Spatiotemporal variation and driving factors of vegetation net primary productivity in the Guan-zhong Plain Urban Agglomeration,China from 2001 to 2020

Studying the spatiotemporal variation and driving mechanisms of vegetation net primary productivity(NPP)in the Guanzhong Plain Urban Agglomeration(GPUA)of China is highly important for regional green and low-carbon development.This study used the Theil-Sen trend analysis,Mann-Kendall trend test,coefficient of variation,Hurst index,and machine learning method(eXtreme Gradient Boosting and SHapley Additive exPlanations(XGBoost-SHAP))to analyze the spatiotemporal variation of NPP in the GPUA from 2001 to 2020 and reveal its response to climate change and human activities.The results found that during 2001-2020,the averageNPP in the GPUA showed a significant upward trend,with an annual growth rate of 10.84 g C/(m^(2)•a).The multi-year average NPP in the GPUA was 484.83 g C/(m^(2)•a),with higher values in the southwestern Qinling Mountains and lower values in the central and northeastern cropland and built-up areas.The average coefficient of variation of NPP in the GPUA was 0.14,indicating a relatively stable state overall,but 72.72%of the study area showed weak anti-persistence,suggesting that NPP in most areas may have declined in the short term.According to XGBoost-SHAP analyses,elevation,land use type and precipitation were identified as the main driving factors of NPP.Appropriate precipitation and higher temperatures promote NPP growth,whereas extreme climates,high population density,and nighttime lighting inhibit NPP.This study has important theoretical and practical significance for achieving regional sustainable development,offers a scientific basis for formulating effective ecological protection and restoration strategies,and promotes green,coordinated,and sustainable development in the GPUA.

Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

Spatio-temporal distribution of net primary productivity along the northeast China transect and its response to climatic change

An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 （P 〈 0.01）. The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 （P 〈 0.01）. The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer （June to August） accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m^-2·a^-1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.

How forest age impacts on net primary productivity: Insights from future multi-scenarios

Forest net primary productivity(NPP)constitutes a key flux within the terrestrial ecosystem carbon cycle and serves as a significant indicator of the forests carbon sequestration capacity,which is closely related to forest age.Despite its significance,the impact of forest age on NPP is often ignored in future NPP projections.Here,we mapped forest age in Hunan Province at a 30-m resolution utilizing a combination of Landsat time series stack(LTSS),national forest inventory(NFI)data,and the relationships between height and age.Subsequently,NPP was derived from NFI data and the relationships between NPP and age was built for various forest types.Then forest NPP was predicted based on the NPP-age relationships under three future scenarios,assessing the impact of forest age on NPP.Our findings reveal substantial variations in forest NPP in Hunan Province under three future scenarios:under the age-only scenario,NPP peaks in 2041(133.56TgC·yr^(−1)),while NPP peaks three years later in 2044(141.14TgC·yr^(−1))under the natural development scenario.The maximum afforestation scenario exhibits the most rapid increase in NPP,with peaking in 2049(197.95TgC·yr^(−1)).However,with the aging of the forest,NPP is projected to then decrease by 7.54%,6.07%,and 7.47%in 2060,and 20.05%,19.74%,and 28.38%in 2100,respectively,compared to their peaks under the three scenarios.This indicates that forest NPP will continue to decline soon.Controlling the age structure of forests through selective logging,afforestation and reforestation,and encouraging natural regeneration after disturbance could mitigate this declining trend in forest NPP,but implications of these measures on the full forest carbon balance remain to be studied.Insights from the future multi-scenarios are expected to provide data to support sustainable forest management and national policy development,which will inform the achievement of carbon neutrality goals by 2060.

