外来入侵植物风传扩散过程模拟模型选择

种子扩散是外来入侵植物异地入侵的重要手段,对入侵种群扩散机制的清晰认识是有效进行入侵防治和生态管理的基础。本文在研究入侵植物风传扩散过程中的主要影响参数的基础上,分析了各参数对不同扩散过程造成的影响,总结出不同的扩散模拟过程中需要应用的影响参数,阐述了这些参数的参数化过程及相应的计算方法。对已有风传扩散模拟模型做了重点介绍,并结合入侵植物自身特征重点解析了其风传扩散过程模拟模型的选择依据。其中,对于风传扩散过程有直接、明显的影响参数是种子释放高度和水平风速。对于入侵草本、灌木植物而言,入侵区域植被高度是必须关注的影响参数。在长距离扩散研究中,以乔木和大灌木为研究对象时需要重点考虑植被叶片与水平风速垂直分布引起的冠层及冠层以上区域大气湍流所带来的影响;小灌木与草本植物则需注意由地表温度引起的温暖气团上升带来的影响。模拟模型中,2Dt模型和混合韦布尔模型可以有效描述扩散体扩散核近端与远端的扩散曲线。对更为详细的长距离扩散模拟模型选择时,需要考虑不同的大气湍流类型、扩散体大小、植被类型、扩散核维度以及模拟精度要求。     

芦苇湿地多时空尺度蒸散模拟研究进展

湿地蒸散是湿地水分损失的主要途径,由于其对气候变化的高度敏感性和重要的反馈作用而倍受关注。湿地蒸散过程可以分为叶片、植株、冠层、景观至区域等多种尺度,如何实现不同时空尺度的过程和参数耦合成为湿地蒸散研究的重点和难点。本文回顾了湿地蒸散模拟研究的发展历程,着重总结了芦苇湿地蒸散监测及模拟方面的技术和方法,并从不同时间尺度(日、月、年)和不同空间尺度(叶片、植株、冠层、区域)以及实现不同时空尺度拓展的关键技术研究等方面进行归纳和总结,探讨了今后的研究重点。实现不同时空尺度的芦苇湿地蒸散过程的准确模拟,关键在于大气-植被-土壤界面的参数耦合,而目前针对不同尺度的芦苇湿地蒸散参数化研究很少,因此,有必要探讨适合芦苇湿地多尺度蒸散模拟及参数化方法,为芦苇湿地蒸散评估模型的选择提供理论支持。     

基于大气辐射校正的广州市植被覆盖度遥感估算

植被覆盖度是描述生态系统的基本参数,也是进行生态系统健康与安全评价的重要生态参数.基于1990、1995、2000和2005年4个时相的TM遥感数据源,以广州市为研究区域,运用减少大气辐射影响的植被指数计算模型,通过非监督分类及图像空间模型运算,修正了大气辐射校正参数,建立了植被覆盖度与校正植被指数的模型,估算了不同时期内广州市的植被覆盖度.结果显示,广州市植被覆盖度在1990—2000年的10年内持续下降,从2000年开始呈上升趋势,符合广州市的经济发展与环境建设实际.所建立的模型适合于区域植被覆盖度动态变化研究,且在植被覆盖度的动态变化特征研究方面有较大优势,其结果适于进行城市生态环境质量与动态评价.     

五种TM影像大气校正模型在植被遥感中的应用

基于2005年7月18日广州市东北部和惠州市北部的TM影像,以表观反射率模型为参照,从植被反射率光谱、地物反射率统计特征、规一化植被指数三方面对4种黑体减法模型和6S模型在植被遥感中的应用进行了评价.结果表明：黑体减法模型DOS4获得了精度较高的植被反射率,其地物反射率与规一化植被指数的信息量最大,适用于研究区的植被遥感研究.对于不同区域的植被遥感研究需要进行具体的比较分析,才能选择到合适的大气校正模型.     

