et al. 2001)• Direct methods
– Field and laboratory measurements
– Eddy covariance• Indirect methods
– Accounting- Stratified accounting
– Remote sensing– Models
Root C
LitterC
Eroded C
Cropland C
Wetland C
Eddy flux
Sampleprobe
Soil profile
Remotesensor
Respired C
Captured C
HeavyfractionC
Woodlot C
Harvested C
Buried C
Lightfraction
C
Respired C
Soil organic C
Soil inorganic C
Simulation modelsDatabases / GIS
SOCt = SOC0 + Cc + Cb - Ch - Cr - Ce
Post et al. (2001)
Objectives
Review components and drivers of the carbon balance in agroecosystemsDiscuss modeling approaches in Century and EPICPresent examples of applications of these models at various scales of spatial resolution• Field• Landscape• Region
Litterfall
Soil erosionSoil deposition
Harvest
Photosynthesis
Decomposition & respiration
Dissolved organic C
Annual Carbon Balance in an Agroecosystem
CO2
CO2
0.1 Mg C ha-1 ??
1 Mg C ha-1
3.5 Mg C ha-1
2 Mg C ha-12.5 Mg C ha-1
?Soil C: 60 Mg C ha-1 + 0.4 Mg C ha-1
Humification
Environmental Variables and Management DetermineCarbon Flux Between Soils and the Atmosphere
CO2
Soil C
Cropping Rotation Practice:Type of Crop, Use ofWinter Cover Crops,Hay in Rotation, Legumes, Cropping Intensification
Tillage Management:Conventional, Reduced or No-till
Residue Management Fertilizer Management
Land-Use Change
Soil Characteristics
Topography
Climate
Irrigation Management
Two terrestrial ecosystem models
Century• Century• DayCent• C-STORE
EPIC• EPIC• APEX
Soil ProcessesWater movement Erosion
Temp & Moisture
Density Changes
Above Gr. LiveAbove Gr. DeadBelow Gr. LiveBelow Gr. Dead
Harvest
Plant Growth
Leaching
Soil Properties, Management, Weather, CO2
PesticidesSurface residuesSubsoil residues
Humus
OrganicTransformations
CO2
NitrificationNH3 Volatilization
DenitrificationPi reactions
InorganicTransformations
NH3, N2O, N2
Processes and drivers
Residue C
Metabolic Litter Biomass C Passive C
Slow C Leached C
Carbon and nitrogen flows
Structural Litter
The C-Store® Soil Carbon ModelThe C-Store® Soil Carbon Model
Crop Yields
Tillage
Crop Growth Timing and Distribution
Weather
Land Use History
Soil Physical Properties
Residue and Manure Mgmt
C-Store® Soil C
C-STORE: Predicting soil C changes at
the field level
Select State and CountyDescribe Land Use History
Specify Soil Type
SpecifyTillageSystem
PastManagement System
(1900-2000)
SpecifyYields
A similar screen is used for2001-2020
Run the Model
Graphic output
Detailed tabular output
Measured vs. Modeled Soil Carbon StocksMeasured Initial C Values
y = 1.0293xR2 = 0.8976
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
modeled (g/m^2)
mea
sure
d (g
/m^2
)
all sitesAkron wf nt 1Arlington LTN1 CCC 0 CTBushland wgf NT 1elansing ccc ctHoytville 1 CB CTIndian Head F-(SW)-SW 1 F-SW-SW NP CTlamberton ccc ct 0nlethbridge2 f-sw 0N ct 1lethbridge8 f-sw blade SM 1Manhattan 1 BW CTmead ccc hfi ctPendleton1 fw 0N CT 1Swift Current F-(SW)-SW 1 F-SW-SW NPWooster 1 CB CTLinear (all sites)
Land Use ChangesCRPAbandoned farmlandConverted to grassland
Rotation Changes
MLRA 108
Kansas wheat-fallow test
0
20
40
60
80
100
120
140
1860 1880 1900 1920 1940 1960 1980 2000
Gra
in Y
ield
Car
bon
(g/m
2)
NASS KSCentury
WN1 N=0 W0 N=0
WN2 N=2
WN2 N=4
Manhattan, KS site data used in simulation. Average monthly weather data used for 1866-1894. Measured monthly precipitaion used for 1895 onward, along with mean monthly tmax, tmin.
N = gN/m2
75% straw removal------->50% straw removal----------------------->grain only removed------------------->
Net Carbon Gains - 1997 rates
0
1
2
3
4
5
6
7
8
MM
T C
/ ye
ar
on conventional-till annual cropland
on reduced-tillannual cropland
on no-till annual cropland
on hay-pasture
on CRP
Aggregate Century results
Century
IPCC
18.4 MMTC yr-1
on 168 Mha cropland
21.2 MMTC yr-1
on 149 Mha cropland
SummarySimulation modeling plays a fundamental role in predicting and understanding soil carbon sequestration at different scales of resolutionC-STORE promises to be a useful tool to develop field-estimates of soil carbon sequestrationLandscape modeling with APEX should help understand the role of erosion in the carbon cycleThe use of Century and EPIC at the regional and national scales will provide independent estimates of soil carbon sequestration