Variations in individual phenological events in response to global change have received considerable attentions. However, the development of phenological stages is relatively neglected, especially ...based on in situ observation data. In this study, the rate of vegetation greenup (Vgreenup) across the Northern Hemisphere was examined for different plant functional types (PFTs) by using eddy covariance flux data from 40 sites (417 site-years). Then, the controls of climatic variables on the spatial distribution of Vgreenup across PFTs were further investigated. The mean Vgreenup was 0.22 ± 0.11 g C m−2 day−2 across all sites, with the largest and lowest values observed in cropland and evergreen needle-leaf forest, respectively. A strong latitude dependence by Vgreenup was observed in both Europe and North America. The spatial variations of Vgreenup were jointly regulated by the duration of greenup (Dgreenup) and the amplitude of greenup (Agreenup). However, the predominant factor was Dgreenup in Europe, which changed to Agreenup in North America. Spring climatic factors exerted significant influences on the spatial distribution of Vgreenup across PFTs. Specifically, increasing temperature tended to shorten Dgreenup and promote Agreenup simultaneously, resulting in an acceleration of Vgreenup. Dryness had a depression effect on Vgreenup for the whole study area, as exhibited by a lower Vgreenup with increasing vapor pressure deficit or decreasing soil moisture. However, Vgreenup in North America was only significantly and positively correlated with temperature. Without the limitation of other climatic factors, the temperature sensitivity of Vgreenup was higher in North America (0.021 g C m−2 day−2 °C−1) than in Europe (0.015 g C m−2 day−2 °C−1). This study provides new cognitions for Vgreenup dynamics from in situ observations in complement to satellite observations, which can improve our understanding of terrestrial carbon cycles.
Vegetation net primary productivity (NPP) is a core parameter regulating carbon cycles of terrestrial ecosystem, which also has close relations with climates. The alpine ecosystems on the Tibetan ...Plateau (TP) are highly sensitive to climate changes. However, systematic analyses on the seasonal and annual responses of NPP to climatic factors in different grassland types on the TP are still lacking. In this study, the spatial and temporal patterns of NPP and their responses to temperature, precipitation and solar radiation during 2001–2015 at seasonal and annual temporal scales were investigated based on outputs of an improved Carnegie–Ames–Stanford Approach (CASA) model. The improved CASA model showed solid performances in simulating NPP in reference to field observations (R2 = 0.79, P < 0.001), resulting in mean error (ME) of −16.68, root mean square error (RMSE) of 87.59 g C·m−2·yr−1, and mean relative error (MRE) of −4.29%, respectively. The annual NPP displayed different altitude dependences between the regions below and above 3500 m, which could be attributed to the altitude associated precipitation variations. The temporal trends of the seasonal and annual NPP exhibited high spatial heterogeneity. For the entire alpine grasslands, solar radiation exerted stronger influences on annual NPP than temperature and precipitation did. The responses of NPP to climatic factors also varied among grassland types and seasons. For alpine meadow, solar radiation and temperature were the dominant climatic factors in controlling the NPP variability in spring and summer, respectively, while the effect of precipitation was weak in all seasons. On the contrary, precipitation played a more crucial role in influencing NPP than temperature and solar radiation in both summer and autumn for alpine steppe. Our results shed further lights on the mechanism underlying the responses of alpine ecosystem to climate changes. The improved understanding can provide guidelines for alpine grassland management.
•The CASA model was improved to simulate NPP in the alpine grasslands.•Altitude dependence of NPP was primarily associated with precipitation variations.•Responses of NPP to climatic factors varied with grassland types and seasons.•Solar radiation and temperature controlled seasonal NPP changes for alpine meadow.•Precipitation played a more important role in mediating NPP for alpine steppe.
