Global patterns in above-ground net primary production and precipitation-use efficiency in grasslands

The above-ground net primary production(ANPP) and the precipitation-use efficiency(PUE) regulate the carbon and water cycles in grassland ecosystems, but the relationships among the ANPP, PUE and precipitation are still controversial. We selected 717 grassland sites with ANPP and mean annual precipitation(MAP) data from 40 publications to characterize the relationships ANPP–MAP and PUE–MAP across different grassland types. The MAP and ANPP showed large variations across all grassland types, ranging from 69 to 2335 mm and 4.3 to 1706 g m^(-2), respectively. The global maximum PUE ranged from 0.19 to 1.49 g m^(-2) mm^(-1) with a unimodal pattern. Analysis using the sigmoid function explained the ANPP–MAP relationship best at the global scale. The gradient of the ANPP–MAP graph was small for arid and semi-arid sites(MAP <400 mm). This study improves our understanding of the relationship between ANPP and MAP across dry grassland ecosystems. It provides new perspectives on the prediction and modeling of variations in the ANPP for different grassland types along precipitation gradients.

Aboveground biomass and net primary production of semi-evergreen tropical forest of Manipur,north-eastern India

The aboveground biomass dynamics and net primary productivity were investigated to assess the productive potential of Dipterocarpus forest in Manipur, Northeast India.Two forest stands(stand I and II) were earmarked randomly in the study site for the evaluation of biomass in the different girth classes of tree species by harvest method.The total biomass was 22.50 t·ha-1 and 18.27 t·ha-1 in forest stand I and II respectively.Annual aboveground net primary production varied from 8.86 to 10.43 t·ha-1 respectively in two forest stands(stand I and II).In the present study, the values of production efficiency and the biomass accumulation ratio indicate that the forest is at succession stage with high productive potential.

Linkages between the biomass of Scomber japonicus and net primary production in the southern East China Sea

Fish biomass is a critical component of fishery stock assessment and management and it is often estimated from ocean primary production（OPP）. However, the relationship between the biomass of a fish stock and OPP is always complicated due to a variety of trophic controls in the ecosystem. In this paper, we examine the quantitative relationship between the biomass of chub mackerel（Scomber japonicus） and net primary production（NPP） in the southern East China Sea（SECS）, using catch and effort data from the Chinese mainland large light-purse seine fishery logbook and NPP derived from remote sensing. We further discuss the mechanisms of trophic control in regulating this relationship. The results show a significant non-linear relationship exists between standardized CPUE（Catch-Per-Unit-Effort） and NPP（P〈0.05）. This relationship can be described by a convex parabolic curve, where the biomass of chub mackerel increases with NPP to a maximum and then decreases when the NPP exceeds this point. The results imply that the ecosystem in the SECS is subject to complex trophic controls. We speculate that the change in abundance of key species at intermediate trophic levels and/or interspecific competition might contribute to this complex relationship.

Assessing Net Primary Production in Montane Wetlands from Proximal, Airborne, and Satellite Remote Sensing

In this study, several vegetation indices were examined in order to determine the most sensitive vegetation index for monitoring southern Appalachian wetlands. Three levels of platforms (in situ, airborne, and satellite) for sensors were also examined in conjunction with vegetation indices. Net primary production (NPP) data were gathered to use as a measure of wetland function. Along with the in situ radiometers, National Agricultural Imagery Program (NAIP) data and Landsat 8 Operational Land Imager (OLI) data were gathered in order to calculate vegetation indices at three platforms. At the in situ level, VARI700 was the most sensitive vegetation index in terms of NPP (r2 = 0.65, p < 0.05). At the airborne level, the NDVI was the most sensitive vegetation index to NPP (r2 = 0.35, p = 0.11). At the satellite level, the DVI appeared to have a positive relationship with NPP. For most indices there was a drop in the coefficient of determination with NPP when the platform altitude increased, with the exception of NDVI when increasing altitude from in situ to airborne. This study provides a novel methodology comparing reflectance and vegetation indices at three platform levels.

