Integrating major agricultural practices into the TRIPLEX-GHG model v2.0 for simulating global cropland nitrous oxide emissions: Development, sensitivity analysis and site evaluation - Song, Hanxiong; Peng, Changhui; Zhang, Kerou; Zhu, Qiuan (Science of The Total Environment, 2022)

Comparative analyses of different biogenic CO2 emission accounting systems in life cycle assessment - Liu, Weiguo; Zhu, Qiuan; Zhou, Xiaolu; Peng, Changhui (Science of The Total Environment, 2019)

Quantification of provincial-level carbon emissions from energy consumption in China - Geng, Yuhuan; Tian, Mingzhong; Zhu, Qiuan; Zhang, Jianjun; Peng, Changhui (Renewable and Sustainable Energy Reviews, 2011)

From plant functional types to plant functional traits: A new paradigm in modelling global vegetation dynamics - Yang, Yanzheng; Zhu, Qiuan; Peng, Changhui; Wang, Han; Chen, Huai (Progress in Physical Geography: Earth and Environment, 2015)
Dynamic global vegetation models (DGVMs) typically track the material and energy cycles in ecosystems with finite plant functional types (PFTs). Increasingly, the community ecology and modelling studies recognize that current PFT scheme is not sufficient for simulating ecological processes. Recent advances in the study of plant functional traits (FTs) in community ecology provide a novel and feasible approach for the improvement of PFT-based DGVMs. This paper reviews the development of current DGVMs over recent decades. After characterizing the advantages and disadvantages of the PFT-based scheme, it summarizes trait-based theories and discusses the possibility of incorporating FTs into DGVMs. More importantly, this paper summarizes three strategies for constructing next-generation DGVMs with FTs. Finally, the method’s limitations, current challenges and future research directions for FT theory are discussed for FT theory. We strongly recommend the inclusion of several FTs, namely specific leaf area (SLA), leaf nitrogen content (LNC), carbon isotope composition of leaves (Leaf δ 13 C), the ratio between leaf-internal and ambient mole fractions of CO 2 (Leaf C i /C a ), seed mass and plant height. These are identified as the most important in constructing DGVMs based on FTs, which are also recognized as important ecological strategies for plants. The integration of FTs into dynamic vegetation models is a critical step towards improving the results of DGVM simulations; communication and cooperation among ecologists and modellers is equally important for the development of the next generation of DGVMs.

Assessment of biomass utilization potential of Caragana korshinskii and its effect on carbon sequestration on the Northern Shaanxi Loess Plateau, China - Liu, Weiguo; Yu, Zhen; Zhu, Qiuan; Zhou, Xiaolu; Peng, Changhui (Land Degradation & Development, 2020)
Abstract Biomass has been promoted as a promising energy resource to mitigate global climate change. To evaluate the contribution of biomass utilization to climate change mitigation under the “Grain for Green” program in Northern Shaanxi, China, a soil carbon dynamic model and a life cycle assessment model were integrated to examine the benefits of using Caragana korshinskii Kom. as an energy crop. We found that the annual dry biomass output is maintained at 0.7 Tg during the simulation period (2020–2097). Due to the compensatory effect of biomass regrowth, the global warming potential of biomass‐derived CO 2 emissions is approximately 0.045; therefore, the total annual biogenic CO 2 emission is 57,211 ± 6,168 Mg CO 2 eq. The total annual life cycle CO 2 emissions approach 867,072 Mg CO 2 eq yr −1 . Under the scenario of no biomass removal, final carbon storage ranges from 15.7 to 19.3 TgC, and the highest carbon sequestration rate is 0.47 TgC yr −1 . In comparison with the no biomass removal scenario, the carbon sequestration rate (close to 0 MgC yr −1 ) in the biomass utilization scenario indicates a carbon loss; however, a portion of the carbon loss (31.39–62.09%) can be offset by carbon emission reductions from the substitution of fossil fuels.

