Altitude pattern of carbon stocks in desert grasslands of an arid land region

For estimating the altitude-distribution pattern of carbon stocks in desert grasslands and analyzing the possible mechanism for this distribution, a detailed study was performed through a series of field vegetation surveys and soil samplings from 90 vegetation plots and 45 soil profiles at 9 sites of the Hexi Corridor region, Northwestern China. Aboveground, belowground, and litter-fall biomass-carbon stocks ranged from 43 to 109, 23 to 64, and 5 to 20 g/m2, with mean values of 80.82,44.91, and 12.15 g/m2, respectively. Soil-carbon stocks varied between 2.88 and 3.98 kg/m2, with a mean value of 3.43 kg/m2 in the 0–100-cm soil layer. Both biomass-and soil-carbon stocks had an increasing tendency corresponding to the altitudinal gradient. A significantly negative correlation was found between soil-carbon stock and mean annual temperature, with further better correlations between soil-and biomass-carbon stocks, and mean annual precipitation. Furthermore, soil carbon was found to be positively correlated with soil-silt and-clay content, and negatively correlated with soil bulk density and the volume percent of gravel. It can be concluded that variations in soil texture and climate condition were the key factors influencing the altitudinal pattern of carbon stocks in this desert-grassland ecosystem. Thus, by using the linear-regression functions between altitude and carbon stocks, approximately 4.18 Tg carbon were predicted from the 1,260 km2 of desert grasslands in the study area.     

中国东部森林土壤腐殖碳组分纬度格局及其影响因素（英文）

Soil humic carbon is an important component of soil organic carbon(SOC) in terrestrial ecosystems. However, no study to date has investigated its geographical patterns and the main factors that influence it at a large scale, despite the fact that it is critical for exploring the influence of climate change on soil C storage and turnover. We measured levels of SOC, humic acid carbon(HAC), fulvic acid carbon(FAC), humin carbon(HUC), and extractable humus carbon(HEC) in the 0–10 cm soil layer in nine typical forests along the 3800-km North-South Transect of Eastern China(NSTEC) to elucidate the latitudinal patterns of soil humic carbon fractions and their main influencing factors. SOC, HAC, FAC, HUC, and HEC increased with increasing latitude(all P0.001), and exhibited a general trend of tropical subtropical temperate. The ratios of humic C fractions to SOC were 9.48%–12.27%(HAC), 20.68%–29.31%(FAC), and 59.37%–61.38%(HUC). Climate, soil texture, and soil microbes jointly explained more than 90% of the latitudinal variation in SOC, HAC, FAC, HEC, and HUC, and interactive effects were important. These findings elucidate latitudinal patterns of soil humic C fractions in forests at a large scale, and may improve models of soil C turnover and storage.     

Combining LPJ-GUESS and HASM to simulate the spatial distribution of forest vegetation carbon stock in China简

It is very important in accurately estimating the forests＇ carbon stock and spatial distribution in the regional scale because they possess a great rate in the carbon stock of the terrestrial ecosystem. Yet the current estimation of forest carbon stock in the regional scale mainly depends on the forest inventory data, and the whole process consumes too much labor, money and time. And meanwhile it has many negative influences on the forest carbon storage updating. In order to figure out these problems, this paper, based on High Accuracy Surface Modeling （HASM）, proposes a forest vegetation carbon storage simulation method. This new method employs the output of LPJ-GUESS model as initial values of HASM and uses the inventory data as sample points of HASM to simulate the distribution of forest carbon storage in China. This study also adopts the seventh forest resources statistics of China as the data source to generate sample points, and it also works as the simulation accuracy test. The HASM simulation shows that the total forest carbon storage of China is 9.2405 Pg, while the calculated value based on forest resources statistics are 7.8115 Pg. The forest resources statistics is taken based on a forest canopy closure, and the result of HASM is much more suitable to the real forest carbon storage. The simulation result also indicates that the southwestern mountain region and the northeastern forests are the important forest carbon reservoirs in China, and they account for 39.82% and 20.46% of the country＇s total forest vegetation carbon stock respectively. Compared with the former value （1975-1995）, it mani- fests that the carbon storage of the two regions do increase clearly. The results of this re- search show that the large-scale reforestation in the last decades in China attains a signifi- cant carbon sink.     

