Global responses of wetland methane emissions to extreme temperature and precipitation.

As global warming continues, events of extreme heat or heavy precipitation will become more frequent, while events of extreme cold will become less so. How wetlands around the globe will react to these extreme events is unclear yet critical, because they are among the greatest natural sources of methane(CH4). Here we use seven indices of extreme climate and the rate of methane emission from global wetlands(WME) during 2000-2019 simulated by 12 published models as input data. Our analyses suggest that extreme cold (particularly extreme low temperatures) inhibits WME, whereas extreme heat (particularly extreme high temperatures) accelerates WME. Our results also suggest that daily precipitation >10 mm accelerates WME, while much higher daily precipitation levels can slow WME. The correlation of extreme high temperature and precipitation with rate of WME became stronger during the study period, while the correlation between extreme low temperature and WME rate became weaker.

An optimized water table depth detected for mitigating global warming potential of greenhouse gas emissions in wetland of Qinghai-Tibetan Plateau.

Climate change and human activities have intensified variations of water table depth (WTD) in wetlands around the world, which may strongly affect greenhouse gas emissions. Here, we analyzed how emissions of CO2, CH4, and N2O from the Zoige wetland on the Qinghai-Tibetan Plateau (QTP) vary with the WTD. Our data indicate that the wetland shows net positive global warming potential (11.72 tCO2-e ha-1 yr-1), and its emissions of greenhouse gases are driven primarily by WTD. Our analysis suggests that an optimal WTD exists, which at our study site was approximately 18 cm, for mitigating increases in global warming potential from the wetland. Our study provides insights into how climate change and human acitivies affect greenhouse gas emissions from alpine wetlands, and they suggest that water table management may be effective at mitigating future increases in emissions.

An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau.

Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Integrating major agricultural practices into the TRIPLEX-GHG model v2.0 for simulating global cropland nitrous oxide emissions: Development, sensitivity analysis and site evaluation.

Nitrous oxide (N2O) emissions from croplands are one of the most important greenhouse gas sources while the estimation of which remains large uncertainties globally. To simulate N2O emissions from global croplands, the process-based TRIPLEX-GHG model v2.0 was improved by coupling the major agricultural activities. Sensitivity experiment was used to measure the impact of the integrated processes to modeled N2O emission found chemical N fertilization have the highest relative effect sizes. While the coefficient of the NO3- consumption rate for denitrification (COEdNO3), controlling the first step of the denitrification process was identified to be the most sensitive parameter based on sensitivity analysis of model parameters. The model performed well when simulating the magnitude of the daily N2O emissions for 39 calibration sites and the continental mean of the parameters were used to producing reasonable estimations for the means of the measured daily N2O fluxes (R2 = 0.87, slope = 1.07) and emission factors (EFs, R2 = 0.70, slope = 0.72) during the experiment periods. The model reliability was further confirmed by model validation. General trend of modeled daily N2O emissions were reasonably consistent with the observations of selected validated sites. In addition, high correlations between the results of modeled and observed mean N2O emissions (R2 = 0.86, slope = 0.82) and EFs (R2 = 0.66, slope = 0.83) from 68 validation sites were obtained. Further improvement on more detailed estimations for the variation of the environmental factors, management effects as well as accurate model input model driving data are required to reduce the uncertainties of model simulations. Consequently, our simulation results demonstrate that the TRIPLEX-GHG model v2.0 can reliably estimate N2O emissions from various croplands at the global scale, which contributes to closing global N2O budget and sustainable development of agriculture.

The Effects of Drought and Re-Watering on Non-Structural Carbohydrates of Pinus tabulaeformis Seedlings.

Intense and frequent drought events strongly affect plant survival. Non-structural carbohydrates (NSCs) are important "buffers" to maintain plant functions under drought conditions. We conducted a drought manipulation experiment using three-year-old Pinus tabulaeformis Carr. seedlings. The seedlings were first treated under different drought intensities (i.e., no irrigation, severe, and moderate) for 50 days, and then they were re-watered for 25 days to explore the dynamics of NSCs in the leaves, twigs, stems, and roots. The results showed that the no irrigation and severe drought treatments significantly reduced photosynthetic rate by 93.9% and 32.6% for 30 days, respectively, leading to the depletion of the starch storage for hydraulic repair, osmotic adjustment, and plant metabolism. The seedlings under moderate drought condition also exhibited starch storage consumption in leaves and twigs. After re-watering, the reduced photosynthetic rate recovered to the control level within five days in the severe drought group but showed no sign of recovery in the no irrigation group. The seedlings under the severe and moderate drought conditions tended to invest newly fixed C to starch storage and hydraulic repair instead of growth due to the "drought legacy effect". Our findings suggest the depletion and recovery of starch storage are important strategies for P. tabulaeformis seedlings, and they may play key roles in plant resistance and resilience under environmental stress.

