CLM5在中国西南地区植被生长模拟的适用性分析

  • 王黎欢 ,
  • 吕雅琼 ,
  • 王梓奕
展开
  • 1. 中国科学院、 水利部成都山地灾害与环境研究所,四川 成都 610299
    2. 中国科学院大学,北京 100049
    3. 成都信息工程大学 大气科学学院/高原大气与环境四川省重点实验室,四川 成都 610225

王黎欢(1999 -), 女, 四川内江人, 硕士研究生, 主要从事陆气相互作用与数值模拟研究. E-mail:

收稿日期: 2023-10-17

  修回日期: 2024-03-27

  网络出版日期: 2024-03-27

基金资助

中国科学院、 水利部成都山地灾害与环境研究所科研项目(IMHE-ZDRW-06); 国家自然科学基金项目(41975153); 中国科学院先导专项(XDA23060601)

Vegetation Growth Simulation of the Community Land Model in the Southwest China

  • Lihuan WANG ,
  • Yaqiong Lü ,
  • Ziyi WANG
Expand
  • 1. Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,Sichuan,China
    2. University of Chinese Academy of Sciences,Beijing 100049,China
    3. College of Atmospheric Sciences,Chengdu University of Information Technology / Sichuan Key Laboratory of Plateau Atmosphere and Environment,Chengdu 610225,Sichuan,China

Received date: 2023-10-17

  Revised date: 2024-03-27

  Online published: 2024-03-27

摘要

在全球变暖的背景下, 西南地区整体上气温升高降水减少, 作为中国重要的碳汇地区, 西南地区的植被动态监测与模拟对深入了解其碳循环机制和促进经济可持续发展具有重要意义。本研究利用陆面过程模式(Community Land Model version5, CLM5), 模拟和分析西南地区2000 -2016年叶面积指数LAI(Leaf Area Index, LAI)和总初级生产力GPP(Gross Primary Productivity, GPP)的时空变化特征, 并与多套遥感数据进行对比, 评估CLM5在西南地区对LAI和GPP模拟的适用性。研究结果表明, CLM5能较好地模拟西南地区LAI和GPP的季节变化规律, 但模拟存在对LAI生长季的高估, 以及对GPP全年低估, 对温带落叶阔叶灌木的LAI、 高寒C3草甸的LAI、 GPP和C3草甸的GPP模拟效果较好。CLM5能够较好地刻画西南地区LAI和GPP空间分布格局, 表现为由东南向西北递减, 但整体上CLM5对西南地区LAI模拟偏高, 特别是对贵州喀斯特地貌地区LAI的模拟偏高。与模型对LAI模拟高估相反, CLM5对西南地区GPP的模拟整体偏低, 特别是云南地区。此外, CLM5对西南地区LAI和GPP的变化趋势模拟效果较差, 特别是在云南大部分地区, 遥感数据主要呈现上升趋势, 而CLM5模拟呈现下降趋势。整体上, CLM5能模拟出西南地区LAI和GPP的季节变化规律和空间分布, 但对云南和贵州部分地区的变化趋势模拟较差, 仍需要针对四川盆地农田、 云南森林、 和贵州喀斯特地区植被发展更深入的参数化方案来提升模拟效果。

本文引用格式

王黎欢 , 吕雅琼 , 王梓奕 . CLM5在中国西南地区植被生长模拟的适用性分析[J]. 高原气象, 2024 , 43(6) : 1586 -1599 . DOI: 10.7522/j.issn.1000-0534.2024.00044