不同区域森林火灾对生态因子的响应及其概率模型

火灾是影响森林生态系统过程的重要干扰之一,其对森林生态系统内各生态因子的响应各不相同.由于植被状况及生态环境的不同,森林火灾的时空分布特征在中国不同植被气候类型内表现不同,根据植被气候类型分类系统,将中国主要森林火灾地区划分为4个区域:东北(冷温带松林)、华北(落叶阔叶林)、东南(常绿阔叶林)和西南(热带雨林),应用遥感监测数据和地面环境数据,以时空变量、生态因子(植被生长变化指数、湿度等)为可选自变量,应用半参数化Logistic回归模型,就森林火险对不同生态影响因子的响应规律进行了分析,建立了基于生态因子的着火概率模型和大火蔓延概率模型,通过模拟及实际数据散点图、火险概率图,评估了模型应用价值.结果表明,土壤湿度及植被含水量在落叶阔叶林、常绿阔叶林、热带雨林地区对着火概率影响显著.在4个植被气候区内,土壤及凋落物湿度对大火蔓延的作用较小.在冷温带松林、落叶阔叶林、常绿阔叶林地区,植被生长的年内变化对火灾发生的影响显著,在常绿阔叶林地区,年内植被生长变化对大火蔓延的作用较小.森林火险概率与各生态因子的相关关系主要呈现出非线性.不同植被气候区内,火险概率受不同生态因子组合的影响,这与不同区域的植被状况及生态环境不同有关.在不同植被气候类型,应用时空变量、生态因子建立半参数化logistic回归模型,进行着火概率和大火蔓延概率的模拟具有可行性和实际应用能力.为进一步分析森林生态系统与火灾之间的动态关系、展开生态系统火灾干扰研究提供了理论基础.     

森林生态系统与大气边界层相互作用的数值模拟

基于大气边界层和植被冠层微气象学基本原理,建立了一个森林生态系统与大气边界层相互作用的数值模式．应用该模式模拟了森林生态系统的热量平衡、植被温度、植被冠层内空气温度、地表温度日变化特征,及森林生态系统下垫面大气边界层风速、位温、比湿、湍流交换系数的时空分布和廓线的日变化特征．该模式还可应用于不同下垫面,模拟陆面物理过程与大气边界层相互作用机制及其区域气候效应的研究,这将为气候模式与生物圈的耦合研究奠定一个良好的基础．     

基于IBIS模型的东北森林净第一性生产力模拟

集成生物圈模型(the integrated biosphere simulator, IBIS)作为目前最复杂的基于动态植被模型的陆面生物模型之一,已经成为模拟大尺度(全球区域)的植被地理分布、净第一性生产力和碳平衡以及预测气候变化对陆地生态系统潜在影响的有效工具.应用IBIS模型对2004～2005年大小兴安岭的植被净第一性生产力(net primary productivity, NPP)进行了定量估算,模拟与研究了大小兴安岭森林生态系统植被NPP的空间分布格局以及不同植被类型的NPP季节变化特征,结果表明:大小兴安岭森林植被年均NPP值为494.7 gCm-2 · a-1,年吸收0.06Pg的大气碳.研究区年均NPP的空间分布主要受热量条件的影响,大兴安岭地区基本上呈现出由北向南增加的趋势,小兴安岭地区除单位面积年均NPP大于1.1kgCm-2 · a-1在小兴安岭北部孙吴和逊克地区分布外,基本上呈现出均匀分布的趋势.加强基础数据研究的同时如何根据中国的实际合理确定模型参数,使模型在我国典型生态系统中应用是值得进一步研究的.     

基于BIOME-BGC模型的长白落叶松林净初级生产力模拟参数敏感性

基于植被生理生态过程的模型包含较多参数,合理的参数取值能够极大地提高模型的模拟能力.参数敏感性分析可以全面分析模型参数对模拟结果的影响程度,在筛选模型敏感参数过程中起到重要作用.本研究以模拟吉林省汪清林业局长白落叶松林净初级生产力（NPP）为例,分析了BIOME-BGC模型的参数敏感性.首先利用样地实测NPP数据与模拟值进行对比分析,检验模型对长白落叶松林NPP的模拟能力;然后利用Morris法和EFAST法筛选出BIOME-BGC模型中对长白落叶松林NPP影响较大的敏感参数.在此基础上,通过EFAST法对所有筛选出的参数进行定量的敏感性分析,计算了敏感参数的全局敏感性指数、一阶敏感性指数和二阶敏感性指数.结果表明： BIOME-BGC模型能够较好地模拟研究区内长白落叶松林NPP的变化趋势;Morris法可以在样本量较少的情况下实现对BIOME-BGC模型敏感参数的筛选,而EFAST法可以定量分析BIOME-BGC模型中单个参数以及不同参数之间交互作用对模拟结果的影响程度;BIOME-BGC模型中对长白落叶松林NPP影响较大的敏感参数为新生茎与叶片的碳分配比和叶片碳氮比,且二者之间的交互作用明显大于其他参数之间的交互作用.     

