Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data‐model comparison

Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data‐model comparisons.     

2001—2018年中国总初级生产力时空变化的遥感研究

总初级生产力（GPP）是绿色植被吸收大气中CO₂进行光合作用生产的有机质,是陆地生态系统碳循环研究的一个关键参数。利用遥感数据和气象数据驱动的双叶光能利用率DTEC模型计算了2001-2018年中国逐月GPP,并结合日光诱导叶绿素荧光（SIF）反演的GOSIF GPP数据集,分析了中国陆地生态系统2001-2018年GPP的时空变化特征。结果表明：（1） GOSIF和DTEC模拟的中国多年GPP平均值分别为7.23 Pg C和6.93 Pg C,在空间分布上呈现东南部高西北部低的特征;（2）2001-2018年,中国GPP呈显著增长（P<0.01）,年增长幅度分别为0.094 PgC/a （GOSIF）和0.073 PgC/a （DTEC）。而已有研究估计的中国GPP年增长幅度约为0.02-0.057 PgC/a,低估了GPP增长趋势。（3）在中国通量网6个通量站的GPP验证表明,两种模型精度高、表现好,都能较好地模拟观测站的GPP季节变化。（4） GOSIF GPP的精度优于DTEC GPP模型,这可能是由于SIF与GPP存在直接机理联系。GOSIF GPP算法能客观地反映植被生产力状况,而DTEC模型更适合自然条件下植被生产力的模拟。     

Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions

Forest ecosystem plays an important role as carbon sinks in Southwest China. Currently, remote sensing technology has been widely used to substantially model the high temporal and spatial variation in gross primary production (GPP) at a site or regional scale. However, during the growing season, the regional uncertainty of GPP in the forest ecosystem and the relative contributions of climate variations to interannual variation (IAV) of GPP are not well understood across Southwest China. Our research focuses on the joint analysis of the three-cornered hat (TCH) algorithm and uses the contribution index to analyse the model's uncertainties varying with plant functional types (PFTs), climate zones, and the contribution of climate variabilities to GPP IAV. Here, three GPP datasets are used to investigate how climate variabilities contribute to the GPP IAV during the growing season. The uncertainties in GPP vary from 829.33 g C m⁻² year⁻¹ to 2031.86 g C m⁻² year⁻¹ for different models in different climate zones and different PFTs. Additionally, the results highlight that precipitation dominates the interannual variation in GPP in forest ecosystem during the growing season in Southwest China. It makes the largest contribution (34.46%) to the IAV of GPP in the climate zone of E (cold subtropical highland area) and the largest contribution (80.83%) to PFTs of the MF (mixed forest). Our study suggests the availability and applicability of GPP products can be used to assess GPP uncertainties and analyse the contributions of climate factors to GPP IAV in forest ecosystem or other ecosystems.     

Discrepancies in vegetation phenology trends and shift patterns in different climatic zones in middle and eastern Eurasia between 1982 and 2015

Changes in vegetation phenology directly reflect the response of vegetation growth to climate change. In this study, using the Normalized Difference Vegetation Index dataset from 1982 to 2015, we extracted start date of vegetation growing season (SOS), end date of vegetation growing season (EOS), and length of vegetation growing season (LOS) in the middle and eastern Eurasia region and evaluated linear trends in SOS, EOS, and LOS for the entire study area, as well as for four climatic zones. The results show that the LOS has significantly increased by 0.27 days/year, mostly due to a significantly advanced SOS (?0.20 days/year) and a slightly delayed EOS (0.07 days/year) over the entire study area from 1982 to 2015. The vegetation phenology trends in the four climatic zones are not continuous throughout the 34‐year period. Furthermore, discrepancies in the shifting patterns of vegetation phenology trend existed among different climatic zones. Turning points (TP) of SOS trends in the Cold zone, Temperate zone, and Tibetan Plateau zone occurred in the mid‐ or late 1990s. The advanced trends of SOS in the Cold zone, Temperate zone, and Tibetan Plateau zone exhibited accelerated, stalled, and reversed patterns after the corresponding TP, respectively. The TP did not occurred in Cold‐Temperate zone, where the SOS showed a consistent and continuous advance. TPs of EOS trends in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone occurred in the late 1980s or mid‐1990s. The EOS in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone showed weak advanced or delayed trends after the corresponding TP, which were comparable with the delayed trends before the corresponding TP. The shift patterns of LOS trends were primarily influenced by the shift patterns of SOS trends and were also heterogeneous within climatic zones.     