1949–2014年中国三种主要作物的地理重心时空迁移分析（英文）

Spatial distribution changes in major crops can reveal important information about cropping systems.Here,a new centroid method that applies physics and mathematics to spatial pattern analysis in agriculture is proposed to quantitatively describe the historical centroids of rice,maize and wheat in China from 1949 to 2014.The geographical centroids of the rice area moved 413.39 km in a 34.32° northeasterly(latitude 3.08°N,longitude 2.10°E) direction at a speed of 6.36 km/year from central Hunan province to Hubei province,while the geographical centroids of rice production moved 509.26 km in the direction of 45.44° northeasterly(latitude 3.22°N,longitude 3.27°E) at a speed of 7.83 km/year from central Hunan province to Henan province.The geographical centroids of the maize area and production moved 307.15 km in the direction of 34.33° northeasterly(latitude 2.29°N,longitude 1.56°E) and 308.16 km in the direction of 30.79° northeasterly(latitude 2.39°N,longitude 1.42°E),respectively.However,the geographical centroids of the wheat area and production were randomly distributed along the border of Shanxi and Henan provinces.We divided the wheat into spring wheat and winter wheat and found that the geographical centroids of the spring wheat area and production were distributed within Inner Mongolia,while the geographical centroids of winter wheat were distributed in Shanxi and Henan provinces.We found that the hotspots of crop cultivation area and production do not always change concordantly at a larger,regional scale,suggesting that the changing amplitude and rate of each crops' yield differ between different regions in China.Thus,relevant adaptation measures should be taken at a regional level to prevent production damage in those with increasing area but decreasing production.     

内蒙古植被覆盖度的时空动态变化及其与气候因子的关系(英文)

The vegetation coverage dynamics and its relationship with climate factors on different spatial and temporal scales in Inner Mongolia during 2001-2010 were analyzed based on MODIS-NDVI data and climate data.The results indicated that vegetation coverage in Inner Mongolia showed obvious longitudinal zonality,increasing from west to east across the region with a change rate of 0.2/10°N.During 2001-2010,the mean vegetation coverage was 0.57,0.4 and 0.16 in forest,grassland and desert biome,respectively,exhibiting evident spatial heterogeneities.Totally,vegetation coverage had a slight increasing trend during the study period.Across Inner Mongolia,the area of which the vegetation coverage showed extremely significant and significant increase accounted for 11.25% and 29.13% of the area of whole region,respectively,while the area of which the vegetation coverage showed extremely significant and significant decrease accounted for 7.65% and 26.61%,respectively.On inter-annual time scale,precipitation was the dominant driving force of vegetation coverage for the whole region.On inter-monthly scale,the change of vegetation coverage was consistent with both the change of temperature and precipitation,implying that the vegetation growth within a year is more sensitive to the combined effects of water and heat rather than either single climate factor.The vegetation coverage in forest biome was mainly driven by temperature on both inter-annual and inter-monthly scales,while that in desert biome was mainly influenced by precipitation on both the two temporal scales.In grassland biome,the yearly vegetation coverage had a better correlation with precipitation,while the monthly vegetation coverage was influenced by both temperature and precipitation.In grassland biome,the impacts of precipitation on monthly vegetation coverage showed time-delay effects.     

省尺度森林植被碳储量空间分布模拟不确定性分析——以江西省为例（英文）

Forest vegetation carbon patterns are significant for evaluating carbon emission and accumulation. Many methods were used to simulate patterns of forest vegetation carbon stock in previous studies, however, uncertainty apparently existed between results of different methods, even estimates of same method in different studies. Three previous methods, including Atmosphere-vegetation interaction model 2(AVIM2), Kriging, Satellite-data Based Approach(SBA), and a new method, High Accuracy Surface Modeling(HASM), were used to simulate forest vegetation carbon stock patterns in Jiangxi Province in China. Cross-validation was used to evaluate methods. The uncertainty and applicability of the four methods on provincial scale were analyzed and discussed. The results showed that HASM had the highest accuracy, which improved by 50.66%, 33.37% and 28.58%, compared with AVIM2, Kriging and SBA, respectively. Uncertainty of simulation of forest biomass carbon stock was mainly derived from modeling error, sampling error and statistical error of forest area. Total forest carbon stock, carbon density and forest area of Jiangxi were 288.62 Tg, 3.06 kg/m~2 and 94.32×109 m~2 simulated by HASM, respectively.     

