水源涵养是评价陆地生态系统服务功能的重要指标，然而学界对水源涵养功能概念和计算方法仍存在诸多争论。这一方面说明水源涵养功能评估具有重要的现实意义，同时也说明其概念的复杂性和模糊性，亟需从生态学和水文学的基本理论出发，厘清水源涵养功能概念的内涵和评估方法，促进科学决策和有效管理。研究水源涵养功能时，生态学家更关注陆地生态系统的蓄水能力（S_max），而水文学家更关注流域的产流量（Q），两者均具有合理性，但各有侧重，若不分别辨析，极易造成概念混淆。理论和数据分析表明，蓄水能力和产流量虽然联系紧密，但概念完全不同。陆地生态系统的S_max决定了流域对降水的分配：即蒸散发（绿水）和Q（蓝水），S_max和Q在降水量一定的情况下往往存在此消彼长的关系。研究发现生态系统的根区蓄水能力（S_R_max）是联系绿水和蓝水的核心要素，是水源涵养功能评估的关键变量。大尺度根区蓄水能力主要由气候决定，可借鉴工程水文中设计水库的累积曲线法，根据生态系统用水的生存策略通过气候反演。最后，本文提出3点建议：① 在实践中分别评估生态系统的绿水和蓝水涵养功能；② 进一步全面考虑冰川积雪、地下水等多要素的水源涵养功能；③ 协同耦合自然和人工水源涵养功能，提高水资源在生态和经济社会系统中的高效优化利用。

Water retention plays a critical role in terrestrial ecosystem service. However, regarding its definition and calculation, there is a long debate in academia, which illustrates its importance in practice, and simultaneously demonstrates the complexity and vagueness of this essential concept. Thus, there is an urgent need to clarify its definition and calculation method based on basic ecological and hydrological theories, and eventually promote science-based decision-making and integrated water management. Interestingly, we observed that for the same term of "water retention function", ecologists intended to concern the terrestrial ecosystem's water storage capacity (Smax), while hydrologists concerned more about the water yield from the catchment (Q). Both perspectives have their own rationality, but with totally different vision and emphasis. By theorical discussion and data analysis, we found that water storage capacity (Smax) and water yield (Q) indeed have strong connection, but they are definitely two different concepts. The Smax of terrestrial ecosystem determined the separation of precipitation into either evaporation (green water) and Q (blue water). The size of Smax in most cases trades off with the amount of Q. We further revealed that the root zone storage capacity (SRmax) of ecosystem is at the heart of water retention function assessment, and plays a key role linking blue and green water. The SRmax is the result of ecosystem's adaption to its climate, and can be derived by the classic method to design reservoir, i.e. the Mass Curve Technique (MCT). Lastly, we gave three recommendations: (1) simultaneously evaluating green water retention capacity and blue water yield in practice; (2) further investigating the water retention functions of more water bodies, e.g. glacier, snow cover, and groundwater; (3) synergizing natural and artificial water retention capacities to enhance the water use efficiency in both the ecosystem and our economic-social system.

\n One source of uncertainty in climate science is how the carbon fertilization effect (CFE) will contribute to mitigation of anthropogenic climate change. Wang\n et al.\n explored the temporal dynamics of CFE on vegetation photosynthesis at the global scale. There has been a decline over recent decades in the contribution of CFE to vegetation photosynthesis, perhaps owing to the limiting effects of plant nutrients such as nitrogen and phosphorus. This declining trend has not been adequately accounted for in carbon cycle models. CFE thus has limitations for long-term mitigation of climate change, and future warming might currently be underestimated.\n

