Soil respiration （SR） is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accur...Soil respiration （SR） is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accurate estimation of CO2 emissions via SR. However, studies on spatio-temporal variation of SR are still scarce for semi-arid regions of North China. In this study, we conducted 12-month SR measurements in six land-use types, including two secondary forests （Populus tomentosa （PT） and Robinia pseudoacacia （RP））, three artificial plantations （Armeniaca sibirica （AS）, Punica granatum （PG） and Ziziphusjujuba （Z J）） and one natural grassland （GR）, to quantify spatio-temporal variation of SR and distinguish its controlling factors. Results indicated that SR exhibited distinct sea- sonal patterns for the six sites. Soil respiration peaked in August 2012 and bottomed in April 2013. The temporal coefficient of variation （CI0 of SR for the six sites ranged from 76.98% to 94.08%, while the spatial CV of SR ranged from 20.28% to 72.97% across the 12-month measurement. Soil temperature and soil moisture were the major controlling factors of temporal variation of SR in the six sites, while spatial variation in SR was mainly caused by the differences in soil total nitrogen （STN）, soil organic carbon （SOC）, net photosynthesis rate, and fine root biomass. Our results show that the annual average SR and Q10 （temperature sensitivity of soil respira- tion） values tended to decrease from secondary forests and grassland to plantations, indicating that the conversion of natural ecosystems to man-made ecosystems may reduce CO2 emissions and SR temperature sensitivity. Due to the high spatio-temporal variation of SR in our study area, care should be taken when converting secondary forests and grassland to plantations from the point view of accurately quantifying C02 emissions via SR at regional scales.展开更多
Net Primary Productivity （NPP） is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate- vegetation interaction is the basis for res...Net Primary Productivity （NPP） is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate- vegetation interaction is the basis for research on the responses of terrestrial ecosystemto global change and mainly comprises two important components： climate vegetation classification and the NPP of the natural vegetation. Comparing NPP estimated from the classification indices-based model with NPP derived from measurements at 3767 sites in China indicated that the classification indices-based model was capable of estimating large scale NPP. Annual cumulative temperature above 0~C and a moisture index, two main factors affecting NPP, were spatially plotted with the ArcGIS grid tool based on measured data in 2348 meteorological stations from 1961 to 2006. The distribution of NPP for potential vegetation classes under present climate conditions was simulated by the classification indices-based model. The model estimated the total NPP of potential terrestrial vegetation of China to fluctuate between 1.93 and 4.54 Pg C year-1. It pro- vides a reliable means for scaling-up from site to regional scales, and the findings could potentially favor China＇s position in reducing global warming gases as outlined in the Kyoto Protocol in order to fulfill China＇s commitment of reducing greenhouse gases.展开更多
基金Under the auspices of Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA05060600)National Natural Science Foundation of China(No.51378306)
文摘Soil respiration （SR） is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accurate estimation of CO2 emissions via SR. However, studies on spatio-temporal variation of SR are still scarce for semi-arid regions of North China. In this study, we conducted 12-month SR measurements in six land-use types, including two secondary forests （Populus tomentosa （PT） and Robinia pseudoacacia （RP））, three artificial plantations （Armeniaca sibirica （AS）, Punica granatum （PG） and Ziziphusjujuba （Z J）） and one natural grassland （GR）, to quantify spatio-temporal variation of SR and distinguish its controlling factors. Results indicated that SR exhibited distinct sea- sonal patterns for the six sites. Soil respiration peaked in August 2012 and bottomed in April 2013. The temporal coefficient of variation （CI0 of SR for the six sites ranged from 76.98% to 94.08%, while the spatial CV of SR ranged from 20.28% to 72.97% across the 12-month measurement. Soil temperature and soil moisture were the major controlling factors of temporal variation of SR in the six sites, while spatial variation in SR was mainly caused by the differences in soil total nitrogen （STN）, soil organic carbon （SOC）, net photosynthesis rate, and fine root biomass. Our results show that the annual average SR and Q10 （temperature sensitivity of soil respira- tion） values tended to decrease from secondary forests and grassland to plantations, indicating that the conversion of natural ecosystems to man-made ecosystems may reduce CO2 emissions and SR temperature sensitivity. Due to the high spatio-temporal variation of SR in our study area, care should be taken when converting secondary forests and grassland to plantations from the point view of accurately quantifying C02 emissions via SR at regional scales.
文摘Net Primary Productivity （NPP） is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate- vegetation interaction is the basis for research on the responses of terrestrial ecosystemto global change and mainly comprises two important components： climate vegetation classification and the NPP of the natural vegetation. Comparing NPP estimated from the classification indices-based model with NPP derived from measurements at 3767 sites in China indicated that the classification indices-based model was capable of estimating large scale NPP. Annual cumulative temperature above 0~C and a moisture index, two main factors affecting NPP, were spatially plotted with the ArcGIS grid tool based on measured data in 2348 meteorological stations from 1961 to 2006. The distribution of NPP for potential vegetation classes under present climate conditions was simulated by the classification indices-based model. The model estimated the total NPP of potential terrestrial vegetation of China to fluctuate between 1.93 and 4.54 Pg C year-1. It pro- vides a reliable means for scaling-up from site to regional scales, and the findings could potentially favor China＇s position in reducing global warming gases as outlined in the Kyoto Protocol in order to fulfill China＇s commitment of reducing greenhouse gases.