Methane emissions respond to soil temperature in convergent patterns but divergent sensitivities across wetlands along altitude.

Among the global coordinated patterns in soil temperature and methane emission from wetlands, a declining trend of optimal soil temperature for methane emissions from low to high latitudes has been witnessed, while the corresponding trend along the altitudinal gradient has not yet been investigated. We therefore selected two natural wetlands located at contrasting climatic zones from foothill and mountainside of Nepal Himalayas, to test: (1) whether the optimal temperature for methane emissions decreases from low to high altitude, and (2) whether there is a difference in temperature sensitivity of methane emissions from those wetlands. We found significant spatial and temporal variation of methane emissions between the two wetlands and seasons. Soil temperature was the dominant driver for seasonal variation in methane emissions from both wetlands, though its effect was perplexed by the level of standing water, aquatic plants, and dissolved organic carbon, particularly in the deep water area. When integrative comparison was conducted by adding the existing data from wetlands of diverse altitudes, and the latitude-for-altitude effect was taken into account, we found the baseline soil temperatures decrease whilst the altitude rises with respect to a rapid increase in methane emission from all wetlands, however, remarkably higher sensitivity of methane emissions to soil temperature (apparent Q10 ) was found in mid-altitude wetland. We provide the first evidence of an apparent decline in optimal temperature for methane emissions with increasing elevation. These findings suggest a convergent pattern of methane emissions with respect to seasonal temperature shifts from wetlands along altitudinal gradient, while a divergent pattern in temperature sensitivities exhibits a single peak in mid-altitude.

Global response of terrestrial gross primary productivity to climate extremes.

Extreme climate events undoubtedly have essential impacts on ecosystem gross primary productivity (GPP), but the global spatio-temporal patterns of GPP responses to climate extremes are unclear. In this study, we analyzed the responses of GPP to temperature and precipitation extremes during historical (1901-2016) and future (2006-2100) periods using climate extreme indices (CEIs) developed by the Expert Team on Climate Change Detection and Indices. Eight temperature-related CEIs and eight precipitation-related CEIs were used for this analysis, along with three future greenhouse gas concentration trajectory scenarios generated by the IPCC: RCP 2.6, RCP 4.5, and RCP 8.5. Our results show that under RCP 4.5 and RCP 8.5, most climate extremes are increasing from the historical period into the future, indicating a warming globe with more frequent and more intense extreme climate events. But the increasing rate is only persistently enhanced with time under scenario RCP 8.5. GPP shows a continuous negative relationship with cold CEIs and positive relationship with wet CEIs from the historical period into the future. In all zonal scales, the changed magnitude of GPP responds strongly to extreme value-related temperature extremes under different scenarios. However, the precipitation-related extremes with the strongest GPP response are various in different regions. In the future, GPP is most sensitive to temperature extremes in upper northern latitudes and in high-altitude regions (e.g., Qinghai-Tibet Plateau) and to precipitation extremes in the tropical zone. This study may provide a basis for predicting how GPP responds to climate extremes and explaining the underlying changes in the carbon cycle.

Progressive nitrogen limitation across the Tibetan alpine permafrost region.

The ecosystem carbon (C) balance in permafrost regions, which has a global significance in understanding the terrestrial C-climate feedback, is significantly regulated by nitrogen (N) dynamics. However, our knowledge on temporal changes in vegetation N limitation (i.e., the supply of N relative to plant N demand) in permafrost ecosystems is still limited. Based on the combination of isotopic observations derived from a re-sampling campaign along a ~3000 km transect and simulations obtained from a process-based biogeochemical model, here we detect changes in ecosystem N cycle across the Tibetan alpine permafrost region over the past decade. We find that vegetation N limitation becomes stronger despite the increased available N production. The enhanced N limitation on vegetation growth is driven by the joint effects of elevated plant N demand and gaseous N loss. These findings suggest that N would constrain the future trajectory of ecosystem C cycle in this alpine permafrost region.