Spatial-temporal patterns of vegetation dynamics and their relationships to climate variations in Qinghai Lake Basin using MODIS time-series data

Global warming has led to significant vegetation changes in recent years. It is necessary to investigate the effects of climatic variations（temperature and precipitation） on vegetation changes for a better understanding of acclimation to climatic change. In this paper, we focused on the integration and application of multi-methods and spatial analysis techniques in GIS to study the spatio-temporal variation of vegetation dynamics and to explore the vegetation change mechanism. The correlations between EVI and climate factors at different time scales were calculated for each pixel including monthly, seasonal and annual scales respectively in Qinghai Lake Basin from the year of 2001 to 2012. The primary objectives of this study are to reveal when, where and why the vegetation change so as to support better understanding of terrestrial response to global change as well as the useful information and techniques for wise regional ecosystem management practices. The main conclusions are as follows：（1） Overall vegetation EVI in the region increased 6% during recent 12 years. The EVI value in growing seasons（i.e. spring and summer） exhibited very significant improving trend, accounted for 12.8% and 9.3% respectively. The spatial pattern of EVI showed obvious spatial heterogeneity which was consistent with hydrothermal condition. In general, the vegetation coverage improved in most parts of the area since nearly 78% pixel of the whole basin showed increasing trend, while degraded slightly in a small part of the area only.（2） The EVI change was positively correlated with average temperature and precipitation. Generally speaking, in Qinghai Lake Basin, precipitation was the dominant driving factor for vegetation growth; however, at different time scale its weight to vegetation has differences.（3） Based on geo-statistical analysis, the autumn precipitation has a strong correlation with the next spring EVI values in the whole region. This findings explore the autumn precipitation is an important indicator     

30年来呼伦贝尔地区草地植被对气候变化的响应(英文)

Global warming has led to significant vegetation changes especially in the past 20 years. Hulun Buir Grassland in Inner Mongolia, one of the world’s three prairies, is undergoing a process of prominent warming and drying. It is essential to investigate the effects of climatic change (temperature and precipitation) on vegetation dynamics for a better understanding of climatic change. NDVI (Normalized Difference Vegetation Index), reflecting characteristics of plant growth, vegetation coverage and biomass, is used as an indicator to monitor vegetation changes. GIMMS NDVI from 1981 to 2006 and MODIS NDVI from 2000 to 2009 were adopted and integrated in this study to extract the time series characteristics of vegetation changes in Hulun Buir Grassland. The responses of vegetation coverage to climatic change on the yearly, seasonal and monthly scales were analyzed combined with temperature and precipitation data of seven meteorological sites. In the past 30 years, vegetation coverage was more correlated with climatic factors, and the correlations were dependent on the time scales. On an inter-annual scale, vegetation change was better correlated with precipitation, suggesting that rainfall was the main factor for driving vegetation changes. On a seasonal-interannual scale, correlations between vegetation coverage change and climatic factors showed that the sensitivity of vegetation growth to the aqueous and thermal condition changes was different in different seasons. The sensitivity of vegetation growth to temperature in summers was higher than in the other seasons, while its sensitivity to rainfall in both summers and autumns was higher, especially in summers. On a monthly-interannual scale, correlations between vegetation coverage change and climatic factors during growth seasons showed that the response of vegetation changes to temperature in both April and May was stronger. This indicates that the temperature effect occurs in the early stage of vegetation growth. Correlations between vegetation growth and precipitation of the month before the current month, were better from May to August, showing a hysteresis response of vegetation growth to rainfall. Grasses get green and begin to grow in April, and the impacts of temperature on grass growth are obvious. The increase of NDVI in April may be due to climatic warming that leads to an advanced growth season. In summary, relationships between monthly-interannual variations of vegetation coverage and climatic factors represent the temporal rhythm controls of temperature and precipitation on grass growth largely.     