考考赖沟流域植被生产力模拟

 该文应用气象数据、土壤物理属性实测数据、土壤水分分布式动态模型和植被表面净辐射模型的模拟结果,利用多元线性回归分析方法,建立 了植被净第一性生产力模型,实现了鄂尔多斯高原东部砂质荒漠化地区考考赖沟流域尺度上30 m×30 m空间分辨率的植被生产力精确模拟,并 且用植被生产力的野外实测数据对模拟结果进行了验证表明: 实测值与模拟值在固定沙丘、半固定沙丘和样线2上都达到0.05显著性相关水平; 不同位置的实测植被生 产力数据多分布在1∶1直线附近,模拟值与实测值吻合较好;植被生产力实测值与模拟值的相对误差范围为3.22%～ 6.27%,偏斜度范围在-12.84%～4.43%。该文的研究方法可以为流域尺度上植被生产力的精确模拟提供借鉴和参考。     

根据花粉模拟的中国植被及6000aBP植被制图的初步探讨

通过建立现代植物花粉与植被的类比关系,运用花粉植被化模拟技术进行植被类型模拟和制图。首先利用中国表土花粉资料,设计了以生态学原理为基础的功能型植物,并组合成植被型,以运行植被模型。该实验能够模拟出中国现代的主要植被类型,其地理分布与实际植被有较好一致性。进一步对中全新世花粉资料运行,初步模拟了１１２个花粉样点上的植被类型,并做出了中全新世的植被分布图。该图能够与已有的研究成果相对比,它为恢复古植被提供了一个较客观地制图法。它的精度可以随着资料密度和花粉类型的增加而不断提高。该研究对国际性的全球古植被制图和对比气候模型驱动的植被模型有着较大的参考价值。     

Environmental variation,vegetation distribution,carbon dynamics and water/energy exchange at high latitudes

Abstract. The responses of high latitude ecosystems to global change involve complex interactions among environmental variables, vegetation distribution, carbon dynamics, and water and energy exchange. These responses may have important consequences for the earth system. In this study, we evaluated how vegetation distribution, carbon stocks and turnover, and water and energy exchange are related to environmental variation spanned by the network of the IGBP high latitude transects. While the most notable feature of the high latitude transects is that they generally span temperature gradients from southern to northern latitudes, there are substantial differences in temperature among the transects. Also, along each transect temperature co‐varies with precipitation and photosynthetically active radiation, which are also variable among the transects. Both climate and disturbance interact to influence latitudinal patterns of vegetation and soil carbon storage among the transects, and vegetation distribution appears to interact with climate to determine exchanges of heat and moisture in high latitudes. Despite limitations imposed by the data we assembled, the analyses in this study have taken an important step toward clarifying the complexity of interactions among environmental variables, vegetation distribution, carbon stocks and turnover, and water and energy exchange in high latitude regions. This study reveals the need to conduct coordinated global change studies in high latitudes to further elucidate how interactions among climate, disturbance, and vegetation distribution influence carbon dynamics and water and energy exchange in high latitudes.     

毛乌素沙地南缘沙漠化临界区域土壤水分和植被空间格局

应用地统计学和经典统计学方法,对毛乌素沙地南缘沙漠化临界区域土壤水分和植被特征的空间分布格局及其相互关系进行研究,结果表明: 0-5 cm和5-10 cm土壤水分符合指数模型,10-15 cm土壤水分和植物群落物种数、植被盖度、植被密度都符合球状模型;0-5 cm土壤水分、植物群落物种数和植被盖度都具有强空间自相关性,5-10 cm、10-15 cm土壤水分和植被密度都具有中等程度的空间自相关性;从牛枝子群落到黑沙蒿群落,各层土壤水分与植物群落物种数之间具有相似的空间格局,都呈先升高后降低的变化趋势,而植被盖度和植被密度呈逐渐减小的变化趋势;0-5 cm土壤水分与植物群落物种数之间具有显著的正相关,是制约植被物种空间分布的关键因素。     

Pan-Arctic soil moisture control on tundra carbon sequestration and plant productivity

Long-term atmospheric CO₂ concentration records have suggested a reduction in the positive effect of warming on high-latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long-term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site-years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.     

Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient

Vegetation is a major environmental factor influencing habitat selection in bird species. High resolution mapping of vegetation cover is essential to model the distribution of populations and improve the management of breeding habitats. However, the task is challenging for grassland birds because microhabitat variations relevant at the territory scale cannot be measured continuously over large areas to delineate areas of higher suitability. Remote sensing may help to circumvent this problem. We addressed this issue by using SPOT 5 imagery and phytosociological data. We mapped grassland vegetation in a floodplain using two methods. We (i) mapped the continuous Ellenberg index of moisture and (ii) identified 5 vegetation classes distributed across the wetness gradient. These two methods produced consistent output maps, but they also provided complementary results. Ellenberg index is a valuable proxy for soil moisture while the class approach provided more information about vegetation structure, and possibly trophic resources. In spite of the apparent uniformity of meadows, our data show that birds do not settle randomly along the moisture and vegetation gradients. Overall birds tend to avoid the driest vegetation classes, i.e. the highest grounds. Thus, vegetation maps based on remote sensing could be valuable tools to study habitat selection and niche partition in grassland bird communities. It is also a valuable tool for conservation and habitat management.     

The eco-hydrological threshold for evaluating the stability of sand-binding vegetation in different climatic zones

Soil moisture dynamics are a determinant of the sustainable development of artificial sand-binding vegetation, which directly prevents and controls desertification and sand hazards, such as the sand burial of farmlands and pastures. How to maintain the stability of sand-binding vegetation is a challenge for ecologists and land managers. An eco-hydrological model coupling the dynamics of sand-binding vegetation cover and soil moisture was used to explore the effect of a stochastic daily precipitation regime on soil moisture and vegetation cover after the establishment of sand-binding vegetation. The simulation results indicate that herbaceous vegetation cover, woody vegetation cover and soil moisture increase nonlinearly with increasing annual rainfall. Specifically, herbaceous vegetation cover first increased and then decreased with increasing annual rainfall. Woody vegetation cover increased by a power-law function within the total community cover, and soil moisture increased exponentially. The eco-hydrological thresholds in different climatic zones and in typical revegetated sandy desert regions of China were determined using an eco-hydrological model. These indexes will not only help to promote dryland ecosystem management and maintain the sustainability of wind-breaks and sand-binding benefits but will also provide a quantifiable reference standard for vegetation recovery and reconstruction in sandy areas in the future.     

Indirect vegetation–soil moisture feedback with application to Holocene North Africa climate1

Using a fully coupled climate–terrestrial ecosystem model, we demonstrate explicitly that an initial perturbation on vegetation induces not only a direct positive vegetation feedback, but also a significant indirect vegetation–soil moisture feedback. The indirect feedback is generated through either fractional cover change or soil moisture depletion. Both indirect feedback mechanisms are triggered by a vegetation perturbation, but involve subsequent effects of soil moisture and evaporation, indirectly. An increase in vegetation tends to reduce bare‐ground evaporation through either the area reduction in bare ground or the depletion of soil moisture; the reduced evaporation may then counter the initial plant transpiration, favoring a negative net vegetation feedback. Furthermore, grasses are more effective in inducing the indirect vegetation–soil feedbacks, because of their limited plant evapotranspiration and shallower roots that tend to change surface soil moisture, and, in turn, evaporation, effectively. In comparison, trees favor a direct positive vegetation feedback due to their strong plant transpiration on subsurface soil moisture as well as a lower albedo.     

