Simulation and Sensitivity for Biomass ofFour Main Forest Types in Zhejiang Using TRIPLEX Model
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摘要: TRIPLEX模型是一种新兴混合模型，提供了一种整合目前模型并能有效解决所面临问题的观念和方法。本文以浙江省21 个县4 种主要林型（常绿阔叶林、针阔混交林、马尾松林和杉木林）、林龄5 ~ 50 a的147 个样地森林生长和产量调查实测作为模型模拟和检验的数据，用TRIPLEX模型模拟和检验了样地的林分密度、树高、胸径、凋落物库、地上和总生物量，结果表明：4 种主要林型的林地生长和产量模拟值和野外实测值的相关系数极高（p < 0.001），两者之间的偏差也较小；各个林型的地上及总生物量的模拟值和实测值相关性均极高（p < 0.001），决定系数r2均以马尾松林的最高（分别为0.95 和0.94），以杉木林的最低；除杉木林外，常绿阔叶林、针阔混交林和马尾松林的地上及总生物量对于温度增长均是负相关，4 种主要林型的地上及总生物量对于相对湿度增长均是正相关，对于降水量变化不相关。TRIPLEX模型最小化了输入参数，对于参数要求低，但未降低预测能力，而且能够在浙江省复杂气候和土壤条件下预测地上和总生物量。Abstract: Inventory data of growth and yield from 147 sample plots aged from 5 to 50 years of four common forest types in 21 counties of Zhejiangprovince was used to test the process-based model of TRIPLEX. The four main forest types are evergreen broad-leaved forest (EF), coniferous andbroad-leaved mixed forest (MF), Pinus massoniana forest (PF) and Cunninghamia lanceolatae forest (CF). Simulation values by TRIPLEX modelwith stand density, diameter at breast height(DBH), height(H), litterfall pool, aboveground and total biomass were compared with measured ones. Theresults showed that significant correlation (p<0.001) between the simulated and measured values of forest stands for four main forest types inZhejiang province was found and the coefficients of determination (r2) was 0.92 for density, 0.77 for DBH, 0.80 for H, 0.53 for litterfall pool, 0.92 for aboveground biomass and 0.91 for total biomass, with small errors of -0.32 for DBH, -0.59 for H, 1.15 for litterfall pool, -0.51 for the aboveground biomass and -2.64 for the total biomass, except for stand density (53.56), and low biases (2.7% for density, -3.3% for DBH, -8.6% for H, -1.0% for the aboveground biomass and -1.5% for the total biomass) except for litterfall pool (16.8%). The simulated and measured values had significant correlation (p<0.001) for the aboveground and total biomass for each forest type, PF had highest coefficient of determination within 0.95 and 0.94 respectively, and CF had lowest coefficient of determination. For the four main forest types, Aboveground and total biomass of tested forest types had negative correlation with temperature except CF, and positive relation with relative humidity, and no correlation with precipitation. Simulated values of biomass and NPP of four forest types in Zhejiang by TRIPLEX model were much closer to the surveyed ones than those by the CEVSA and CASA models. These results suggest that TRIPLEX model did not decrease the predictive accuracy at the low demand for parameters, which can simulate and predict biomass with complex conditions in climate and soil.
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Keywords:
- TRIPLEX /
- biomass /
- NPP /
- model test /
- Pinus massoniana forest /
- Cunninghamia lanceolatae /
- broad-leaved forest
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[1] Dixon R K, Brown S, Houghton R A, et al. Carbon pools and fluxes of global forest ecosystems [J]. Science, 1994（263）：185－190.
[2] IPCC. The Science of Climate Change [M]. Cambridge: Cambridge University Press, 2000.
[3] Smith R. Global Forest Resources Assessment 2000. Main report [M]. Rome: Food and Agriculture Organization (FAO), 2002
[4] Houghton R A. Terrestrial source and sinks of carbon inferred from terrestrial data [J]. Tellus B, 1996（48）：420－432.
[5] Friedlingstein P, Dufresne J L, Cox P M, et al. How positive is the feedback between climate change and the carbon cycle [J]. Tellus, 2003（55B）：692－700.
[6] Landsberg J J. Physiological Ecology of Forest Production [M]. Sydney: Academic Press, 1986.
[7] Kimmins J P. Modeling the sustainability of forest production and yield for a changing and uncertain future[J]. Forestry Chronicle, 1990, 66（3）：271－280.
[8] Mohren G M L, Burkhart H E, Jansen J J. Contrasts between biologically-based process models and management-oriented growth and yield models [J]. For Ecol Manage, 1994（69）：1－5.
[9] Landsberg J J, Coops N C. Modeling forest productivity across large areas and long periods [J]. Nat Resoure Model, 1999（12）：383－411.
[10] M?kel? A, Landsberg J, E k A, et al. Process-based models for forest ecosystem management: current state of the art and challenges for practical implementation [J]. Tree Physiol, 2000（20）：289－298.
[11] Peng C H. Understanding the role of forest simulation models in sustainable forest management [J]. Environ Impact Assess Rev, 2000（20）：481－501.
[12] Peng C H. Growth and yield models for uneven-aged stands: past, present and future [J]. For Ecol Manage, 2000（132）：259－279.
[13] Shugart H H, Smith T M, Post W M. The application of individual-based simulation models for assessing the effects of global change [J]. Annu Rev Ecol Syst, 1992（23）：15－38.
