沿水分梯度草原群落NPP动态及对气候变化响应的模拟分析

doi:10.3773/j.issn.1005-264x.2008.03.002

摘要/Abstract

摘要：

水分条件不仅影响半干旱区群落的组成, 而且在一定程度上决定了群落的功能。处于不同水分条件生境下群落的优势物种在水分利用和同化物利用效率方面的功能特征会存在差异, 这些差异将导致群落对于气候变化产生不同的响应, 进而影响到景观和区域尺度上对于全球变化下碳动态和格局的分析。该文选取了锡林河流域典型草原区沿水分梯度的4个代表群落, 在野外实验测定并结合长期定位研究成果基础上, 利用BIOME-BGC模型对代表群落的长期净初级生产力(Net primary productivity, NPP)动态进行了模拟和模型验证。通过分析该地区1953~2005年气候变化趋势, 推测了未来可能的气候变化情景, 进而模拟了气候变化下4个群落长期NPP动态的响应。结果表明, 当前气候条件下, 羊草(Leymus chinensis)群落NPP平均值为197.76 gC·m^-2 (SE=7.11), 大针茅(Stipa grandis)群落NPP平均值为198.95 gC·m^-2 (SE=6.41), 贝加尔针茅(Stipa baicalensis)群落NPP平均值为210.41 gC·m^-2 (SE=7.87), 克氏针茅(Stipa krylovii)群落NPP平均值为144.92 gC·m^-2 (SE=4.64), 4个群落NPP平均值为188.01 gC·m^-2 (SE=3.72); 气候变化情景下, 温度增加下(P0T1),NPP平均下降14.2%,降水增加下(P1T0), NPP平均增加13.2%,温度与降水都增加情景下(P1T1), NPP平均下降2.7%, 但由于生境水分条件差别和优势物种功能特征差异, 4个群落表现出了增减幅度不同的趋势。对气候因子的敏感性分析及回归分析表明, 降水是该地区NPP最主要的决定因子, 而温度决定作用相对较小,主要通过影响植物的呼吸和水分蒸散等过程影响NPP。在最有可能代表未来气候变化的温度增加的两种情景下(P0T1、P1T1), NPP均呈下降趋势。群落NPP对气候变化的响应趋势与水分胁迫系数(Water stress index, WSI)、碳胁迫系数(Carbon stress index, CSI)变化密切相关。克氏针茅群落由于所处生境水分条件差,WSI高,对降水的依赖程度最大;贝加尔针茅群落一方面处于较好的水分生境,具有较小的WSI,另一方面,由于具有高碳氮比,维持呼吸消耗的光合产物比例低,CSI远低于其它3个群落, 未来气候变化下, NPP较其它3个群落仍较高。

关键词: 典型草原, BIOME-BGC, 净初级生产力, 气候变化, 胁迫系数

Abstract:

Aims Water availability influences community composition and function in semi-arid areas. Habitat water availability may differentiate functional traits on water and carbon assimilation use efficiencies of dominant species, which may induce different response patterns of communities to climatic change. Our objectives are to 1) quantitatively analyze long-term net primary productivity (NPP) dynamics of typical grassland communities along a water gradient based on field measurements, long-time monitoring results and a process-based model; 2) analyze trends of temperature and precipitation based on local long-term meteorological data and predict future climate change scenarios and 3) predict NPP responses of the communities along the water gradient to climate change and interpret the results by means of water stress index (WSI) and carbon stress index (CSI).

Methods We selected four grassland communities along a water gradient in the Xilingol River Basin of China as our study sites: Stipa baicalensis, Leymus chinensis, Stipa grandis, and Stipa krylovii. We conducted field surveys and measurements of physiological parameters and soil parameters in summer 2005 and used local meteorological data (1953 to 2005) to analyze trends of precipitation and temperature and prescribe future scenarios. A process-based model, BIOME-BGC, was parameterized and validated using field data and long-term monitoring data and run on daily steps to simulate NPP dynamics of the four communities under the current climate and future scenarios. We calculated water and carbon stress indices for each community under each scenario to interpret possible mechanisms.