Seasonal influence of freshwater discharge on spatio-temporal variations in primary productivity, sea surface temperature, and euphotic zone depth in the northern Bay of Bengal

Ocean productivity is the foundation of marine food web,which continuously removes atmospheric carbon dioxide and supports life at sea and on land.Spatio-temporal variability of net primary productivity(NPP),sea surface temperature(SST),sea surface salinity(SSS),mixed layer depth(MLD),and euphotic zone depth(EZD) in the northern B ay of Bengal(BoB) during three monsoon seasons were examined in this study based on remote sensing data for the period 2005 to 2020.To compare the NPP distribution between the coastal zones and open BoB,the study area was divided into five zones(Z1-Z5).Results suggest that most productive zones Z2 and Zl are located at the head bay area and are directly influenced by freshwater discharge together with riverine sediment and nutrient loads.Across Z1-Z5,the NPP ranges from 5 315.38 mg/(m^(2)·d) to 346.7 mg/(m^(2)·d)(carbon,since then the same).The highest monthly average NPP of 5 315.38 mg/(m^(2)·d) in February and 5 039.36 mg/(m^(2)·d) in June were observed from Z2,while the lowest monthly average of 346.72 mg/(m^(2)·d) was observed in March from Z4,which is an oceanic zone.EZD values vary from 6-154 m for the study area,and it has an inverse correlation with NPP concentration.EZD is deeper during the summer season and shallower during the wintertime,with a corresponding increase in productivity.Throughout the year,monthly SST shows slight fluctuation for the entire study area,and statistical analysis shows a significant correlation among NPP,and EZD,overall positive between NPP and MLD,whereas no significant correlation among SSS,and SST for the northern BoB.Long-term trends in SST and productivity were significantly po sitive in head bay zones but negatively productive in the open ocean.The findings in this study on the distribution of NPP,SST,SSS,MLD,and EZD and their seasonal variability in five different zones of BoB can be used to further improve the management of marine resources and overall environmental condition in response to climate changes in BoB as they are of utmost relevance to the fisheries for the three bordering countries.

Decomposition and decoupling effect of energy eco-footprint based on global net primary productivity in urban agglomerations

Net primary productivity(NPP)is an important breakthrough point of current research on ecological footprint improvement.The energy eco-footprint(EEF)of the Four-City Area in Central China(FCACC)was measured by constructing an EEF-NPP model.This work has made the following efforts:(1)Gini coefficient was employed to analyze the degree of matching between the EEF and economic growth,population,and energy consumption.(2)LMDI decomposition method was used to explore the impacts of multiple factors on the EEF in the FCACC.(3)Tapio decoupling model was applied to verify the decoupling relationships between the above influencing factors and the EEF.(4)LMDI decomposition formula was embedded into the decoupling model to analyze the impacts of technical and non-technical factors on the decoupling elasticity of the above.The main findings show that from 2010 to 2020:(1)the degree of matching of EEF-GDP,EEF-population,and EEF-energy consumption increased.(2)energy intensity and per capita GDP were the main factors that affected the EEF.(3)the decoupling states between total energy consumption,energy consumption structure,energy intensity,per capita GDP,and population size with the EEF were expansive negative decoupling,expansive negative decoupling,strong negative decoupling,weak decoupling,and expansive negative decoupling,respectively.(4)the impact of non-technical factors was greater than that of technical factors,and their impacts were always in opposite directions.

A process model for simulating net primary productivity (NPP) based on the interaction of water-heat process and nitrogen: a case study in Lantsang valley

Terrestrial carbon cycle and the global atmospheric CO2 budget are important foci in global climate change research. Simulating net primary productivity （NPP） of terrestrial ecosystems is important for carbon cycle research. In this study, a plant-atmosphere-soil continuum nitrogen （N） cycling model was developed and incorporated into the Boreal Ecosystem Productivity Simulator （BEPS） model. With the established database （leaf area index, land cover, daily meteorology data, vegetation and soil） at a 1 km resolution, daily maps of NPP for Lantsang valley in 2007 were produced, and the spatial-temporal patterns of NPP and mechanisms of its responses to soil N level were further explored. The total NPP and mean NPP of Lantsang valley in 2007 were 66.5 Tg C and 416 g？m-2？a-1 C, respectively. In addition, statistical analysis of NPP of different land cover types was conducted and investigated. Compared with BEPS model （without considering nitrogen effect）, it was inferred that the plant carbon fixing for the upstream of Lantsang valley was also limited by soil available nitrogen besides temperature and precipitation. However, nitrogen has no evident limitation to NPP accumulation of broadleaf forest, which mainly distributed in the downstream of Lantsang valley.