Vegetation phenology is one of the most direct and sensitive indicators of seasonal and interanual variations of environmental conditions. Phenological changes reflect quick change of terrestrial ecosystems in response to climate change. Satellite remote-sensing techniques capture canopy reflectance and can be used for studies of vegetation phenology. In this study, satellite-derived Start of Season (SOS) dates are obtained from the GIMMS AVHRR NDVI dataset by different methods such as Dynamic Threshold method, Delayed Moving Average methods, Double Logistic analysis and Savitzky-Golay method. The derived SOS data are compared and analyzed for the ecoregions from China to Russia, and the Dynamic Threshold method is decided to be most suitable for Eurasia scale. Based on the analysis of the changes of vegetation phenology and the response of phenology to climate change from 1982 to 2006, it is concluded that the Dynamic Threshold method has high retrieval rate for the SOS dates in Eurasia, and the data show a stable trend along the latitudinal gradient. The retrieved SOS dates for boreal forests and tundra ecosystems are most stable in the long term, while in the vegetation areas of low latitudes the dates show higher variability. It is found that from 1982 to 2006, there is a trend of SOS dates becoming earlier for the majority of vegetation types, and the forest coverage areas show even stronger trend of SOS dates becoming earlier, with a change rate of 11.45-15.61 days/25 years, due to global warming. With the exception of the closed to open (>15%) shrubland (<5 m), for most other types of vegetation, there is a negative correlation between vegetation phenology and the average temperature of the month. In other words, for each one degree increase, there is 1.32-3.47 days decrease to SOS date in spring, which is consistent with global warming in recent years.

多变的气候和复杂的地理环境使得青藏高原植被对气候变化响应敏感,因此分析高原植被与气候因子之间的动态关系对气候变化研究和生态系统管理具有重要意义。论文基于1982—2012年青藏高原气象数据(气温、降水)以及GIMMS NDVI3g遥感数据,在像素级别上运用格兰杰因果关系检验方法,在月尺度和季节尺度上分析了高原植被NDVI(主要是草原)与平均气温、降水量之间的响应情况及因果关系。研究表明：① 月尺度上NDVI与平均气温之间、NDVI与降水量之间的时序平稳性比例高于季节尺度,月尺度下达到平稳性的植被区域分别占99.13%和98.68%,季节尺度下分别占64.01%和71.97%;② 月尺度下高原平均气温和降水量对NDVI影响的滞后期都集中在第12~13个月,荒漠草原、典型草原和草甸3种植被类型的滞后期一致,季节尺度下平均气温和降水量对NDVI影响的滞后期主要分布在第3~4和第6个季度,3种植被类型的滞后期差异性较大;③ 月尺度下,青藏高原约98.95%的植被覆被区的平均气温是引起NDVI变化的格兰杰原因,反之,大部分地区(约89.05%,除高原东南区域)内NDVI也是引起平均气温变化的格兰杰原因;季节尺度下,青藏高原中部以外植被区域(约92.03%)内的平均气温是引起NDVI变化的格兰杰原因,而在东部和西部部分地区(约50.55%)中NDVI也是引起平均气温变化的格兰杰原因;④ 月尺度下,高原东北和西北地区(约72.05%)内的降水量是引起NDVI变化的格兰杰原因,大部分地区(约94.86%,除东南部少量区域)中NDVI是引起降水量变化的格兰杰原因;季节尺度下,高原东南部(约61.43%)地区内的降水量是引起NDVI变化的格兰杰原因,高原中东部地区(约48.98%)中NDVI是引起降水量变化的格兰杰原因。总之,高原植被NDVI与气温、降水的相互作用显著,彼此均可构成格兰杰因果效应,但总体上气候因子的影响程度大于植被的反馈作用,月尺度的效应区域大于季节尺度的效应区域。