Important findings Minimum and maximum temperatures increased from 1953 to 2005, while the precipitation varied. Average NPP of the four communities decreased 14.2% under the scenario of increasing temperature, increased 13.2% under the scenario of increasing precipitation, and declined 2.7% under the scenario of increasing both. Precipitation is the predominant factor on NPP dynamics in this semi-arid area, while temperature mainly influences plant respiration and evapotranspiration and also affects NPP. Due to the differences in water availability and functional traits of dominant species, the four communities presented different responses and sensitivities to the climate changes in precipitation and temperature, which closely related to variations in WSI and CSI. Stipa krylovii community maintains a high WSI and temperature, reflected by variations in WSI and CSI, shows great dependence on precipitation mainly because of the poor habitat water availability. Stipa baicalensis community has higher NPP under both current and future climate change scenarios largely because it has 1) lower WSI because of better habitat water availability and 2) lower CSI because its higher carbon to nitrogen ratio contributes to less photosynthetic products consumed by maintenance respiration.

Key words: typical steppe, BIOME-BGC, NPP (Net primary productivity), climate change, stress index

董明伟, 喻梅. 沿水分梯度草原群落NPP动态及对气候变化响应的模拟分析. 植物生态学报, 2008, 32(3): 531-543. DOI: 10.3773/j.issn.1005-264x.2008.03.002

DONG Ming-Wei, YU Mei. SIMULATION ANALYSIS ON NET PRIMARY PRODUCTIVITY OF GRASSLAND COMMUNITIES ALONG A WATER GRADIENT AND THEIR RESPONSES TO CLIMATE CHANGE. Chinese Journal of Plant Ecology, 2008, 32(3): 531-543. DOI: 10.3773/j.issn.1005-264x.2008.03.002

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.plant-ecology.com/CN/10.3773/j.issn.1005-264x.2008.03.002