Abstract

Under the background of global warming, the temperature has increased frequently in the southwest, and the ecosystem in the southwest is vulnerable and sensitive to climate change in the past few decades.The southwest region is an important carbon sink area in China.Monitoring and simulation of vegetation variations is of great significance for an in-depth understanding of the carbon cycle mechanism and promoting sustainable economic development.Leaf Area Index (LAI) and Gross Primary Productivity (GPP), as indicators of vegetation health and ecosystem stability, can be used to quantify vegetation studies and characterize dynamic changes of vegetation.Vegetation dynamic Model is one of the important means to study vegetation growth and change.Community Land Model (CLM) is one of the earliest land model with the function of vegetation dynamic simulation, the most developed and widely used land model in the world.Model evaluation is an indispensable part of model development, which provides a basis for model development and improvement.This study uses the Community Land Model version5 (CLM5) to simulate and analyze the spatial and temporal variations of the leaf area index (LAI) and total primary productivity (GPP) in the southwest region across 2000 -2016, and compare it with multiple sets of remote sensing data to evaluate LAI and GPP simulations of CLM5 in the southwest.The results showed that CLM5 could well simulate the seasonal variation of LAI and GPP in southwest China, but overestimated LAI in growing season.The CLM5 can reasonably simulate LAI of temperate deciduous broadleaf shrubs, LAI, GPP of alpine C3 meadow and GPP of C3 meadow.CLM5 could capture the spatial distribution pattern of LAI and GPP in the southwest, which is decreasing from southeast to northwest, but CLM5 overestimates LAI in the whole southwest region, especially in the karst landform area of Guizhou.Contrary to the overestimation of LAI simulation, CLM5's overall simulation of GPP in Southwest China is low, especially in Yunnan province.In addition, CLM5 has a poor simulation of LAI and GPP trend in the southwest.Especially in most parts of Yunnan, remote sensing data mainly shows an upward trend, while CLM5 simulation shows a downward trend.In a word, CLM5 can simulate the seasonal change and spatial distribution of LAI and GPP in the southwest, but the simulation of the trend in some areas of Yunnan and Guizhou is poor, and more in-depth parametric schemes for the development of farmland in Sichuan Basin, Yunnan forests, and karst vegetation in Guizhou are needed to improve the simulation.