Stand carbon density drivers and changes under future climate scenarios across global forests - Guo, Yanrong; Peng, Changhui; Trancoso, Ralph; Zhu, Qiuan; Zhou, Xiaolu (Forest Ecology and Management, 2019)

Application of machine learning methods for paleoclimatic reconstructions from leaf traits - Wei, Gang; Peng, Changhui; Zhu, Qiuan; Zhou, Xiaolu; Yang, Bin (International Journal of Climatology, 2021)
Abstract Digital leaf physiognomy (DLP) is considered as one of the most promising methods for estimating past climate. However, current models built using the DLP data set still lack precision, especially for mean annual precipitation (MAP). To improve predictive power, we developed five machine learning (ML) models for mean annual temperature (MAT) and MAP respectively, and then tested the precision of these models and some of their averaging compared with that obtained from other models. The precision of all models was assessed using a repeated stratified 10‐fold cross‐validation. For MAT, three combinations of models ( R 2 = .77) presented moderate improvements in precision over the multiple linear regression (MLR) model ( R 2 = .68). For log e (MAP), the averaging of the support vector machine (SVM) and boosting models improved the R 2 from .19 to .63 compared with that of the MLR model. For MAP, the R 2 of this model combination was 0.49, which was much better than that of the artificial neural network (ANN) model ( R 2 = .21). Even the bagging model, which had the lowest R 2 (.37) for log e (MAP), demonstrated better precision ( R 2 = .27) for MAP. Our palaeoclimate estimates for nine fossil floras were also more accurate, because they were in better agreement with independent paleoclimate evidence. Our study confirms that our ML models and their averaging can improve paleoclimatic reconstructions, providing a better understanding of the relationship between climate and leaf physiognomy.

Contribution of the Order Ericales to Improving Paleoclimate Reconstructions - Wei, Gang; Peng, Changhui; Zhu, Qiuan; Zhou, Xiaolu; Liu, Weiguo (Sustainability, 2022)
Paleobotanists have long built leaf climate models based on site mean of leaf physiognomic characteristics of woody dicotyledons species (WDS) for estimating past climate. To explore the potential of the order Ericales in estimating paleoclimate, we developed two linear models for each climatic factor. One is based on WDS, and the other is based on both WDS and leaf physiognomic characters of the order Ericales (WDS-E). We found that, compared with WDS models, WDS-E models improved greatly in mean annual precipitation (MAP), growing season precipitation (GSP) and mean annual range in temperature (MART). When the minimum species number of the order Ericales is three per site, the WDS-E models improved the r2 from 0.64 to 0.78 for MART, from 0.23 to 0.61 for ln(MAP), and from 0.37 to 0.64 for ln(GSP) compared with the WDS models. For mean annual temperature (MAT), the WDS-E model (r2 = 0.86) also exhibited a moderate improvement in precision over the WDS model (r2 = 0.82). This study demonstrates that other patterns, such as those of the order Ericales, can contribute additional information towards building more precise paleoclimate models.

Assessment of frozen ground organic carbon pool on the Qinghai-Tibet Plateau - Jiang, Lin; Chen, Huai; Zhu, Qiuan; Yang, Yanzheng; Li, Mingxu; Peng, Changhui; Zhu, Dan; He, Yixin (Journal of Soils and Sediments, 2019)

Delayed spring phenology on the Tibetan Plateau may also be attributable to other factors than winter and spring warming - Chen, Huai; Zhu, Qiuan; Wu, Ning; Wang, Yanfen; Peng, Chang-Hui (Proceedings of the National Academy of Sciences, 2011)

Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada - Fang, Xiuqin; Zhu, Qiuan; Ren, Liliang; Chen, Huai; Wang, Kai; Peng, Changhui (Remote Sensing of Environment, 2018)

Quantification of methane emissions from municipal solid waste landfills in China during the past decade - Du, Mingxi; Peng, Changhui; Wang, Xiaoge; Chen, Huai; Wang, Meng; Zhu, Qiuan (Renewable and Sustainable Energy Reviews, 2017)

Direct and Indirect Effects of UV-B Exposure on Litter Decomposition: A Meta-Analysis - Song, Xinzhang; Peng, Changhui; Jiang, Hong; Zhu, Qiuan; Wang, Weifeng (PLoS ONE, 2013)