Due to the complex plateau climate and unique geographical environment, the vegetation responds strongly to climatic shifts on the Tibetan Plateau. Therefore, it is of great significance to discuss the causality between vegetation and climate changes. Using the meteorological dataset including average temperature and precipitation and the GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g remote sensing data from 1982 to 2012 to analyze the causal relationship between NDVI and its influencing factors at the monthly and seasonal scales by the Granger causality test on the pixel level, this study examined the response of plateau vegetation (mainly grassland) to average temperature and precipitation change and causality. The results show that: 1) The stationarity proportion of vegetation NDVI and average temperature (99.13%), NDVI and precipitation (98.68%) at the monthly scale was higher than at the seasonal scale (64.01% and 71.97% respectively). 2) Lagging effects of average temperature on NDVI and precipitation on NDVI were around 12-13 months at the monthly scale and mainly 3, 4, and 6 quarters at the seasonal scale on the Tibetan Plateau. The three vegetation types—desert steppe, typical steppe, and meadow steppe—showed high similarities at the monthly scale, while greater heterogeneity was observed at the seasonal scale. 3) For 98.95% of the area covered by vegetation on the Tibetan Plateau, it is believed that average temperature change was the Granger cause of NDVI change, while for 89.05% of the region (except for the southeast), NDVI change was supposed to be the Granger cause of average temperature change at the monthly scale. At the seasonal scale, average temperature change was considered the Granger cause of NDVI change in 92.03% of the regsion (except for the central part of the Tibetan Plateau). Nevertheless, in the eastern and western parts of the plateau (about 50.55% of the region), NDVI change was interpreted as the Granger cause of average temperature change. 4) In the northeast and northwest of the region (about 98.95% of the area) precipitation change was believed to be the Granger cause of NDVI change, while in 94.86% of the region (except for a few areas in the southeast) NDVI change was supposed to be the Granger cause of precipitation change at the monthly scale. At the seasonal scale, precipitation change was considered the Granger cause of NDVI change in the southeastern part of the plateau (61.43% of the area). Nevertheless, in the central and eastern parts of the region (about 48.98% of the area), NDVI change was interpreted as the Granger cause of precipitation change. Overall, climatic factors on the Tibetan Plateau have an interactive relationship with vegetation and each can cause a Grainger effect to the other, with climatic factors having stronger Grainger effect on vegetation than the other way round. The Granger effect region on the Tibetan Plateau at the monthly scale is larger than the Granger effect region at the seasonal scale.

论文基于2000—2019年秦岭—淮河南北MODIS-NDVI植被覆盖数据,对中国南北过渡带植被时空变化进行分析,并探讨植被动态变化驱动因素。结果表明：① 在趋势变化上,2000—2019年秦岭—淮河南北植被显著恢复。其中,秦巴山区植被恢复面积占比最高,其次是巫山山区和关中平原;植被退化区面积占比仅为6.4%,主要分布于长三角城市群。② 在气候因素上,NDVI变化与气温显著相关(P&lt;0.05)面积占比为9.1%,低于降水(13.1%)和日照时数(14.5%)显著区域,无显著相关区域分布面积最广,说明在关键生长季(5—9月),区域水热条件较好,植被年际波动受气候变化影响区域较少。③ 在驱动因素上,受气候因素和生态建设驱动绿化占比分别为19.2%和30.0%,其中,生态建设驱动绿化区与秦巴山区、大别山生态修复工程,川东、长江中下游撂荒地空间格局一致,说明耕地转为生态用地是区域植被快速恢复的主要原因。研究结果对量化湿润—半湿润地区植被驱动因素,优化生态建设评估模型具有启示意义。

Using the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data (MOD13Q1) and land cover data, and the observed daily temperature, precipitation, and sunlight hours data obtained from 196 meteorological stations in the north and south of the Qinling-Huaihe region, this study explored the spatiotemporal characteristics of the vegetation dynamics in the region. It also analyzed the effects of the driving forces of climate change and human activities (such as urbanization and ecological restoration programs) on vegetation restoration and degradation between 2000 and 2019. As China's north-south transitional zone, the Qinling-Huaihe region has experienced overall greening (54.6% significant NDVI increase at P<0.05) and partial degradation (6.4% significant NDVI decrease at P< 0.05) from 2000 to 2019. The area of vegetation restoration was mainly in the Qinling-Daba Mountains, which are characterized by multi-dimensional zonal structures and biological diversity; and the percentage of NDVI increase was 96.7%. The other areas of restoration were the Qinba Mountains, Wushan Mountains and the Guanzhong Plain, which are all key implementation areas of ecological restoration programs. In terms of vegetation degradation, there was an NDVI decrease of 6.4%, which was mainly concentrated in the Yangtze River Delta urban agglomeration and sporadically spread across other rapid urbanization areas. With regard to the impacts of climatic factors on vegetation dynamics, the areas of NDVI change affected by temperature only accounted for 9.1% of the total area of the region, which is lower than the areas affected by precipitation (13.1%) and sunlight (14.5%). It indicates that the areas with significant (P<0.05) correlation between climatic factors and NDVI are limited. The reason is that there are the good hydrothermal conditions and irrigation facilities in the study region, climatic factors are not the primary limiting factors of vegetation growth. With regard to the driving forces of vegetation change, among the areas of significant greening (54.6%), 19.2% and 30.0% were identified to be related to climate change and ecological restoration programs, respectively. Ecological engineering had an influence on the vegetation dynamics of the Qinling-Huaihe region. The spatial pattern of significant restoration was consistent with the ecological restoration programs, such as the Qinling-Daba Mountains ecological protection and restoration and the Dabie Mountains ecological restoration programs. These findings enrich our understanding of the relationship between vegetation expansion, climate change, and human activities in the subtropical and warm-temperate zones of China.