Critical land change information enhances the understanding of carbon balance in the United States.

Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

The significant contribution of lake depth in regulating global lake diffusive methane emissions.

Global lakes have been identified as an important component of natural methane (CH4) sources. Given that lake CH4 emissions involve multiple, complex processes influenced by various environmental factors, estimates of global lake CH4 emissions are largely uncertain. In this study, we compiled global CH4 emission data on 744 lakes from published studies, and found a significantly negative correlation (R2 = 0.50, p < 0.01) between diffusive CH4 flux and lake maximum depth. Further analysis indicated that no significant differences in global sediment CH4 production were found for the different maximum depths investigated. Owing to the longer oxidation pathway, presence of oxycline layer, and the lower nutrient environment, deeper lakes yield less diffusive CH4 efflux compared to shallower lakes. Additionally, we also found that lake area was negatively correlated (R2 = 0.13, p < 0.01) to diffusive CH4 flux. Therefore, based on empirical correlations between lake morphometry (maximum depth and area) and diffusive CH4 emission, as well as the combination of two lake databases, we estimated that the annual diffusive CH4 emission from global lakes is approximately 11.2 (6.2-19.5) Tg CH4/yr, and greater than 84% is emitted from lakes with a mean depth of less than 5 m. Furthermore, two regions, 40-70° N (30.4%) and 20° S∼10° N (37.4%), were found to be the dominant contributors of global lake diffusive CH4 emissions, resulting from the considerable total lake area and the extensive shallow lakes in these regions. This study highlights the significance of the 'depth-effect' which controls the spatial distribution of lake diffusive CH4 flux and allows for the quantification of global lake diffusive CH4 emissions.

Global vegetation biomass production efficiency constrained by models and observations.

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

Simulation of dissolved organic carbon concentrations and fluxes in Chinese monsoon forest ecosystems using a modified TRIPLEX-DOC model.

Dissolved organic carbon (DOC) plays an important role in global and regional carbon cycles. However, the quantification of DOC in forest ecosystems remains uncertain. Here, the processed-based biogeochemical model TRIPLEX-DOC was modified by optimizing the function of soil organic carbon distribution with increasing depths, as well as DOC sorption-desorption efficiency. The model was validated by field measurements of DOC concentration and flux at five forest sites and Beijiang River basin in monsoon regions of China. Model validation indicated that seasonal patterns of DOC concentration across climatic zones were different, and these differences were captured by our model. Importantly, the modified model performed better than the original model. Indeed, model efficiency of the modified model increased from -0.78 to 0.19 for O horizon predictions, and from -0.46 to 0.42 for the mineral soils predictions. Likewise, DOC fluxes were better simulated by the modified model. At the site scale, the simulated DOC fluxes were strongly correlated with the observed values (R2 = 0.97, EF = 0.91). At the regional scale, the DOC flux predicted in the Beijiang River basin was 16.44 kg C/ha, which was close to the observed value of 17 kg C/ha. Using sensitivity analysis, we showed that temperature, precipitation and temperature sensitivity of DOC decomposition (Q10) were the most sensitive parameters when predicting DOC concentrations and fluxes in forest soils. We also found that both the percentage of DOC flux to forest net ecosystem productivity, and the retention of DOC by mineral soil were highly correlated with the amount of precipitation. Overall, our model validations indicated that the modified TRIPLEX-DOC model is a useful tool for simulating the dynamics of DOC concentrations and fluxes in forest ecosystems. We highlight that more accurate estimates of parameter Q10 in deep mineral soils can reduce model uncertainty, when simulating DOC concentrations and fluxes in forest soils.

Comparison of methane emissions among invasive and native mangrove species in Dongzhaigang, Hainan Island.

