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

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

Responses of gross primary productivity to different sizes of precipitation events in a temperate grassland ecosystem in Inner Mongolia, China

Changes in the sizes of precipitation events in the context of global climate change may have profound impacts on ecosystem productivity in arid and semiarid grasslands. However, we still have little knowledge about to what extent grassland productivity will respond to an individual precipitation event. In this study, we quantified the duration, the maximum, and the time-integrated amount of the response of daily gross primary productivity (GPP) to an individual precipitation event and their variations with different sizes of precipitation events in a typical temperate steppe in Inner Mongolia, China. Results showed that the duration of GPP-response (τR) and the maximum absolute GPP-response (GPPmax) increased linearly with the sizes of precipitation events (Pes), driving a corresponding increase in time-integrated amount of the GPP-response (GPPtotal) because variations of GPPtotal were largely explained by τR and GPPmax. The relative contributions of these two parameters to GPPtotal were strongly Pes-dependent. The GPPmax contributed more to the variations of GPPtotal when Pes was relatively small (<20 mm), whereas τR was the main driver to the variations of GPPtotal when Pes was relatively large. In addition, a threshold size of at least 5 mm of precipitation was required to induce a GPP-response for the temperate steppe in this study. Our work has important implications for the modeling community to obtain an advanced understanding of productivity-response of grassland ecosystems to altered precipitation regimes.

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

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

Responses in gross primary production of Stipa krylovii and Allium polyrhizum to a temporal rainfall in a temperate grassland of Inner Mongolia, China

In the arid and semi-arid areas of China, rainfall and drought affect the growth and photosynthetic activities of plants.Gross primary productivity(GPP) is one of the most important indices that measure the photosynthetic ability of plants.This paper focused on the GPP of two representative grassland species(Stipa krylovii Roshev.and Allium polyrhizum Turcz.ex Regel) to demonstrate the effect of a temporal rainfall on the two species.Our research was conducted in a temperate grassland in New Barag Right Banner, Hulun Buir City, Inner Mongolia Autonomous Region of China, in a dry year 2015.We measured net ecosystem productivity(NEP) and ecosystem respiration flux(ER) using a transparent chamber system and monitored the photosynthetically active radiation(PAR), air and soil temperature and humidity simultaneously.Based on the measured values of NEP and ER, we calculated the GPP of the two species before and after the rainfall.The saturated GPP per aboveground biomass(GPPAGB) of A.polyrhizum remarkably increased from 0.033(±0.018) to 0.185(±0.055) μmol CO2/(gdw·s) by 5.6-fold and that of S.krylovii decreased from 0.068(±0.021) to 0.034(±0.011) μmol CO2/(gdw·s) by 0.5-fold on the 1st and 2nd d after a 9.1 mm rainfall event compared to the values before the rainfall at low temperatures below 35℃.However, on the 1st and 2nd d after the rainfall, both of the saturated GPPAGB values of S.krylovii and A.polyrhizum were significantly lower at high temperatures above 35℃(0.018(±0.007) and 0.110(±0.061) μmol CO2/(gdw·s), respectively) than at low temperatures below 35℃(0.034(±0.011) and 0.185(±0.055) μmol CO2/(gdw·s), respectively).The results showed that the GPP responses to the temporal rainfall differed between S.krylovii and A.polyrhizum and strongly negative influenced by temperature.The temporal rainfall seems to be more effective on the GPP of A.polyrhizum than S.krylovii.These differences might be related to the different physiological and structural features, the coexistence of the species and their species-specific survival strategies.

Response of Growing Season Gross Primary Production to El Nino in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging(BMA).The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Nino years indicated that GPP response to El Nino varies with Pacific Decadal Oscillation(PDO) phases: when the PDO was in the cool phase,it was likely that GPP was greater in northern China(32°–38°N,111°–122°E) and less in the Yangtze River valley(28°–32°N,111°–122°E);in contrast,when PDO was in the warm phase,the GPP anomalies were usually reversed in these two regions.The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon.The previously published findings on how El Nino during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Nino in this study theoretically credible.This paper not only introduces an effective way to use BMA in grids that have mixed plant function types,but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Nino and PDO.