Revealing Invisible Water: Moisture Recycling as an Ecosystem Service

An ecosystem service is a benefit derived by humanity that can be traced back to an ecological process. Although ecosystem services related to surface water have been thoroughly described, the relationship between atmospheric water and ecosystem services has been mostly neglected, and perhaps misunderstood. Recent advances in land-atmosphere modeling have revealed the importance of terrestrial ecosystems for moisture recycling. In this paper, we analyze the extent to which vegetation sustains the supply of atmospheric moisture and precipitation for downwind beneficiaries, globally. We simulate land-surface evaporation with a global hydrology model and track changes to moisture recycling using an atmospheric moisture budget model, and we define vegetation-regulated moisture recycling as the difference in moisture recycling between current vegetation and a hypothetical desert world. Our results show that nearly a fifth of annual average precipitation falling on land is from vegetation-regulated moisture recycling, but the global variability is large, with many places receiving nearly half their precipitation from this ecosystem service. The largest potential impacts for changes to this ecosystem service are land-use changes across temperate regions in North America and Russia. Likewise, in semi-arid regions reliant on rainfed agricultural production, land-use change that even modestly reduces evaporation and subsequent precipitation, could significantly affect human well-being. We also present a regional case study in the Mato Grosso region of Brazil, where we identify the specific moisture recycling ecosystem services associated with the vegetation in Mato Grosso. We find that Mato Grosso vegetation regulates some internal precipitation, with a diffuse region of benefit downwind, primarily to the south and east, including the La Plata River basin and the megacities of Sao Paulo and Rio de Janeiro. We synthesize our global and regional results into a generalized framework for describing moisture recycling as an ecosystem service. We conclude that future work ought to disentangle whether and how this vegetation-regulated moisture recycling interacts with other ecosystem services, so that trade-offs can be assessed in a comprehensive and sustainable manner.     

山西太岳山小流域土壤水分空间异质性及其影响因子

以山西太岳山华北落叶松林地为主的小流域作为研究对象,采用地统计学方法结合地理信息系统(GIS)技术手段,研究了接石沟小流域土壤水分(0—60cm)的空间变异特征,以及植被分布和地形因子对其影响规律。结果表明:在时间稳定性的前提下,土壤水分含量和变异系数随土层加深逐渐降低。三层土壤水分半方差函数的最优拟合模型为球状模型,变程范围在1.1—1.4 km,均具有强烈的空间自相关性,其中0—20 cm和20—40 cm层土壤水分的空间异质性程度高于40—60 cm土层,以中间层的结构因素占总变异比例最大。自然结构因素(地形、母质、植被和土壤等)对不同土层土壤水分的总空间变异性起主导作用(81.4%—91.3%),而随机因素(取样误差、人为干扰等)的影响相对较小(8.7%—18.6%)。沿着集水线由西-东方向,从边缘的土壤水分高值斑块区逐渐过渡到明显的低值斑块区,梯度变化明显。研究发现,在植被覆盖异质性小的山地,土壤水分的空间异质性主要由地形因素引起,具体表现为其与坡向指数(TRASP)、坡度、海拔和土壤有机碳、全氮呈极显著相关关系(P0.01),而与植被指数(NDVI)呈弱的负相关关系。叠加分析显示,在阴坡、坡度较缓(15°)及高海拔叠合的区域土壤水分含量较高。研究结果可为山地人工林构建和植被恢复中土壤水资源的利用以及水分管理策略的制定提供理论依据。     

含植被覆盖影响的石羊河流域土壤水分遥感估算及空间格局分析

土壤水分是地表和大气循环的纽带,对植被生长和高效农业灌溉起着关键作用。以石羊河流域为研究区,采用植被覆盖度/表面反照率梯形特征空间散点图计算裸土反照率,减少植被对遥感获取土壤水分误差,以提高遥感土壤水分估算精度。同时通过稳定性、空间自相关和地理探测器等分析了SM的空间格局及其影响因素。结果表明：（1）裸土反照率模型在石羊河流域的SM反演精度较高,为流域尺度的SM计算提供了新的方法思考。（2） SM具有明显的空间自相关性,Moran''s值为0.88（Z-score=1852.94,P<0.01）,上游林地高-高聚集,下游荒漠低-低聚集,且SM与FVC显著相关（P<0.01）。（3）石羊河流域年内SM稳定性整体良好,其中稳定性好和较好区域占研究区88.34%。（4） SM空间分布受多因子影响,各因子解释能力存在显著差异,其中植被覆盖度 > 土壤类型 > 高程 > 土地利用,且因子间交互作用增强了对SM空间分异的解释力。（5）不同土地利用类型的SM差异较大,其中未利用地大部分SM小于7%;草地和耕地SM居于中等水平,SM值为7%-15%;林地水平最高,SM值大于25%。     

Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region

Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.