https://www.plant-ecology.com/CN/Y2008/V32/I3/531

图/表 6

参考文献 82

[1]	Bai YF (白永飞) (1999). Influence of seasonal distribution of precipitation primary productivity of Stipa krylovii community . Acta Phytoecologica Sinica (植物生态学报), 23,155-160. (in Chinese with English abstract)
[2]	Bai YF (白永飞), Li LH (李凌浩), Wang QB (王其兵), Zhang LX (张丽霞), Zhang Y (张焱), Chen ZZ (陈佐忠) (2000). Changes in plant species diversity and productivity along gradients of precipitation and elevation in the Xilin River Basin. Inner Mongolia. Acta Phytoecologica Sinica (植物生态学报), 24,667-673. (in Chinese with English abstract)
[3]	Bristow KL, Campbell GS (1984). On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31,159-166.
[4]	Cao MK, Tao B, Li KR, Shao XM, Prience SD (2003). Interannual variation in terrestrial ecosystem carbon fluxes in China from 1981-1998. Acta Botanica Sinica, 45,552-560.
[5]	Chen B (陈波) (2001). Progress in the response of net primary productivity of terrestrial vegetations to global climate changes. Journal of Zhejiang Forestry College (浙江林学院学报), 18,445-449. (in Chinese with English abstract)
[6]	Chen SQ (陈四清) (2002). Study on Land-use/Cover Change and Carbon Cycle of Xilin River,Inner Mongolia Based on Remote Sensing and GIS (基于遥感和GIS的内蒙古锡林河流域土地利用/土地覆盖变化和碳循环研究). PhD dissertation, Graduate School of Chinese Academy of Sciences, Beijing. (in Chinese with English abstract)
[7]	Churkina G, Running SW (1998). Contrasting climatic controls on the estimated productivity of global terrestrial biomes. Ecosystems, 1,206-215.
[8]	Churkina G, Tenhunen J, Thornton PE, Falge EM, Elbers JA, Erhard M, Grünwald T, Kowalski AS, Rannik Á, Sprinz D (2003). Analyzing the ecosystem carbon dynamics of four european coniferous forests using a biogeochemistry model. Ecosystems, 6,168-184.
[9]	Ciais P, Reichstein M, Viovy N, Granier A, Ogée J, Allard V, Aubinet M, Buchmann N, Bernhofer C, Carrara A, Chevallier F, De Noblet N, Friend AD, Friedlingstein P, Grünwald T, Heinesch B, Keronen P, Knohl A, Krinner G, Loustau D, Manca G, Matteucci G, Miglietta F, Ourcival JM, Papale D, Pilegaard K, Rambal S, Seufert G, Soussana JF, Sanz MJ, Schulze ED, Vesala T, Valentini R (2005). Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437,529-533. URL PMID
[10]	Clery D (2006). Climate change demands action, says U.K. report. Science, 311,592-592.
[11]	Coops NC, Waring RH, Brown SR, Running SW (2001). Comparisons of predictions of net primary production and seasonal patterns in water use derived with two forest growth models in Southwestern Oregon. Ecological Modelling, 142,61-81.
[12]	Cramer W, Kicklighter DW, Bondeau A, Moore III B, Churkina G, Nemry B, Ruimy A, Schloss AL (1999). Comparing global models of terrestrial net primary productivity ( NPP): overview and key results . Global Change Biology, 5 (Suppl.1),1-15.
[13]	Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA, Brovkin V, Cox PM, Fisher V, Foley JA, Friend AD, Kucharik C, Lomas MR, Ramankutty N, Sitch S, Smith B, White A, Young-Molling C (2001). Global response of terrestrial ecosystem structure and function to CO ₂ and climate change: results from six dynamic global vegetation models . Global Change Biology, 7,357-373.
[14]	Dong WJ, Qi Y, Li HM, Zhou DJ, Shi DH, Sun LY (2005). Modeling carbon and water budgets in the Lushi Bssin with BIOME-BGC. Chinese Journal of Population,Resources and Environment, 3(2),27-34.
[15]	Dong WJ (董文娟), Qi Y (齐晔), Li HM (李惠民), Zhou DJ (周大杰) (2005). Modeling net primary production spatial distribution of vegetation with BIOME-BGC: a case study of the upper Lushi Catchment in the Xiaohuajian section of the Yellow River Basin. Geography and Geo-Information Science (地理与地理信息科学), 21,105-109.
[16]	Fang JY (方精云), Tang YH (唐艳鸿), Lin JD (林俊达) (2000). Global Ecology—Climate Change and Ecological Responses (全球生态学——气候变化与生态响应). Higher Education Press, Beijing. (in Chinese)
[17]	Fang JY, Piao SL, Tang ZY (2001). Interannual variability in net primary productivity and precipitation. Science, 293,1723a.
[18]	Feng XF (冯险峰), Liu GH (刘高焕), Chen SP (陈述彭), Zhou WZ (周文佐) (2004). Study on process model of net primary productivity of terrestrial ecosystems. Journal of Natural Resources (自然资源学报), 19,369-378. (in Chinese with English abstract)
[19]	Gao Q (高琼), Yu M (喻梅), Zhang XS (张新时), Guan F (关烽) (1997). Dynamic modeling of Northeast China Transect responses to global change—a regional vegetation model driven by remote sensing information. Acta Botanica Sinica (植物学报), 39,800-810. (in Chinese with English abstract)
[20]	Gao Q, Yu M, Yang XS (2000). An analysis of sensitivity of terrestrial ecosystems in China to climatic change using spatial simulation. Climatic Change, 47,373-400.
[21]	Glassy JM, Running SW (1994). Validating diurnal climatology of the MT-CLIM model across a climatic gradient in Oregon. Ecological Applications, 4,248-257.
[22]	Hunt ER Jr, Piper SC, Nemani RR, Keeling CD, Otto RD, Running SW (1996). Global net carbon exchange and intra-annual atmospheric CO ₂ concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model . Global Biogeochemical Cycles, 10,431-456.
[23]	IGBP Terrestrial Carbon Working Group. (1998). The terrestrial carbon cycle: implication for the Kyoto Protocol. Science, 280,1393-1394.
[24]	Ji JJ (季劲钧), Huang M (黄玫), Liu Q (刘青) (2005). Modeling studies of response mechanism of steppe productivity to climate change in middle latitude semiarid regions in China. Acta Meteorologica Sinica (气象学报), 63,257-266.
[25]	Jiang GM, He WM (1999). Species- and habitat-variability of photosynthesis, transpiration and water use efficiency of different plant species in Maowusu sand area. Acta Botanica Sinica, 41,1114-1124.
[26]	Kang S, Kimball JS, Running SW (2006). Simulating effects of fire disturbance and climate change on boreal forest productivity and evapotranspiration. Science of the Total Environment, 362,85-102.
[27]	Kimball JS, Running SW, Nemani RR (1997a). An improved method for estimating surface humidity from daily minimum temperature. Agricultural and Forest Meteorology, 85,87-98.
