The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variatio...The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variations is well-documented,spatial variations(from local to regional scales)remain inadequately understood.To evaluate the scale-dependent effects of productivity,predictions from the Biome-BGC model were compared with moderate-resolution imaging spectroradiometer(MODIS)and biometric NPP data in a large temperate forest region at both local and regional levels.Linear mixed-effect models and variance partitioning analysis were used to quantify the effects of environmental heterogeneity and trait variation on simulated NPP at varying spatial scales.Results show that NPP had considerable predictability at the local scale,with a coefficient of determination(R^(2))of 0.37,but this predictability declined significantly to 0.02 at the regional scale.Environmental heterogeneity and photosynthetic traits collectively explained 94.8%of the local variation in NPP,which decreased to 86.7%regionally due to the reduced common effects among these variables.Locally,the leaf area index(LAI)predominated(34.6%),while at regional scales,the stomatal conductance and maximum carboxylation rate were more influential(41.1%).Our study suggests that environmental heterogeneity drives the photosynthetic processes that mediate NPP variations across spatial scales.Incorporating heterogeneous local conditions and trait variations into analyses could enhance future research on the relationship between climate and carbon cycles at larger scales.展开更多
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 de...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.展开更多
Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and...Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.展开更多
The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the cont...The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau(QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index(NDVI) and the Global Inventory Modeling and Mapping Studies(GIMMS) NDVI_(3g) datasets to obtain a NDVI dataset(1982–2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach(CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m^(2)·a), and 217.52 g C/(m^(2)·a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982–2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m^(2)·a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation(R99p), simple daily intensity index(SDII), and hottest day(TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index(CSDI) and warm spell duration index(WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.展开更多
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 d...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.展开更多
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 ...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.展开更多
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 P...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.展开更多
It is necessary to quantitatively study the relationship between climate and human factors on net primary productivity(NPP)inorder to understand the driving mechanism of NPP and prevent desertification.This study inve...It is necessary to quantitatively study the relationship between climate and human factors on net primary productivity(NPP)inorder to understand the driving mechanism of NPP and prevent desertification.This study investigated the spatial and temporal differentiation features of actual net primary productivity(ANPP)in the Ili River Basin,a transboundary river between China and Kazakhstan,as well as the proportional contributions of climate and human causes to ANPP variation.Additionally,we analyzed the pixel-scale relationship between ANPP and significant climatic parameters.ANPP in the Ili River Basin increased from 2001 to 2020 and was lower in the northeast and higher in the southwest;furthermore,it was distributed in a ring around the Tianshan Mountains.In the vegetation improvement zone,human activities were the dominant driving force,whereas in the degraded zone,climate change was the primary major driving force.The correlation coefficients of ANPP with precipitation and temperature were 0.322 and 0.098,respectively.In most areas,there was a positive relationship between vegetation change,temperature and precipitation.During 2001 to 2020,the basin’s climatic change trend was warm and humid,which promoted vegetation growth.One of the driving factors in the vegetation improvement area was moderate grazing by livestock.展开更多
