1) Land cover (preferably in time series); (including Reserves and protected areas, Potential vegetation) 2) Climate data (monthly precipitation, monthly max and min air temperatures); 3) Initial soil organic matter content; 4) Soil texture (% sand, % clay,% silt) 5) Drainage/water holding capacity; GIS Data for Carbon Simulation
GIS Data for Carbon Simulation. Land cover (preferably in time series); (including Reserves and protected areas, Potential vegetation) Climate data (monthly precipitation, monthly max and min air temperatures); Initial soil organic matter content; Soil texture (% sand, % clay,% silt) - PowerPoint PPT Presentation
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1) Land cover (preferably in time series);
(including Reserves and protected areas, Potential vegetation)
2) Climate data (monthly precipitation, monthly max and min air temperatures);
3) Initial soil organic matter content;
4) Soil texture (% sand, % clay,% silt)
5) Drainage/water holding capacity;
GIS Data for Carbon Simulation
1) Crop yield or net primary productivity;
2) Land use (fertilization, cultivation, tree or crop species; Rotation probability; Harvesting practices, Fertilization; Irrigation; Grazing; Organic matter addition, Land fire; Cultivation....); times of inputs and occurrences
1) C:N ratios of plant tissues and SOC pools;
2) Soil erosion and deposition.
GIS or Tabular Data for Carbon Simulation
Objectives
1. Adapt GEMS (general ensemble biogeochemical modeling system) for Africa
2. Quantify carbon dynamics in the Department of Velingara from 1900 to 2100
3. Assess the impacts of land use and climate change on carbon dynamics
Data were collected around six villages in five land cover classes.Unit: MgC ha-1
SOC contents in the top 0 – 20 cm layer were estimated from those in the 0 – 40 cm layer according to Jobbagy and Jackson (2000).
Spatially-Explicit Biogeochemical Modeling
The General Ensemble biogeochemical Modeling System (GEMS) is developed to simulate carbon dynamics over large areas. It consists of
Encapsulated ecosystem biogeochemical model(s).
Data assimilation system
Input/output processor
User-friendly GUI
Time
Spatial and Temporal Changes of Land Cover, Carbon Stock in Vegetation and Soils
EcosystemBiogeochemical
Model
Input Files
Data AssimilationSystem
JFD Table
DatabasesJFD Cover
OverlayOperation
OutputFiles
Land Cover
Land Use Info UnitsClimateSoils
GIS Coverages
Structure of ENSEMBLE
land cover Soil
climateLand use
N deposition.......
Spatial and Temporal C Dynamics and Uncertainties
Major Components of GEMS
JFD Grid/Table
Meaning of Each Column
in the JFD Table
Number of Simulations
for Each Cohort, n
Read a Cohort
Copy Default Input Files From Library
Update Default Input Files
Run CENTURY
Write Output
Stop
K = 0
K = k + 1
yes
no
yes
no
End of JFD Table?
K>n?
Diagram of information flow linking the CENTURY model with GIS data to produce estimates of regional emissions in GEMS
jfd_vlg.xtr JFD filevar_order_vlg.xtr variable order in the JFD file/edcsnw64/data/sliu/velingara/edc100files default century 100 filesstatus0.bin file name specifying the starting status of simulations; use NONE if no file2 previous status based on potential vegetation types (=1), or JFD cases (=2)status1.bin file name specifying the ending status of simulations; use NONE if no file0 spinning up run under potential vegetation (=1), otherwise (=0)0 MONTE_CARLO (yes = 1; no=0)5 Number of runs for each unique JFD case2 Land cover choice (1 --- ag census data; 2 --- GIS grid Time Series; 3 --- both) 6 total number of LC datasets9 max number of years that remote sensing can pick up clearcutting activities1900 Init_landcov1973 lc01978 lc11984 lc21990 lc31999 lc4soil.data soil data base soil_dr_vlg.data soil drainage dataprec_tab.txt monthly precipitationMinTemp.txt monthly min temperature maxTemp.txt monthly max temperaturecroprotat.data crop trasition probability generated from NRIn_depo.txt atmospheric deposition data baselc2cent.map land cover codes and default site filecrop_comp.data crop composition datafallow.data fallow infomanager.data data on fire, forest harvest, grazing etc.
1. It was assumed that agricultural will not expand in 21st century2. High Climate Change Scenario (HCCS) poses a great threat to food
security
Net Primary Productivity
1. NPP varies between 3 and 4 MgC/ha/y.2. Large inter-annual variability caused by precipitation fluctuation.3. NPP decreases under HCCS (i.e., large climate change)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1800 1850 1900 1950 2000 2050 2100
Year
NP
P (
MgC
/ha/
y) HCCS, more CUT
LCCS, more CUT
NCCS, more CUT
HCCS, less CUT
NCCS, less CUT
LCCS, less CUT
Carbon in Live Biomass
1. C stock in undisturbed dry and moist tropical forest is 88 and 135 MgC/ha, respectively
2. C stock has decreased by 46% from 1900 to 2000 in Velingara3. Woodfuel production has a larger impact than climate change
0
20
40
60
80
100
120
1800 1850 1900 1950 2000 2050 2100
Year
Liv
e B
iom
ass
(MgC
/ha)
HCCS, more CUT
LCCS, more CUT
NCCS, more CUT
HCCS, less CUT
NCCS, less CUT
LCCS, less CUT
0
5
10
15
20
25
30
35
1800 1850 1900 1950 2000 2050 2100
Year
SO
C (
MgC
/ha) HCCS, more CUT
LCCS, more CUT
NCCS, more CUT
HCCS, less CUT
NCCS, less CUT
LCCS, less CUT
Soil Organic Carbon
1. SOC stock in undisturbed dry and moist tropical forest is 29 and 35 MgC/ha, respectively
2. SOC stock has decreased by 9% from 1900 to 2000 in Velingara3. The max difference caused by management and climate change
options is about 5 MgC/ha in 2100
Total C Stock in Vegetation and Soil
1. Total C stock has decreased 37% from 1900 to 20002. Live biomass reduction accounts for 88% of the total C loss3. Selective logging has a significant impact4. Large climate change (HCCS) reduces C stock