A change in climate would be expected to shift plant distribution as species expand in newly favorable areas and decline in increasingly hostile locations. We compared surveys of plant cover that were made in 1977 and 2006-2007 along a 2,314-m elevation gradient in Southern California's Santa Rosa Mountains. Southern California's climate warmed at the surface, the precipitation variability increased, and the amount of snow decreased during the 30-year period preceding the second survey. We found that the average elevation of the dominant plant species rose by approximately 65 m between the surveys. This shift cannot be attributed to changes in air pollution or fire frequency and appears to be a consequence of changes in regional climate.

系统评价气候变化和人为因素引起的植被退化，对有效开展生态工程建设、实现生态环境的可持续发展具有重要意义。目前绝大多研究主要分析气候因素对植被变化的影响，分析气候因素和人类活动对植被变化的双重影响少有研究。本文主要介绍这一双重影响对植被变化的贡献程度的主要研究方法——定性-半定量法、回归分析法、残差趋势法、基于净第一生产力的评价方法。综述分析了气候和人为因素对植被变化驱动的主要结论，总结目前各研究方法存在的问题，提出了未来需要重点加强气候和人为因素对植被变化驱动因素的分解研究，加强模型评价和不确定性量化研究等。

Understanding how the dynamics of vegetation growth respond to climate change and human activities at different temporal and spatial scales is critical to project future ecosystem dynamics. At present, most of the studies mainly analyze the influence of climate change on vegetation, but there are few studies on the dual effects of climate change and human activities on vegetation. We systematically reviewed the methods of how to identify contributions of climate change and human activities on vegetation at quantitative aspects, including the qualitative and semi-quantitative assessment, regression analysis, residual trend method, and the first Net Primary Production based evaluation method. By summarizing and analyzing the progresses of previous researches on quantitative evaluation of the contributions and the existing problems, we found that it would be necessary to strengthen the study of disassembling the driving mechanisms of climate change and human activities on vegetation, as well as model evaluation and uncertainty analysis in the future.

利用1961—2011 年中国西南5 省市113 个气象站的观测资料，基于Penman-Monteith 蒸散模型计算了各个站点逐月潜在蒸散和干湿指数，研究了近50 a 来西南地区气候干湿状况的时空变化特征。结果表明：西南地区气候整体较为湿润，但存在较大的区域差异，呈“东湿西干”的空间分布特征。近50 a 来西南区域的气候有“暖干化”的变化趋势，这种趋势在进入21 世纪以后有进一步加剧的迹象。西南地区干湿季特征鲜明，夏季最为湿润，冬季最干燥。近50 a 来，西南地区的气候干湿状况有两次显著的转变过程，第一次时间点在1992 年前后，此时气候开始湿润化，进入相对湿润期；另一次在2002 年前后，变化趋势由湿润化转为干旱化，进入相对干旱期。降水量是西南地区气候干湿状况的决定因素，日照时数与相对湿度等气象要素对干湿状况也产生较大影响。

Using the monthly measurements during 1961 to 2011 of 113 meteorological stations in Southwest China, the potential evapotranspiration and aridity index were calculated with the Penman-Monteith model and then the characteristics of spatial-temporal change of surface dry and wet conditions in Southwest China in recent 50 years are discussed. The results show that the dry and wet conditions in Southwest China are spatially uneven with a characteristic named "humid east and arid west" while they are moist as a whole. The climate dry and wet conditions tend to be hotter and drier in this area in recent 50 years and are growing more serious in the last 12 years. The climate dry and wet condition of summer is the moistest in four seasons while it is the driest in winter. There are two time points where the climate dry and wet conditions changed abruptly in Southwest China in the past 50 years. One is around 1992 when it tends to be moist and the other is around 2002 when it becomes dryer instead of moister. Precipitation plays a dominant role in the climate dry and wet conditions of Southwest China region meanwhile the factors such as sunshine duration and relative humidity also have a significant impact on them.

