Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests

Thornton, P. E.; Law, B. E.; Gholz, Henry L.; Clark, Kenneth L.; Falge, Eva; Ellsworth, David S.; Goldstein, Allen H.; Monson, Russell K.; Hollinger, David Y.; Falk, Matthias; Chen, Jiquan; Sparks, Jed P.

doi:10.1016/s0168-1923(02)00108-9

Cited by 821 publications

(794 citation statements)

References 35 publications

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“…The model uses a daily time step and simulates processes including photosynthesis and plant respiration. The autotrophic respiration algorithm in this version uses biomass nitrogen content and temperature (as in Thornton et al, 2002). The algorithm for net photosynthesis is based on the Farquhar biochemical model, and GPP is calculated as the sum of net photosynthesis and daytime foliar respiration.…”

Section: Process Model Applicationmentioning

confidence: 99%

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

Turner

2003

Remote Sensing of Environment

131

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The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km 2 ) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm-daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms. D

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Section: Process Model Applicationmentioning

confidence: 99%

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

Turner

2003

Remote Sensing of Environment

131

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“…models simulating the forest as a composite of small patches of (potentially) different composition and successional stage) thus employ an onelayer bucket model (i.e. models assuming a single well-mixed body of water for a stand) specified by field capacity to permanent wilting point (e.g., Bugmann and Solomon, 2000: FORCLIM;Thornton et al, 2002: BIOME-BGC). Examples for process-models utilizing a more complex soil architecture are given by Grote and Pretzsch (2002: BALANCE) and Lasch et al (2005: 4C).…”

Section: Droughtmentioning

confidence: 99%

Modelling natural disturbances in forest ecosystems: a review

Seidl

Fernandes

Fonseca

et al. 2011

Ecological Modelling

356

219

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“…Next to it could be an old-growth stand (450 years old) with the same fPAR but with a balance of NPP and Rh such that NEP is zero on average across 10 or 20 years. Simu lations and eddy covariance flux tower data at the ecosystem scale show that a major influence on forest NEP is disturbance, with important secondary influences of climate [Thornton et al, 2002].…”

Section: Page 200mentioning

confidence: 99%

On “Estimating carbon budgets for U.S. ecosystems”

Turner¹

2006

EoS Transactions

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In a recent Eos article, Potter et al. [2006] describe an approach to developing terrestrial biogenic carbon budgets at regional to continental scales. Their primary scaling tool is the Carnegie‐Ames‐Stanford (CASA) model, and spatially explicit inputs include climate as well as vegetation type and vegetation greenness (from satellite imagery). Net primary production (NPP) is simulated with a light use efficiency approach, and heterotrophic respiration (Rh) is based on turnover rates of several litter and soil carbon pools. This approach is well‐suited to characterizing the response of net ecosystem production (NEPNPP‐Rh, as in the work by Lovett et al. [2006]) to interannual variation in climate, but it is worth pointing out that it largely misses the effects of forest management on NEE These management effects have a significant influence on the absolute carbon exchange with the atmosphere, and on the kind of carbon accounting needed for carbon dioxide emissions inventories.

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Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests

Cited by 821 publications

References 35 publications

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

Modelling natural disturbances in forest ecosystems: a review

On “Estimating carbon budgets for U.S. ecosystems”

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