The strength of methane (CH4) source of mangroves is not well understood, especially when including all CH4 pathways in consideration. This study measured CH4 fluxes by five pathways (sediments, pneumatophores, water surface, leaves, and stems) from four typical mangrove forests, including Kandelia candel without pneumatophores and three species with pneumatophores: Sonneratia apetala, Laguncularia racemosa and Bruguiera gymnorhiza-Bruguiera sexangula. The CH4 fluxes from sediments were 4.82±1.46mgCH4m-2h-1 for K. candel and 1.36±0.17mgCH4m-2h-1 for the other three with pneumatophores. Among the three communities with pneumatophores, S. apetala community had significantly greater emission rate than the other two (P<0.05). Pneumatophores in S. apetala were found to significantly decrease CH4 emission from sediments (P<0.01), while those in B. gymnorhiza-B. sexangula were significantly increase it (P<0.05). CH4 fluxes from waters were 3.48±1.11mgCH4m-2h-1, with the highest emission rate in the K. candel community for the duck farming. Leaves of mangroves except for those of K. candel were a weak CH4 daytime sink, but stems were a weak source. The total 72ha of mangroves in the Changning river basin emitted about 8.10Gg CH4 per year, with a weighted emission rate of about 1.29mgCH4m-2h-1. Our results suggested that mangroves are only a small methane source to atmosphere with great contribution from sediments and waters, only slight contribution from leaves and stems. Pneumatophores of different mangrove species played different roles in CH4 fluxes from sediments.

Modelling the impacts of climate and land use changes on soil water erosion: Model applications, limitations and future challenges.

The world is experiencing serious soil losses. Soil erosion has become an important environmental problem in certain regions and is strongly affected by climate and land use changes. By selecting and reviewing 13 extensively used soil water erosion models (SWEMs) from the published literature, we summarize the current model-based knowledge on how climate factors (e.g., rainfall, freeze-thaw cycles, rainstorms, temperature and atmospheric CO2 concentrations) and land use change impact soil erosion worldwide. This study also provides a critical review of the application of these 13 SWEMs. By comparing model structures, features, prediction accuracies, and erosion processes, we recommend the most suitable SWEMs for different regions of the globe (Asia, Europe, Africa and the America) based on the evaluations of 13 SWEMs. Future soil erosion could be simulated using the RUSLE, LISEM, WEPP v2010.1, SWAT, EPIC, KINEROS and AGNPS models in Asia; the RUSLE, WEPP v2010.1, SWAT, EPIC, WATEM-SEDEM, MEFIDIS, AGNPS and AnnAGNPS models in Europe; the RUSLE, LISEM, SWAT, and AGNPS models in Africa; and the WEPP v2010.1, SWAT, EPIC, KINEROS, AGNPS and AnnAGNPS models in America. Finally, the limitations and challenges of the 13 SWEMs are highlighted.

Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China.

Dynamic global vegetation models (DGVMs) suffer insufficiencies in tracking biochemical cycles and ecosystem fluxes. One important reason for these insufficiencies is that DGVMs use fixed parameters (mostly traits) to distinguish attributes and functions of plant functional types (PFTs); however, these traits vary under different climatic conditions. Therefore, it is urgent to quantify trait covariations, including those among specific leaf area (SLA), area-based leaf nitrogen (N area), and leaf area index (LAI) (in 580 species across 218 sites in this study), and explore new classification methods that can be applied to model vegetation dynamics under future climate change scenarios. We use a redundancy analysis (RDA) to derive trait-climate relationships and employ a Gaussian mixture model (GMM) to project vegetation distributions under different climate scenarios. The results show that (1) the three climatic variables, mean annual temperature (MAT), mean annual precipitation (MAP), and monthly photosynthetically active radiation (mPAR) could capture 65% of the covariations of three functional traits; (2) tropical, subtropical and temperate forest complexes expand while boreal forest, temperate steppe, temperate scrub and tundra shrink under future climate change scenarios; and (3) the GMM classification based on trait covariations should be a powerful candidate for building new generation of DGVM, especially predicting the response of vegetation to future climate changes. This study provides a promising route toward developing reliable, robust and realistic vegetation models and can address a series of limitations in current models.

Comparative analyses of different biogenic CO2 emission accounting systems in life cycle assessment.