Net primary productivity （NPP） is the structure and function of the ecosystem. NPP can most important index that represents the be simulated by dynamic global vegetation models （DGVM）, which are designed to repres...Net primary productivity （NPP） is the structure and function of the ecosystem. NPP can most important index that represents the be simulated by dynamic global vegetation models （DGVM）, which are designed to represent vegetation dynamics relative to environ- mental change. This study simulated the NPP of China＇s ecosystems based on the DGVM Integrated Biosphere Simulator （IBIS） with data on climate, soil, and topography. The appli- cability of IBIS in the NPP simulation of China＇s terrestrial ecosystems was verified first. Comparison with other relevant studies indicates that the range and mean value of simula- tions are generally within the limits of observations; the overall pattern and total annual NPP are close to the simulations conducted with other models. The simulations are also close to the NPP estimations based on remote sensing. Validation proved that IBIS can be utilized in the large-scale simulation of NPP in China＇s natural ecosystem. We then simulated NPP with climate change data from 1961 to 2005, when warming was particularly striking. The following are the results of the simulation. （1） Total NPP varied from 3.61 GtC/yr to 4.24 GtC/yr in the past 45 years and exhibited minimal significant linear increase or decrease. （2） Regional differences in the increase or decrease in NPP were large but exhibited an insignificant overall linear trend. NPP declined in most parts of eastern and central China, especially in the Loess Plateau. （3） Similar to the fluctuation law of annual NPP, seasonal NPP also displayed an insignificant increase or decrease; the trend line was within the general level. （4） The re- gional differences in seasonal NPP changes were large. NPP declined in spring, summer, and autumn in the Loess Plateau but increased in most parts of the Tibetan Plateau.展开更多
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 Carnegi...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.展开更多
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 App...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.展开更多
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 ...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.展开更多
We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts ...We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.展开更多
The Three-River Headwater Region（TRHR）, known as the ＂Water Tower of China＂, is an important ecological shelter for national security interests and regional sustainable development activities for many downstream r...The Three-River Headwater Region（TRHR）, known as the ＂Water Tower of China＂, is an important ecological shelter for national security interests and regional sustainable development activities for many downstream regions in China and a number of Southeast Asian countries. The TRHR is a high-elevation, cold environment with a unique, but typical alpine vegetation system. Net primary productivity（NPP） is a key vegetation parameter and ecological indicator that can reflect both natural environmental changes and carbon budget levels. Given the unique geographical environment and strategic location of the TRHR, many scholars have estimated NPP of the TRHR by using different methods; however, these estimates vary greatly for a number of reasons. To date, there is no paper that has reviewed and assessed NPP estimation studies conducted in the TRHR. Therefore, in this paper, we（1） summarized the related methods and results of NPP estimation in the TRHR in a systematic review of previous research;（2） discussed the suitability of existing methods for estimating NPP in the TRHR and highlighted the most significant challenges; and（3） assessed the estimated NPP results. Finally, developmental directions of NPP estimation in the TRHR were prospected.展开更多
Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in th...Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index（NDVI） and an improved Carnegie-Ames-Stanford（CASA） model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination（R2） of 0.65 and root mean square error（RMSE） of 20.86 g C/（m^2·a）. Temporally, annual NPP of the Ili River Valley was 599.19 g C/（m^2·a） and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of –3.51 g C/（m^2·a）. However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.展开更多
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 dat...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.展开更多
Grazing is a main human activity in the grasslands of Xinjiang, China. It is vital to identify the effects of grazing on the sustainable utilization of local grasslands. However, the effects of grazing on net primary ...Grazing is a main human activity in the grasslands of Xinjiang, China. It is vital to identify the effects of grazing on the sustainable utilization of local grasslands. However, the effects of grazing on net primary productivity （NPP）, evapotranspiration （ET） and water use efficiency （WUE） in this region remain unclear. Using the spatial Biome-BGC grazing model, we explored the effects of grazing on NPP, ET and WUE across the different regions and grassland types in Xinjiang during 1979-2012. NPP, ET and WUE under the grazed scenario were generally lower than those under the ungrazed scenario, and the differences showed increasing trends over time. The decreases in NPP, ET and WUE varied significantly among the regions