Examining Forest Net Primary Productivity Dynamics and Driving Forces in Northeastern China During 1982–2010

Forest net primary productivity （NPP） is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegie-Ames-Stanford Approach （CASA） model with normalized difference vegetation index （NDVI） sequences derived from Advanced Very High Resolution Radiometer （AVHRR） Global Invento y Modeling and Mapping Studies （GIMMS） and Terra Moderate Resolution Imaging Spectroradiometer （MODIS） products. To address the problem of data inconsistency between AVHRR and MODIS data, a per-pixel unary linear regres- sion model based on least ~;quares method was developed to derive the monthly NDVI sequences. Results suggest that estimated forest NPP has mean relative error of 18.97% compared to observed NPP from forest inventory. Forest NPP in the northeastern China in- creased significantly during the twenty-nine years. The results of seasonal dynamic show that more clear increasing trend of forest NPP occurred in spring and awmnn. This study also examined the relationship between forest NPP and its driving forces including the climatic and anthropogenic factors. In spring and winter, temperature played the most pivotal role in forest NPR In autumn, precipitation acted as the most importanl factor affecting forest NPP, while solar radiation played the most important role in the summer. Evaportran- spiration had a close correlation with NPP for coniferous forest, mixed coniferous broadleaved forest, and broadleaved deciduous forest. Spatially, forest NPP in the Da Hinggan Mountains was more sensitive to climatic changes than in the other ecological functional re- gions. In addition to climalie change, the degradation and improvement of forests had important effects on forest NPP. Results in this study are helpful for understanding the regional carbon sequestration and can enrich the cases for the monitoring of vegetation during long time series.

Impacts of Climate Change on Net Primary Productivity in Arid and Semiarid Regions of China

In recent years, with the constant change in the global climate, the effect of climate factors on net primary productivity(NPP) has become a hot research topic. However, two opposing views have been presented in this research area: global NPP increases with global warming, and global NPP decreases with global warming. The main reasons for these two opposite results are the tremendous differences among seasonal and annual climate variables, and the growth of plants in accordance with these climate variables. Therefore, it will fail to fully clarify the relation between vegetation growth and climate changes by research that relies solely on annual data. With seasonal climate variables, we may clarify the relation between vegetation growth and climate changes more accurately. Our research examined the arid and semiarid areas in China(ASAC), which account for one quarter of the total area of China. The ecological environment of these areas is fragile and easily affected by human activities. We analyzed the influence of climate changes, especially the changes in seasonal climate variables, on NPP, with Climatic Research Unit(CRU) climatic data and Moderate Resolution Imaging Spectroradiometer(MODIS) satellite remote data, for the years 2000–2010. The results indicate that: for annual climatic data, the percentage of the ASAC in which NPP is positively correlated with temperature is 66.11%, and 91.47% of the ASAC demonstrates a positive correlation between NPP and precipitation. Precipitation is more positively correlated with NPP than temperature in the ASAC. For seasonal climatic data, the correlation between NPP and spring temperature shows significant regional differences. Positive correlation areas are concentrated in the eastern portion of the ASAC, while the western section of the ASAC generally shows a negative correlation. However, in summer, most areas in the ASAC show a negative correlation between NPP and temperature. In autumn, precipitation is less important in the west, as opposed to the east, in which it is critically important. Temperatures in winter are a limiting factor for NPP throughout the region. The findings of this research not only underline the importance of seasonal climate variables for vegetation growth, but also suggest that the effects of seasonal climate variables on NPP should be explored further in related research in the future.