[28]	Kimball JS, Thornton PE, White MA, Running SW (1997b). Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region. Tree Physiology, 17,589-599.
[29]	Kimball JS, White MA, Running SW (1997c). BIOME-BGC simulations of stand hydrologic processes for BOREAS. Journal of Geophysical Research, 102(D24),20043-20051.
[30]	Kong QF (孔庆馥), Bai YL (白云龙), Yan FL (阎福林), Chen JF (陈杰范), Li GZ (李桂珍), Hao XZ (郝秀芝), Wang XP (王小平), Yan GX (阎贵兴), Hu W (胡薇), Li J (李京) (1990). The List of Chemical Component and Nutrition Value of China Feeding Plant (中国饲用植物化学成分及营养价值表). China Agriculture Press, Beijing. (in Chinese)
[31]	Lagergren F, Grelle A, Lankreijer H, Mølder M, Lindroth A (2006). Current carbon balance of the forested area in Sweden and its sensitivity to global change as simulated by BIOME-BGC. Ecosystems, 9,894-908.
[32]	Li B (李博), Yong SP (雍世鹏), Li ZH (李忠厚) (1988). The vegetation of Xilin River Basin and its utilization. Research on Grassland Ecosystem (草原生态系统研究), 3,84-183. (in Chinese with English abstract)
[33]	Li B (李博), Ren ZB (任志弼), Shi PJ (史培军) (1993). Study on Dynamic Inspection in Grassland and Husbandry of North China (中国北方草地畜牧业动态监测研究). China Agriculture Press, Beijing, 129-190. (in Chinese)
[34]	Li LH (李凌浩), Liu XH (刘先华), Chen ZZ (陈佐忠) (1998). Study on the carbon of Leymus chinensis steppe in the Xilin River Basin . Acta Botanica Sinica (植物学报), 40,955-961. (in Chinese with English abstract)
[35]	Li ZZ (李自珍), Shi WL (施维林), Tang HP (唐海萍), Wang XP (王新平) (2001). Studies on numerical simulation of moisture niche-fitness procedure of arid plants. Journal of Desert Research (中国沙漠), 21,281-285. (in Chinese with English abstract)
[36]	Liu XP (刘新平), Zhang TH (张铜会), Zhao HL (赵哈林), Zhao XY (赵学勇), He YH (何玉惠) (2005). Research advances on moisture dynamic of desertified lands in arid and semi-arid regions. Research of Soil and Water Conservation (水土保持研究), 12 (1),63-68. (in Chinese with English abstract)
[37]	Liu YH (刘颖慧), Jia HK (贾海坤), Gao Q (高琼) (2006). Review on researches of photoassimilates partitioning and its models. Acta Ecologica Sinica (生态学报), 26,1981-1992. (in Chinese with English abstract)
[38]	Mcguire AD, Joyce LA, Kicklighter DW, Melillo JM, Esser G, Vorosmarty CJ (1993). Productivity response of climax temperate forests to elevated temperature and carbon dioxide: a North American comparison between two global models. Climatic Change, 24,287-310.
[39]	Melillo JM, McGuire AD, Kicklighter DW, Moore IIIB, Vorosmarty CJ, Schloss AL (1993). Global climate change and terrestrial net primary production. Nature, 363,234-240.
[40]	Melillo JM, Borchere J, Chaney J (1995). Vegetation ecosystem modeling and analysis project: comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO ₂ doubling . Global Biogeochemical Cycles, 9,407-437.
[41]	Monteith JL (1965). Evaporation and Environment, Proceedings of the 19th Symposium of Society for Experimental Biology. Cambridge University Press, New York, 205-233.
[42]	Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, Myneni RB, Running SW (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300,1560-1563.
[43]	Ni J (2004). Estimating net primary productivity of grasslands from field biomass measurements in temperate northern China. Plant Ecology, 174,217-234.
[44]	Niu SL (牛书丽), Han XG (韩兴国), Ma KP (马克平), Wan SQ (万师强) (2007). Field facilities in global warming and terrestrial ecosystem reseach. Journal of Plant Ecology(Chinese Version) (植物生态学报), 31,262-271. (in Chinese with English abstract)
[45]	Pan QM (潘庆民), Bai YF (白永飞), Han XG (韩兴国), Yang JC (杨景成) (2005). Effects of nitrogen additions on a Leymus chinensis population in typical steppe of Inner Mongolia . Acta Phytoecologica Sinica (植物生态学报), 29,311-317. (in Chinese with English abstract)
[46]	Pan YD, Melillo JM, McGuire AD, Kicklighter DW, Pitelka LF, Hibbard K, Pierce LL, Running SW, Ojima DS, Parton WJ, Schimel DS (1998). Modeled responses of terrestrial ecosystems to elevated atmospheric CO ₂: a comparison of simulations by the biogeochemistry models of the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) . Oecologia, 114,389-404. URL PMID
[47]	Patz JA, Lendrum DC, Holloway T, Foley JA (2005). Impact of regional climate change on human health. Nature, 438,310-317.
[48]	Peng CH, Michael JA (1999). Modelling the response of net primary productivity ( NPP) of boreal forest ecosystems to changes in climate and fire disturbance regimes . Ecological Modelling, 122,175-193.
[49]	Piao SL (朴世龙), Fang JY (方精云), Guo QH (郭庆华) (2001a). Application of CASA model to the estimation of Chinese terrestrial net primary production. Acta Phytoecologica Sinica (植物生态学报), 25,603-608. (in Chinese with English abstract)
[50]	Piao SL (朴世龙), Fang JY (方精云), Guo QH (郭庆华) (2001b). Terrestrial net primary productivity and its spatial temporal patterns in China during 1982-1999. Acta Scientiarum Naturalium Universitatis Pekinensis (北京大学学报自然科学版), 37,563-569. (in Chinese with English abstract)
[51]	Pørtner HO, Knust R (2007). Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science, 315,95-97.
[52]	Running SW, Hunt RE (1993). Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer JR, Field CB eds. Scaling Physiologic Processes: Leaf to Globe. Academic Press, San Diego, CA, USA, 141-158.
[53]	Running SW, Coughlan JC (1988). A general model of forest ecosystem processes for regional applications. Ⅰ. Hydrologic balance, canopy gas exchange and primary production processes. Ecological Modelling, 42,125-154.
[54]	Running SW, Gower ST (1991). FOREST-BGC, a general model of forest ecosystem processes for regional applications. Ⅱ. Dynamic carbon allocation and nitrogen budgets. Tree Physiology, 9,147-160. URL PMID
[55]	Ruan MG (1991). A simple method for estimating gross carbon budgets for vegetation in forest ecosystems. Tree Physiology, 9,255-266.
[56]	Running SW, Nemani RR (1991). Regional hydrologic and carbon balance response of forests resulting from potential climate change. Climatic Change, 19,349-386.