and grassland types. NPP decreased as follows： among the regions, Northern Xinjiang （16.60 g C/（m2·a））, Tianshan Mountains （15.94 g C/（m2·a）） and Southern Xinjiang （-3.54 g C/（m2·a））; and among the grassland types, typical grasslands （25.70 g C/（m2·a））, swamp meadows （25.26 g C/（m2·a））, mid-mountain meadows （23.39 g C/（m2·a））, alpine meadows （6.33 g C/（m2·a））, desert grasslands （5.82 g C/（m2·a）） and saline meadows （2.90 g C/（me.a））. ET decreased as follows： among the regions, Tianshan Mountains （28.95 mm/a）, Northern Xinjiang （8.11 mm/a） and Southern Xinjiang （7.57 mm/a）; and among the grassland types, mid-mountain meadows （29.30 mm/a）, swamp meadows （25.07 mm·a）, typical grasslands （24.56 mm/a）, alpine meadows （20.69 mm/a）, desert grasslands （11.06 mm/a） and saline meadows （3.44 mm/a）. WUE decreased as follows： among the regions, Northern Xinjiang （0.053 g C/kg H2O）, Tianshan Mountains （0.034 g C/kg H2O） and Southern Xinjiang （0.012 g C/kg H2O）; and among the grassland types, typical grasslands （0.0609 g C/kg H2O）, swamp meadows （0.0548 g C/kg H2O）, mid-mountain meadows （0.0501 g C/kg H2O）, desert grasslands （0.0172 g C/kg H2O）, alpine meadows （0.0121 g C/kg H2O） and saline meadows （0.0067 g C/kg H2O）. In general, the decreases in NPP and WUE were more significant in the regions with relatively high levels of vegetation growth because of the high grazing intensity in these regions. The decreases in ET were significant in mountainous areas due to the terrain and high grazing intensity.展开更多
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 ...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）.展开更多
Northeast China has experienced frequent droughts over the past fifteen years.However,the effects of droughts on net primary productivity(NPP)in Northeast China remain unclear.In this paper,the droughts that occurred ...Northeast China has experienced frequent droughts over the past fifteen years.However,the effects of droughts on net primary productivity(NPP)in Northeast China remain unclear.In this paper,the droughts that occurred in Northeast China between 1999 and 2013 were identified using the Standardized Precipitation Evapotranspiration Index(SPEI).The NPP standardized anomaly index(NPP-SAI)was used to evaluate NPP anomalies.The years of 1999,2000,2001,and 2007 were further investigated in order to explore the influence of droughts on NPP at different time scales(3,6,and 12 months).Based on the NPP-SAI of normal areas,we found droughts overall decreased NPP by 112.06 Tg C between 1999 and 2013.Lower temperatures at the beginning of the growing season could cause declines in NPP by shortening the length of the growing season.Mild drought or short-term drought with higher temperatures might increase NPP,and weak intensity droughts intensified the lag effects of droughts on NPP.展开更多
An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial...An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial distribution and dynamic change of NPP and fractional vegetation cover in the Yellow River Basin from 1982 to 1999 are analyzed. Finally, the effect of rainfall on NDVI is examined. Results show that mean NPP and fractional vegetation cover have an inclining trend for the whole basin, and rainfall in flood season influences vegetation cover most.展开更多
基金supported by the National Key R&D Program of China(No.2023YFF1304001-01)the Science and Technology Project of the Department of Transportation of Heilongjiang Province(No.HJK2023B024-3)the Program of National Natural Science Foundation of China(No.32371870).
文摘The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variations is well-documented,spatial variations(from local to regional scales)remain inadequately understood.To evaluate the scale-dependent effects of productivity,predictions from the Biome-BGC model were compared with moderate-resolution imaging spectroradiometer(MODIS)and biometric NPP data in a large temperate forest region at both local and regional levels.Linear mixed-effect models and variance partitioning analysis were used to quantify the effects of environmental heterogeneity and trait variation on simulated NPP at varying spatial scales.Results show that NPP had considerable predictability at the local scale,with a coefficient of determination(R^(2))of 0.37,but this predictability declined significantly to 0.02 at the regional scale.Environmental heterogeneity and photosynthetic traits collectively explained 94.8%of the local variation in NPP,which decreased to 86.7%regionally due to the reduced common effects among these variables.Locally,the leaf area index(LAI)predominated(34.6%),while at regional scales,the stomatal conductance and maximum carboxylation rate were more influential(41.1%).Our study suggests that environmental heterogeneity drives the photosynthetic processes that mediate NPP variations across spatial scales.Incorporating heterogeneous local conditions and trait variations into analyses could enhance future research on the relationship between climate and carbon cycles at larger scales.