Net primary production in three bioenergy crop systems following land conversion

Aims Identifying the amount of production and the partitioning to above-and belowground biomass is generally the first step toward select-ing bioenergy systems.There are very few existing studies on the dynamics of production following land conversion.The objectives of this study were to(i)determine the differences in aboveground net primary production(ANPP),belowground net primary produc-tion(BNPP),shoot-to-root ratio(S:R)and leaf area index in three bioenergy crop systems and(ii)evaluate the production of these three systems in two different land use conversions.Methods This investigation included biometric analysis of NPP on three agri-cultural sites converted from conservation reserve program(CRP)management to bioenergy crop production(corn,switchgrass and prairie mix)and three sites converted from traditional agriculture production to bioenergy crop production.Important findings The site converted from conventional agriculture produced smaller ANPP in corn(19.03±1.90 standard error[SE]Mg ha^(−1) year^(−1))than the site converted from CRP to corn(24.54±1.43 SE Mg ha^(−1) year^(−1)).The two land conversions were similar in terms of ANPP for switchgrass(4.88±0.43 SE for CRP and 2.04±0.23 SE Mg ha^(−1) year^(−1) for agriculture)and ANPP for prairie mix(4.70±0.50 SE for CRP and 3.38±0.33 SE Mg ha^(−1) year^(−1) for agriculture).The BNPP at the end of the growing season in all the bioenergy crop systems was not significantly different(P=0.75,N=8).

Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982

Based on the GIMMS AVHRR NDVI data （8 km spatial resolution） for 1982-2000, the SPOT VEGETATION NDVI data （1 km spatial resolution） for 1998-2009, and observa- tional plant biomass data, the CASA model was used to model changes in alpine grassland net primary production （NPP） on the Tibetan Plateau （TP）. This study will help to evaluate the health conditions of the alpine grassland ecosystem, and is of great importance to the pro- motion of sustainable development of plateau pasture and to the understanding of the func- tion of the national ecological security shelter on the TP. The spatio-temporal characteristics of NPP change were investigated using spatial statistical analysis, separately on the basis of physico-geographical factors （natural zone, altitude, latitude and longitude）, river basin, and county-level administrative area. Data processing was carried out using an ENVI 4.8 platform, while an ArcGIS 9.3 and ANUSPLIN platform was used to conduct the spatial analysis and mapping. The primary results are as follows： （1） The NPP of alpine grassland on the TP gradually decreases from the southeast to the northwest, which corresponds to gradients in precipitation and temperature. From 1982 to 2009, the average annual total NPP in the TP alpine grassland was 177.2x1012 gC yrl（yr represents year）, while the average annual NPP was 120.8 gC m^-2 yr^-1. （2） The annual NPP in alpine grassland on the TP fluctuates from year to year but shows an overall positive trend ranging from 114.7 gC m^-2 yr^-1 in 1982 to 129.9 gC m^-2 yr^-1 in 2009, with an overall increase of 13.3%; 32.56% of the total alpine grassland on the TP showed a significant increase in NPP, while only 5.55% showed a significant decrease over this 28-year period. （3） Spatio-temporal characteristics are an important control on an- nual NPP in alpine grassland： a） NPP increased in most of the natural zones on the TP, only showing a slight decrease in the Ngari montane desert-steppe and desert zone. The positive trend in NPP in the high-cold shrub-meadow zone, high-cold meadow steppe zone and high-cold steppe zone is more significant than that of the high-cold desert zone; b） with in- creasing altitude, the percentage area with a positive trend in annual NPP follows a trend of ＂increasing-stable-decreasing＂, while the percentage area with a negative trend in annual NPP follows a trend of ＂decreasing-stable-increasing＂, with increasing altitude; c） the varia- tion in annual NPP with latitude and longitude co-varies with the vegetation distribution; d） the variation in annual NPP within the major river basins has a generally positive trend, of which the growth in NPP in the Yellow River Basin is most significant. Results show that, based on changes in NPP trends, vegetation coverage and phonological phenomenon with time, NPP has been declining in certain places successively, while the overall health of the alpine grassland on the TP is improving.

Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century

A wide variety of studies have estimated the magnitude of global terrestrial net primary production （NPP）, but its variations, both spatially and temporally, still remain uncertain. By using an improved process-based terrestrial ecosystem model （DLEM, Dynamic Land Ecosystem Model）, we provide an estimate of global terrestrial NPP induced by multiple environmental factors and examine the response of terrestrial NPP to climate variability at biome and global levels and along latitudes throughout the first decade of the 21st century. The model simulation estimates an average global terrestrial NPP of 54.6 Pg C yr-1 during 2000-2009, varying from 52.8 Pg C yr-1 in the dry year of 2002 to 56.4 Pg C yr-1 in the wet year of 2008. In wet years, a large increase in terrestrial NPP compared to the decadal mean was prevalent in Amazonia, Africa and Australia. In dry years, however, we found a 3.2% reduction in global terrestrial NPP compared to the decadal mean, primarily due to limited moisture supply in tropical regions. At a global level, precipitation explained approximately 63% of the variation in terrestrial NPP, while the rest was attributed to changes in temperature and other environmental factors. Precipitation was the major factor determining inter-annual variation in terrestrial NPP in low-latitude regions. However, in midand high-latitude regions, temperature variability largely controlled the magnitude of terrestrial NPP. Our results imply that pro- jected climate warming and increasing climate extreme events would alter the magnitude and spatiotemporal patterns of global terrestrial NPP.

Effects of Climate Change and Shifts in Forest Composition on Forest Net Primary Production

Forests are dynamic in both structure and species composition, and these dynamics are strongly influenced by climate. However, the net effects of future tree species composition on net primary production （NPP） are not well understood. The objective of this work was to model the potential range shifts of tree species （DISTRIB Model） and predict their impacts on NPP （PnET-Ⅱ Model） that will be associated with alterations in species composition. We selected four 200 × 200 km areas in Wisconsin, Maine, Arkansas, and the Ohio-West Virginia area, representing focal areas of potential species range shifts. PnET-Ⅱ model simulations were carried out assuming that all forests achieved steady state, of which the species compositions were predicted by DISTRIB model with no migration limitation. The total NPP under the current climate ranged from 552 to 908 g C/m^2 per year. The effects of potential species redistributions on NPP were moderate （-12% to ＋8%） compared with the influence of future climatic changes （-60% to ＋25%）. The direction and magnitude of climate change effects on NPP were largely dependent on the degree of warming and water balance. Thus, the magnitude of future climate change can affect the feedback system between the atmosphere and biosphere.

Recent patterns of terrestrial net primary production in Africa influenced by multiple environmental changes

Terrestrial net primary production(NPP)is of fundamental importance to food security and ecosystem sustainability.However,little is known about how terrestrial NPP in African ecosystems has responded to recent changes in climate and other environmental factors.Here,we used an integrated ecosystem model(the dynamic land ecosystem model;DLEM)to simulate the dynamic variations in terrestrial NPP of African ecosystems driven by climate and other environmental factors during 1980-2009.We estimate a terrestrial NPP of 10.22(minimum-maximum range of 8.9-11.3)Pg C/yr during the study period.Our results show that precipitation variability had a significant effect on terrestrial NPP,explaining 74%of interannual variations in NPP.Over the 30-yr period,African ecosystems experienced an increase in NPP of 0.03 Pg C/yr,resulting from the combined effects of climate variability,elevated atmospheric CO_(2)concentration,and nitrogen deposition.Our further analyses show that there is a difference in NPP of 1.6 Pg C/yr between wet and dry years,indicating that interannual climatic variations play an important role in determining the magnitude of terrestrial NPP.Central Africa,dominated by tropical forests,was the most productive region and accounted for 50%of the carbon sequestered as NPP in Africa.Our results indicate that warmer and wetter climatic conditions,together with elevated atmospheric CO_(2)concentration and nitrogen deposition,have resulted in a significant increase in African terrestrial NPP during 1980-2009,with the largest contribution from tropical forests.

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.