Ocean-atmosphere Teleconnections Play a Key Role in the Interannual Variability of Seasonal Gross Primary Production in China

Since the 1950s,the terrestrial carbon uptake has been characterized by interannual variations,which are mainly determined by interannual variations in gross primary production(GPP).Using an ensemble of seven-member TRENDY(Trends in Net Land-Atmosphere Carbon Exchanges)simulations during 1951-2010,the relationships of the interannual variability of seasonal GPP in China with the sea surface temperature(SST)and atmospheric circulations were investigated.The GPP signals that mostly relate to the climate forcing in terms of Residual Principal Component analysis(hereafter,R-PC)were identified by separating out the significant impact from the linear trend and the GPP memory.Results showed that the seasonal GPP over China associated with the first R-PC1(the second R-PC2)during spring to autumn show a monopole(dipole or tripole)spatial structure,with a clear seasonal evolution for their maximum centers from springtime to summertime.The dominant two GPP R-PC are significantly related to Sea Surface Temperature(SST)variability in the eastern tropical Pacific Ocean and the North Pacific Ocean during spring to autumn,implying influences from the El Niño-Southern Oscillation(ENSO)and the Pacific Decadal Oscillation(PDO).The identified SST and circulation factors explain 13%,23%and 19%of the total variance for seasonal GPP in spring,summer and autumn,respectively.A clearer understanding of the relationships of China’s GPP with ocean-atmosphere teleconnections over the Pacific and Atlantic Ocean should provide scientific support for achieving carbon neutrality targets.

How the enhanced East Asian summer monsoon regulates total gross primary production in eastern China

Recognizing the relationship between gross primary production(GPP)and precipitation in eastern China,the East Asian Summer Monsoon(EASM)plays a crucial role in shaping GPP.Despite confirmation of the strong link between EASM and GPP,there remains a notable research gap in understanding the specific impact of the EASM on GPP in different regions of eastern China.Here we used simulations from Trends in Net Land-Atmosphere Carbon Exchanges(TRENDY)models from 1951 to 2010 and divided eastern China into five subregions for the study.We also used the New East Asian Summer Monsoon Index(NEWI)as a quantitative metric to distinguish between periods of strong and weak EASM.Building on this,this study aims to investigate the response of GPP in different subregions of eastern China.Regionally,under strengthened EASM years(1954,1957,1965,1969,1977,1980,1983,1987,1993 and 1998),East China experienced the most pronounced increase in GPP at 12±21(mean±1 sigma)gC m^(-2) mon^(-1) compared to the weak EASM years(1958,1961,1972,1973,1978,1981,1985,1994,1997 and 2004).In contrast,Southwest China showed a decline in GPP at-4±10 gC m^(-2) mon^(-1).Moreover,GPP also increased in Northeast and North China when EASM strengthened,while South China showed a decline in GPP.This indicated that GPP changed with monsoon intensity.According to the mechanism analysis,during strong EASM,there was intense moisture convergence through alterations in the atmospheric circulation field over East China and abundant precipitation,which further contributed to the increase in GPP.Downward solar radiation in Southwest China decreased with EASM enhancement,which suppressed GPP and hindered vegetation growth.Overall,the results highlight the importance of accurately predicting the impact of different EASM intensities of regional carbon fluxes.

Changes in Water Use Efficiency Caused by Climate Change,CO_(2) Fertilization,and Land Use Changes on the Tibetan Plateau

Terrestrial ecosystem water use efficiency(WUE)is an important indicator for coupling plant photosynthesis and transpiration,and is also a key factor linking the carbon and water cycles between the land and atmosphere.However,under the combination of climate change and human intervention,the change in WUE is still unclear,especially on the Tibetan Plateau(TP).Therefore,satellite remote sensing data and process-based terrestrial biosphere models(TBMs)are used in this study to investigate the spatiotemporal variations of WUE over the TP from 2001 to 2010.Then,the effects of land use and land cover change(LULCC)and CO_(2) fertilization on WUE from 1981-2010 are assessed using TBMs.Results show that climate change is the leading contributor to the change in WUE on the TP,and temperature is the most important factor.LULCC makes a negative contribution to WUE(-20.63%),which is greater than the positive contribution of CO_(2) fertilization(11.65%).In addition,CO_(2) fertilization can effectively improve ecosystem resilience on the TP.On the northwest plateau,the effects of LULCC and CO_(2) fertilization on WUE are more pronounced during the driest years than the annual average.These findings can help researchers understand the response of WUE to climate change and human activity and the coupling of the carbon and water cycles over the TP.