平坦低地农业区人类活动影响下地形对森林分布的影响（英文）

Complex topography buffers forests against deforestation in mountainous regions.However,it is unknown if terrain also shapes forest distribution in lowlands where human impacts are likely to be less constrained by terrain.In such regions,if important at all,topographic effects will depend on cultural-historical factors and thus be human-driven(anthropogenic) rather than natural,except in regions where the general climate or extreme soils limit the occurrence of forests.We used spatial regression modeling to assess the extent to which topographic factors explain forest distribution(presence-absence at a 48×48 m resolution) in a lowland agricultural region(Denmark,43,075 km2) at regional and landscape scales(whole study area and 10×10 km grid cells,respectively),how landscape-scale forest-topography relationships vary geographically,and which potential drivers(topographic heterogeneity,forest cover,clay content,coastal/inland location) determine this geographic heterogeneity.Given a moist temperate climate and non-extreme soils all landscapes in Denmark would naturally be largely forest covered,and any topographic relationships will be totally or primarily human-driven.At regional scale,topographic predictors explained only 5% of the distribution of forest.In contrast,the explanatory power of topography varied from 0%–61% at landscape scale,with clear geographic patterning.Explanatory power of topography at landscape scale was moderately dependent on the potential drivers,with topographic control being strongest in areas with high topographic heterogeneity and little forest cover.However,these conditioning effects were themselves geographically variable.Our findings show that topography by shaping human land-use can affect forest distribution even in flat,lowland regions,but especially via localized,geographically variable effects.     

青藏高原高寒草地净初级生产力(NPP)时空分异(英文)

Based on the GIMMS AVHRR NDVI data(8 km spatial resolution) for 1982–2000, the SPOT VEGETATION NDVI data(1 km spatial resolution) for 1998–2009, and observational plant biomass data, the CASA model was used to model changes in alpine grassland net primary production(NPP) on the Tibetan Plateau(TP). This study will help to evaluate the health conditions of the alpine grassland ecosystem, and is of great importance to the promotion of sustainable development of plateau pasture and to the understanding of the function of the national ecological security shelter on the TP. The spatio-temporal characteristics of NPP change were investigated using spatial statistical analysis, separately on the basis of physico-geographical factors(natural zone, altitude, latitude and longitude), river basin, and county-level administrative area. Data processing was carried out using an ENVI 4.8 platform, while an ArcGIS 9.3 and ANUSPLIN platform was used to conduct the spatial analysis and mapping. The primary results are as follows:(1) The NPP of alpine grassland on the TP gradually decreases from the southeast to the northwest, which corresponds to gradients in precipitation and temperature. From 1982 to 2009, the average annual total NPP in the TP alpine grassland was 177.2×1012gC yr-1(yr represents year), while the average annual NPP was 120.8 gC m-2yr-1.(2) The annual NPP in alpine grassland on the TP fluctuates from year to year but shows an overall positive trend ranging from 114.7 gC m-2yr-1in 1982 to 129.9 gC m-2yr-1in 2009, with an overall increase of 13.3%; 32.56% of the total alpine grassland on the TP showed a significant increase in NPP, while only 5.55% showed a significant decrease over this 28-year period.(3) Spatio-temporal characteristics are an important control on annual NPP in alpine grassland: a) NPP increased in most of the natural zones on the TP, only showing a slight decrease in the Ngari montane desert-steppe and desert zone. The positive trend in NPP in the high-cold shrub-meadow zone, high-cold meadow steppe zone and high-cold steppe zone is more significant than that of the high-cold desert zone; b) with increasing altitude, the percentage area with a positive trend in annual NPP follows a trend of"increasing-stable-decreasing", while the percentage area with a negative trend in annual NPP follows a trend of "decreasing-stable-increasing", with increasing altitude; c) the variation in annual NPP with latitude and longitude co-varies with the vegetation distribution; d) the variation in annual NPP within the major river basins has a generally positive trend, of which the growth in NPP in the Yellow River Basin is most significant. Results show that, based on changes in NPP trends, vegetation coverage and phonological phenomenon with time, NPP has been declining in certain places successively, while the overall health of the alpine grassland on the TP is improving.     

