Modeling Approaches for Understanding and Predicting · PDF fileModeling Approaches for...
Transcript of Modeling Approaches for Understanding and Predicting · PDF fileModeling Approaches for...
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
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1000
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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)
C-Store® Development Status
Copyright 2003The Natural Resource Ecology Laboratory
Colorado State University
Initial Model
Development
InitialTesting
BuildValidation & Verification
DatasetIntegrate with
Economic Models
Web InterfaceDevelopment, Beta Testing
Final Testing
ReleaseFall, 2004
Verification andUncertainty
Process
APEX, a watershed model to simulate plant growth, hydrology, soil erosion and nutrient cycling on multiple fields Routing runoff & sediments
(soil, nutrients, pesticides)
Belowground flow
Carbon and nutrient cycling
Study S ubareaDrainage Path
< 7580859095100105110115> 120
Slope from DEM01
23
456
5m Contours from DE M
Study SubareaDrainage Path
Crop type from imageryCornSoybeansWinter WheatDouble-Cropped WW/SBOther Row CropsOther GrassWoods
Digital Elevation Model help delineate drainage area
Remote Sensing imagery helps
determine crop types and management
Seven fields on a hillslope in Frederick Co., MD
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DtB
DtB
AfB
DtB
DuB
DuB
HcBDrainage Path
Deliniated farm fieldsCornCorn/SoybeanHaySoybean
Field NumberDominant Soil Type
Tributary Main Channel TributaryField Routing Number
1 2 4 3 5 6 7
Deposition of sediment (tons/ha) 1.26 ----- 0.94
4.91 ----- 0.15 ----- Deposition of carbon (tons/ha) 0.104 ----- 0.070
0.345 ----- 0.012 -----
Soluble Nutrients in Q
Nutrient loss in sediment Soil C Change Subarea Runoff (Q) Sediment in Q
P N P N Top 0.25m Profile mm t/ha ------------------------------kg/ha---------------------------- --------------t/ha-------------- 1 164 2.1 1.4 6.2 1.5 10.5 -1.8 7.5 2 61 5.5 1.1 1.5 5.3 12.3 -6.3 1.1 3 162 4.1 1.6 2.3 2.2 7.4 -2.3 8.5 4 129 3.0 0.9 3.6 1.4 12.2 3.0 19.5 5 129 2.2 2.1 2.0 1.8 8.4 7.5 7.4 6 58 2.8 1.1 5.5 2.8 7.5 -3.7 10.1 7 126 1.5 0.9 3.3 0.8 7.3 3.2 18.9
Runoff, sediments, nutrients and soil C changes
Routing and deposition of sediments and carbon
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Landscape modeling is helping our understanding of
the role of erosion in the carbon cycle
The EPIC model: validation and application to estimate soil carbon sequestration under no till at the national scale
Long term experiments
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0 20 40 60 80 100SIMULATED SOIL CARBON (MG C/HA)
OB
SER
VED
SO
IL C
AR
BO
N (M
G C
/HA
)
ALBERTANEBRASKAKANSASTEXASILLINOISOHIOARGENTINA
Adj r2 = 0.94
Overview of the data assembly and modeling system in EPIC
National Resource Inventory
Cropping Practice Survey
Populated Model Run Databases--"The Library of Runs"
RunBuilder
Census of Agriculture
EPIC Field-Scale
Model
Tillage Systems
Nutrient Applications
Soil and Topography
Weather and Climate
Irrigation Schedules
Manure Applications
Conservation Practices
Crop Growth Data
Data Reduction Processes
I_Epic Interface
Model Run Input Databases
Analytical Framework-National Datasets
Notill C Benefit for Dryland Corn by Soil Cluster for Selected States(Soils in each state sorted in descending order of NT benefit)
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-10
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Car
bon
Sequ
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red
(g/m
2/yr
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GAIAMSOHOKPASD
Truncated, one in IA at -176.7, and one in OH at -108.9
NT Benefit (g/m2/yr) = ((NT30-NT1)- (Till30-Till1))/30
Soil Carbon Over Time for Soybeans on 2 Iowa Soils by Tillage Type
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1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029 2032 2035 2038
Met
ric
Ton
s/Hec
tare
S1_PlowS1_MulchS1_NotillS2_PlowS2_MulchS2_Notill
The Century model: validation and application at the national scale
Background image shows all MLRA’s that have more than 5% agricultural cropland
Climatic DataMonthly TemperatureMonthly Precipitation
Historical Cropping Practices
Recent Cropping PracticesCrop RotationTillageFertilizerIrrigation
Native Vegetation
Soil CharacteristicsTextureDrainage
INPUT DATAMODELED
SCENARIOS
Tillage ChangesNo TillReduced TillageConventional Tillage
Land Use ChangesCRPAbandoned farmlandConverted to grassland
Rotation Changes
MLRA 108
Kansas wheat-fallow test
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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
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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