Seasonal,interannual and decadal drivers of tree and grass productivity in an Australian tropical savanna

Tree–grass savannas are a widespread biome and are highly valued for their ecosystem services. There is a need to understand the long‐term dynamics and meteorological drivers of both tree and grass productivity separately in order to successfully manage savannas in the future. This study investigated the interannual variability (IAV) of tree and grass gross primary productivity (GPP) by combining a long‐term (15 year) eddy covariance flux record and model estimates of tree and grass GPP inferred from satellite remote sensing. On a seasonal basis, the primary drivers of tree and grass GPP were solar radiation in the wet season and soil moisture in the dry season. On an interannual basis, soil water availability had a positive effect on tree GPP and a negative effect on grass GPP. No linear trend in the tree–grass GPP ratio was observed over the 15‐year study period. However, the tree–grass GPP ratio was correlated with the modes of climate variability, namely the Southern Oscillation Index. This study has provided insight into the long‐term contributions of trees and grasses to savanna productivity, along with their respective meteorological determinants of IAV.     

Belowground biomass of alpine shrublands across the northeast Tibetan Plateau

Although belowground biomass (BGB) plays an important role in global cycling, the storage of BGB and climatic effects on it are remaining unclear. With data from 49 sites, we aimed to investigate BGB and its climatic controls in alpine shrublands in the Tibetan Plateau. Our study showed that the BGB (both grass‐layer and shrub‐layer biomass) storage in the alpine shrublands was 67.24 Tg, and the mean BGB density and shrublands area were 1,567.38 g/m² and 4.29 × 10⁴ km², respectively. Shrub layer had a larger BGB stock and accounted for 66% of total BGB this area, while only 34% was accumulated in the grass layer. BGB of the grass layer in the Tibetan Plateau shrublands was larger than that of Tibetan alpine grasslands, indicating that shrubland ecosystem played a critical importance role in carbon cycle on the Tibetan Plateau. The BGB in the grass layer and shrub layer demonstrated different correlations with climatic factors. Specifically, the effects from mean annual temperature on shrub‐layer BGB were not significant, similarly to the relationship between mean annual precipitation and grass‐layer BGB. But shrub‐layer BGB had a significantly positive relationship with mean annual precipitation (p < .05), while grass‐layer BGB showed a trend of decrease with increasing mean annual temperature (p < .05). Consequently, the actual and potential increases of BGB varied due to different increases of mean annual precipitation and temperature among different areas of the Tibetan Plateau. Therefore, in the warmer and wetter scenario, due to contrary relationships from mean annual precipitation and temperature on shrub‐layer BGB and grass‐layer BGB, it is necessary to conduct a long‐term monitoring about dynamic changes to increase the precision of assessment of BGB carbon sequestration in the Tibetan Plateau alpine shrublands.     