The biomass-derived CO2 emission is usually treated as neutral to climate change. However, due to the stay of biomass-derived CO2 in the atmosphere, many researchers believe that biomass-derived CO2 also has climate change benefit. Therefore, many methods to account the global warming potential of biomass-derived CO2 (GWPbio) were proposed. Based on those new methods, we developed an accounting system for climate change impact of biomass utilization in this study, and compared it with the conventional accounting system which follows the carbon neutral assumption. A case study of caragana-to-pellet bioenergy production system was simulated to test the performance of the GWPbio accounting system. The CENTURY model was used to simulate carbon dynamics of caragana plantation in the Loess Plateau in China, and life cycle assessment (LCA) model was developed to estimate the life cycle emissions of the caragana-to-pellet system. Attributed to short rotation of caragana plantation and fast biomass accumulation after harvest, the GWPbio values around 0.044 were obtained. When the GWPbio was applied to LCA, significant high life cycle CO2 emission was found in comparison to the conventional method. However, the GWPbio accounting system has lower positive climate change impact than the conventional accounting system in assessing the overall impact of biomass utilization. This indicated that the application of GWPbio accounting system would encourage the utilization of biomass and allow a fair comparison with fossil fuels. In the sensitivity analysis, we found the accounting system was sensitive to biomass accumulation and all the corresponding factor affecting biomass accumulation.

Change in Autumn Vegetation Phenology and the Climate Controls From 1982 to 2012 on the Qinghai-Tibet Plateau.

Autumn vegetation phenology plays a critical role in the survival and reproduction of vegetation in changing environments. Using GIMMS3g (Global Inventory Modeling and Mapping Studies), MODIS (Moderate Resolution Imaging and Spectroradiometer), and SPOT (Systeme Probatoire d'Observation de la Terre) remote sensing data, we investigated the spatial and temporal dynamics of the vegetation dormancy onset date (DOD) and its response to temperature, precipitation, and cold degree days (CDD) in different biomes on the Qinghai-Tibet Plateau (QTP) from 1982 to 2012. Our results indicated that there was no significant temporal trend in the DOD for the vegetation on the QTP but found clear regional characteristics in the DOD trends with a notably advancing trend in the central region and a widespread delay in the southwestern region (>1 day year-1, P < 0.05). Our results also indicated that temperature plays an important role in the trend of delays in vegetation autumn phenology; in particular, the preseason temperature can delay the DOD significantly; the positive correlations were observed in more than 71% of the study areas. Consistent with previous studies, we observed significant negative correlations between preseason CDD and DOD; the negative correlations were observed in more than 72% of the study areas for all the data sets. In contrast, the effects of precipitation on DOD were biome dependent. We found that precipitation could promote the extension of the growing season in meadow and grass biomes but produce weak effects on vegetation dormancy in forest biomes. Therefore, not only the magnitude but also the timing of changes in temperature and precipitation determines the effects of climate factors on DOD and further suggests that biome-specific phenological responses also need to be integrated into vegetation phenology models for future climate change investigations on the QTP.

Dynamics of vegetation autumn phenology and its response to multiple environmental factors from 1982 to 2012 on Qinghai-Tibetan Plateau in China.

Autumn phenological shifts induced by environmental change have resulted in substantial impacts on ecosystem processes. However, autumn phenology and its multiple related controlling factors have not been well studied. In this study, the spatiotemporal patterns of the end date of the vegetation growing season (EGS) and their multiple controls (climate change, summer vegetation growth and human activities) over the Qinghai-Tibetan Plateau (QTP) were investigated using the satellite-derived normalized difference vegetation index (NDVI) based on GIMMS3g datasets during 1982-2012. The results showed that there was no significant temporal trend in the EGS during the period of 1982-2012. Spatially, there was a notable advancing trend in the southwest region and a delayed trend in the other regions of the QTP during 1982-2000, and this spatial trend was reversed during 2001-2012. We found average temperature, precipitation and sunshine duration of autumn exerted positive effects on EGS on the QTP, while average temperature and sunshine duration of summer exerted negative effects. Our results indicated that vegetation growth in summer tends to induce an earlier EGS in alpine vegetation, whereas summer vegetation degradation could delay the EGS on the QTP. In contrast, moderate grazing delays vegetation browning in autumn, while overgrazing leads to advancement of grass senescence. This study improves our understanding of how multiple environmental variables jointly affect autumn phenology and highlights the importance of biotic controls for autumn phenology on the QTP.