[57]	Schrøter D, Cramer W, Leemans R, Prentice IC, Araújo MB, Arnell NW, Bondeau A, Bugmann H, Carter TR, Garcia CA, de la Vega-Leinert AC, Erhard M, Ewert F, Glendining M, House JI, Kankaanpää S, Klein RJ, Lavorel S, Lindner M, Metzger MJ, Meyer J, Mitchell TD, Reginster I, Rounsevell M, Sabaté S, Sitch S, Smith B, Smith J, Smith P, Sykes MT, Thonicke K, Thuiller W, Tuck G, Zaehle S, Zierl B (2005). Ecosystem service supply and vulnerability to global change in Europe. Science, 310,1333-1337.
[58]	Sun R (孙睿), Zhu QJ (朱启疆) (2000). Distribution and seasonal change of net primary productivity in China from April, 1992 to March, 1993. Acta Geographica Sinica (地理学报), 55,36-45.
[59]	Su HX, Sang WG (2004). Simulations and analysis of net primary productivity in Quercus liaotungensis forest of Donglingshan Mountain Range in response to different climate change scenarios . Acta Botanica Sinica, 46,1281-1291.
[60]	Su HX (苏宏新) (2005). Analyzing and Simulating the Growth of Picea schrenkiana Forests in Xinjiang Under Global Climate Change (全球气候变化条件下新疆天山云杉林生长的分析与模拟). PhD dissertation, Institute of Botany, the Chinese Academy of Sciences, Beijing. (in Chinese with English abstract)
[61]	Suttle KB, Thomsen MA, Power ME (2007). Species interactions reverse grassland responses to changing climate. Science, 315,640-642.
[62]	Tao B (陶波), Li KR (李克让), Shao XM (邵雪梅), Cao MK (曹明奎) (2003). Temporal and spatial pattern of net primary production of terrestrial ecosystems in China. Acta Geographica Sinica (地理学报), 58,372-380. (in Chinese with English abstract)
[63]	Thornton PE, White JD (1996). Biogeochemical characterization of the CRB using the BGC model, ecophysiological inputs, and landscape descriptions. Final report RJVA-94933. [Irregular pagination]. On file with: U.S. Department of Agriculture, Forest Service, Intermountain Fire Sciences Lab, PO Box 8089,Missoula, MT 59807.
[64]	Thornton PE, Running SW (1999). An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 93,211-228.
[65]	Thornton PE, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP (2002). Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology, 113,185-222.
[66]	Tian HQ, Melillo JM, Kicklighter DW, Mcguire AD, Helfrich III JVK, Moore III B, Vørøsmarty CJ (1998). Effect of interannual climate variability on carbon storage in Amazonian ecosystems. Nature, 396,664-667.
[67]	Tian HQ (田汉勤), Xu XF (徐小锋), Song X (宋霞) (2007). Drought impacts on terrestrial ecosystem productivity. Journal of Plant Ecology (Chinese Version) (植物生态学报), 31,231-241. (in Chinese with English abstract)
[68]	Wang JB (王军邦) (2004). Chinese Terrestrial Net Ecosystem Productive Model Applied Remote Sensing Data (中国陆地净生态系统生产力遥感模型研究). PhD dissertation, Graduate School of Chinese Academy of Sciences, Beijing. (in Chinese with English abstract)
[69]	Wang YH, Zhou GS, Wang YH (2007). Modeling response of the meadow steppe dominated by Leymus chinensis to climate change . Climatic Change, 82,437-452.
[70]	Wei G, Gao ZQ, Slusser J, Pan XL, Ma YJ (2003). The responses of net primary production (NPP) to different climate scenarios with BIOME-BGC model in oasis areas along the Tianshan Mountains in Xinjiang, China. Published by SPIE, Bellingham, WA. USA, 4890,141-150.
[71]	Weltzin JF, Loik ME, Schwinning S, Williams DG, Fay PA, Haddad BM, Harte J, Huxman TE, Knapp AK, Lin GH, Pockman WT, Shaw MR, Small EE, Smith MD, Smith SD, Tissue DT, Zak JC (2003). Assessing the response of terrestrial ecosystems to potential changes in precipitation. BioScience, 53,941-952.
[72]	White MA, Thornton PE, Running SW, Nemani RR (2000). Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interactions, 4(3),1-85.
[73]	Winslow JC, Hunt ER, Piper SC (2003). The influence of seasonal water availability on global C3 versus C4 grassland biomass and its implications for climate change research. Ecological Modelling, 163,153-173.
[74]	Xiao X, Melillo JM, Kicklighter DW, Pan Y, McGuire AD, Helfrich J (1998). Net primary production of terrestrial ecosystems in China and its equilibrium responses to changes in climate and atmospheric CO ₂ concentration . Acta Phytoecologica Sinica (植物生态学报), 22,97-118.
[75]	Xiao XM (肖向明), Wang YF (王义凤), Chen ZZ (陈佐忠) (1996). Dynamics of primary productivity and soil organic matter of typical steppe in the Xilin River Basin of Inner Mongolia and their response to climate change. Acta Botanica Sinica (植物学报), 38,45-52. (in Chinese with English abstract)
[76]	Xu BC (许炳成), Shan L (山仑), Chen YM (陈云明) (2003). Review and discuss on the effect and influence factors of vegetation construction on soil water in semi-arid area on Loess Plateau. Science of Soil and Water Conservation (中国水土保持科学), 1(4),32-35. (in Chinese with English abstract)
[77]	Xu XF (徐小锋), Tian HQ (田汉勤), Wan SQ (万师强) (2007). Climate warming impacts on carbon in terrestrial ecosystems. Journal of Plant Ecology (Chinese Version) (植物生态学报), 31,175-188. (in Chinese with English abstract)
[78]	Zhang XS (张新时) (1993). A vegetation-climate classification system for global change studies in China. Quaternary Sciences (第四纪研究), 2,157-169. (in Chinese with English abstract)
[79]	Zhao MS, Heinsch FA, Nemani RR, Running SW (2005). Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95,164-176.
[80]	Zhou GS, Wang YH, Jiang YL, Xu ZZ (2002). Carbon balance along Northeast China Transect (NECT-IGBP). Science in China (Series C), 45,18-29.
[81]	Zhu WQ (朱文泉), Pan YZ (潘耀忠), Long ZH (龙中华), Chen YH (陈云浩), Li J (李京), Hu HB (扈海波) (2005). Estimating net primary productivity of terrestrial vegetation based on GIS and RS: a case study in Inner Mongolia, China. Journal of Remote Sensing (遥感学报), 9,300-307. (in Chinese with English abstract)
[82]	Zimmermann NE, Thornton PE, Jolly WM, Cherubini P, Kräuchi N, Kienast F, Wildi O, Forster T, Dobbertin M, Schaub M (2001). Scaling carbon fluxes from stands to landscapes: calibrating and testing BIOME-BGC along multiple environmental gradients. WSL, http://www.wsl.ch/staff/marcus.schaub/index-en.php.