参考文献

null
Ali A A Fisher J B Rogers A, et al, 2016.A global scale mechanistic model of photosynthetic capacity (LUNA V1.0)[J].Geoscientific Model Development9(2): 587-606.DOI: 10.5194/gmd-9-587-2016 .
null
Beer C Reichstein M Tomelleri E, et al, 2010.Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate[J].American Association for the Advancement of Science329(5993): 834-8.DOI: 10.1126/SCIENCE.1184984 .
null
Bi W J He W Zhou Y Z, et al, 2022.A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020[J].Scientific Data9(1): 2052-4463.DOI: 10.1038/s41597-022-01309-2 .
null
Cai L B Chen X Huang R C, et al, 2022.Runoff change induced by vegetation recovery and climate change over carbonate and non-carbonate areas in the karst region of South-west China[J].Journal of Hydrology604(127231): 0022-1694.DOI: 10.1016/j.jhydrol.2021.127231 .
null
Cao M, Woodward, et al, 1998.Dynamic responses of terrestrial ecosystem carbon cycling to global climate change[J].Nature393(6682): 249-249.DOI: 10.1038/30460 .
null
Deng M S Meng X H Lu Y Q, et al, 2022.The response of vegetation to regional climate change on the Tibetan Plateau based on remote sensing products and the dynamic global vegetation model[J].Remote Sensing14(27): 7540-7552.DOI: 10.1029/2019JD030481 .
null
Dickinson R E Henderson-Sellers A Kennedy P J, et al, 1986.Biosphere-atmosphere transfer scheme (BATS) for the NCAR Community Climate Model[J].University Corporation for Atmospheric Research213(26): 2052-4463.DOI: 10.5065/D6668B58 .
null
Duan Q Schaake J, V.Andréassian, et al, 2006.Model Parameter Estimation Experiment (MOPEX): an overview of science strategy and major results from the second and third workshops[J].Journal of Hydrology320(1-2): 3-17.DOI: 10.1016/j.jhydrol.2005.07.031 .
null
Foley J A Levis S Costa M H, et al, 2000.Incorporating dynamic vegetation cover within global climate models[J].Ecological Applications10(6): 1620-1632.DOI: 10.2307/2641227 .
null
Foley J A Prentice I C Ramankutty N, et al, 1996.An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics[J].Global Biogeochemical Cycles10(4): 603-628.DOI: 10.1029/96GB02692 .
null
Hantson S Arneth A Harrison S P, et al, 2016.The status and challenge of global fire modelling[J].Biogeosciences Discussions13(11): 3359-3375.DOI: 10.5194/bg-13-3359-2016 .
null
Hu Q W Li T T Deng X, et al, 2022.Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models[J].Science of the Total Environment810(18): 152231.DOI: 10.1016/j.scitotenv.2021.152231 .
null
Jiang C Y Ryu Y Fang H L, et al, 2017.Inconsistencies of interannual variability and trends in long-term satellite leaf area index products[J].Global Change Biology23(10): 4133-4146.DOI: 10.1111/gcb.13787 .
null
Ke Y Leung L R Huang M, et al, 2012.Development of high resolution land surface parameters for the Community Land Model[J].Geoscientific Model Development5(6): 1341-1362.DOI: 10.5194/gmd-5-1341-2012 .
null
Lawrence D M Fisher R A Koven C D, et al, 2010.The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty[J].Journal of Advances in Modeling Earth Systems, 2019, 11(12).DOI: 10.1029/2018MS001583 .
null
Lawrence P J ChaseT N2010.Investigating the climate impacts of global land cover change in the community climate system model[J].International Journal of Climatology30(13): 2066-2087.DOI: 10.1002/joc.2061 .
null
Lawrence P J Lawrence D M Hurtt G C2018.Attributing the carbon cycle impacts of CMIP5 historical and future land use and land cover change in the community earth system model (CESM1)[J].Journal of Geophysical Research: Biogeosciences123(5): 1732-1755 DOI: 10.1029/2017JG004348 .
null
Lawrence P J Chase T N2010.Investigating the climate impacts of global land cover change in the community climate system model[J].International Journal of Climatology30(13): 2066-2087.DOI: 10.1002/joc.2061