Improving GPP estimates by partitioning green APAR from total APAR in two deciduous forest sites

Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected to improve by removing these components.However,their infl uence in GPP estimations has not been quantitatively evaluated for deciduous forests.Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components(FAPAR_(green))for partitioning APAR green(photosynthetically active radiation absorbed by photosynthetic components).In this study,the enhanced vegetation index(EVI)estimated FAPAR_(green)and to separate the photosynthetically active radiation absorbed by photosynthetic components(APAR green)from total APAR observations(APAR_(total))at two deciduous forest sites.The eddy covariance-light use effi ciency(EC-LUE)algorithm was employed to evaluate the infl uence of non-photosynthetic components and to test the performance of APAR green in GPP estimation.The results show that the infl uence of non-photosynthetic components have a seasonal pattern at deciduous forest sites,large diff erences are observed with normalized root mean square error(RMSE*)values of APAR green-based GPP and APAR_(total)-based GPP between tower-based GPP during the early and end stages,while slight diff erences occurred during peak growth seasons.In addition,daily GPP estimation was significantly improved using the APAR green-based method,giving a higher coeffi cient of determination and lower normalized root mean square error against the GPP estimated by the APAR_(total)-based method.The results demonstrate the signifi cance of partitioning APAR green from APAR_(total)for accurate GPP estimation in deciduous forests.

Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0

Gross primary production(GPP)is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively,yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations.This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model(BCC_AVIM2.0)in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6(CMIP6)experiments.Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008.Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset,increasing from cold(tundra)and dry(desert)biomes to warm(temperate)and humid(tropical rainforest)biomes.BCC_AVIM2.0 overestimates GPP in most parts of the globe,especially in boreal forest regions and Southeast China,while underestimating GPP in subhumid regions in eastern South America and tropical Africa.The four datasets broadly agree on the GPP seasonal cycle,but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations.The observation-based datasets exhibit slight interannual variability(IAV)and weak GPP linear trends,while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia.Regarding the possible relationships between annual means of GPP and climate factors,BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation,especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa,while the observed GPP in the above regions is temperature-or radiation-dominant.The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth,while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.

Seasonal and Interannual Variations of Carbon Exchange over a Rice–Wheat Rotation System on the North China Plain

Rice-wheat （R-W） rotation systems are ubiquitous in South and East Asia, and play an important role in modulating the carbon cycle and climate. Long-term, continuous flux measurements help in better understanding the seasonal and interannual variation of the carbon budget over R-W rotation systems. In this study, measurements of CO2 fluxes and meteorological variables over an R-W rotation system on the North China Plain from 2007 to 2010 were analyzed. To analyze the abiotic factors regulating Net Ecosystem Exchange （NEE）, NEE was partitioned into gross primary production （GPP） and ecosystem respiration. Nighttime NEE or ecosystem respiration was controlled primarily by soil temperature, while daytime NEE was mainly determined by photosythetically active radiation （PAR）. The responses of nighttime NEE to soil temperature and daytime NEE to light were closely associated with crop development and photosynthetic activity, respectively. Moreover, the interannual variation in GPP and NEE mainly depended on precipitation and PAR. Overall, NEE was negative on the annual scale and the rotation system behaved as a carbon sink of 982 g C m 2 per year over the three years. The winter wheat field took up more CO2 than the rice paddy during the longer growing season, while the daily NEE for wheat and rice were -2.35 and -3.96 g C m-2, respectively. After the grain harvest was subtracted from the NEE, the winter wheat field became a moderately strong carbon sink of 251-334 g C m-2 per season, whereas the rice paddy switched to a weak carbon sink of 107-132 per season.