Carbon budgets of wetland ecosystems in China

Wetlands contain a large proportion of carbon (C) in the biosphere and partly affect climate by regulating C cycles of terrestrial ecosystems. China contains Asia's largest wetlands, accounting for about 10% of the global wetland area. Although previous studies attempted to estimate C budget in China's wetlands, uncertainties remain. We conducted a synthesis to estimate C uptake and emission of wetland ecosystems in China using a dataset compiled from published literature. The dataset comprised 193 studies, including 370 sites representing coastal, river, lake and marsh wetlands across China. In addition, C stocks of different wetlands in China were estimated using unbiased data from the China Second Wetlands Survey. The results showed that China's wetlands sequestered 16.87 Pg C (315.76 Mg C/ha), accounting for about 3.8% of C stocks in global wetlands. Net ecosystem productivity, jointly determined by gross primary productivity and ecosystem respiration, exhibited annual C sequestration of 120.23 Tg C. China's wetlands had a total gaseous C loss of 173.20 Tg C per year from soils, including 154.26 Tg CO₂‐C and 18.94 Tg CH₄‐C emissions. Moreover, C stocks, uptakes and gaseous losses varied with wetland types, and were affected by geographic location and climatic factors (precipitation and temperature). Our results provide better estimation of the C budget in China's wetlands and improve understanding of their contribution to the global C cycle in the context of global climate change.     

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%–15% and 23%–34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%–18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit–shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (V_cmax), which together decreased photosynthesis in sunlit canopy by 5.9–7.2 PgC year⁻¹ while enhanced photosynthesis by 6.9–8.2 PgC year⁻¹ in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year⁻¹ net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.     

Grassland gross carbon dioxide uptake based on an improved model tree ensemble approach considering human interventions: global estimation and covariation with climate

Grassland ecosystems act as a crucial role in the global carbon cycle and provide vital ecosystem services for many species. However, these low‐productivity and water‐limited ecosystems are sensitive and vulnerable to climate perturbations and human intervention, the latter of which is often not considered due to lack of spatial information regarding the grassland management. Here by the application of a model tree ensemble (MTE‐GRASS) trained on local eddy covariance data and using as predictors gridded climate and management intensity field (grazing and cutting), we first provide an estimate of global grassland gross primary production (GPP). GPP from our study compares well (modeling efficiency NSE = 0.85 spatial; NSE between 0.69 and 0.94 interannual) with that from flux measurement. Global grassland GPP was on average 11 ± 0.31 Pg C yr^?1 and exhibited significantly increasing trend at both annual and seasonal scales, with an annual increase of 0.023 Pg C (0.2%) from 1982 to 2011. Meanwhile, we found that at both annual and seasonal scale, the trend (except for northern summer) and interannual variability of the GPP are primarily driven by arid/semiarid ecosystems, the latter of which is due to the larger variation in precipitation. Grasslands in arid/semiarid regions have a stronger (33 g C m^?2 yr^?1/100 mm) and faster (0‐ to 1‐month time lag) response to precipitation than those in other regions. Although globally spatial gradients (71%) and interannual changes (51%) in GPP were mainly driven by precipitation, where most regions with arid/semiarid climate zone, temperature and radiation together shared half of GPP variability, which is mainly distributed in the high‐latitude or cold regions. Our findings and the results of other studies suggest the overwhelming importance of arid/semiarid regions as a control on grassland ecosystems carbon cycle. Similarly, under the projected future climate change, grassland ecosystems in these regions will be potentially greatly influenced.     

Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data‐model intercomparison

To predict forest response to long‐term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short‐term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry‐season intensities and lengths, to determine how well four state‐of‐the‐art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry‐season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry‐season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry‐season GPP resulted from a combination of internal biological (leaf‐flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (R_e) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry‐season declines in GPP and R_e consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light‐harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf‐level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.     

Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate

Aim To examine the global pattern of the net primary production (NPP)/gross primary production (GPP) ratio of the Earth's land area along geographical and climatic gradients. Location The global planetary ecosystem. Methods The 4‐year average annual NPP/GPP ratio of the Earth's land area was calculated using 2000–03 Moderate Resolution Imaging Spectroradiometer (MODIS) data. The global pattern of the NPP/GPP ratio was investigated by comparing it among each typical terrestrial ecosystem and plotting it along a geographical and climatic gradient, including latitude, altitude, temperature and precipitation. Results The global terrestrial ecosystem had an average NPP/GPP ratio value of 0.52 with minor variation from 2000 to 2003. However, the NPP/GPP ratio showed considerable spatial variation associated with ecosystem type, geographical location and climate. Densely vegetated ecosystems had a lower NPP/GPP ratio than sparsely vegetated ecosystems. Forest ecosystems had a lower NPP/GPP ratio than shrub and herbaceous ecosystems. Geographically, the NPP/GPP ratio increased with altitude. In the Southern Hemisphere, the NPP/GPP ratio decreased along latitude from 30° to 10° and it exhibited high fluctuation in the Northern Hemisphere. Climatically, the NPP/GPP ratio exhibited a decreasing trend along enhanced precipitation when it was less than 2300 mm year^?1 and a static trend when the annual precipitation was over 2300 mm. The NPP/GPP ratio showed a decreasing trend along temperature when it was between –20 °C and 10 °C, and showed an increasing trend along rising temperature when it was between –10 °C and 20 °C. Within each ecosystem, the NPP/GPP ratio revealed a similar trend to the global trend along temperature and precipitation. Conclusions The NPP/GPP ratio exhibited a pattern depending on the main climatic characteristics such as temperature and precipitation and geographical factors such as latitude and altitude. The findings of this research challenge the widely held assumption that the NPP/GPP ratio is consistent regardless of ecosystem type.     

Significant soil acidification across northern China's grasslands during 1980s–2000s

Anthropogenic acid deposition may lead to soil acidification, with soil buffering capacity regulating the magnitude of any soil pH change. However, little evidence is available from large‐scale observations. Here, we evaluated changes in soil pH across northern China's grasslands over the last two decades using soil profiles obtained from China's Second National Soil Inventory during the 1980s and a more recent regional soil survey during 2001–2005. A transect from the central‐southern Tibetan Plateau to the eastern Inner Mongolian Plateau, where Kriging interpolation provided robust predictions of the spatial distribution of soil pH, was then selected to examine pH changes during the survey period. Our results showed that soil pH in the surface layer had declined significantly over the last two decades, with an overall decrease of 0.63 units (95% confidence interval = 0.54–0.73 units). The decline of soil pH was observed in both alpine grasslands on the Tibetan Plateau and temperate grasslands on the Inner Mongolian Plateau. Soil pH decreased more intensively in low soil carbonate regions, while changes of soil pH showed no significant associations with soil cation exchange capacity. These results suggest that grassland soils across northern China have experienced significant acidification from the 1980s to 2000s, with soil carbonates buffering the increase in soil acidity. The buffering process may induce a large loss of carbon from soil carbonates and thus alter the carbon balance in these globally important ecosystems.     

2001-2014年中国植被净初级生产力时空变化及其与气象因素的关系

植被净初级生产力（NPP）是表征植被活动的关键变量,对于评估生态系统承载力,理解陆地生态系统碳循环有着重要意义。以全球陆表特征数据集（GLASS）为基础,对2001-2014年中国植被NPP进行了估算。在此基础上,利用一元线性回归、经验正交分解（EOF）分析了我国植被NPP时空变化,利用逐象元相关性分析、奇异值分解（SVD）两种方法分析了我国植被NPP与温度、降雨量的相关性。结果表明：（1）我国植被NPP空间上基本呈由东南向西北递减的分布趋势,主要是由于植被分布和气候条件决定的。研究期间我国植被NPP呈波动增加趋势,总量在3.02-3.49PgC/a之间,平均约为3.25 PgC/a。（2）一元线性回归与EOF分析结果较为一致,表明我国长江中下游、华北平原和东北长白山地区NPP呈减少趋势,而青藏高原、西北、内蒙古中部及东南沿海地区NPP呈增加趋势。（3） NPP与气象要素逐象元相关性分析表明,长白山、青藏高原及南方地区NPP与温度呈正相关,内蒙东部和西北地区NPP与降雨量呈正相关,东北、长江下游地区NPP与降雨量呈负相关。SVD分析结果与逐象元分析结果基本一致表明,NPP与温度、降雨量均存在明显相关性,长白山、内蒙古、青藏高原地区NPP与温度正相关,西北、内蒙中部地区NPP与降雨量呈正相关,长江下游、东北地区NPP与降雨量呈负相关。     

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO₂ between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO₂ release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO₂ fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NN_C‐part). NN_C‐part incorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R² > .94) and RECO (R² > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NN_C‐part performed more realistic than the traditional methods for predicting additional patterns of gross CO₂ fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO₂ fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NN_C‐part is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.     