null
Levis S2010.Modeling vegetation and land use in models of the earth system[J].Wiley Interdisciplinary Reviews Climate Change1(6): 840-856.DOI: 10.1002/wcc.83 .
null
Levis S Bonan G B Bonfils C2004.Soil feedback drives the mid-Holocene North African monsoon northward in fully coupled CCSM2 simulations with a dynamic vegetation model[J].Climate Dynamics23(7/8): 791-802.DOI: 10.1007/s00382-004-0477-y .
null
Li Z X He Y Q Wang P Y, et al, 2012.Changes of daily climate extremes in southwestern China during 1961-2008[J].Global & Planetary Change, 80-81(0921-8181): 255-272.DOI: 10. 1016/j.gloplacha.2011.06.008 .
null
Liang M L Xie Z H2008.Improving the vegetation dynamic simulation in a land surface model by using a statistical-dynamic canopy interception scheme[J].Advances in Atmospheric Sciences25(4): 610–618.DOI: 10.1007/s00376-008-0610-7 .
null
Like N Jun X Chesheng Z, et al, 2016.Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis[J].Journal of Arid Land8(4): 506-520.DOI: 10.1007/s40333-016-0126-4 .
null
Liu Y Liu R Chen J M2012.Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data[J].Journal of Geophysical Research: Biogeosciences117(G4): 2169-8953.DOI: 10.1029/2012jg002084 .
null
Lu X Du Z Huang Y, et al, 2020.Full implementation of matrix approach to biogeochemistry module of Community Land Model version 5 (CLM5)[J].Journal of Advances in Modeling Earth Systems12(11).DOI: 10.1029/2020MS002105 .
null
Madani N Parazoo N C2020.Global monthly GPP from an improved light use efficiency model, 1982-2016[M].ORNL Distributed Active Archive Center.
null
Ninomiya H Kato T Vegh L, et al, 2023.Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)[J].Geoscientific Model Development16(14): 4155-4170.DOI: 10.5194/gmd-16-4155-2023 .
null
Piao S L Fang J Y, et al, 2009.The carbon balance of terrestrial ecosystems in China[J].Nature458(7241): 1009-13.DOI: 10. 1038/nature07944 .
null
Sato, H, Itoh, A, Kohyama, T, 2007.SEIB-DGVM: A new dynamic global vegetation model using a spatially explicit individual-based approach[J].Ecological Modelling200(3-4): 279-307.DOI: 10.1016/j.ecolmodel.2006.09.006 .
null
Sellers P J1997.Modeling the exchanges of energy, water, and carbon between continents and the atmosphere[J].Science275(5299): 502-509.DOI: doi: 10.1126/science.275.5299.502 .
null
Shao P Zeng X D2011.The impact of interannual climate variability on the mean global vegetation distribution[J].Acta Ecologica Sinica31(6): 1494-1505.DOI: 10.3724/SP.J.1077.2011.00311 .
null
Song X Wang D Y Li F, et al, 2021.Evaluating the performance of CMIP6 Earth system models in simulating global vegetation structure and distribution[J].Advances in Climate Change Research12(4): 584-595.DOI: 10.1016/j.accre.2021.06.008 .
null
Sun G D2009.Simulation of potential vegetation distribution and estimation of carbon flux in China from 1981 to 1998 with LPJ dynamic global vegetation model[J].Climatic and Environmental Research14(4): 341-351.DOI: 10.1016/S1003-6326(09)60084-4 .
null
Sun X F Yue T X Fan Z M, et al, 2013.Spatiotemporal trends in global vegetation carbon storage[J].Resources Science35(4): 782-91.DOI: 1007-7588(2013)35: 4<782: QQZBTC>2.0.TX; 2-X .
null
Thonicke K Venevsky S Sitch S, et al, 2001.The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model[J].Global Ecology and Biogeography10(6): 661-677.DOI: 10.1046/j.1466-822X.2001.00175.x .
null
Viovy N2018.CRUNCEP version 7-atmospheric forcing data for the community land model[M].Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory; Boulder, CO.
null
Wang H Yao F Zhu H S, et al, 2020.Spatiotemporal variation of vegetation coverage and its response to climate factors and human activities in arid and semi-arid areas: case study of the Otindag Sandy Land in China[J].Sustainability12(12): 5214.DOI: 10.3390/su12125214 .