2000-2010年黄河上游流域植被年际变化及其对气候要素的响应(英文)

To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index(NDVI) time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil–Sen median trend analysis and the Mann–Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000–2005 than in 2006–2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20–30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive: regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.     

山体效应对生态垂直格局的影响（英文）

The varied altitudinal gradient of climate and vegetation is further complicated by mass elevation effect(MEE), especially in high and extensive mountain regions. However, this effect and its implications for mountain altitudinal belts have not been well studied until recently. This paper provides an overview of the research carried out in the past 5 years. MEE is virtually the heating effect of mountain massifs and can be defined as the temperature difference on a given elevation between inside and outside of a mountain mass. It can be digitally modelled with three factors of intra-mountain base elevation(MBE), latitude and hygrometric continentality; MBE usually acts as the primary factor for the magnitude of MEE and, to a great extent, could represent MEE. MEE leads to higher treelines in the interior than in the outside of mountain masses. It makes montane forests to grow at 4800–4900 m and snowlines to develop at about 6000 m in the southern Tibetan Plateau and the central Andes, and large areas of forests to live above 3500 m in a lot of high mountains of the world. The altitudinal distribution of global treelines can be modelled with high precision when taking into account MEE and the result shows that MEE contributes the most to treeline distribution pattern. Without MEE, forests could only develop upmost to about 3500 m above sea level and the world ecological pattern would be much simpler. The quantification of MEE should be further improved with higher resolution data and its global implications are to be further revealed.     

陕甘宁地区植被恢复对气候变化和人类活动的响应(英文)

The "Grain for Green Project" initiated by the governments since 1999 were the dominant contributors to the vegetation restoration in the agro-pastoral transitional zone of northern China. Climate change and human activities are responsible for the improvement and degradation to a certain degree. In order to monitor the vegetation variations and clarify the causes of rehabilitation in the Shaanxi-Gansu-Ningxia Region, this paper, based on the MODIS-NDVI and climate data during the period of 2000-2009, analyzes the main charac-teristics, spatial-temporal distribution and reasons of vegetation restoration, using methods of linear regression, the Hurst Exponent, standard deviation and other methods. Results are shown as follows. (1) From 2000 to 2009, the NDVI of the study area was improved progres-sively, with a linear tendency being 0.032/10a, faster than the growth of the Three-North Shelter Forest Program (0.007/10a) from 1982 to 2006. (2) The vegetation restoration is characterized by two fast-growing periods, with an "S-shaped" increasing curve. (3) The largest proportion of the contribution to vegetation restoration was observed in the slightly improved area, followed by the moderate and the significantly improved area; the degraded area is distributed sporadically over southern part of Ningxia Hui Autonomous Region as well as eastern Dingbian of Shaanxi province, Huanxian and Zhengyuan of Gansu province. (4) Climate change and human activities are two driving forces in vegetation restoration; more-over anthropogenic factors such as "Grain for Green Project" were the main causes leading to an increasing trend of NDVI on local scale. However, its influencing mechanism remains to be further investigated. (5) The Hurst Exponent of NDVI time series shows that the vegetation restoration was sustainable. It is expected that improvement in vegetation cover will expand to the most parts of the region.     

Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010

To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index（NDVI） time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil–Sen median trend analysis and the Mann–Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000–2005 than in 2006–2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20–30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive： regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.     