Using triple oxygen isotopes and oxygen-argon ratio to quantify ecosystem production in the mixed layer of northern South China Sea slope region

Quantifying the gross and net production is an essential component of carbon cycling and marine ecosystem studies.Triple oxygen isotope measurements and the O_(2)/Ar ratio are powerful indices in quantifying the gross primary production and net community production of the mixed layer zone,respectively.Although there is a substantial advantage in refining the gas exchange term and water column vertical mixing calibration,application of mixed layer depth history to the gas exchange term and its contribution to reducing indices error are unclear.Therefore,two cruises were conducted in the slope regions of the northern South China Sea in October 2014(autumn)and June 2015(spring).Discrete water samples at Station L07 in the upper 150 m depth were collected for the determination ofδ^(17)0,δ^(18)O,and the O_(2)/Ar ratio of dissolved gases.Gross oxygen production(GOP)was estimated using the triple oxygen isotopes of the dissolved O_(2),and net oxygen production(NOP)was calculated using O_(2)/Ar ratio and O_(2)concentration.The vertical mixing effect in NOP was calibrated via a N_(2)O based approach.GOP for autumn and spring was(169±23)mmol/(m^(2)·d)(by O_(2))and(189±26)mmol/(m^(2)·d)(by O_(2)),respectively.While NOP was 1.5 mmol/(m^(2)·d)(by O_(2))in autumn and 8.2 mmol/(m^(2)·d)(by O_(2))in spring.Application of mixed layer depth history in the gas flux parametrization reduced up to 9.5%error in the GOP and NOP estimations.A comparison with an independent O_(2)budget calculation in the diel observation indicated a26%overestimation in the current GOP,likely due to the vertical mixing effect.Both GOP and NOP in June were higher than those in October.Potential explanations for this include the occurrence of an eddy process in June,which may have exerted a submesoscale upwelling at the sampling station,and also the markedly higher terrestrial impact in June.

Large-scale estimates of gross primary production on the Qinghai-Tibet plateau based on remote sensing data

Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficiency model(i.e.EC-LUE)to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP.In general,EC-LUE model performed well in predicting GPP at different time scale over QTP.Annual GPP over the entire QTP ranged from 575 to 703 Tg C,and showed a significantly increasing trend from 1982 to 2013.However,there were large spatial heterogeneities in long-term trends of GPP.Throughout the entire QTP,air temperature increase had a greater influence than solar radiation and precipitation(PREC)changes on productivity.Moreover,our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations.When compared with GPP estimates of the EC-LUE model,most Coupled Model Intercomparison Project(CMIP5)GPP products overestimate the magnitude and increasing trends of regional GPP,which potentially impact the feedback of ecosystems to regional climate changes.

Net Ecosystem CO2 Flux and Effect Factors in Peatland Ecosystem of Central China

Peatland ecosystems play an important role in the global carbon cycle because they act as a pool or sink for the carbon cycle. However, the relationship between seasonality effect factors and net ecosystem CO2 exchange (NEE) remains to be clarified, particularly for the non-growing season. Here, based on the eddy covariance technique, NEE in the peatland ecosystem of Central China was examined to measure two years’ (2016 and 2017) accumulation of carbon dioxide emissions with contrasting seasonal distribution of environmental factors. Our results demonstrate the cumulative net ecosystem CO2 emissions during the study period was in the first non-growing season 2.94 ± 4.83 μmolCO2 m-2.s-1 with the lowest values in the same year in first growing season was -2.79 ± 4.92 μmolCO2 m-2.s-1. The results indicate the effect of seasonal variations of NEE can be directly reflected in daily and seasonal variations in growth and respiration of peatland ecosystem by environmental parameters over different growing stages.

Influence of vapor pressure deficit on vegetation growth in China

Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.

