Based on the widely used Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ) for climate change study, and according to the features of natural environment in China, the operation mechanism of the model was adjusted, and the parameters were modified. With the modified LPJ model and taking 1961-1990 as baseline period, the responses of natural vegetation net primary productivity (NPP) in China to climate change in 1991-2080 were simulated under the Special Report on Emissions Scenarios (SRES) B2 scenario. In 1961-1990, the total NPP of natural vegetation in China was about 3.06 Pg C a(-1); in 1961-2080, the total NPP showed a fluctuant decreasing trend, with an accelerated decreasing rate. Under the condition of slight precipitation change, the increase of mean air temperature would have definite adverse impact on the NPP. Spatially, the NPP decreased from southeast coast to northwest inland, and this pattern would have less variation under climate change. In eastern China with higher NPP, especially in Northeast China, east of North China, and Loess Plateau, the NPP would mainly have a decreasing trend; while in western China with lower NPP, especially in the Tibetan Plateau and Tarim Basin, the NPP would be increased. With the intensive climate change, such a variation trend of NPP would be more obvious.

Climate change risk has been a challenge for the development of society. As the ecosystems in the arid/humid transition zone (AHTZ) in northern China are characterized by sensitive and complex response to climatic fluctuations, assessing the climate change risk for ecosystems in the AHTZ is of scientific significance to regional climate governance and risk management. This study utilized the net primary productivity (NPP) as the indice for risk assessment. Based on the climate data of five global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and four Representative Concentration Pathways (RCPs), the NPP was simulated using an improved LPJ (Lund-Potsdam-Jena). A climate change risk assessment method was built to identify the climate change risk levels and their spatio-temporal distributions in the AHTZ at different periods in the future. Results show that NPP loss would gradually extend and aggravate the risk in the AHTZ from mid-term period to long-term period of the 21st century. The risk under the high-emission scenario would be more serious, mainly manifested as a negative anomaly and a downward trend of NPP. In particular, under RCP8.5, 81.85% of the area may face climate change risk, and 54.71% will reach a high risk. During 2071-2099, under RCP8.5, the NPP anomaly in the high-risk area will reach (-96.00±46.95) gC m-2 a-1, and the changing rate of NPP will reach (-3.56±3.40) gC m-2 a-1. The eastern plain of AHTZ and the eastern grasslands of Inner Mongolia are expected to become the main risk concentration areas. The future vegetation growth in these areas may be adversely affected by climate change. Increasing warming and intensifying drying may be important causes for future climate change risks.

气候变化风险是人类社会发展面临的严峻挑战,评估识别对气候波动响应敏感且复杂的干湿过渡区生态系统所面临的气候变化风险是一个重要科学问题,对区域气候治理和风险管理具有科学意义。本文利用参与耦合模式比较计划第五阶段（CMIP5）的多气候模式多情景数据,通过改进和验证Lund-Potsdam-Jena（LPJ）动态全球植被模型,辨识未来不同时段生态系统生产力的气候变化风险等级及其时空分布,明晰气候因子对净初级生产力（NPP）风险的作用特征。结果表明：未来中远期干湿过渡区生态系统生产力面临的气候变化风险面积将可能扩大,风险等级将可能提升,高排放情景下的风险更加严重,主要表现为NPP距平为负,且仍有继续下降的趋势。尤其是典型浓度路径（RCP8.5）情景下,81.85%的地区将可能面临气候变化风险,54.71%将达到高风险。2071—2099年,RCP8.5高风险区的NPP距平将达到(-96.00±46.95) gC m^-2 a^-1,NPP变化速率将达到(-3.56±3.40) gC m^-2 a^-1。干湿过渡区东部平原和内蒙古东部草原区预估将可能成为风险主要集中区域,这些地区未来的植被生长将可能受到气候变化的不利影响,增温加剧和干旱程度加重可能是未来气候变化风险的重要驱动因素。