Satellite-derived Normalized Difference Vegetation Index (NDVI), a proxy of vegetation productivity, is known to be correlated with temperature in northern ecosystems. This relationship, however, may change over time following alternations in other environmental factors. Here we show that above 30 degrees N, the strength of the relationship between the interannual variability of growing season NDVI and temperature (partial correlation coefficient RNDV-GT) declined substantially between 1982 and 2011. This decrease in RNDVI-GT is mainly observed in temperate and arctic ecosystems, and is also partly reproduced by process-based ecosystem model results. In the temperate ecosystem, the decrease in RNDVI-GT coincides with an increase in drought. In the arctic ecosystem, it may be related to a nonlinear response of photosynthesis to temperature, increase of hot extreme days and shrub expansion over grass-dominated tundra. Our results caution the use of results from interannual time scales to constrain the decadal response of plants to ongoing warming.

Grassland degradation has emerged as a serious socio-economic and ecological problem, endangering both long-term usage and the regional biogeochemical cycle. Climate change and human activities are the two leading factors leading to grassland degradation. However, it is unclear what the degradation level caused by these two factors is. Using the normalized difference vegetation index (NDVI) and coefficient of variation of NDVI (CV), the spatial distribution features of grassland degradation or restoration were analyzed in Qilian County in the northeast of the Qinghai-Tibet Plateau. The dominant climate variables affecting NDVI variation were selected through the combination of random forest model and stepwise regression method to improve the residual trend analysis, and on this basis, twelve possible scenarios were established to evaluate the driving factors of different degraded grasslands. Finally, used the Hurst index to forecast the trend of grassland degradation or restoration. The results showed that approximately 55.0% of the grassland had been degraded between 2000 and 2019, and the area of slight degradation (NDVI> 0; CV> 0; NDVI> 0.2) accounted for 48.6%. These regions were centered in the northwest of Qilian County. Climate and human activities had a joint impact on grassland restoration or degradation. Human activities played a leading role in grassland restoration, while climate change was primarily a driver of grassland degradation. The regions with slight degradation or re-growing (NDVI> 0; CV> 0), moderate degradation (NDVI< 0; CV> 0), and severe degradation or desertification (NDVI< 0; CV< 0) were dominated by the joint effects of climate and anthropogenic activity accounted for 34.3%, 3.3%, and 1.3%, respectively, of the total grassland area. Grasslands in most areas of Qilian County are forecasted to continue to degrade, including the previously degraded areas, with continuous degradation areas accounting for 54.78%. Accurately identifying the driving factors of different degraded grassland and predicting the dynamic change trend of grassland in the future is the key to understand the mechanism of grassland degradation and prevent grassland degradation. The findings offer a reference for accurately identifying the driving forces in grassland degradation, as well as providing a scientific basis for the policy-making of grassland ecological management.© 2022. The Author(s).

In recent decades, the trade-off between urbanization and vegetation dynamics has broken the balance between human activities and social-economic dimensions. Our understanding towards the complex human–nature interactions, particularly the gradient of vegetation growth pattern across different city size, is still limited. Here, we selected 35 typical cities in China and classified them into five categories according to their resident population (e.g., megacities, megapolis, big cities, medium cities, and small cities). The spatial-temporal dynamics of vegetation growth for all 35 cities were inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). We found that averaged NDVI for all cities slightly decreased during 2000 and 2020, at a rate of 1.6 × 10−4 per year. Most cities were characterized with relatively lower NDVI in urban areas than its surrounding area (determined by a series of buffer zones, i.e., 1–25 km outside of the city boundary). The percentage of greening pixels increased from urban area to the 25 km buffer zone at a rate of 4.7 × 10−4 per km. We noticed that negative impact of urbanization on vegetation growth reduced as the distance to urban area increased, with an exception for megacities (e.g., Shanghai, Beijing, and Shenzhen). In megacities and megapolis, greening pixels were more concentrated at core urban area, implying that the positive urbanization effect on vegetation growth is much more apparent. We argue that urbanization in China might facilitate vegetation growth to a certain extent, for which an appropriate urban planning such as purposeful selection of city sizes could be a scientific guidance while targeting the city’s sustainable development goals in future.