Vegetation phenology is considered a sensitive indicator of climate change, which controls carbon, nitrogen, and water cycles within terrestrial ecosystems. The Moderate Resolution Imaging ...Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) is an important moderate resolution remote sensing data for monitoring vegetation phenology. However, Terra MODIS Collection 5 (C5) vegetation index products were identified to be affected by sensor degradation, which has been addressed in the recently released MODIS Collection 6 (C6) vegetation index products. In order to compare the difference between MODIS C5 and C6 NDVI in monitoring vegetation phenology, the start and end of growing season (SOS and EOS) of the alpine grassland on the Tibetan Plateau (TP) were extracted using four common methods. Then, the C5 and C6 NDVI-derived SOS (SOSC5 and SOSC6) and EOS (EOSC5 and EOSC6) were compared with ground-observed phenology data. Results showed that the multi-year average growing season NDVIs of C6 were lower than those of C5 in most areas, while the inter-annual variation patterns of regional average SOSC5 and SOSC6 (EOSC5 and EOSC6) were consistent. However, large spatial differences in phenological trends were found between C5 and C6 NDVI products. From C5 to C6, pixels with a SOS (EOS) trend shifting from significant to insignificant or from insignificant to significant accounted for at least 14.58% (9.07%) of the total pixels. SOSC5 was more consistent than SOSC6 with the ground-observed green-up dates. C5 NDVI may be more appropriate for monitoring SOS than C6 NDVI in the study region, but more ground-observed phenology records are needed to confirm it due to only four observational sites in this study. However, large differences and poor correlations existed between EOSC5 (EOSC6) and the ground-observed beginning of leaf coloring. To further evaluate the uncertainty of MODIS C5 and C6 NDVI in monitoring vegetation phenology, higher resolution near-surface remote sensing data and corresponding validation methods should be applied.
As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the ...underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, we investigated the direct and lagged seasonal responses of net primary productivity (NPP) to the beginning of growing season (BGS) along a precipitation gradient by integrating field observations, remote sensing monitoring and ecosystem model simulations. The results revealed distinct response patterns of seasonal NPP to BGS. Specifically, the BGS showed a significant and negative correlation with spring NPP (R = −0.73, p < 0.01), as evidenced by the direct boosting effects of earlier BGS on spring NPP. Moreover, spring NPP was more responsive to BGS in areas with more annual precipitation. The boosting effects of earlier BGS on NPP tended to weaken in summer compared with that in spring. Sequentially, BGS exhibited stronger positive correlation with autumn NPP in areas with less annual precipitation, which suggested the enhanced lagged suppressing effects of earlier spring phenology on ecosystem carbon assimilation during the later growing season under aggravated water stress. Overall, the strengthened NPP in spring was offset by its decrement in autumn, resulting in no obvious relationship between BGS and annual NPP (R = −0.34, p > 0.05) for the entire grasslands on the TP. The findings of this study imply that the lagged effects of phenology on the ecosystem productivity during the subsequent seasons should not be neglected in the future studies.
•Temporal change in relationship between NDVI and diurnal temperature was explored.•RNDVI-TMX and RNDVI-TMN showed distinct variations between mid- and high latitudes.•Weakening effect of TMX on ...vegetation growth was more apparent than that of TMN.•TRENDY models didn’t capture dynamic relation of vegetation growth to TMX or TMN.
Global warming has boosted vegetation growth to a large extent, but this stimulation effect has significantly weakened in recent years. Among the set of possible driving forces, the asymmetric daytime and nighttime warming effect has been largely neglected. To improve our understanding on the relationship between vegetation growth and global warming, this study tries to attribute the respective effects of daytime and nighttime temperature on vegetation growth and reveal their temporal trends in the extratropical Northern Hemisphere (30–90 °N). The results showed there had been significant warming trends in growing season maximum (TMX, 0.37 °C per decade) and minimum temperatures (TMN, 0.38 °C per decade) during 1982–2015, especially in high latitudes of the NH. Under the asymmetric diurnal warming, the effects of TMX and TMN on normalized difference vegetation index (NDVI) exhibited distinct temporal variations between mid- (<55 °N) and high latitudes (>55 °N). The positive correlation between NDVI and TMX (RNDVI-TMX) weakened in high latitudes, as well as the negative correlation between NDVI and TMN (RNDVI-TMN). However, the RNDVI-TMX and RNDVI-TMN changed little in mid-latitudes. Moreover, the weakening effect of TMX on NDVI was more apparent than that of TMN in high latitudes. The area with significantly (p < 0.1) positive RNDVI-TMX and significantly (p < 0.1) negative RNDVI-TMN both shrank from 1982–1998 to 1999–2015, with prior (15.97%) twice the latter one (7.09%) in shrunk area in high latitudes. With regard to vegetation type, decline in area with significantly (p < 0.1) positive RNDVI-TMX and negative RNDVI-TMN was extremely obvious in savannas, deciduous broadleaf forests and deciduous needleleaf forests. Besides, we also disclosed the poor performance of ecosystem process models in capturing the dynamic relationship between vegetation growth and diurnal temperature, which might be caused by their totally relying on diurnal mean temperature, instead of daytime and nighttime temperature, in forcing the models. This study can further advance our understandings on ecosystem responses to climate warming, and efficiently improve performance of ecosystem models.
Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the ...development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location.
•This is the CO2 enrichment field experiment conducted on the highest elevation.•Elevated CO2 (eCO2) stimulates both GEP and ER, and causes a neutral effect on NEP.•Simultaneous N addition and eCO2 ...stimulate carbon sink.•Excessive precipitation suppresses the eCO2 effects on carbon fluxes.
It's generally believed that elevated CO2 (eCO2) could stimulate plant growth and the ecosystem carbon (C) sink. However, great uncertainties exist in terms of the CO2 fertilization effect (CFE) magnitude, and how it is regulated by other global change factors. The lack of experimental evidence from the Alpine Region also limits our cognition on the CFE. By conducting a five-year manipulative field experiment in a semi-arid grassland of the Tibetan Plateau, we are aimed to explore the behavior of ecosystem C exchange in response to eCO2 and N availability under contrasting natural precipitation regimes. The experiment showed that eCO2 stimulated both gross ecosystem productivity (GEP) and ecosystem respiration (ER), and resulted in a neutral effect on net ecosystem productivity (NEP). The reduction of leaf N concentration under eCO2 constrained the eCO2 effects on C fluxes, especially on GEP and NEP. As N addition replenishes N availability in soil and leaf, GEP benefited more from the N addition than the ER. The eCO2 strengthened the C sink when exogenous N was added simultaneously. Furthermore, precipitation variability played an importance role in mediating the eCO2 effect among growing seasons. The eCO2 effects on C fluxes tended to decline with increased water availability. The CFE was suppressed with excessive precipitation when the water-use efficiency (WUE) response was weak and eCO2-induced water-saving disappeared. The negative impact of precipitation on the CFE may also be attributed to the short precipitation intervals and insufficient radiation caused by high-frequency precipitation. Our study demonstrates that eCO2 only stimulates net C uptake under conditions of N addition or during drier periods. Given the widespread N limitation, the efficacy of terrestrial ecosystems in mitigating climate change under rising CO2 may be weaker than projected and is closely related to the precipitation variability.
Adults without diabetes are not completely healthy; they are probably heterogeneous with several potential health problems. The management of hemoglobin A1c (HbA1c) is crucial among patients with ...diabetes; but whether similar management strategy is needed for adults without diabetes is unclear. Thus, this study aimed to investigate the associations of visit-to-visit HbA1c variability with incident dementia and hippocampal volume among middle-aged and older adults without diabetes, providing potential insights into this question.
We conducted a prospective analysis for incident dementia in 10,792 participants (mean age 58.9 years, 47.8 % men) from the UK Biobank. A subgroup of 3793 participants (mean age 57.8 years, 48.6 % men) was included in the analysis for hippocampal volume. We defined HbA1c variability as the difference in HbA1c divided by the mean HbA1c over the 2 sequential visits (latter − former/mean). Dementia was identified using hospital inpatient records with ICD-9 codes. T1-structural brain magnetic resonance imaging was conducted to derive hippocampal volume (normalized for head size). The nonlinear and linear associations were examined using restricted cubic spline (RCS) models, Cox regression models, and multiple linear regression models.
During a mean follow-up (since the second round) of 8.4 years, 90 (0.8 %) participants developed dementia. The RCS models suggested no significant nonlinear associations of HbA1c variability with incident dementia and hippocampal volume, respectively (All P > 0.05). Above an optimal cutoff of HbA1c variability at 0.08, high HbA1c variability (increment in HbA1c) was associated with an increased risk of dementia (Hazard Ratio, 1.88; 95 % Confidence Interval, 1.13 to 3.14, P = 0.015), and lower hippocampal volume (coefficient, −96.84 mm3, P = 0.037), respectively, in models with adjustment of covariates including age, sex, etc. Similar results were found for a different cut-off of 0. A series of sensitivity analyses verified the robustness of the findings.