川渝地区气温随地形、经度和纬度的变化(英文)

Using the daily temperature data of 95 meteorological stations from Si-chuan-Chongqing Region and its surrounding areas, this paper adopted these methods (e.g., linear regression, trend coefficient, geographical statistics, gray relational analysis and spatial analysis functions of GIS) to analyze the relations of temperature variability with topography, latitude and longitude. Moreover, the rank of gray correlation between temperature variability and elevation, longitude, latitude, topographic position and surface roughness also was measured. These results indicated: (1) The elevation affected temperature variability most obviously, followed by latitude, and longitude. The slope of the linear regression between temperature change rate and elevation, latitude and longitude was 0.4142, 0.0293 and -0.3270, respectively. (2) The rank of gray correlation between temperature change rate and geographic factors was elevation > latitude > surface roughness > topographic position > longitude. The gray correla-tion degree between temperature change rate and elevation was 0.865, followed by latitude with 0.796, and longitude with 0.671. (3) The rate of temperature change enhanced with the increase of elevation. Especially, the warming trend was significant in the plateau and mountain areas of western Sichuan, and mountain and valley areas of southwestern Sichuan (with the warming rate of 0.74℃/10a during the 1990s). However, there was a weak warming trend in Sichuan Basin and its surrounding low mountain and hilly areas. (4) The effects of latitude on temperature change rate presented the specific regulation, which the warming rate of low-latitude areas was more significant than that of high-latitude areas. However, they were consistent with the regulation that the increasing of low temperature controlled most of the warming trend, due to the effects of terrain and elevation on annual mean temperature. (5) Ba-sically, temperature variability along longitude direction resulted from the regular change of elevation along longitude. It was suggested that, in Sichuan-Chongqing Region, special features of temperature variability largely depended on the terrain complexity (e.g., undulations, mutations and roughness). The elevation level controlled only high or low annual mean temperature and the range of temperature change rate in the macro sense.     

Evaluation of Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products for drought monitoring over the middle and lower reaches of the Yangtze River Basin,China

Drought is one of the most frequent and widespread natural disasters and has tremendous agricultural, ecological, societal, and economic impacts. Among the many drought indices, the standardized precipitation index(SPI) based on monthly precipitation data is simple to calculate and has multiscale characteristics. To evaluate the applicability of high spatiotemporal resolution satellite precipitation products for drought monitoring, based on the Tropical Rainfall Measuring Mission(TRMM) products and station-based meteorological data, the SPI values at different time scales(1, 3, 6, and 12 months) were calculated for the period of 1998–2016 in the middle and lower reaches of the Yangtze River Basin(MLRYRB). The temporal correlations show that there is a high degree of consistency between calculations at the different time scales(1, 3, 6 and 12 months) based on the two data sources and that the amplitude of fluctuations decreases with increasing time scale. In addition, the Mann-Kendall(MK) test method was applied to analyze the trends from 1998 to 2016, and the results suggest that wetting trends clearly prevailed over drying trends. Moreover, a correlation analysis of the two data sources based on 60 meteorological stations was performed with the SPI values at different time scales. The correlation coefficients at the short time scales(1, 3, and 6 months) are all greater than 0.7, and the correlation coefficient at the long time scale(12 months) is greater than 0.5. In summary, the results demonstrate that the TRMM 3 B43 precipitation product provides a new data source that can be used for reliable drought monitoring in the MLRYRB.     

Imbalance of inter-provincial forest carbon sequestration rate from 2010 to 2060 in China and its regulation strategy

Forest ecosystem, as a predominant component of terrestrial ecosystems in view of carbon sinks, has a high potential for carbon sequestration. Accurately estimating the carbon sequestration rate in forest ecosystems at provincial level, is a prerequisite and basis for scientifically formulating the technical approaches of carbon neutrality and the associated regulatory policies in China. However, few researches on future carbon sequestration rates(CSRs) for Chinese forest ecosystems for provinci...     