[14] Landsberg J J, Gower S T. Applications of Physiological Ecology to Forest Management[M]. San Diego: Academic Press, 1997.
[15] Korzukhin M D, Ter-Mikaelian M T, Wagner R G. Process versus empirical models: which approach for forest ecosystem management [J]. Can J For Res, 1996（26）：879－887.
[16] Sands P J, Battaglia M, Mummery D. Application of process-based models to forest management: experience with PROMOD, a simple plantation productivity model [J]. Tree Physiol, 2000（20）：383－392.
[17] Johnsen K, Samuelson L, Teskey R, et al. Process models as tools in forestry research and management [J]. For Sci, 2001（47）：2－8.
[18] Peng C H, Jiang H, Apps M J, et al. Effects on harvesting regimes on carbon and nitrogen dynamics of boreal forests in central Canada: a process model simulation [J]. Ecol Model, 2002（155）：177－189.
[19] Zhang J, Chu Z Y, Ge Y, et al. TRIPLEX Model Testing and Application for Predicting Forest Growth and Biomass Production in the Subtropical Forest Zone of China’s Zhejiang Province [J]. Ecol Model, 2008（219）：264－275.
[20] Zhao M F, Xiang W H, Deng X W, et al. Application of TRIPLEX model for predicting Cunninghamia lanceolata and Pinus massoniana forest stand production in Hunan Province, southern China[J]. Ecol Model, 2013（250）：58－71.
[21] 浙江省森林资源状况及其生态功能价值（2013）[EB/OL]. http://zjrb.zjol.com.cn/html/2015－01/21/content_2845042.htm [22] 张骏，袁位高，葛滢，等. 浙江省生态公益林碳储量和固碳现状及潜力[J]. 生态学报，2010，30（14）：3 839－3 848. [23] Landsberg J J, Waring R H. A generalized model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning [J]. Ecol Model, 1997（95）：209－228.
[24] Peng C H, Liu J, Dang Q, et al. TRIPLEX: A generic hybrid model for predicting forest growth and carbon and nitrogen dynamics [J]. Ecol Model, 2002（153）：109－130.
[25] Bossel, H. TREEDYN3 Forest Simulation Model [J]. Ecol Model, 1996，90（3）：187－227.
[26] Parton W J, Scurlock J M, Ojima D S, et al. Observations and modelling of biomass and soil organic matter dynamics for the grassland biome worldwide [J]. Glob Biogeochem Cycle, 1993，7（4）：785－809.
[27] Zhang J, Ge Y, Chang J, et al. Carbon storage by ecological service forests in Zhejiang Province, subtropical China [J]. For Ecol Manage, 2007（245）：64－75.
[28] Zhou X L, Peng C H, Dang Q L. Assessing the generality and accuracy of the TRIPLEX model using in situ data of boreal forests in central Canada [J]. Environ Model Software, 2004（19）：35－46.
[29] Zhou X L, Peng C H, Dang Q L, et al. Predicting forest growth and yield in northeastern Ontario using the process-based carbon dynamic model of TRIPLEX 1.0 [J]. Can J For Res, 2005（35）：2 268－2 280.
[30] Zhou X L, Peng C H, Dang Q L, et al. A simulation of temporal and spatial variations in carbon at landscape level: A case study for Lake Abitibi Model Forest in Ontario, Canada [J]. Mitigat Adapt Strateg Glob Change, 2007, 12（4）：525－543.
[31] Kimball J S, Thornton P E, White M A, et al. Simulating forest productivity and surface–atmosphere carbon exchange in the BOREAS study region [J]. Tree Physiol, 1997（17）：589－599.
[32] Ryan M G, Lavigne M B, Gower S T. Annual carbon cost of autotrophic respiration in boreal forest ecosystems in relation to species and climate [J]. J Geophys Res, 1997, 102: 28871－28883.
[33] Metherell A K, Harding L A, Cole C V, et al. CENTURY Soil Organic Matter Model Environment Technical Documentation Agroecosystem Version 4.0[J]. GPSR Technical Report No. 4, United States Department of Agriculture, Agricultural Research Service, Great Plains Systems Research Unit. http://www.nrel.colostate.edu/ projects/century/ [cited 10 May 2004], 1993.
[34] 方精云，刘国华，徐嵩龄. 我国森林植被的生物量和净生产量[J]. 生态学报，1996，16（5）：497－508. [35] Zhao M, Zhou G S. Estimation of biomass and net primary productivity of major planted forests in China based on forest inventory data[J]. For Ecol Manage, 2005（207）：295－313.
[36] Fang J Y, Piao S L, Field C B, et al. Increasing net primary production in China from 1982 to 1999 [J]. Front Ecol Environ, 2003（1）：293－297.
[37] Cao M K, Woodward F I. Net primary and ecosystem production and carbon stocks of terrestrial ecosystem and their response to climatic change [J]. Glob Change Biol, 1998（4）：185－198.
[38] Cao M K, Tao B, Li K R. Interannual variation in terrestrial ecosystem carbon Fluxes in China from 1981 to 1998 [J]. 植物学报（英文版）,2003，45（5）：552－560. [39] Chapin F S, Callaghan T V, Bergeron Y. Global change and the boreal forest: thresholds, shifting states or gradual change[J]. Ambio, 2004, 33（6）：361－365.
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