Terrestrial and aquatic responses to climate change and human impact on the southeastern Tibetan Plateau during the past two centuries

Rapid population growth and economic development have led to increased anthropogenic pressures on the Tibetan Plateau, causing significant land cover changes with potentially severe ecological consequences. To assess whether or not these pressures are also affecting the remote montane‐boreal lakes on the SE Tibetan Plateau, fossil pollen and diatom data from two lakes were synthesized. The interplay of aquatic and terrestrial ecosystem response was explored in respect to climate variability and human activity over the past 200 years. Nonmetric multidimensional scaling and Procrustes rotation analysis were undertaken to determine whether pollen and diatom responses in each lake were similar and synchronous. Detrended canonical correspondence analysis was used to develop quantitative estimates of compositional species turnover. Despite instrumental evidence of significant climatic warming on the southeastern Plateau, the pollen and diatom records indicate very stable species composition throughout their profiles and show only very subtle responses to environmental changes over the past 200 years. The compositional species turnover (0.36–0.94 SD) is relatively low in comparison to the species reorganizations known from the periods during the mid‐ and early‐Holocene (0.64–1.61 SD) on the SE Plateau, and also in comparison to turnover rates of sediment records from climate‐sensitive regions in the circum arctic. Our results indicate that climatically induced ecological thresholds are not yet crossed, but that human activity has an increasing influence, particularly on the terrestrial ecosystem in our study area. Synergistic processes of post‐Little Ice Age warming, 20th century climate warming and extensive reforestations since the 19th century have initiated a change from natural oak‐pine forests to seminatural, likely less resilient pine‐oak forests. Further warming and anthropogenic disturbances would possibly exceed the ecological threshold of these ecosystems and lead to severe ecological consequences.     

Application of two remote sensing GPP algorithms at a semiarid grassland site of North China

Aims Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. However, for a given algorithm, it usually has its limitation on applications to a wide range of vegetation types and/or under diverse environmental conditions. This study was conducted to compare the performance of two remote sensing GPP algorithms, the MODIS GPP and the vegetation photosynthesis model (VPM), in a semiarid temperate grassland ecosystem.Methods The study was conducted at a typical grassland site in Ujimuqin of Inner Mongolia, North China, over 2 years in 2006 and 2007. Environmental controls on GPP measured by the eddy covariance (EC) technique at the study site were first investigated with path analysis of meteorological and soil moisture data at a daily and 8-day time steps. The estimates of GPP derived from the MODIS GPP and the VPM with site-specific inputs were then compared with the values of EC measurements as ground truthing at the site. Site-specific ? max (α) was estimated by using rectangular hyperbola function based on the 7-day flux data at 30-min intervals over the peak period of the growing season (May to September).Important findings Between the two remote sensing GPP algorithms and various estimates of the fraction of absorbed photosynthetic active radiation (FPAR), the VPM based on FPAR derived from the enhanced vegetation index (EVI) works the best in predicting GPP against the ground truthing of EC GPP. A path analysis indicates that the EC GPP in this semiarid temperate grassland ecosystem is controlled predominantly by both soil water and temperature. The site water condition is slightly better simulated by the moisture multiplier in the VPM than in the MODIS GPP algorithm, which is a most probable explanation for a better performance of the VPM than MODIS GPP algorithm in this semiarid grassland ecosystem.     

Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model

Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.     