地点 Site	经度 Longitude (°E)	纬度 Latitude (°N)	海拔 Altitude (m)	年降水量 Annual precipitation (mm)	不同深度土壤含水量 Soil water content under different depth (%)
地点 Site	经度 Longitude (°E)	纬度 Latitude (°N)	海拔 Altitude (m)	年降水量 Annual precipitation (mm)	0~15 cm	15~40 cm	40~60 cm
贝加尔针茅群落Stipa baicalensis community	116.82	43.51	1 397	>450	11.01	8.23	8.11
大针茅群落 S. grandis community	116.55	43.54	1 130	350~450	9.37	7.52	6.53
羊草群落 Leymus chinensis community	116.67	43.55	1 245	350~450	6.69	5.53	4.63
克氏针茅群落 S. krylovii community	115.90	44.09	982	250~350	6.76	5.47	4.31

地点 Site	经度 Longitude (°E)	纬度 Latitude (°N)	海拔 Altitude (m)	年降水量 Annual precipitation (mm)	不同深度土壤含水量 Soil water content under different depth (%)
地点 Site	经度 Longitude (°E)	纬度 Latitude (°N)	海拔 Altitude (m)	年降水量 Annual precipitation (mm)	0~15 cm	15~40 cm	40~60 cm
贝加尔针茅群落Stipa baicalensis community	116.82	43.51	1 397	>450	11.01	8.23	8.11
大针茅群落 S. grandis community	116.55	43.54	1 130	350~450	9.37	7.52	6.53
羊草群落 Leymus chinensis community	116.67	43.55	1 245	350~450	6.69	5.53	4.63
克氏针茅群落 S. krylovii community	115.90	44.09	982	250~350	6.76	5.47	4.31