<p>The Dynamic Land Ecosystem Model (DLEM) was developed to meet critical needs for understanding and predicting the large-scale patterns and processes of terrestrial ecosystems and continental margins, and complex interactions among climate, ecosystem and human in the context of multifactor global change. The DLEM couples major biophysical, biogeochemical, vegetation dynamical and land use processes, and works at multiple scales in time step ranging from daily to yearly and spatial resolution from meters to kilometers, from region to globe. The DLEM is characterized by the following features: 1) multiple factors driven; 2) fully-coupled cycles of carbon, nitrogen and water; 3) concurrently simulation of major greenhouse gases (CO₂, CH₄, N₂O, & H₂O); 4) dynamically tracking changes in land cover/use and vegetation distribution. The model has been validated against site-specific measurements across the globe and applied at various scales. In this paper, we have briefly addressed model structure, parameters, key processes and major input/output variables. As a case study, we presented the simulated global fluxes of net primary productivity, evapotranspiration and methane during 1948-2005 and their spatial patterns in the year 2000. We also identified major gaps in terrestrial ecosystem modeling and field observations, and further discussed some critical future research needs.</p>

人类社会从陆地生态系统获取生产和生活资料的同时也作为一种干扰形式改变着地气之间的动态平衡。这三个既独立又相互耦合的子系统共同组成了一个复杂的陆地系统。如何深入理解这一系统的过程和机制是人类应对气候变化挑战的前提条件。陆地生态系统模型作为一种集成工具,已广泛应用于全球变化研究的各个领域,但从输入数据到模型结构和过程等诸多方面仍存在很大的不确定性。近年来,随着大气和地面生态观测网络的不断完善以及遥感等空间技术的不断强大,使陆地生态系统模型进一步发展和突破成为可能。新一代多因子驱动的陆地生态系统动态模型(Dynamic Land Ecosystem Model, DLEM) 正是在这一背景下应运而生的。本文旨在介绍DLEM的主体框架、输入输出变量、关键过程、主要功能和特点。

Exchanges of energy and matter between terrestrial biosphere and atmosphere and hydrosphere create critical feedbacks to Earth’s climates. To quantify how terrestrial ecosystems respond and feedback to global changes, terrestrial biosphere model (TBM) has been developed and applied in global change ecology during the past decades. In TBMs, myriad of biogeophysical, biogeochemical, hydrological cycles and dynamics processes on different spatial and temporal scales are represented. The TBMs have been applied on assessing and attributing past changes in terrestrial biosphere, and on predicting future changes and their feedbacks to climates. Here, we provide an overview of processes included in TBMs and TBMs applications on carbon and hydrological cycles, as well as their application on exploring human impacts on terrestrial ecosystems. Finally, we outline perspectives for future development and application of TBMs.

陆地生物圈与大气圈和水圈之间能量、水和碳氮等元素的交换和循环对整个地球系统产生了深刻的影响。陆地生物圈模型(TBM)是研究陆地生态系统如何响应和反馈全球变化的重要方法和工具。通过对从生态系统到区域和全球陆地生物圈不同空间尺度的植被动态、生物地球物理和生物地球化学循环过程、水循环和水文过程、自然干扰和人类活动等过程时间动态的模拟, 陆地生物圈模型被广泛地应用于评估和归因过去陆地生物圈的时空变化和预测陆地生物圈对未来全球变化的响应和反馈。该文简要回顾了陆地生物圈模型的发展, 总结了模型对陆地生态系统主要过程的刻画和模型在生态系统生态学的应用, 并对未来陆地生物圈模型的发展和应用进行了展望。

