Modeling plant-soil-atmosphere carbon dioxide exchange using optimality principles
The exchange of carbon dioxide (CO2) between terrestrial ecosystems and the atmosphere plays a central role in the ecology of the biosphere and the climate system. Towards quantification of ecosystem-atmosphere CO 2 exchange, a generalized model of plant-soil-atmosphere CO2 exchange (OPTICAL) was de...
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University of New Hampshire Scholars' Repository
2000
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| Online Access: | https://scholars.unh.edu/dissertation/2131 https://scholars.unh.edu/cgi/viewcontent.cgi?article=3130&context=dissertation |
| Summary: | The exchange of carbon dioxide (CO2) between terrestrial ecosystems and the atmosphere plays a central role in the ecology of the biosphere and the climate system. Towards quantification of ecosystem-atmosphere CO 2 exchange, a generalized model of plant-soil-atmosphere CO2 exchange (OPTICAL) was described and evaluated using eddy covariance measurements of net ecosystem exchange of CO2 (NEE) in arctic, boreal, temperate, and tropical landscapes. The model requires no calibration and is based on theories of plant resource optimization and plant-soil nutrient feedbacks. The model predicts canopy photosynthetic capacity (Pcmax), canopy photosynthesis (P c), plant respiration (Rp), and soil heterotrophic respiration (RH). It can be applied globally using satellite-derived estimates of canopy light absorptance (f APAR), incident radiation (PAR), and air temperature (T air). The model provides the means by which to relate satellite observations such as the Normalized Difference Vegetation Index (NDVI) to the physiological status of vegetation and to ecosystem-atmosphere carbon exchange. A unique aspect of the model is its use of a recursive filter for calculating photosynthetic acclimation based on the integrated effect of environmental conditions. Good agreement was found between modeled and observed Pcmax (r2 = 0.76), the latter derived from light response curves fit to estimates of gross ecosystem exchange (GEE). Consistent with theories of resource optimization, P cmax varied strongly with time-averaged absorbed PAR and temperature. Modeled Pcmax combined with a 'big-leaf' canopy model explained 74 to 85% of the variability in GEE. The photo-acclimation model not only performed better than a traditional time-invariant model and as good or better than calibrated site-specific models, it did not require knowledge of vegetation type. The process of photo-acclimation appeared most important during periods of greatest transition in plant physiological status (e.g. spring and fall). Agreement between modeled and ... |
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