Spatio-temporal Pattern of Net Primary Productivity in Hengduan Mountains area, China： Impacts of Climate Change and Human Activities

Net primary productivity(NPP), a metric used to define and identify changes in plant communities, is greatly affected by climate change, human activities and other factors. Here, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate the NPP of plant communities in Hengduan Mountains area of China, and to explore the relationship between NPP and altitude in this region. We examined the mechanisms underlying vegetation growth responses to climate change and quantitatively assessed the effects of ecological protection measures by partitioning the contributions of climate change and human activities to NPP changes. The results demonstrated that: 1) the average total and annual NPP values over the years were 209.15 Tg C and 468.06 g C/(m2·yr), respectively. Their trend increasingly fluctuated, with spatial distribution strongly linked to altitude(i.e., lower and higher NPP in high altitude and low altitude areas, respectively) and 2400 m represented the marginal altitude for vegetation differentiation; 2) areas where climate was the main factor affecting NPP accounted for 18.2% of the total research area, whereas human activities were the primary factor influencing NPP in 81.8% of the total research area, which indicated that human activity was the main force driving changes in NPP. Areas where climatic factors(i.e., temperature and precipitation) were the main driving factors occupied 13.6%(temperature) and 6.0%(precipitation) of the total research area, respectively. Therefore, the effect of temperature on NPP changes was stronger than that of precipitation; and 3) the majority of NPP residuals from 2001 to 2014 were positive, with human activities playing an active role in determining regional vegetation growth, possibly due to the return of farmland back to forest and natural forest protection. However, this positive trend is decreasing. This clearly shows the periodical nature of ecological projects and a lack of long-term effectiveness.

Classification and Net Primary Productivity of the Southern China's Grasslands Ecosystem Based on Improved Comprehensive and Sequential Classification System(CSCS) Approach

This research classified vegetation types and evaluated net primary productivity （NPP） of southern China＇s grasslands based on the improved comprehensive and sequential classification system （CSCS）, and proposed 5 thermal grades and 6 humidity grades. Four classes of grasslands vegetation were recognized by improved CSCS, namely, tundra grassland class, typical grassland class, mixed grassland class and alpine grassland class. At the type level, 14 types of vegetations （9 grasslands and 5 forests） were classified. The NPP had a trend to decrease from east to west and south to north, and the annual mean NPP was estimated to be 656.3 g C m-2 yr-1. The NPP value of alpine grassland class was relatively high, generally more than 1200 g C m2 yr-1. The NPP value of mixed grassland class was in a range from 1 000 to 1200 g C m-2 yr-1. Tundra grassland class was located in southeastern Tibet with high elevation, and its NPP value was the lowest （〈600 g C m＇2yrl）. The typical grassland class distributed in most of the area, and its NPP value was generally from 600 to 1000 g C m-2 yr-1. The total NPP value in the study area was 68.46 Tg C. The NPP value of typical grassland class was the highest （48.44 Tg C）, and mixed grassland class was the second （16.54 Tg C）, followed by alpine grassland class （3.22 Tg C）, with tundra grassland class being the lowest （0.25 Tg C）. For all the grasslands types, the total NPP of forest meadow was the highest （34.81 Tg C）, followed by sparse forest brush （16.54 Tg C）, and montane meadow was the lowest （0.01 Tg C）.

Estimation of Net Primary Productivity of Terrestrial Vegetation in China by Remote Sensing

Among the many approaches for studying the net primary productivity (NPP), a new method by using remote sensing was introduced in this paper. With spectral information source (the visible band, near infrared band and thermal infrared band) of NOAA-AVHRR, we can get the relative index and parameters, which can be used for estimating NPP of terrestrial vegetation. By means of remote sensing, the estimation of biomass and NPP is mainly based on the models of light energy utilization. In other words, the biomass and NPP can be calculated from the relation among NPP, absorbed photosynthetical active radiation (APAR) and the rate (epsilon) of transformation of APAR to organic matter, thus: NPP = ( FPAR x PAR) x [epsilon * x sigma (T) x sigma (E) x sigma (S) x (1 - Y-m) x (1 - Y-g)]. Based upon remote sensing ( RS) and geographic information system (GIS), the NPP of terrestrial vegetation in China in every ten days was calculated, and the annual NPP was integrated. The result showed that the total NPP of terrestrial vegetation in China was 6.13 x 10(9) t C . a(-1) in 1990 and the maximum NPP was 1 812.9 g C/m(2). According to this result, the spatio-temporal distribution of NPP was analyzed. Comparing to the statistical models, the RS model, using area object other than point one, can better reflect the distribution of NPP, and match the geographic distribution of vegetation in China.