Modeling grassland net primary productivity and water-use efficiency along an elevational gradient of the Northern Tianshan Mountains

Mountainous ecosystems are considered highly sensitive and vulnerable to natural disasters and cli- rnatic changes. Therefore, quantifying the effects of elevation on grassland productivity to understand ecosys- tem-climate interactions is vital for mountainous ecosystems. Water-use efficiency （WUE） provides a useful index for understanding the metabolism of terrestrial ecosystems as well as for evaluating the degradation of grasslands. This paper explored net primary productivity （NPP） and WUE in grasslands along an elevational gradient ranging from 400 to 3,400 m asl in the northern Tianshan Mountains-southern Junggar Basin （TMJB）, Xinjiang of China, using the Biome-BGC model. The results showed that： 1 ） the NPP increased by 0.05 g C/（m2-a） with every increase of 1-m elevation, reached the maximum at the mid-high elevation （1,600 m asl）, and then decreased by 0.06 g C/（m2.a） per 1-m increase in elevation; 2） the grassland NPP was positively correlated with temperature in alpine meadow （AM, 2,700-3,500 m asl）, mid-mountain forest meadow （MMFM, 1,650-2,700 m asl） and low-mountain dry grassland （LMDG, 650-1,650 m asl）, while positive correlations were found between NPP and annual precipitation in plain desert grassland （PDG, lower than 650 m asl）; 3） an increase （from 0.08 to 1.09 g C/（m2.a）） in mean NPP for the grassland in TMJB under a real climate change scenario was observed from 1959 to 2009; and 4） remarkable differences in WUE were found among different elevations, in general, WUE increased with decreasing elevation, because water availability is lower at lower elevations; however, at elevations lower than 540 m asl, we did observe a decreasing trend of WUE with decreasing elevation, which may be due to the sharp changes in canopy cover over this gradient. Our research suggests that the NPP simulated by Biome-BGC is consistent with field data, and the modeling provides an opportunity to further evaluate interactions between environmental factors and ecosystem productivity.

Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity

Satellite-derived phytoplankton pigment absorption （aph） has been used as a key predictor of phytoplankton photosynthetic efficiency to estimate global ocean net primary production （NPP）. In this study, an aph-based NPP model （AbPM） with four input parameters including the photosynthetically available radiation （PAR）, diffuse attenuation at 490 nm （Ka（490））, euphotic zone depth （Zeu） and the phytoplankton pigment absorption coefficient （aph） is compared with the chlorophyll-based model and carbon-based model. It is found that the AbPM has significant advantages on the ocean NPP estimation compared with the chlorophyll-based model and carbon- based model. For example, AbPM greatly outperformed the other two models at most monitoring sites and had the best accuracy, including the smallest values of RMSD and bias for the NPP estimate, and the best correlation between the observations and the modeled NPPs. In order to ensure the robustness of the model, the uncertainty in NPP estimates of the AbPM was assessed using a Monte Carlo simulation. At first, the frequency histograms of simple difference （fi）, and logarithmic difference （~LOG） between model estimates and in situ data confirm that the two input parameters （Zeu and PAR） approximate the Normal Distribution, and another two input parameters （aph and Ka（490）） approximate the logarithmic Normal Distribution. Second, the uncertainty in NPP estimates in the AbPM was assessed by using the Monte Carlo simulation. Here both the PB （percentage bias）, defined as the ratio of ANPP to the retrieved NPP, and the CV （coefficient of variation）, defined as the ratio of the standard deviation to the mean are used to indicate the uncertainty in the NPP brought by input parameter to AbPM model. The uncertainty related to magnitude is denoted by PB and the uncertainty related to scatter range is denoted by CV. Our investigations demonstrate that PB of NPP uncertainty brought by all parameters with an annual mean of 5.5% covered a range of -5%-15% for the global ocean. The PB uncertainty of AbPM model was mainly caused by aph; the PB of NPP uncertainty brought by aph had an annual mean of 4.1% for the global ocean. The CV brought by all the parameters with an annual mean of 105% covered a range of 98%-134% for global ocean. For the coastal zone of Antarctica with higher productivity, the PB and CV of NPP uncertainty brought by all parameters had annual means of 7.1% and 121%, respectively, which are significantly larger than those obtained in the global ocean. This study suggests that the NPPs estimated by AbPM model are more accurate than others, but the magnitude and scatter range of NPP errors brought by input parameter to AbPM model could not be neglected, especially in the coastal area with high productivity. So the improving accuracy of satellite retrieval of input parameters should be necessary. The investigation also confirmed that the SST related correction is effective for improving the model accuracy in low temperature condition.

