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Construction And Application Of New Generation Of Dynamic Global Vegetation Models(DGVMS) Based On Plant Functional Traits

Posted on:2016-04-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y Z YanFull Text:PDF
GTID:1310330461466819Subject:Ecology
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This thesis focused on the theories of plant functional traits(FTs), and the aim is applying FTs in improving the dynamic global vegetation models(DGVMs). The main contents including the collection of FTs and building a FTs database, analyzing the relationships between FTs and climate factors, analyzing the relationships between FTs and ecosystem functions, summarizing the strategies for improving DGVMs, adding the hybrid framework based on FTs into DGVMs and evaluating the improved model by simulating the carbon balance. These themes showed that FTs theories played an important role in improving DGVMs and also presented a huge space to improve in applications. Although the traitclimate relationship is not the only candidate useful for predicting vegetation distributions and analyzing climatic sensitivity, it sheds new light on developing the next generation of traitbased DGVMs. Main results and conclusions are listed as follows.(1) It is feasible to construct the new generation of DGVMs based on plant functional traits and Gaussian mixture modelAs part of the ongoing development of plant functional trait(FT) hybrid models, a Gaussian mixture model(GMM) was adapted for and applied to predicting the distribution of vegetation in China and investigating the sensitivity of vegetation to changing climate on the basis of trait-climate relationships in China. First, three key FTs, including leaf mass per area(LMA), area-based leaf nitrogen(Narea), and mass-based leaf nitrogen(Nmass) were collected from the available literature. In addition, one structural trait of plant communities, leaf area index(LAI), was extracted from MODIS products across China. Second, trait-climate relationships were extracted, and then different trait combinations was used in training a GMM to model vegetation distribution. Finally, the GMM trained by the LMA-Nmass-LAI combination was applied to investigate the climate sensitivity of vegetation under different climate scenarios in China. The results demonstrated the following: all four traits effectively captured the relationships between climate variables and traits, as well as effectively predicted vegetation distribution and helped analyze environmental sensitivity; the LMA-Nmass-LAI combination yielded an accuracy of 72%, providing more detailed parameter information regarding community structures and ecosystem function, and was therefore selected fortraining GMMs to predict vegetation distribution; a sensitivity analysis indicated that increasing temperatures shifted the boundaries of most vegetation northward and westward.Because the forests in these regions are well adapted to growth under rainy conditions,increasing precipitation is predicted to expand the boundaries of forests compared with the baseline vegetation distribution. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analyzing climatic sensitivity, it sheds new light on developing the next generation of trait-based DGVMs.(2) Results of application of PFTs-FT hybrid model in simulating the vegetation distribution under future climatic scenarios were consistent with the rules of vegetationclimate relationshipsA hybrid model was proposed and applied in simulating the vegetation distribution under three important climatic scenarios of four common climatic models. Climatic data was resampled into 0.085 degree and vegetation distribution was obtained with the help of Gaussian Mixture Model(GMM). The results showed the vegetation distribution of four model had litter differences. Temperate deserts and subtropical vegetation increase significantly, but boreal evergreen trees, deciduous shrubs, tundra and temperate crop decrease in future climatic scenarios. Forest biomes present a decreasing tend between 2006 and 2100, and shift northwestern; subtropical crop adapted living in hot conditions and expand to a larger area compared with the baseline map. The results of vegetation distribution are consistent with the life strategies of all vegetation.(3) Modelling results based on plant functional types and plant functional traits were similar when they were applied in modelling terrestrial ecosystem bugets in ChinaThis thesis predicted the carbon budget pattern of terrestrial ecosystem in China from1960 to 2013 through adding FTs into IBIS and with the help of Gaussian Mixture Models(GMMs). Similar results were obtained through FTs and PFTs methods. The Net ecosystem productivity(NEP) values were located in southeastern forest regions, Qin Mountains,Xiaoxinganling Mountains and Changbai Mountains; the low net ecosystem productivity values were located in Yungui Plateau, Southern of North China plain, and middle and lower reaches of Yangtze River. The lowest NEP located in western desert, crop growing area of China.(4) The carbon budget patterns of Chinese terrestrial ecosystem were silmulated with reasonable results from 1984 to 2013 using Integrated Biosphere SimulatorEvaluating the carbon budget is one of the key scientific questions for global change biology. In this paper, Integrated Biosphere Simulator(IBIS) was employed in evaluating theeffects of climate change and elevated CO2 concentration on the temporal and spatial variation of carbon budget pattern in the terrestrial ecosystem of China during 1984 to2013.The total NPP of the terrestrial ecosystem of China showed an increasing trend and the range of NPP was between 2.4 3GtC/a and 3.16 GtC/a, with the mean value of 2.77 GtC/a; the highest net primary productivity(NPP) was located in southeastern and southwestern China while the lowest NPP was distributed in the northwestern China; the NPP showed an increasing trend from 1984 to 2013. The net ecosystem productivity(NEP) showed that most of Chinese terrestrial ecosystem acted as carbon sinks; NPP was significantly correlated with precipitation and temperature; the northwestern China and southwestern part of Tibet plateau acted as small carbon source; Daxinganling Mountains, Xiaoxinganling Mountains, Changbai Mountains, southeastern China and southwestern China acted as large carbon sink; Above all,IBIS achieved a reasonable and reliable result about the terrestrial carbon budget in China,which could be applied in predicting the potential of carbon sequestration and providing the scientific basis for regional carbon management.
Keywords/Search Tags:plant functional types(PFTs), IBIS, vegetation mapping, carbon budget patterns, sensitivity analysis
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