植被生长季长度和生长强度是形态上影响植被生产力变化的重要因子。全球变暖情景下,北半球中高纬度大部分地区植被生长季显著延长并对植被生产力产生正向反馈,而植被生长强度变化情形及对生产力的控制作用并不清晰。中国东北地区属于中纬度温带地区,具有较高的植被覆盖度和丰富的植被类型,探索其植被生长季长度和强度的变化及对生产力的控制作用有利于理解和应对该地区的生态系统变化。以中国东北为研究区,基于1982—2015年长时序遥感植被指数数据（NDVI3g）,利用曲率求导法确定植被生长季开始点（SOS）、结束点（EOS）、生长季长度（LOS）和夏季最大生长季强度（GM）等关键物候参数,然后利用相对重要性（RI）方法定量分析了生长季长度和强度对植被生产力长期变化趋势的相对贡献及时空格局。结果表明：① 研究区整体的植被生产力和生长强度呈现增强趋势,而生长季长度呈现缩短趋势,导致生长强度成为控制生产力变化趋势的主要因素（RI = 70%）;② 在不同植被覆盖区域,生长季长度和生长强度对生产力的影响程度具有显著的空间差异。西部草原区植被生产力受生长强度控制最为显著（RI = 93%）,其次为针叶林（RI = 66%）和阔叶林区（RI = 62%）,农作物区生产力受生长强度影响最小（RI = 56%）。生长季长度对植被生产力的控制在农作物区最为显著（RI = 40%）,在其他区域的影响约为27%~35%。各植被覆盖区生长强度与生产力均为正相关,生长季长度与生产力均为负相关;③ 气候因素（降水、温度）和物候变化均对主要贡献因子生长强度产生影响,其中SOS的变化对生长强度的影响程度和空间范围最为显著,主要表现为SOS推迟促进生长强度增强。本研究基于遥感数据发现1982—2015年间中国东北地区植被生长更加旺盛,但是植被生长活动主要受生长强度的影响,该研究可以为植被生产力变化模拟的参数选择提供新的线索。

Evapotranspiration is a key variable and key process in global water cycle, and it is crucial for understanding how anthropogenic and climate changes have impacts on terrestrial water cycle. In the last decade, global vegetation changes are dramatic, reflected by land use and land cover changes and increase in leaf area index. It remains unclear how these changes influence terrestrial evapotranspiration processes. This study uses a coupled evapotranspiration and gross primary product model (PML-V2) that is run at 500 m and 8-day resolutions across the globe to investigate the impacts of vegetation changes on spatial pattern and dynamics of evapotranspiration in the period of 2003-2017. We found that evapotranspiration across the globe has increased noticeably because of vegetation changes, which is characterized by clear regional and non-regional patterns. Transpiration has strongly increased in the central and northern parts of North America, Europe, eastern China, southern Africa, and eastern and northern Australia. Under different land cover types, shrubs and cropland have been influenced strongly, and their impact is stronger in the post-2012 period than that in the pre-2012 period. The recent total increase from these two land cover types amounts to about 0.41 ×103 km3 a-1, which is about 8 times of natural annual runoff from the Yellow River Basin. The results from this study can help improve the understanding of how vegetation changes caused by recent land use and land cover changes influence terrestrial water cycle and the potential local and regional climate change.

蒸散发是陆面水循环的关键环节和过程,是研究水循环对人类活动和气候变化响应的关键要素。过去十几年,全球下垫面的植被变化剧烈,但如何影响全球陆面蒸散发仍未得到清晰的揭示。本文采用500 m分辨率MODIS数据驱动PML-V2模型,定量解析了2003—2017年植被变化对全球陆面蒸散发的影响。结果显示：在全球尺度上,植被变绿使得全球蒸散发呈现显著的增加趋势,使陆地水循环加快;区域尺度上,植被变化对蒸散发的影响则存在明显的地带性和非地带性特征,如在北美洲中北部、欧洲、中国东部、非洲南部和澳大利亚东北部等地区,蒸散发总量的增加主要是由植被蒸腾增加而引起的。分析不同植被功能类型区的贡献显示,下垫面变化对灌木和耕地影响尤为明显,并在2012年以后呈现增强趋势;这2个植被类型区的全球年总蒸散发累积增加量为0.41×10³ km³ a^-1,约为黄河流域多年平均径流量的8倍。该研究结果有助于进一步加强关于下垫面变化对陆地水循环的影响及其可能带来的局部气候变化的认识。