基金funded by the Xi'an Social Science Fund(24QL38).
文摘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.
基金funded by the National Natural Science Foundation of China[grant numbers 42075115 and 41991285]the Joint Open Project of KLME&CIC-FEMD[grant number KLME201901]。
文摘Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.
基金supported by the National Natural Science Foundation of China (U2243227)。
文摘The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau(QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index(NDVI) and the Global Inventory Modeling and Mapping Studies(GIMMS) NDVI_(3g) datasets to obtain a NDVI dataset(1982–2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach(CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m^(2)·a), and 217.52 g C/(m^(2)·a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982–2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m^(2)·a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation(R99p), simple daily intensity index(SDII), and hottest day(TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index(CSDI) and warm spell duration index(WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.
基金This paper was supported by the National Natural Sci-ence Foundation of China (Grant No. 40371001) and the Youth Foundation of Beijing Normal University
文摘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.
文摘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.
基金National Natural Sciences Foundation of China (Nos. 40501072 and 40673067)the Major State Basic Research Develop-ment Program of China (No. 2002CB 412503)the Knowledge In-novation Program of the Institute of Geographic Sciences and Natural Resources Research,CAS "The effect of human activities on regional envi-ronmental quality, the health risk and the environmental remediation"
文摘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.
基金Under the auspices of the Key Laboratory of Xinjiang Science and Technology Department(No.2022D04009)National Social Science Foundation of China’s Major Program(No.17ZDA064)。
文摘It is necessary to quantitatively study the relationship between climate and human factors on net primary productivity(NPP)inorder to understand the driving mechanism of NPP and prevent desertification.This study investigated the spatial and temporal differentiation features of actual net primary productivity(ANPP)in the Ili River Basin,a transboundary river between China and Kazakhstan,as well as the proportional contributions of climate and human causes to ANPP variation.Additionally,we analyzed the pixel-scale relationship between ANPP and significant climatic parameters.ANPP in the Ili River Basin increased from 2001 to 2020 and was lower in the northeast and higher in the southwest;furthermore,it was distributed in a ring around the Tianshan Mountains.In the vegetation improvement zone,human activities were the dominant driving force,whereas in the degraded zone,climate change was the primary major driving force.The correlation coefficients of ANPP with precipitation and temperature were 0.322 and 0.098,respectively.In most areas,there was a positive relationship between vegetation change,temperature and precipitation.During 2001 to 2020,the basin’s climatic change trend was warm and humid,which promoted vegetation growth.One of the driving factors in the vegetation improvement area was moderate grazing by livestock.
基金"Strategic Priority Research Program of China"of the Chinese Academy of Sciences,No.XDA05090307National Key Technology R&D Program of the 12th Five-Year Plan,No.2012BAC19B10Open Project of Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration,No.SHUES2012A04
文摘Net primary productivity （NPP） is the structure and function of the ecosystem. NPP can most important index that represents the be simulated by dynamic global vegetation models （DGVM）, which are designed to represent vegetation dynamics relative to environ- mental change. This study simulated the NPP of China＇s ecosystems based on the DGVM Integrated Biosphere Simulator （IBIS） with data on climate, soil, and topography. The appli- cability of IBIS in the NPP simulation of China＇s terrestrial ecosystems was verified first. Comparison with other relevant studies indicates that the range and mean value of simula- tions are generally within the limits of observations; the overall pattern and total annual NPP are close to the simulations conducted with other models. The simulations are also close to the NPP estimations based on remote sensing. Validation proved that IBIS can be utilized in the large-scale simulation of NPP in China＇s natural ecosystem. We then simulated NPP with climate change data from 1961 to 2005, when warming was particularly striking. The following are the results of the simulation. （1） Total NPP varied from 3.61 GtC/yr to 4.24 GtC/yr in the past 45 years and exhibited minimal significant linear increase or decrease. （2） Regional differences in the increase or decrease in NPP were large but exhibited an insignificant overall linear trend. NPP declined in most parts of eastern and central China, especially in the Loess Plateau. （3） Similar to the fluctuation law of annual NPP, seasonal NPP also displayed an insignificant increase or decrease; the trend line was within the general level. （4） The re- gional differences in seasonal NPP changes were large. NPP declined in spring, summer, and autumn in the Loess Plateau but increased in most parts of the Tibetan Plateau.