参数 Parameter	LC	SG	SB	SK
有效土壤深度 Effective soil depth (m)	0.6	0.6	0.6	0.6
无石土壤中砂粒体积百分比Sand percentage by volume in rock-free soil (%)	77	77	74	85
无石土壤中粉粒体积百分比Silt percentage by volume in rock-free soil (%)	10	11	16	6
无石土壤中粘粒体积百分比Clay percentage by volume in rock-free soil (%)	13	12	10	9
海拔 Site elevation (m)	1 245	1 173	1 397	982
短波反射率 Site shortwave albedo (DIM)	0.2	0.2	0.2	0.2
新生细根碳∶新叶碳 New fine root C∶new leaf C	1.5	1.5	1.5	1.5
叶子碳氮比C∶N of leaves (kg C·kg^-1 N)	12.55	13.5	27.22	14.01
叶子凋落物中易挥发物质比例Leaf litter labile proportion (DIM)	0.550	0.539	0.555	0.608
叶子凋落物中纤维素比例Leaf litter cellulose proportion (DIM)	0.378	0.391	0.374	0.333
叶子凋落物中木质素比例Leaf litter lignin proportion (DIM)	0.072	0.070	0.071	0.059
冠层平均叶面积比Canopy average specific leaf area (m²·kg^-1C)	20.803	16.714	19.695	14.214
最大气孔导度 Maximum stomatal conductance (m·s^-1)	0.006	0.006	0.006	0.006
表皮导度 Cuticular conductance (m·s^-1)	0.000 06	0.000 06	0.000 06	0.000 06
边界层导度 Boundary layer conductance (m·s^-1)	0.04	0.04	0.04	0.04
导度开始减小时叶片水势Leaf water potential∶start of conductance reduction (MPa)	-0.73	-0.73	-0.73	-0.73
导度减小完成时叶片水势Leaf water potential∶complete conductance reduction (MPa)	-2.7	-2.7	-2.7	-2.7
导度开始减小时蒸汽压亏损Vapor pressure deficit∶start of conductance reduction (Pa)	1 200	1 400	1 800	600
导度减小完成时蒸汽压亏损Vapor pressure deficit∶complete conductance reduction (Pa)	7 200	6 200	4 700	4 800