2001-2020年内蒙古地区水分利用效率的时空变化及其影响因素

Water use efficiency(WUE)is an important variable to explore coupled relationships in carbon and water cycles.In this study,we first compared the spatial variations of annual gross primary productivity(GPP)and evapotranspiration(ET)using four GPP and ET products.Second,we selected the products closest to the flux towers data to estimate WUE.Finally,we quantitatively analyzed the impact of climate change and soil water content on WUE.The results showed that:(1)Four GPP and ET products provided good performance,with GOSIF-GPP and FLDAS-ET exhibiting a higher correlation and the smallest errors with the flux tower data.(2)The spatial pattern of WUE is consistent with that of GPP and ET,gradually decreasing from the northeast to the southwest.Higher WUE values appeared in the northeast forest ecosystem,and lower WUE values occurred in the western Gobi Desert,with a value of 0.28 gC m^(-2)mm^(-1).The GPP and ET products showed an increasing trend,while WUE showed a decreasing trend(55.15%)from 2001 to 2020.(3)The spatial relationship between WUE and driving factors reveal the variations in WUE of Inner Mongolia are mainly affected by soil moisture between 0 and 10 cm(SM0–10cm),vapor pressure deficit(VPD),and precipitation,respectively.(4)In arid regions,VPD and precipitation exhibit a major influence on WUE.An increase in VPD and precipitation has a negative and positive effect on WUE,with threshold values of approximately 0.36 k Pa and 426 mm,respectively.(5)In humid regions,SM0–10cm,VPD,SM10–40cm,and SM40–100cm exert a significant impact on WUE,especially SM0–10cm,and weakens with increasing soil depths,these differences may be related to physiological structure and living characteristics of vegetation types in different climate regimes.Our results emphasize the importance of VPD and soil moisture in regional variability in WUE.

在显著变绿的地区叶面积指数控制总初级生产力的变化

The leaf area index(LAI)shows a significant increasing trend from global to regional scales,which is known as greening.Greening will further enhance photosynthesis,but it is unclear whether the contribution of greening has exceeded the CO_(2)fertilization effect and become the dominant factor in the gross primary productivity(GPP)variation.We took the Yangtze River Delta(YRD)of China,where cropland and natural vegetation are significantly greening,as an example.Based on the boreal ecosystem productivity simulator(BEPS)and Revised-EC-LUE models,the GPP in the YRD from 2001 to 2020 was simulated,and attribution analysis of the interannual variation in GPP was performed.In addition,the reliability of the GPP simulated by the dynamic global vegetation model(DGVM)in the area was further investigated.The research results showed that GPP in the YRD had three significant characteristics consistent with LAI:(1)GPP showed a significant increasing trend;(2)the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP;and(3)cropland GPP showed double-high peak characteristics.The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation(4.29 Tg C yr^(-1)for BEPS and 2.73 Tg C yr^(-1)for the Revised-EC-LUE model)determined the interannual variation in GPP,which was much higher than the CO_(2)fertilization effect(2.29 Tg C yr^(-1)for BEPS and 0.67 Tg C yr^(-1)for the Revised-EC-LUE model).The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models.The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon.Our study highlights that in significant greening areas,LAI has dominated GPP variation,both spatially and temporally,and DGVM can correctly simulate GPP only if it accurately simulates LAI variation.

Extracting subpixel vegetation NDVI time series for evaluating the mixed pixel effect on GPP estimation in urban areas

The normalized difference vegetation index(NDVI)is the most widely used vegetation index for monitoring vegetation vigor and cover.As NDVI time series are usually derived at coarse or medium spatial resolutions,pixel size often represents a mixture of vegetated and non-vegetated surfaces.In heterogeneous urban areas,mixed pixels impede the accurate estimation of gross primary productivity(GPP).To address the mixed pixel effect on'NDVI time series and GPP estimation,we proposed a framework to extract subpixel vegetation NDVI(NDVI_(vege))from Landsat OLI images in urban areas,using endmember fractions,mixed NDVI(NDVI_(mix)),and NDVI.of non-vegetation,endmembers.Results demonstrated that the NDVI_(vege) extracted by this framework agreed well with the true NDVI_(vege) cross seasons and vegetation fractions,with R^(2) ranging from 0.74 to 0.82 and RMSE ranging from 0.03 to 0.04.The NDVI_(vege) time series was applied to evaluate vegetation GP in Wuhan,China.The total annual GPp estimated with NDVI_(vege) was 28-35%higher than the total annual GPP estimated with NDVI_(mix) implying uncertainty in the GPP estimations caused by mixed pixels.This study showed the potential of the proposed framework to resolve NDVI_(vege) for characterizing vegetation dynamics in heterogeneous areas.