基金Under the auspices of Key Program of Chinese Academy of Sciences(No.KZZD-EW-08-02)CAS/SAFEA(Chinese Academy of Science/State Administration of Foreign Experts Affairs)International Partnership Program for Creative Research Teams(No.KZZD-EW-TZ-07)Strategic Frontier Program of Chinese Academy of Sciences-Climate Change:Carbon Budget and Relevant Issues(No.XDA05050101)
文摘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.
基金Under the auspices of National Key Basic Research Program of China(No.2015CB452706)National Natural Science Foundation of China(No.41401198,41571527)+1 种基金Youth Talent Team Program of the Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(No.SDSQB-2015-01)Youth Innovation Promotion Association,Chinese Academy of Sciences(No.2016332)
文摘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.
基金the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues of Chinese Academy of Sciences(No.XDA05060104)
文摘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.
基金Project supported by the National High-Tech Research and Development Program (863) of China (No. 2006AA120101)the National Natural Science Foundation of China (Nos. 40871158 and 40875070)the Key Technologies Research and Development Program of China (No. 2006BAD10A01)
文摘We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.
基金National Key Research and Development Program of China,No.2016YFC0500205National Basic Research Program of China(973 Program),No.2015CB954103,No.2015CB954101
文摘The Three-River Headwater Region（TRHR）, known as the ＂Water Tower of China＂, is an important ecological shelter for national security interests and regional sustainable development activities for many downstream regions in China and a number of Southeast Asian countries. The TRHR is a high-elevation, cold environment with a unique, but typical alpine vegetation system. Net primary productivity（NPP） is a key vegetation parameter and ecological indicator that can reflect both natural environmental changes and carbon budget levels. Given the unique geographical environment and strategic location of the TRHR, many scholars have estimated NPP of the TRHR by using different methods; however, these estimates vary greatly for a number of reasons. To date, there is no paper that has reviewed and assessed NPP estimation studies conducted in the TRHR. Therefore, in this paper, we（1） summarized the related methods and results of NPP estimation in the TRHR in a systematic review of previous research;（2） discussed the suitability of existing methods for estimating NPP in the TRHR and highlighted the most significant challenges; and（3） assessed the estimated NPP results. Finally, developmental directions of NPP estimation in the TRHR were prospected.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19030204)the West Light Program of Chinese Academy of Sciences(2015-XBQN-B-17)
文摘Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index（NDVI） and an improved Carnegie-Ames-Stanford（CASA） model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination（R2） of 0.65 and root mean square error（RMSE） of 20.86 g C/（m^2·a）. Temporally, annual NPP of the Ili River Valley was 599.19 g C/（m^2·a） and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of –3.51 g C/（m^2·a）. However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.
基金National Natural Science Foundation of China, No. 49871055 No. 39990490 key basic research project of China, No. 95-Y-38
文摘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.