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.

Estimation of net primary productivity and its driving factors in the Ili River Valley,China

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.

Effects of grazing on net primary productivity,evapotranspiration and water use efficiency in the grasslands of Xinjiang,China

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.

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）.

Carbon uptake and change in net primary productivity of oasis-desert ecosystem in arid western China with remote sensing technique

Arid and semi-arid ecosystems exhibit a spatially complex biogeophysical structure. According to arid western special climate-vegetation characters, the fractional cover of photosynthetic vegetation （PV）, non-photosynthetic vegetation （NPV）, bare soil and water are unmixed, using the remote sensing spectral mixture analysis. We try the method to unmix the canopy funation structure of arid land cover in order to avoid the differentiation of regional vegetation system and the disturbance of environmental background. We developed a modified production efficiency model NPP-PEM appropriate for the arid area at regional scale based on the concept of radiation use efficiency. This model refer to the GLO-PEM and CASA model was driven with remotely sensed observations, and calculates not just the conversion efficiency of absorbed photosynthetically active radiation but also the carbon fluxes that determine net primary productivity （NPP）. We apply and validate the model in the Kaxger and Yarkant river basins in arid western China. The NPP of the study area in 1992 and 1998 was estimated based on the NPP-PEM model. The results show that the improved PEM model, considering the photosynthetical activation of heterogeneous functional vegetation, is in good agreement with field measurements and the existing literature. An accurate agreement （R2= 0.85, P〈0.001） between the estimates and the ground-based measurement was obtained. The spatial distribution of mountain-oasis-desert ecosystem shows an obvious heterogeneous carbon uptake. The results are applicable to arid ecosystem studies ranging from characterizing carbon cycle, carbon flux over arid areas to monitoring change in mountain-oasis-desert productivity, stress and management.

Dynamic change of net primary productivity and fractional vegetation cover in the Yellow River Basin using multi-temporal AVHRR NDVI Data

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.

Temporal and spatial variations of net primary productivity and its response to groundwater of a typical oasis in the Tarim Basin, China

Net primary productivity (NPP) of the vegetation in an oasis can reflect the productivity capacity of a plant community under natural environmental conditions. Owing to the extreme arid climate conditions and scarce precipitation in the arid oasis regions, groundwater plays a key role in restricting the development of the vegetation. The Qira Oasis is located on the southern margin of the Taklimakan Desert (Tarim Basin, China) that is one of the most vulnerable regions regarding vegetation growth and water scarcity in the world. Based on remote sensing images of the Qira Oasis and daily meteorological data measured by the ground stations during the period 2006-2019, this study analyzed the temporal and spatial patterns of NPP in the oasis as well as its relation with the variation of groundwater depth using a modified Carnegie Ames Stanford Approach (CASA) model. At the spatial scale, NPP of the vegetation decreased from the interior of the Qira Oasis to the margin;at the temporal scale, NPP of the vegetation in the oasis fluctuated significantly (ranging from 29.80 to 50.07 g C/(m2•month)) but generally showed an increasing trend, with the average increase rate of 0.07 g C/(m2•month). The regions with decreasing NPP occupied 64% of the total area of the oasis. During the study period, NPP of both farmland and grassland showed an increasing trend, while that of forest showed a decreasing trend. The depth of groundwater was deep in the south of the oasis and shallow in the north, showing a gradual increasing trend from south to north. Groundwater, as one of the key factors in the surface change and evolution of the arid oasis, determines the succession direction of the vegetation in the Qira Oasis. With the increase of groundwater depth, grassland coverage and vegetation NPP decreased. During the period 2008-2015, with the recovery of groundwater level, NPP values of all types of vegetation with different coverages increased. This study will provide a scientific basis for the rational utilization and sustainable management of groundwater resources in the oasis.

Quantitative Assessment of the Relative Contributions of Climate and Human Factors to Net Primary Productivity in the Ili River Basin of China and Kazakhstan

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.