null
Xie X Y Li A N Jin H A, et al, 2019.Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models[J].Science of The Total Environment690(10): 1120-1130.DOI: 10.1016/j.scitotenv.2019.06.516 .
null
Xiong Q Sun Z Y Cui W, et al, 2022.A study on sensitivities of tropical forest GPP responding to the characteristics of drought- case study in Xishuangbanna, China[J].Water14(2): 157.DOI: 10.3390/w14020157 .
null
Xu K Wang X P Jiang C, et al, 2020.Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity[J].Forest Ecosystems7(3): 12.DOI: 10.1186/s40663-020-00239-y .
null
Xue B L Guo Q H Hu T Y, et al, 2017.Evaluation of modeled global vegetation carbon dynamics: analysis based on global carbon flux and above-ground biomass data[J].Ecological Modelling355(38): 84-96.DOI: 10.1016/j.ecolmodel.2017.04.012 .
null
Yu M Li Q Hayes M J, et al, 2014.Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951-2010?[J].International Journal of Climatology34(3): 545-558.DOI: 10.1002/joc.3701 .
null
Yuan Q Z Wu S H Zhao D S, et al, 2014.Modeling net primary productivity of the terrestrial ecosystem in China from 1961 to 2005[J].Journal of Geographical Sciences24(1): 3-17.DOI: 10. 1007/s11442-014-1069-3 .
null
Zhu Z C Bi J Pan Y Z, et al, 2013.Global data sets of vegetation Leaf Area Index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the Period 1981 to 2011[J].Remote Sensing5(2): 927-948.DOI: 10.3390/rs5020927 .
null
鲍艳, 王玉琦, 南素兰, 等, 2023.动态植被模型对青藏高原植被的模拟检验[J].高原气象42(2): 333-343, DOI: 10.7522/j.issn.1000-0534.2021.00062.Bao Y
null
Wang Y Q Nan S L, et al, 2023.Evaluation of vegetation characteristics over Qinghai- Xizang Plateau simulated by a vegetation dynamic model[J].Plateau Meteorology42(2): 333-343.DOI: 10.7522/j.issn.1000-0534.2021.00062 .
null
车明亮, 陈报章, 王瑛, 等, 2014.全球植被动力学模型研究综述[J].应用生态学报25(1): 263-271.DOI: 10.13287/j.1001-9332.2014.01.036.Che M L
null
Chen B Z Wang Y, et al, 2014.Review of dynamic global vegetation models (DGVMs)[J].Chinese Journal of Applied Ecology25(1): 263-271.DOI: 10.13287/j.1001-9332.2014.01.036 .
null
陈锋, 谢正辉, 2009.基于中国植被数据的陆面覆盖及其对陆面过程模拟的影响[J].大气科学33(4): 681-697.DOI: 10.3878/j.issn.1006-9895.2009.04.03.Chen F
null
Xue Z H2009.A land cover dataset based on chinese vegetation data and its impact on land surface simulations[J].Chinese Journal of Atmospheric Sciences33(4): 681-697.DOI: 10.3878/j.issn.1006-9895.2009.04.03 .
null
付春伟, 胡泽勇, 卢珊, 等, 2022.基于CLM4.5模式的季节冻土区土壤参数化方案的模拟研究[J].高原气象41(1): 93-106.DOI: 10.7522/j.issn.1000-0534.2021.00050.Fu C W
null
Hu Z Y Lu S, et al, 2022.A simulation study on soil parameterization scheme of seasonally frozen ground regions based on CLM4.5[J].Plateau Meteorology41(1): 93-106.DOI: 10.7522/j.issn.1000-0534.2021.00050 .
null
洪辛茜, 黄勇, 孙涛, 2021.我国西南喀斯特地区2001—2018年植被净初级生产力时空演变[J].生态学报41(24): 9836-9846.DOI: 10.5846/stxb202009122381.Hong X Q
null
Huang Y Sun T2021.Spatiotemporal evolution of vegetation net primary productivity in the karst region of southwest China from 2001 to 2018[J].Acta Ecologica Sinica, 2021, 41(24): 9836-9846.DOI: 10.5846/stxb202009122381 .
null
侯吉宇, 周艳莲, 刘洋, 2020.不同叶面积指数遥感数据模拟中国总初级生产力的时空差异[J].遥感技术与应用35(5): 1015-1027.DOI: 10.11873/j.issn.1004-0323.2020.5.1015.Hou J Y
null
Zhou Y L Liu Y2020.Spatial and temporal differences of GPP simulated by different satellite-derived LAI in China[J].Remote Sensing Technology and Application35(5): 1015-1027.DOI: 10.11873/j.issn.1004-0323.2020.5.1015 .
null
李美丽, 尹礼昌, 张园, 等, 2021.基于MODIS-EVI的西南地区植被覆盖时空变化及驱动因素研究[J].生态学报41(3): 1138-1147..DOI: 10.5846/stxb201907101451.Li M L
null
Yin L C Zhang Y, et al, 2021.Spatio-temporal dynamics of fractional vegetation coverage based on MODIS-EVI and its driving factors in Southwest China[J].41(3): 1138-1147.DOI: 10.5846/stxb201907101451 .