Evaluating the reliability of global historical land use scenarios for forest data in China

Global historical land use scenarios are widely used to simulate the climatic and ecological effects of changes in land cover; however, reliability evaluation of these scenarios for data on China's forests is missing. By using a historical document-derived Chinese forest dataset(CHFD) for the years 1700–2000, we evaluated the reliability of data on forests in China over three global scenarios—SAGE(Center for Sustainability and the Global Environment), PJ(Pongratz Julia), and KK10(Kaplan and Krumhardt 2010)—through trend-related, quantitative, and spatial comparisons. The results show the following:(1) Although the area occupied by forests in China in the SAGE, PJ, KK10, and CHFD datasets decreased over the past 300 years, there were large differences between global scenarios and CHFD. The area occupied by forests in China in the SAGE scenario for 1700–1990 was 20%–40% more than that according to CHFD, and that occupied by forests in the KK10 from 1700 to 1850 was 32%–46% greater than that in CHFD. The difference between the PJ and CHFD was lower than 20% for most years.(2) Large differences were detected at the provincial and grid cell scales, where the PJ scenario was closer to CHFD in terms of total forested area. Provinces with large differences in terms of trend and quantity were 84% and 92% of all provinces, respectively. Grid cells with relative differences greater than 70% accounted for 60%–80% of all grids.(3) These global historical land use scenarios do not accurately reveal the spatiotemporal pattern of Chinese forests due to differences in the data sources, methods of reconstruction, and spatial scales.     

山体效应及其对林线分布的影响(英文)

The concept of mass elevation effect(massenerhebungseffect,MEE) was intro-duced by A.de Quervain about 100 years ago to account for the observed tendency for temperature-related parameters such as tree line and snowline to occur at higher elevations in the central Alps than on their outer margins.It also has been widely observed in other ar-eas of the world,but there have not been significant,let alone quantitative,researches on this phenomenon.Especially,it has been usually completely neglected in developing fitting mod-els of timberline elevation,with only longitude or latitude considered as impacting factors.This paper tries to quantify the contribution of MEE to timberline elevation.Considering that the more extensive the land mass and especially the higher the mountain base in the interior of land mass,the greater the mass elevation effect,this paper takes mountain base elevation(MBE) as the magnitude of MEE.We collect 157 data points of timberline elevation,and use their latitude,longitude and MBE as independent variables to build a multiple linear regres-sion equation for timberline elevation in the southeastern Eurasian continent.The results turn out that the contribution of latitude,longitude and MBE to timberline altitude reach 25.11%,29.43%,and 45.46%,respectively.North of northern latitude 32°,the three factors’ contribu-tion amount to 48.50%,24.04%,and 27.46%,respectively;to the south,their contribution is 13.01%,48.33%,and 38.66%,respectively.This means that MBE,serving as a proxy indi-cator of MEE,is a significant factor determining the elevation of alpine timberline.Compared with other factors,it is more stable and independent in affecting timberline elevation.Of course,the magnitude of the actual MEE is certainly determined by other factors,including mountain area and height,the distance to the edge of a land mass,the structures of the mountains nearby.These factors need to be included in the study of MEE quantification in the future.This paper could help build up a high-accuracy and multi-scale elevation model for alpine timberline and even other altitudinal belts.     

Contrasting vegetation changes in dry and humid regions of the Tibetan Plateau over recent decades

An overall greening over the Tibetan Plateau(TP) in recent decades has been established through analyses of remotely sensed Normalized Difference Vegetation Index(NDVI), though the regional pattern of the changes and associated drivers remain to be explored. This study used a satellite Leaf Area Index(LAI) dataset(the GLASS LAI dataset) and examined vegetation changes in humid and arid regions of the TP during 1982–2012. Based on distributions of the major vegetation types, the TP was divided roughly into a humid southeastern region dominated by meadow and a dry northwestern region covered mainly by steppe. It was found that the dividing line between the two regions corresponded well with the lines of mean annual precipitation of 400 mm and the mean LAI of 0.3. LAI=0.3 was subsequently used as a threshold for investigating vegetation type changes at the interanual and decadal time scales: if LAI increased from less than 0.3 to greater than0.3 from one time period to the next, it was regarded as a change from steppe to meadow, and vice versa. The analysis shows that changes in vegetation types occurred primarily around the dividing line of the two regions, with clear growth(reduction) of the area covered by meadow(steppe), in consistency with the findings from using another independent satellite product. Surface air temperature and precipitation(diurnal temperature range) appeared to contribute positively(negatively) to this change though climate variables displayed varying correlation with LAI for different time periods and different regions.