Among middle-aged and older adults without diabetes, increasing visit-to-visit HbA1c variability was associated with an increased dementia risk and lower hippocampal volume. The findings highlight the importance of monitoring and controlling HbA1c fluctuation in apparently healthy adults without diabetes.
•High visit-to-visit HbA1c variability was associated with an increased dementia risk.•High visit-to-visit HbA1c variability was associated with low hippocampal volume.•Monitoring HbA1c fluctuation is of great importance in adults without diabetes.
ABSTRACTAccurately estimating gross primary productivity (GPP), the largest carbon flux in terrestrial ecosystems, is crucial for advancing our understanding of global carbon cycle and predicting ...climate feedbacks. The advancements in remote sensing (RS) have facilitated the development of GPP estimation models at regional and global scales in recent decades. This article systemically reviews the development of RS-based GPP estimation in three main aspects: theoretical foundation, key parameters and methods. Regarding the theoretical foundation, RS generally excels in representing key characteristics during the light transmission process of photosynthesis. However, it exhibits a relatively weaker ability to describe the carbon reaction process, severely limiting the in-depth understanding of the mechanisms of RS-based GPP estimation. Concerning key parameters, the definition of traditional parameters, such as leaf area index (LAI), photosynthetically active radiation (PAR), and fraction of absorbing PAR, has been detailed in the development of RS (e.g. LAI is divided into sunlit LAI and shaded LAI). However, their accuracy still needs improvement. Additionally, researchers have developed effective parameters (e.g. photochemical reflectance index, sun-induced chlorophyll fluorescence, and the maximum carboxylation rate) that possess increased capability to represent and interpret the carbon reaction process of photosynthesis. Regarding estimation methods, although the four main categories of RS-based GPP estimation models (statistical model, light use efficiency model, RS-based process model and machine learning-based model) have made significant progress in parameter optimization, the estimation accuracy and mechanism innovation remain less than satisfactory. Finally, we summarize the current issues of RS-based GPP estimation related to parameters performance and accuracy, model mechanisms and capabilities, as well as scale and connotation mismatch. Integrating more adequate in situ and comprehensive observations would enhance the interpretability of GPP estimation models, providing more reliable insights into the mechanisms in future studies. This article contributes to understanding of the photosynthetic process and RS-based GPP estimation, potentially aiding in the development of parameter optimization (improving the estimation accuracy of existing parameters and developing new ones) and model design (introducing new parameters and exploring new mechanistic models).
The warming-wetting climates in Chinese drylands, together with a series of ecological engineering projects, had caused apparent changes to vegetation therein. Regarding the vegetation greening ...trend, different remote sensing data had yielded distinct findings. It was critical to evaluate vegetation dynamics in Chinese drylands using a series of remote sensing data. By comparing the three most commonly used remote sensing datasets i.e., MODIS, Advanced Very High Resolution Radiometer (AVHRR), and Landsat, this study comprehensively investigated vegetation dynamics for Chinse drylands. All three remote sensing datasets exhibited evident vegetation greening trends from 2000 to 2020 in Chinese drylands, especially in the Loess Plateau and Northeast China. However, Landsat identified the largest greening areas (89.8%), while AVHRR identified the smallest greening area (58%). The vegetation greening areas identified by Landsat comprise more small patches than those identified by MODIS and AVHRR. The MODIS data exhibited a higher consistency with Landsat than with AVHRR in terms of detecting vegetation greening areas. The three datasets exhibited high consistency in identifying vegetation greening in Northeast China, Loess Plateau, and Xinjiang. The percentage of inconsistent areas among the three datasets was 39.56%. The vegetation greening areas identified by Landsat comprised more small patches. Sensors and the atmospheric effect are the two main reasons responsible for the different outputs from each NDVI product. Ecological engineering projects had a great promotion effect on vegetation greening, which can be detected by the three NDVI datasets in Chinese drylands, thereby combating desertification and reducing dust storms.
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