<p>The ratio of transpiration to evapotranspiration (T/ET) is a key parameter for quantifying water use efficiency of ecosystems and understanding the interaction between ecosystem carbon uptake and water cycling in the context of global change. The estimation of T/ET has been paid increasing attention from the scientific community in recent years globally. In this paper, we used the Priestly-Taylor Jet Propulsion Laboratory Model (PT-JPL) driven by regional remote sensing data and gridded meteorological data, to simulate the T/ET in forest ecosystems along the North-South Transect of Eastern China (NSTEC) during 2001-2010, and to analyze the spatial distribution and temporal variation of T/ET, as well as the factors influencing the variation in T/ET. The results show that: (1) The PT-JPL model is suitable for the simulation of evapotranspiration and its components of forest ecosystems in Eastern China, and has relatively good stability and reliability. (2) Spatial distribution of T/ET in forest ecosystems along NSTEC was heterogeneous, i.e., T/ET was higher in the north and lower in the south, with an averaged value of 0.69; and the inter-annual variation of T/ET showed a significantly increasing trend, with an increment of 0.007/yr (<i>p </i>< 0.01). (3) Seasonal and inter-annual variations of T/ET had different dominant factors. Temperature and EVI can explain around 90% (<i>p </i>< 0.01) of the seasonal variation in T/ET, while the inter-annual variation in T/ET was mainly controlled by EVI (53%, <i>p </i>< 0.05).</p>

植被蒸腾与蒸散的比值（transpiration/evapotranspiration, T/ET）表征了植被蒸腾对生态系统蒸散的贡献率,是准确量化生态系统水分利用效率的关键参数,对研究植被水分运移的生理生态机理以及碳水循环关系具有重要意义。基于站点数据验证PT-JPL模型（Priestly-Taylor Jet Propulsion Laboratory Model）模拟精度,集成遥感数据和气象栅格数据模拟中国东部南北样带森林生态系统2001-2010年T/ET,并分析其时空变化及影响因子。结果表明：① PT-JPL模型适用于中国东部森林生态系统蒸散及其组分模拟,具有较高的稳定性和可靠性;② 中国东部南北样带森林生态系统T/ET空间差异显著,整体呈南部低、北部高,主要由夏季T/ET空间格局主导;样带整体T/ET均值为0.69,2001-2010年呈显著缓慢上升趋势,增幅为0.007/yr（p p p

Based on MODIS-NDVI and climate data, using an artificial influence model based on coefficient of variation to quantitatively calculate the human impact of vegetation cover(NDVIH) in China from 2001 to 2015, Sen+Mann-Kendall model and Hurst index were used to analyze the spatial-temporal feature and the future trends. It was found that: (1) In the year from 2001 to 2015, the Spatial Differentiation of NDVIH in China was more obvious from southern part to northern part, with an average annual mean value of -0.0102, the vegetation coverage decreased slightly under human activities, the negative impact area accounting for 51.59% which is slightly larger than the positive impact area. (2) The interannual variation of NDVIH in China is obvious, showing the negative impact volatility decreased, the rate of decline is 0.5%/10a; among which the positive and negative effects all showed an increasing trend, the positive growth rate (0.3%/10a) is much larger than the negative impact (0.02%/10a). (3) During 2001-2015, the center of gravity of positive impact has moved to the northeast, the center of gravity of negative impact has moved to the southwest, vegetation cover in northeastern China has improved under the influence of mankind, and human activities in the southwest have increased the degree of vegetation destruction. (4) The proportion of "negative impact reduction" and "positive impact increase" trend of NDVIH in China appeared to be the largest which accounting for 28.14% and 25.21% of the total, and the ecological environment is improving. (5) The reverse characteristics of NDVIH change were stronger than the same characteristic in China, mainly showed a negative impact which decreased at the first and then increased with the rate of 15.59% of the total area.