Storage of biomass and net primary productivity in desert shrubland of Artemisia ordosica on Ordos Plateau of Inner Mongolia, China

Biomass and net primary productivity （NPP） are two important parameters in determining ecosystem carbon pool and carbon sequestration. The biomass storage and NPP in desert shrubland of Artemisia ordosica on Ordos Plateau were investigated with method of harvesting standard size shrub in the growing season （June-October） of 2006. Results indicated that above- and belowground biomass of the same size shrubs showed no significant variation in the growing season （p〉0.1）, but annual biomass varied significantly （p〈 0.01）. In the A. ordosica community, shrub biomass storage was 699.76-1246.40 g.m^-2 and annual aboveground NPP was 224.09 g-m^-2·a^-1. Moreover, shrub biomass and NPP were closely related with shrub dimensions （cover and height） and could be well predicted by shrub volume using power regression.

江西省NPP估算及其与气候因子的关联分析-基于改进CASA模型

通过改进太阳辐射参数和水分子胁迫系数计算方法提高了CASA(Carnegie-Ames-Stanford Approach)模型估算净初级生产力(NPP)的精度,并在此基础上对NPP和气象因子进行相关性和趋势分析.结果表明,基于改进后模型,NPP与实地观测数据的相关性达0.62;2001~2022年,江西省年均NPP整体呈上升趋势,年均值超过1000gC/(m^(2)⋅a);NPP月均值为秋季>夏季>冬季>春季,月均值最大值出现在7月;NPP年均值上最大值、最小值出现在2018年、2010年;趋势变化和相关性分析的结果表明,2001~2022年江西省太阳辐射量呈现下降趋势,但NPP的变化未受显著影响;最小二乘法回归模型结果表明,温度每增加一个单位,NPP平均随温度的增加而增加,随太阳辐射的减少而减少;NPP在近几年(2019~2022年)极端事件增加的情况下,NPP未出现显著下降.

Simulation of the Ecosystem Productivity Responses to Aerosol Diffuse Radiation Fertilization Effects over the Pan-Arctic during 2001–19

The pan-Arctic is confronted with air pollution transported from lower latitudes.Observations have shown that aerosols help increase plant photosynthesis through the diffuse radiation fertilization effects(DRFEs).While such DRFEs have been explored at low to middle latitudes,the aerosol impacts on pan-Arctic ecosystems and the contributions by anthropogenic and natural emission sources remain less quantified.Here,we perform regional simulations at 0.2o×0.2ousing a well-validated vegetation model(Yale Interactive terrestrial Biosphere,YIBs)in combination with multi-source of observations to quantify the impacts of aerosol DRFEs on the net primary productivity(NPP)in the pan-Arctic during 2001-19.Results show that aerosol DRFEs increase pan-Arctic NPP by 2.19 Pg C(12.8%)yr^(-1)under clear-sky conditions,in which natural and anthropogenic sources contribute to 8.9% and 3.9%,respectively.Under all-sky conditions,such DRFEs are largely dampened by cloud to only 0.26 Pg C(1.24%)yr^(-1),with contributions of 0.65% by natural and 0.59% by anthropogenic species.Natural aerosols cause a positive NPP trend of 0.022% yr^(-1)following the increased fire activities in the pan-Arctic.In contrast,anthropogenic aerosols induce a negative trend of-0.01% yr^(-1)due to reduced emissions from the middle latitudes.Such trends in aerosol DRFEs show a turning point in the year of 2007 with more positive NPP trends by natural aerosols but negative NPP trends by anthropogenic aerosols thereafter.Though affected by modeling uncertainties,this study suggests a likely increasing impact of aerosols on terrestrial ecosystems in the pan-Arctic under global warming.

Assessing the Dynamics of Grassland Net Primary Productivity in Response to Climate Change at the Global Scale

Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.

