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Non-CO2 Greenhouse Gases Workshop, Boulder, CO, October 22-24, 2008. Spatial and temporal patterns of CH 4 and N 2 O fluxes from North America as estimated by process-based ecosystem model. Hanqin Tian, Xiaofeng Xu and other ESRA Members Ecosystem Science and Regional Analysis Laboratory
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Non-CO2 Greenhouse Gases Workshop, Boulder, CO, October 22-24, 2008 Spatial and temporal patterns of CH4 and N2O fluxes from North America as estimated by process-based ecosystem model Hanqin Tian, Xiaofeng Xu and other ESRA Members Ecosystem Science and Regional Analysis Laboratory Auburn University
Modeling Measuring Flask Data Satellite Data Eddy Flux Ecosystem Experiments The Integrated Ecosystem Modeling Approach DLEM Synthesis
Outline • Why do we estimate CH4 and N2O fluxes using ecosystem model? • A case study in North America • DLEM: a process-based ecosystem model • Site level model verification • Comparison with other studies • Model application on the regional level
Why do we use ecosystem modeling approach? • Attributing controls on non-CO2 fluxes Anthropogenic and natural factors • Spatial and temporal extrapolation • Prediction
The Dynamic Land Ecosystem Model - Key components and interactions (a) (a)
The Dynamic Land Ecosystem Model - Key processes, fluxes and pools
Methane module of DLEM Three methane-associated processes are incorporated in DLEM: methane production in soil, the oxidation of produced methane during transportation, atmospheric methane oxidation
Nitrous oxide module of DLEM Nitrification and denitrification are determined by environmental conditions as soil moisture, temperature, pH
Model input data • Climate dataset (precipitation, temperature, humidity) • Nitrogen deposition • Ozone concentration • Land use and land cover change • Historical CO2 concentration • Fertilizer, irrigation area
Model validation for CH4 fluxes Durham forest (42N, 73W) The observed data are from BOREAS
Site level Validation on CH4 Durham forest (42N, 73W) The observed data are from BOREAS
Model validation for N2O fluxes N2O from wetland (33.5E, 47.58N) Observed data are from Song et al. (2008)
DLEM-based estimation of CH4 and N2O emission from North America terrestrial ecosystems • Study Period: 1979-2005 • Spatial resolution: 32 km • Time step: Daily • Forces: multiple factors
1 Tundra 2 Boreal broad-leaf deciduous forests 3 Boreal needle-leaf evergreen forests 4 Boreal needle-leaf deciduous forests 5 Temperate broad-leaf deciduous forests 6 Temperate broad-leaf evergreen forests 7 Temperate needle-leaf evergreen forests 8 Temperate needle-leaf deciduous forests 9 Tropical/subtropical broad-leaf deciduous forests 10 Tropical/subtropical broad-leaf evergreen forests 11 Open shrub 12 Close shrub 13 C3 grassland 14 C4 grassland 15 Grass peatland 16 Forest peatland 17 Grass permanent wetland 18 Forest permanent wetland 19 Grass seasonal wetland 20 Forest seasonal wetland 21 Desert 22 Mixed forests 24 Temperate needle-leaf evergreen forests in tropical area
Input data:Nitrogen fertilization Data are from FAO and USDA
Nitrogen fertilization effects on N2O fluxes in Conterminous US during 1945-2005
Summary • DLEM is capable of capturing spatial and temporal patterns of CH4 and N2O fluxes in North American terrestrial ecosystems. • DLEM could be used to quantify the relative contribution of multiple factors. Our simulated results suggest that climate (temperature and precipitation) is the primary control over interannual variability of CH4 and N2O over North America during1979- 2005, the air pollution and land cover/land use change could substantially alter the fluxes of CH4 and N2O over the region. • Ecosystem modeling approach can add a new dimension of the NACP non-CO2 greenhouse gases synthesis.
Needs for ecosystem modeling approach to CH4 and N2O fluxes • Data needs: • Vegetation maps, particularly wetland area/distribution • Land management (fertilization, irrigation) • Validation: • Site level: validate against long-term observations • Regional Level: comparison with inverse modeling and bottom-up inventories • Model improvement: To better address some key processes such as soil thawing.