基于碳-水-氮耦合过程改进模型的温带草地生态系统生产力模拟研究

基于碳-水-氮耦合过程改进模型的温带草地生态系统生产力模拟研究 预测气候变化背景下生态系统总初级生产力的响应是全球变化生态学研究领域的一项核心任务。然而,对模型研究领域来说,准确模拟干旱生态系统总初级生产力的年际变异仍然是一个巨大的挑战。土壤含水量和总初级生产力对土壤水敏感性的精确模拟,是预测干旱生态系统中总初级生产力年际变异的两个关键方面。为此,本研究以一个广泛应用的生态系统模型(Biome-BGC模型)为例,旨在改进温带草地生态系统的模型模拟效果。一方面,通过对蒸散模块、土壤水沿剖面的垂直分布和田间持水量计算的改进和调整,模型实现了对土壤水模拟的更新。另一方面,我们改进了影响水-氮关系的函数,从而调节了总初级生产力对土壤水的敏感性。研究结果表明,原有模型高估了土壤含水量,低估了总初级生产力敏感性的年际变异,从而导致模拟总初级生产力的年际变异低于观测值。例如,原模型严重低估了总初级生产力在干旱年份的减少。相比之下,改进后的模型准确地模拟了观测土壤水的季节和年际变化,特别是表层土壤水。通过优化影响氮矿化的参数,改进后的模型改善了总初级生产力对土壤水敏感性的模拟,使其更接近观测值。因此,改进后模型对总初级生产力年际变异的模拟得到了很大程度的提高。我们的结果表明,在对干旱生态系统总初级生产力年际变异进行模拟时,应优先考虑表层土壤水及其对氮有效性的影响。     

Higher than expected CO2 fertilization inferred from leaf to global observations

Several lines of evidence point to an increase in the activity of the terrestrial biosphere over recent decades, impacting the global net land carbon sink (NLS) and its control on the growth of atmospheric carbon dioxide (c_a). Global terrestrial gross primary production (GPP)—the rate of carbon fixation by photosynthesis—is estimated to have risen by (31 ± 5)% since 1900, but the relative contributions of different putative drivers to this increase are not well known. Here we identify the rising atmospheric CO₂ concentration as the dominant driver. We reconcile leaf‐level and global atmospheric constraints on trends in modeled biospheric activity to reveal a global CO₂ fertilization effect on photosynthesis of 30% since 1900, or 47% for a doubling of c_a above the pre‐industrial level. Our historic value is nearly twice as high as current estimates (17 ± 4)% that do not use the full range of available constraints. Consequently, under a future low‐emission scenario, we project a land carbon sink (174 PgC, 2006–2099) that is 57 PgC larger than if a lower CO₂ fertilization effect comparable with current estimates is assumed. These findings suggest a larger beneficial role of the land carbon sink in modulating future excess anthropogenic CO₂ consistent with the target of the Paris Agreement to stay below 2°C warming, and underscore the importance of preserving terrestrial carbon sinks.     

青藏高原高寒灌丛2003-2016年生长季的不同月份CO2通量对气温日较差的响应

全球气候变化引起的气温日较差（ADT）减小，将会对高寒生态系统的碳平衡造成深刻影响。基于涡度相关系统，利用2003-2016年的涡度相关系统观测资料，做了青藏高原高寒灌丛在生长季（6-9月）不同月份的ADT对CO₂通量影响的研究。结果表明：2003-2016年的生长季中，最高气温（MaxTa）和最低气温（MinTa）呈先升高后降低的单峰变化趋势，ADT没有呈现明显的变化趋势。逐日总初级生产力（GPP）和生态系统呼吸（Re）呈先增加后降低的单峰趋势，逐日净生态系统CO₂交换（NEE）呈先下降后上升的"V"型变化趋势。高寒灌丛在生长季为碳汇，整个生长季总NEE、GPP和Re平均值分别为（-161.2±30.1）、（501.9±60.2）、（340.7±54.4） gCm^-2。在高寒灌丛生长季（6-9月）的每个月份，MaxTa、MinTa和ADT分别是GPP（P<0.001）、Re（P<0.001）和NEE（P<0.01）变化的主要控制因子。高寒灌丛的ADT的增大有利于生态系统的碳固持，暗示在未来气候变化背景下ADT的减小将会削弱高寒灌丛生态系统的碳汇能力。