The net primary productivity of Mid-Jurassic peatland and its control factors: Evidenced by the Ordos Basin

Using the large-scale thick 4# coal seam from the Mid-Jurassic in the southern Ordos Basin as an example, this paper studied the net primary productivity(NPP) level of the Mid-Jurassic peatland, and discussed its control factors. Geophysical logging signals were used for a spectrum analysis to obtain the Milankovitch cycle parameters in coal seam. These were then used to calculate the accumulation rate of the residual carbon in 4# coal seam. The carbon loss can be calculated according to the density and residual carbon content of 4# coal seam. Then, the total carbon accumulation rate of the peatland was further derived, and the NPP of peatland was determined. The results show that the NPP of MidJurassic peatland is higher than that of Holocene at the same latitude. Comprehensive analysis indicates that the temperature, carbon dioxide and oxygen levels in atmosphere are the main control factors of the NPP of Mid-Jurassic peatland.

Variation of net primary productivity and its drivers in China’s forests during 2000-2018

Background:Net primary productivity(NPP)in forests plays an important role in the global carbon cycle.However,it is not well known about the increase rate of China’s forest NPP,and there are different opinions about the key factors controlling the variability of forest NPP.Methods:This paper established a statistics-based multiple regression model to estimate forest NPP,using the observed NPP,meteorological and remote sensing data in five major forest ecosystems.The fluctuation values of NPP and environment variables were extracted to identify the key variables influencing the variation of forest NPP by correlation analysis.Results:The long-term trends and annual fluctuations of forest NPP between 2000 and 2018 were examined.The results showed a significant increase in forest NPP for all five forest ecosystems,with an average rise of 5.2 gC·m-2·year-1 over China.Over 90%of the forest area had an increasing NPP range of 0-161 gC·m-2·year-1.Forest NPP had an interannual fluctuation of 50-269 gC.m-2·year-1 for the five major forest ecosystems.The evergreen broadleaf forest had the largest fluctuation.The variability in forest NPP was caused mainly by variations in precipitation,then by temperature fluctuations.Conclusions:All five forest ecosystems in China exhibited a significant increasing NPP along with annual fluctuations evidently during 2000-2018.The variations in China’s forest NPP were controlled mainly by changes in precipitation.

Estimation of net primary productivity in China using remote sensing data

It is significant to estimate terrestrial net primary productivity (NPP) accurately not only for global change research, but also for natural resources management to achieve sustainable development. Remote sensing data can describe spatial distribution of plant resources better. So, in this paper an NPP model based on remote sensing data and climate data is developed. And 1km resolution AVHRR NDVI data are used to estimate the spatial distribution and seasonal change of NPP in China. The results show that NPP estimated using remote sensing data are more close to truth. Total annual NPP in China is 2.645X109 tC. The spatial distribution of NPP in China is mainly affected by precipitation and has the trend of decreasing from southeast to northwest.

Effects of climate change on phenology and primary productivity in the desert steppe of Inner Mongolia

Variations in temperature and precipitation affect local ecosystems. Considerable spatial and temporal heterogeneity exists in arid ecosystems such as desert steppes. In this study, we analyzed the spatiotemporal dy- namics of climate and vegetation phenology in the desert steppe of Inner Mongolia, China using meteorological data （1961-2010） from 11 stations and phenology data （2004-2012） from 6 ecological observation stations. We also estimated the gross primary production for the period of 1982-2009 and found that the annual mean tem- perature increased at a rate of 0.47~C/decade during 1961-2010, with the last 10 years being consistently warmer than the 50 years as an average. The most significant warming occurred in winters. Annual precipitation slightly decreased during the 50-year period, with summer precipitation experiencing the highest drop in the last 10 years, and spring precipitation, a rise. Spatially, annual precipitation increased significantly in the northeastern and eastern central areas next to the typical steppe. From 2004 to 2012, vegetation green-up and senescence date advanced in the study area, shortening the growing season. Consequently, the primary productivity of the desert steppe de- creased along the precipitation gradient from southeast to northwest. Temporally, productivity increased during the period of 1982-1999 and significantly decreased after 2000. Overall, the Last decade witnessed the most dramatic climatic changes that were likely to negatively affect the desert steppe ecosystem. The decreased primary produc- tivity, in particular, decreases ecosystem resilience and impairs the livelihood of local farmers and herdsmen.