基于MODIS-NDVI和气温、降水数据,使用基于变异系数的人为影响模型定量计算了2001-2015年中国植被覆盖人为影响,辅以趋势分析、Hurst指数等方法探讨了中国植被覆盖人为影响的时空变化特征及未来演变趋势。研究发现：① 2001-2015年,中国植被覆盖人为影响由南向北空间分异愈发明显,年均值为-0.0102,植被覆盖在人类活动影响下轻微减少,负影响面积占51.59%,略大于正影响面积。② 中国植被覆盖人为影响年际变化特征明显,整体呈负影响波动减少趋势,降速为0.5%/10a,其中正影响、负影响均呈增大趋势,正影响增速（0.3%/10a）远大于负影响（0.02%/10a）。③ 2001-2015年间,中国植被覆盖人为正影响重心向东北方向移动,负影响重心向西南方向移动,东北部植被覆盖在人为影响下不断改善,西南部人类活动对植被破坏程度不断增大。④ 中国植被覆盖人为影响主要呈负影响减少和正影响增大趋势,面积占比分别为28.14%和25.21%,生态环境趋于改善。⑤ Hurst指数分析表明,中国植被覆盖人为影响未来演变趋势的反向特征强于正向特征,主要呈人为负影响先减少后增大趋势,面积占比15.59%。

基于中国603个气象站的地表气温和降水观测资料以及GIMMS NDVI3g数据,采用变化趋势分析和多元回归残差分析等方法研究了1982—2015年中国植被NDVI变化特征及其主要驱动因素（即气候变化和人类活动）的相应贡献。结果表明：① 1982—2015年中国植被恢复明显,在选择的32个省级行政区中,山西、陕西和重庆的生长季NDVI增加最快,仅上海生长季NDVI呈减小趋势。② 气候变化和人类活动的共同作用是中国植被NDVI呈现整体快速增加和巨大空间差异的主要原因,其中气候变化对各省生长季NDVI变化的影响在-0.01×10 ^-3~1.05×10 ^-3 a ^-1之间,而人类活动的影响在-0.32×10 ^-3~1.77×10 ^-3 a ^-1之间。③ 气候变化和人类活动分别对中国近34年来植被NDVI的增加贡献了40%和60%;人类活动贡献率超过80%的区域主要集中在黄土高原中部、华北平原以及中国东北和西南等地;人类活动贡献率大于50%的省份有22个,其中贡献率最大的3个地区为上海、黑龙江和云南。研究结果建议应更加重视人类活动在植被恢复中的作用。

Climate change is a major driver of vegetation activity, and thus its complex processes become a frontier and difficulty in global change research. To understand the complex relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change from 1982 to 2013 in China were investigated by the geographically weighted regression (GWR) model. The GWR was run based on the combined datasets of satellite vegetation index (GIMMS NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results noted that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China. The significant negative temperature-vegetation relationship was distributed in northeast, northwest and southeast parts of the country, while the positive correlation was more concentrated from southwest to northeast. And then, by comparing the normalized regression coefficients for different climate factors, regions with moisture dominants for NDVI were observed in North China and the Tibetan Plateau, and regions with temperature dominants for NDVI were distributed in the East, Central and Southwest China, where the annual mean maximum temperature accounts for the largest areas. In addition, regression coefficients between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, there was a positive correlation between vegetation dynamics and climate variability. However, the continuous and dynamic responding process of vegetation activity to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the description of climate-induced vegetation activity from its rise to decline in different regions is expected to provide a scientific basis for initiating ecosystem-based adaptation strategies in response to global climate change.

为研究气候变化与植被活动之间的复杂关系,采用1982-2013年GIMMS NDVI与气象站点温度与水分的监测资料,应用基于像元的地理加权回归方法,探究了中国植被NDVI及其动态特征对气候变化响应的空间格局。中国植被NDVI与地表温度呈空间非平稳关系,在空间上的负相关关系主要集中在东北、西北及东南部分地区,空间正相关则更为集中和连片;针对不同气候指标的标准化系数对比可知,植被NDVI受水分控制作用较为显著的区域主要集中在北方地区以及青藏高原,温度的主导作用区域则分布在华东、华中及西南地区,其中年均最高气温对NDVI的主导区域范围最广;植被NDVI动态与气候变率的回归结果表明,增温速率的升高会通过加剧干旱等机制对植被活动产生抑制作用,水分变率对植被活动的强弱起到了重要的调节作用。