null
李睿, 张黎, 景元书, 等, 2019.基于FLUXNET的CLM模型总初级生产力模拟评价与误差分析[J].生态学杂志38(9): 2883-2895.DOI: 10.13292/j.1000-4890.201909.020.Li R
null
Zhang L Jing Y S, et al, 2019.Evaluation and error analysis of gross primary productivity using land surface model CLM over FLUXNET[J].Chinese Journal of Ecology38(9): 2883-2895.DOI: 10.13292/j.1000-4890.201909.020 .
null
刘佳凤, 2023.青藏高原东缘森林通用陆面模式植被生长参数优化[D].成都: 成都山地灾害与环境研究所, 1-115.
null
Liu J F2023.Optimization of vegetation growth parameters in the Community Land Model for the forest on the eastern edge of the Tibetan Plateau[D].Chengdu: Institute of Mountain Hazards and Environment, 1-115.
null
刘远, 周买春, 2017.遥感反演植被叶面积指数的不确定性来源综述[J].江苏农业科学45(12): 12-19.DOI: 10.15889/j.issn.1002-1302.2017.12.003.Liu Y
null
Zhou M C2017.Uncertain sources of remote sensing inversion of vegetation leaf area index: an overview[J].Jiangsu Agricultural Sciences45(12): 12-19.DOI: 10.15889/j.issn.1002-1302.2017.12.003 .
null
苏秀程, 王磊, 李奇临, 等, 2014.近50 a中国西南地区地表干湿状况研究[J].自然资源学报29(1): 104-116.DOI: 10.11849/zrzyxb.2014.01.010.Su X C
null
Wang L Li Q L, et al, 2014.Study of surface dry and wet conditions in southwest China in recent 50 years[J].Journal of Natural Resoueces29(1): 104-116.DOI: 10.11849/zrzyxb.2014.01.010 .
null
田晓瑞, 赵凤君, 舒立福, 等, 2010.西南林区卫星监测热点及森林火险天气指数分析[J].林业科学研究23(4): 523-529.
null
Tian X R Zhao F J Shu L F, et al, 2010.Hotspots from satellite monitoring and forest fire weather index analysis for southwest China[J].Forest Research23(4): 523-529.
null
王旭峰, 马明国, 姚辉, 2009.动态全球植被模型的研究进展[J].遥感技术与应用24(2): 246-251.DOI: 10.11873/j.issn.1004-0323.2009.2.246.Wang X F
null
Ma M G Yao H2009.Advance in dynamic global vegetation models[J].Remote Sensing Technology and Application24(2): 246-251.DOI: 10.11873/j.issn.1004-0323.2009.2.246 .
null
王媛媛, 谢正辉, 贾炳浩, 等, 2015.基于陆面过程模式CLM4的中国区域植被总初级生产力模拟与评估[J].气候与环境研究20(1): 97-110.DOI: 10.3878/j.issn.1006-9585.2014.13208.Wang Y Y
null
Xie Z H Jia B H, et al, 2015.Simulation and evaluation of gross primary productivity in China by using land surface model CLM4[J].Climatic and Environmental Research20(1): 97-110.DOI: 10.3878/j.issn.1006-9585.2014.13208 .
null
杨春艳, 严小冬, 夏阳, 等, 2021.近56 a西南区域降水分布及持续性干旱的研究[J].中低纬山地气象45(2): 15-22.DOI: 10.3969/j.issn.1003-6598.2021.02.003.Yang C Y
null
Yan X D Xia Y, et al, 2021.Study on precipitation distribution and persistent drought in southwest China in recent 56 years[J].Mid-low Latitude Mountain Meteorology45(2): 15-22.DOI: 10.3969/j.issn.1003-6598.2021.02.003 .
null
杨明鑫, 肖天贵, 李勇, 等, 2022.CMIP6模式对我国西南地区夏季气候变化的模拟和预估[J].高原气象41(6): 15.DOI: 10.7522/j.issn.1000-0534.2021.00119.Yang M X
null
Xiao T G Li Y, et al, 2022.Evaluation and projection of climate change in southwest China using CMIP6 model [J].Plateau Meteorology41(6): 15.DOI: 10.7522/j.issn.1000-0534.2021.00119 .
null
张雪梅, 王克林, 岳跃民, 等, 2017.生态工程背景下西南喀斯特植被变化主导因素及其空间非平稳性 [J].生态学报37(12): 4008-4018.DOI: 10.5846/stxb201611192354.Zhang X M
null
Wang K L Yue Y M, et al, 2017.Factors impacting on vegetation dynamics and spatial non-stationary relationships in karst regions of southwest China[J].Acta Ecologica Sinica37(12): 4008-4018.DOI: 10.5846/stxb201611192354 .
null
甄英, 李永飞, 何静, 2019.川西北高原甘孜州地区降水变化特征及旱涝研究[J].水土保持研究26(6): 191-197.
null
Zhen Y Li Y F He J2019.Research on Characteristics of Precipitation change and drought and flood in ganzi region of northwest Sichuan Plateau[J].Research of Soil and Water Conservation26(6): 191-197.
null
郑朝菊, 曾源, 赵玉金, 等, 2017.近15 年中国西南地区植被覆盖度动态变化[J].国土资源遥感29(3): 128-136.DOI: 10.6046/gtzyyg.2017.03.19.Zheng C J
null
Zeng Y Zhao Y J, et al, 2017.Monitoring and dynamic analysis of fractional vegetation cover in southwestern China over the past 15 years based on MODIS data[J].Remote Sensing for Land & Resources29(3): 128-136.DOI: 10.6046/gtzyyg.2017.03.19 .
文章导航

/