Drylands are home to more than 38% of the total global population and are one of the most sensitive areas to climate change and human activities(1,2). Projecting the areal change in drylands is essential for taking early action to prevent the aggravation of global desertification(3,4). However, dryland expansion has been underestimated in the Fifth Coupled Model Intercomparison Project (CMIP5) simulations(5) considering the past 58 years (1948-2005). Here, using historical data to bias-correct CMIP5 projections, we show an increase in dryland expansion rate resulting in the drylands covering half of the global land surface by the end of this century. Dryland area, projected under representative concentration pathways (RCPs) RCP8.5 and RCP4.5, will increase by 23% and 11%, respectively, relative to 1961-1990 baseline, equalling 56% and 50%, respectively, of total land surface. Such an expansion of drylands would lead to reduced carbon sequestration and enhanced regional warming(6,7), resulting in warming trends over the present drylands that are double those over humid regions. The increasing aridity, enhanced warming and rapidly growing human population will exacerbate the risk of land degradation and desertification in the near future in the drylands of developing countries, where 78% of dryland expansion and 50% of the population growth will occur under RCP8.5.

宁夏自2000年后实施退耕还林以来,局部地区的生态环境得到明显改善,为探求近15来年宁夏地区植被的动态变化及其影响因子,本文以MOD13Q1为数据源,结合DEM数据、土地利用分类图,采用Sen+Mann-Kendall非参数检验方法和Hurst模型,分析了宁夏不同行政区、不同海拔、不同坡度、不同坡向及不同植被类型生长季NDVI的空间变化特征及未来变化趋势;并利用重心迁移模型和转移矩阵分析宁夏2000-2014年间植被覆盖的时空演变特征。结果表明：①从空间分布看,宁夏南部六盘山、北部贺兰山及引黄灌溉区植被长势较好,中部干旱地区植被长势较差;且植被NDVI与海拔高程和坡度呈显著正相关。②从植被覆盖的转移矩阵看,较高植被覆盖的面积占比从2000年的17.29%增长到2014年的31.55%,主要是由较低植被覆盖转化而来的。③从重心迁移方向看,中度植被覆盖和较高植被覆盖的重心迁移最为明显,分别向东北方向偏移了129.49 km和向东南方向偏移了89.49 km。④从变化趋势看,生长季植被NDVI整体呈上升趋势,明显改善的面积占总面积的59.63%,轻微改善区域占31.72%;林地和水田显著改善的面积分别占总面积的71.50%和70.80%;显著改善的面积比例随海拔高程和坡度的增加均先增加后减少,且南部各行政区植被改善的面积均高于北部。⑤从可持续性看,植被恢复的持续性较强,89.24%的植被NDVI呈现持续改善的趋势;南部地区的持续改善的面积大于北部地区。

The Normalized Difference Vegetation Index (NDVI) is sensitive to changes in surface vegetation cover. Research into how climate change impacts surface vegetation cover is essential to manage ecological systems and promote green development. The Western Henan Mountains, located in the transitional zone between the northern subtropical and warm temperate zones of China, is an ideal location to study the impacts of climate change on surface vegetation cover. Combining a digital elevation model (DEM) with temperature and precipitation data; and MODIS-NDVI imagery (2000∼2017) for the Western Henan Mountains, this study explores variations in the growing season NDVI and its response to climate change. Results show that there are significant changes with fluctuations in NDVI values from 2000 to 2017. NDVI increased at a growth rate of 0.027 per decade (p &amp;lt; 0.05) overall, indicating vegetation conditions have gradually improved. Although the NDVI value showed an overall increasing trend, 19.12% of the areas showed a decreasing trend, interspersing and intersecting spatially, showing significant spatial differences. NDVI increased initially, but then decreased as a function of elevation, which was shown to be proportional to slope and independent of aspect. Variables including elevation and slope gradient are shown to provide high explanation of NDVI variability, whilst temperature is shown to have a more significant impact on NDVI than precipitation. However, vegetation responses to temperature and precipitation covaried with both slope and aspect. Positive NDVI trends were strongest at low elevations (i.e., &amp;lt;1,100 masl), which we attribute to vegetation restoration activities. Lower NDVI values characterized gentle slopes (&amp;lt;5°), whilst higher values were, in contrast, associated with steeper slopes (5∼10°). This study highlights the complex mechanisms and their relations governing vegetation response to climate change and should form an instructive basis for both future modeling studies investigating the response of vegetation to future global warming.