参数 Parameter	LC	SG	SB	SK
有效土壤深度 Effective soil depth (m)	0.6	0.6	0.6	0.6
无石土壤中砂粒体积百分比Sand percentage by volume in rock-free soil (%)	77	77	74	85
无石土壤中粉粒体积百分比Silt percentage by volume in rock-free soil (%)	10	11	16	6
无石土壤中粘粒体积百分比Clay percentage by volume in rock-free soil (%)	13	12	10	9
海拔 Site elevation (m)	1 245	1 173	1 397	982
短波反射率 Site shortwave albedo (DIM)	0.2	0.2	0.2	0.2
新生细根碳∶新叶碳 New fine root C∶new leaf C	1.5	1.5	1.5	1.5
叶子碳氮比C∶N of leaves (kg C·kg^-1 N)	12.55	13.5	27.22	14.01
叶子凋落物中易挥发物质比例Leaf litter labile proportion (DIM)	0.550	0.539	0.555	0.608
叶子凋落物中纤维素比例Leaf litter cellulose proportion (DIM)	0.378	0.391	0.374	0.333
叶子凋落物中木质素比例Leaf litter lignin proportion (DIM)	0.072	0.070	0.071	0.059
冠层平均叶面积比Canopy average specific leaf area (m²·kg^-1C)	20.803	16.714	19.695	14.214
最大气孔导度 Maximum stomatal conductance (m·s^-1)	0.006	0.006	0.006	0.006
表皮导度 Cuticular conductance (m·s^-1)	0.000 06	0.000 06	0.000 06	0.000 06
边界层导度 Boundary layer conductance (m·s^-1)	0.04	0.04	0.04	0.04
导度开始减小时叶片水势Leaf water potential∶start of conductance reduction (MPa)	-0.73	-0.73	-0.73	-0.73
导度减小完成时叶片水势Leaf water potential∶complete conductance reduction (MPa)	-2.7	-2.7	-2.7	-2.7
导度开始减小时蒸汽压亏损Vapor pressure deficit∶start of conductance reduction (Pa)	1 200	1 400	1 800	600
导度减小完成时蒸汽压亏损Vapor pressure deficit∶complete conductance reduction (Pa)	7 200	6 200	4 700	4 800