Temporal and Spatial Variation of Wetland CH4 Emissions from the Qinghai–Tibet Plateau under Future Climate Change Scenarios - Zhang, Xian; Wang, Jieyi; Zhang, Jiang; Chen, Huai; Peng, Changhui; Zhu, Qiuan (Atmosphere, 2022)
Wetlands are an important natural source of methane (CH4), so it is important to quantify how their emissions may vary under future climate change conditions. The Qinghai–Tibet Plateau contains more than a third of China’s wetlands. Here, we simulated temporal and spatial variation in CH4 emissions from natural wetlands on the Qinghai–Tibet Plateau from 2008 to 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5. Based on the simulation results of the TRIPLEX-GHG model forced with data from 24 CMIP5 models of global climate, we predict that, assuming no change in wetland distribution on the Plateau, CH4 emissions from natural wetlands will increase by 35%, 98% and 267%, respectively, under RCP 2.6, 4.5 and 8.5. The predicted increase in atmospheric CO2 concentration will contribute 10–28% to the increased CH4 emissions from wetlands on the Plateau by 2100. Emissions are predicted to be majorly in the range of 0 to 30.5 g C m−2·a−1 across the Plateau and higher from wetlands in the southern region of the Plateau than from wetlands in central or northern regions. Under RCP8.5, the methane emissions of natural wetlands on the Qinghai–Tibet Plateau increased much more significantly than that under RCP2.6 and RCP4.5.

Contribution of Incorporating the Phosphorus Cycle into TRIPLEX-CNP to Improve the Quantification of Land Carbon Cycle - Ding, Juhua; Zhu, Qiuan; Li, Hanwei; Zhou, Xiaolu; Liu, Weiguo; Peng, Changhui (Land, 2022)
Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.

Soil properties and species composition under different grazing intensity in an alpine meadow on the eastern Tibetan Plateau, China - Yang, Zhen’an; Xiong, Wan; Xu, Yingyi; Jiang, Lin; Zhu, Erxiong; Zhan, Wei; He, Yixin; Zhu, Dan; Zhu, Qiuan; Peng, Changhui; Chen, Huai (Environmental Monitoring and Assessment, 2016)

Effects of soil warming, rainfall reduction and water table level on CH 4 emissions from the Zoige peatland in China - Yang, Gang; Chen, Huai; Wu, Ning; Tian, Jianqing; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; He, Yixin; Zheng, Qunying; Zhang, Changbing (Soil Biology and Biochemistry, 2014)

Assessing the spatio-temporal variation and uncertainty patterns of historical and future projected water resources in China - Zhu, Qiuan; Jiang, Hong; Peng, Changhui; Liu, Jinxun; Fang, Xiuqin; Chen, Huai; Liu, Shirong (Journal of Water and Climate Change, 2013)
The spatial and temporal variation and uncertainty of precipitation and runoff in China were compared and evaluated between historical and future periods under different climate change scenarios. The precipitation pattern is derived from observed and future projected precipitation data for historical and future periods, respectively. The runoff is derived from simulation results in historical and future periods using a dynamic global vegetation model (DGVM) forced with historical observed and global climate models (GCMs) future projected climate data, respectively. One GCM (CGCM3.1) under two emission scenarios (SRES A2 and SRES B1) was used for the future period simulations. The results indicated high uncertainties and variations in climate change effects on hydrological processes in China: precipitation and runoff showed a significant increasing trend in the future period but a decreasing trend in the historical period at the national level; the temporal variation and uncertainty of projected precipitation and runoff in the future period were predicted to be higher than those in the historical period; the levels of precipitation and runoff in the future period were higher than those in the historical period. The change in trends of precipitation and runoff are highly affected by different climate change scenarios. GCM structure and emission scenarios should be the major sources of uncertainty.