The ongoing changes in vegetation spring phenology in temperate/cold regions are widely attributed to temperature. However, in arid/semiarid ecosystems, the correlation between spring temperature and phenology is much less clear. We test the hypothesis that precipitation plays an important role in the temperature dependency of phenology in arid/semiarid regions. We therefore investigated the influence of preseason precipitation on satellite-derived estimates of starting date of vegetation growing season (SOS) across the Tibetan Plateau (TP). We observed two clear patterns linking precipitation to SOS. First, SOS is more sensitive to interannual variations in preseason precipitation in more arid than in wetter areas. Spatially, an increase in long-term averaged preseason precipitation of 10 mm corresponds to a decrease in the precipitation sensitivity of SOS by about 0.01 day mm(-1). Second, SOS is more sensitive to variations in preseason temperature in wetter than in dryer areas of the plateau. A spatial increase in precipitation of 10 mm corresponds to an increase in temperature sensitivity of SOS of 0.25 day °C(-1) (0.25 day SOS advance per 1 °C temperature increase). Those two patterns indicate both direct and indirect impacts of precipitation on SOS on TP. This study suggests a balance between maximizing benefit from the limiting climatic resource and minimizing the risk imposed by other factors. In wetter areas, the lower risk of drought allows greater temperature sensitivity of SOS to maximize the thermal benefit, which is further supported by the weaker interannual partial correlation between growing degree days and preseason precipitation. In more arid areas, maximizing the benefit of water requires greater sensitivity of SOS to precipitation, with reduced sensitivity to temperature. This study highlights the impacts of precipitation on SOS in a large cold and arid/semiarid region and suggests that influences of water should be included in SOS module of terrestrial ecosystem models for drylands. © 2015 John Wiley & Sons Ltd.

Under the combined influence of climate change and human activities, vegetation ecosystem has undergone profound changes. It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods. Therefore, it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle. Based on the data of climate elements (sunshine hours, precipitation and temperature), human activities (population intensity and GDP intensity) and other natural factors (altitude, slope and aspect), this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method, a trend analysis, and a gravity center model, and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model. The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest. During 1981-2019, the temporal variation of vegetation NDVI showed an overall increasing trend. The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County, Gansu Province, and the center moved northeastwards from 1981 to 2019. During 1981-2000 and 2001-2019, the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest. During the study period (1981-2019), the dominant factors influencing vegetation NDVI shifted from natural factors to human activities. These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

The effects of ecological restoration based on ecosystem services (ES) have attracted more and more attention, while the simulation and cost-benefit analysis of ecological restoration scenarios are not well investigated. In this study, four ecological restoration scenarios were simulated at a typical watershed on the Qinghai-Tibetan Plateau (QTP) based on the land use conversion. Scenario 1 was only grassland restoration, Scenario 2 and 3 were mainly farmland to shrub, and Scenario 4 was mainly grassland restoration with bare land converting to forest and shrub. The ecosystem services value (ESV) and the cost-benefits of these scenarios were quantified and compared in 25 years after the restoration investment. The results showed there were significant differences in the ESV under four scenarios, among which Scenario 4 had the largest ESV and Scenario 1 had the smallest ESV. The spatial distribution of ESV was uneven, and high-value regions were concentrated in the southwest and central regions. There were great variations between original scenario and simulated scenarios, but a little difference between Scenarios 2, 3, and 4. The largest loss of farmland abandonment was regulating service, followed by supporting service, provisioning service, and cultural service. From the perspective of payback period, Scenario 1 was the fastest, and it could obtain net benefits first. From the short- and long-term (6 and 25 years after investment) benefits, Scenarios 1 and 4 had the largest cumulative ESV increase, respectively. The results of this study can provide a basis for the formulation and implementation of ecological policies.