基金supported financially by the National Natural Science Foundation of China(41361140361,41271126)the Project of State Key Laboratory of Desert and Oasis Ecology(Y471163)
文摘Grazing is a main human activity in the grasslands of Xinjiang, China. It is vital to identify the effects of grazing on the sustainable utilization of local grasslands. However, the effects of grazing on net primary productivity （NPP）, evapotranspiration （ET） and water use efficiency （WUE） in this region remain unclear. Using the spatial Biome-BGC grazing model, we explored the effects of grazing on NPP, ET and WUE across the different regions and grassland types in Xinjiang during 1979-2012. NPP, ET and WUE under the grazed scenario were generally lower than those under the ungrazed scenario, and the differences showed increasing trends over time. The decreases in NPP, ET and WUE varied significantly among the regions and grassland types. NPP decreased as follows： among the regions, Northern Xinjiang （16.60 g C/（m2·a））, Tianshan Mountains （15.94 g C/（m2·a）） and Southern Xinjiang （-3.54 g C/（m2·a））; and among the grassland types, typical grasslands （25.70 g C/（m2·a））, swamp meadows （25.26 g C/（m2·a））, mid-mountain meadows （23.39 g C/（m2·a））, alpine meadows （6.33 g C/（m2·a））, desert grasslands （5.82 g C/（m2·a）） and saline meadows （2.90 g C/（me.a））. ET decreased as follows： among the regions, Tianshan Mountains （28.95 mm/a）, Northern Xinjiang （8.11 mm/a） and Southern Xinjiang （7.57 mm/a）; and among the grassland types, mid-mountain meadows （29.30 mm/a）, swamp meadows （25.07 mm·a）, typical grasslands （24.56 mm/a）, alpine meadows （20.69 mm/a）, desert grasslands （11.06 mm/a） and saline meadows （3.44 mm/a）. WUE decreased as follows： among the regions, Northern Xinjiang （0.053 g C/kg H2O）, Tianshan Mountains （0.034 g C/kg H2O） and Southern Xinjiang （0.012 g C/kg H2O）; and among the grassland types, typical grasslands （0.0609 g C/kg H2O）, swamp meadows （0.0548 g C/kg H2O）, mid-mountain meadows （0.0501 g C/kg H2O）, desert grasslands （0.0172 g C/kg H2O）, alpine meadows （0.0121 g C/kg H2O） and saline meadows （0.0067 g C/kg H2O）. In general, the decreases in NPP and WUE were more significant in the regions with relatively high levels of vegetation growth because of the high grazing intensity in these regions. The decreases in ET were significant in mountainous areas due to the terrain and high grazing intensity.
基金the National Basic Research Program of China(2010CB950702)the National High-Technology Reaearch and Development Program of China(2007AA10Z231)the Asia-Pacific Network for Global Change Research Project(ARCP201106CMY-Li)
文摘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）.
基金Under the auspices of Special Issue of National Remote Sensing Survey and Assessment of Eco-Environment Change Between 2000 and 2010(No.STSN-09-03)
文摘Northeast China has experienced frequent droughts over the past fifteen years.However,the effects of droughts on net primary productivity(NPP)in Northeast China remain unclear.In this paper,the droughts that occurred in Northeast China between 1999 and 2013 were identified using the Standardized Precipitation Evapotranspiration Index(SPEI).The NPP standardized anomaly index(NPP-SAI)was used to evaluate NPP anomalies.The years of 1999,2000,2001,and 2007 were further investigated in order to explore the influence of droughts on NPP at different time scales(3,6,and 12 months).Based on the NPP-SAI of normal areas,we found droughts overall decreased NPP by 112.06 Tg C between 1999 and 2013.Lower temperatures at the beginning of the growing season could cause declines in NPP by shortening the length of the growing season.Mild drought or short-term drought with higher temperatures might increase NPP,and weak intensity droughts intensified the lag effects of droughts on NPP.
基金National Key Research Program of Basic Science, No. G1999043601 National Natural Science Foundation of China,No. 49871055
文摘An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial distribution and dynamic change of NPP and fractional vegetation cover in the Yellow River Basin from 1982 to 1999 are analyzed. Finally, the effect of rainfall on NDVI is examined. Results show that mean NPP and fractional vegetation cover have an inclining trend for the whole basin, and rainfall in flood season influences vegetation cover most.