The role of shrub(Potentilla fruticosa)on ecosystem CO_(2) fluxes in an alpine shrub meadow

Aims Recent studies have shown that alpine meadows on the Qinghai-Tibetan plateau act as significant CO_(2)sinks.On the plateau,alpine shrub meadow is one of typical grassland ecosystems.The major alpine shrub on the plateau is Potentilla fruticosa L.(Rosaceae),which is distributed widely from 3200 to 4000 m.Shrub species play an important role on carbon sequestration in grassland ecosystems.In addition,alpine shrubs are sensitive to climate change such as global warming.Considering global warming,the biomass and productivity of P.fruticosa will increase on Qinghai-Tibetan Plateau.Thus,understanding the carbon dynamics in alpine shrub meadow and the role of shrubs around the upper distribution limit at present is essential to predict the change in carbon sequestration on the plateau.However,the role of shrubs on the carbon dynamics in alpine shrub meadow remains unclear.The objectives of the present study were to evaluate the magnitude of CO_(2)exchange of P.fruticosa shrub patches around the upper distribution limit and to elucidate the role of P.fruticosa on ecosystem CO_(2)fluxes in an alpine meadow.Methods We used the static acrylic chamber technique to measure and estimate the net ecosystem productivity(NEP),ecosystem respiration(Re),and gross primary productivity(GPP)of P.fruticosa shrub patches at three elevations around the species’upper distribution limit.Ecosystem CO_(2)fluxes and environmental factors were measured from 17 to 20 July 2008 at 3400,3600,and 3800 m a.s.l.We examined the maximum GPP at infinite light(GPPmax)and maximum Re(Remax)during the experimental time at each elevation in relation to aboveground biomass and environmental factors,including air and soil temperature,and soil water content.Important Findings Patches of P.fruticosa around the species’upper distribution limit absorbed CO_(2),at least during the daytime.Maximum NEP at infinite light(NEPmax)and GPPmax of shrub patches in the alpine meadow varied among the three elevations,with the highest values at 3400 m and the lowest at 3800 m.GPPmax was positively correlated with the green biomass of P.fruticosa more strongly than with total green biomass,suggesting that P.fruticosa is the major contributor to CO_(2)uptake in the alpine shrub meadow.Air temperature influenced the potential GPPat the shrub-patch scale.Remax was correlated with aboveground biomass and Remax normalized by aboveground biomass was influenced by soil water content.Potentilla fruticosa height(biomass)and frequency increased clearly as elevation decreased,which promotes the large-scale spatial variation of carbon uptake and the strength of the carbon sink at lower elevations.

Divergent apparent temperature sensitivity of terrestrial ecosystem respiration

Aims Recent studies revealed convergent temperature sensitivity of ecosys-tem respiration(Re)within aquatic ecosystems and between terrestrial and aquatic ecosystems.We do not know yet whether various terres-trial ecosystems have consistent or divergent temperature sensitivity.Here,we synthesized 163 eddy covariance flux sites across the world and examined the global variation of the apparent activation energy(Ea),which characterizes the apparent temperature sensitivity of and its interannual variability(IAV)as well as their controlling factors.Methods We used carbon fluxes and meteorological data across FLUXNET sites to calculate mean annual temperature,tempera-ture range,precipitation,global radiation,potential radiation,gross primary productivity and Re by averaging the daily values over the years in each site.Furthermore,we analyzed the sites with>8 years data to examine the IAV of Ea and calculated the standard deviation of Ea across years at each site to character-ize IAV.Important Findings The results showed a widely global variation of Ea,with significantly lower values in the tropical and subtropical areas than in temperate and boreal areas,and significantly higher values in grasslands and wetlands than that in deciduous broadleaf forests and evergreen for-ests.Globally,spatial variations of Ea were explained by changes in temperature and an index of water availability with differing contribution of each explaining variable among climate zones and biomes.IAV and the corresponding coefficient of variation of Ea decreased with increasing latitude,but increased with radiation and corresponding mean annual temperature.The revealed patterns in the spatial and temporal variations of Ea and its controlling factors indicate divergent temperature sensitivity of Re,which could help to improve our predictive understanding of Re in response to climate change.