黑河流域是密云水库上游潮白河水系的重要组成部分。从20 世纪80 年代开始，国家在黑河流域开展了大 面积的生态保护和植树造林活动。为正确认识植树造林等活动对流域水资源的影响，本研究利用20 世纪80 年代 和2000 年两期土地利用数据和流域1959～2000 年降雨、径流资料，分析了生态保护和植树造林活动影响下的流域 河川径流量的变化情况。研究结果表明：1959～2000 年黑河流域年降水量和河川径流量年际变化较大，但整体变化 趋势性不显著；黑河流域以林地、草地和耕地面积为主，从20 世纪80 年代到2000 年林地面积增加了8x10²hm²，草 地和耕地面积分别减少了7x10²hm² 和1x10²hm²；1980～2000 年开展植树造林活动期较1959～1979 年多年平均径流 系数下降了4.1%；1980 年后20 年的生态保护和植树造林活动对河川径流量的影响为- 0.3mm/hm²，且有丰水年份 对流域河川径流量影响最大，平水年次之，在枯水年份对流域河川径流量影响最小的现象。

Heihe basin is an important part of the Chaobai river system, which lies on the upstream of Miyun reservoir. Since 1980s, the government has launched many activities on ecological protection and afforestation. In order to understand the impact of these activities on water resource in Heihe basin, this paper analyzes the changes of streamflow by using land use data in the 1980s and 2000 and the precipitation - runoff data from 1959 to 2000. The results show: the interannual variation of precipitation and streamflow in Heihe basin from 1959 to 2000 is large, but has generally shown no obvious trend of rise or decline; and the heihe basin area is dominated by woodland, grasslands and farmland. From 1980s to 2000, the area of woodland had increased by 800 hectares while grassland and farmland had decreased, respectively, by 700 hectares and 100 hectares. During 1980 to 2000, when lots of ecological protection and afforestation activities were carried out, the average runoff coefficient got a reduction of 4.1%, compared to that in the control period of 1959 ~1979. According to the data of two periods in discussion, the impact of ecological protection and afforestation on streamflow is - 0.308mm/hm2 and it indicates a phenomenon that the impact is the largest in wet years, smaller in normal years, and the smallest in dry years.

基于GIMMS NDVI以及MODIS NDVI数据,分析内蒙古地区1981-2010年的植被变化趋势,并结合气候、社会经济数据,以旗县为单位定量分析气候变化和人类活动对植被变化的影响,结果表明：①1981-2010年间,内蒙古地区植被变化具有典型的空间异质性,其中植被显著增加区域主要集中在西南部的阿拉善盟、鄂尔多斯市以及东部通辽市等地区,显著减少区域主要集中在北部的锡林郭勒盟以及东北部的呼伦贝尔市的部分地区;②对于植被显著增加区域,人类活动作用的影响面积最大,其次为气候因素,气候与人类活动的耦合作用也对植被增加有一定显著影响;内蒙古西部降雨量的增加、围封禁牧政策的实施以及农作物播种面积的增加为驱动植被增加的主要因素;③对于植被显著减少区域,人类活动的作用略大于气候因素;内蒙古中东部地区降雨减少以及近10年来部分旗县风速的增加是导致植被显著减少的重要气候因素;虽然人工造林、农作物播种面积会增加局部植被盖度,但在县域尺度不足以抵消干旱对植被生长的不利影响,反而会导致区域植被退化。

This study constructed growing season NDVI in 1981-2010 based on GIMMS NDVI and MODIS NDVI data in Inner Mongolia. The characteristics of NDVI change were analyzed and natural and human influencing factors were investigated in each county (banner) by trend analysis and multiple regression analysis. The results indicate that NDVI changes in Inner Mongolia showed great heterogeneity. The regions that experienced significantly increased vegetation cover were mainly distributed in Erdos City and Alashan Prefecture in southwestern and Tongliao City in eastern Inner Mongolia, and the regions that experienced significantly decreased vegetation cover were in HulunBuir and Xilinguole Prefectures in northern Inner Mongolia. For the increased vegetation cover regions, human activities were the dominant factor, and climate change played the second role; the coupling of climate change and human activities also had certain impact on the vegetation increase. The increase of the rainfall, implementation of banned grazing policies and increase of cropping area were the main factors driving the increase of vegetation. However, for the decreased vegetation cover regions, the role of human activities was slightly greater than that of climate change. The reduction of rainfall in the central and eastern Inner Mongolia and the raise of wind speed in some counties in nearly 10 years were the main climatic factors driving the significant decrease of vegetation. Although afforestation and the increase of cropping area might lead to the increase of vegetation cover at local scale, it was not enough to counteract the adverse effects of drought on vegetation growth at county scale; nevertheless, it might lead to regional vegetation degradation.

A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000–2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.