Quantifying patterns, sources and uncertainty of nitrous oxide emissions from global grazing lands: Nitrogen forms are the determinant factors for estimation and mitigation - Song, Hanxiong; Peng, Changhui; Zhang, Kerou; Li, Tong; Yang, Mingxia; Liu, Qiuyu; Zhu, Qiuan (Global and Planetary Change, 2023)

Modeling Global Soil Carbon and Soil Microbial Carbon by Integrating Microbial Processes into the Ecosystem Process Model TRIPLEX‐GHG - Wang, Kefeng; Peng, Changhui; Zhu, Qiuan; Zhou, Xiaolu; Wang, Meng; Zhang, Kerou; Wang, Gangsheng (Journal of Advances in Modeling Earth Systems, 2017)
Abstract Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process‐based model TRIPLEX‐GHG was developed by coupling it with the new MEND (Microbial‐ENzyme‐mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX‐MICROBE) shows considerable improvement over the previous version (TRIPLEX‐GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well‐regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. (2014). We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and mineral‐associated organic carbon (MOC). However, our work represents the first step toward a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles. , Key Points Traditional soil carbon models are lacking in their representation of key microbial processes that control the soil carbon response to global climate change A Ecosystem model (TRIPLEX‐MICROBE) offers considerable improvement over a previous version (TRIPLEX‐GHG) in simulating soil organic carbon Our work is the first step toward a new generation of ecosystem process models that integrate key microbial processes into soil carbon cycles

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites - Zhang, TingLong; Sun, Rui; Peng, ChangHui; Zhou, GuoYi; Wang, ChunLing; Zhu, QiuAn; Yang, YanZheng (Science China Earth Sciences, 2016)

A novel pathway of direct methane production and emission by eukaryotes including plants, animals and fungi: An overview - Liu, Jiangong; Chen, Huai; Zhu, Qiuan; Shen, Yan; Wang, Xue; Wang, Meng; Peng, Changhui (Atmospheric Environment, 2015)

MODELING INDIVIDUAL TREE MORTALITY RATES USING MARGINAL AND RANDOM EFFECTS REGRESSION MODELS - Ma, Zhihai; Peng, Changhui; Li, Weizhong; Zhu, Qiuan; Wang, Weifeng; Song, Xinzhang; Liu, Jianwei (Natural Resource Modeling, 2013)
A bstract Developing models to predict tree mortality using data from long‐term repeated measurement data sets can be difficult and challenging due to the nature of mortality as well as the effects of dependence on observations. Marginal (population‐averaged) generalized estimating equations (GEE) and random effects (subject‐specific) models offer two possible ways to overcome these effects. For this study, standard logistic, marginal logistic based on the GEE approach, and random logistic regression models were fitted and compared. In addition, four model evaluation statistics were calculated by means of K ‐fold cross‐valuation. They include the mean prediction error, the mean absolute prediction error, the variance of prediction error, and the mean square error. Results from this study suggest that the random effects model produced the smallest evaluation statistics among the three models. Although marginal logistic regression accommodated for correlations between observations, it did not provide noticeable improvements of model performance compared to the standard logistic regression model that assumed impendence. This study indicates that the random effects model was able to increase the overall accuracy of mortality modeling. Moreover, it was able to ascertain correlation derived from the hierarchal data structure as well as serial correlation generated through repeated measurements.

The Effects of Drought and Re-Watering on Non-Structural Carbohydrates of Pinus tabulaeformis Seedlings - Guo, Xinyi; Peng, Changhui; Li, Tong; Huang, Jingjing; Song, Hanxiong; Zhu, Qiuan; Wang, Meng (Biology, 2021)
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.

Woody plants reduce the sensitivity of soil extracellular enzyme activity to nutrient enrichment in wetlands: A meta-analysis - Li, Tong; Peng, Changhui; Bu, Zhaojun; Zhu, Qiuan; Song, Hanxiong; Guo, Xinyi; Wang, Meng (Soil Biology and Biochemistry, 2021)

The significant contribution of lake depth in regulating global lake diffusive methane emissions - Li, Mingxu; Peng, Changhui; Zhu, Qiuan; Zhou, Xiaolu; Yang, Gang; Song, Xinzhang; Zhang, Kerou (Water Research, 2020)

Effects of Land Cover Changes on Net Primary Productivity in the Terrestrial Ecosystems of China from 2001 to 2012 - Li, Hanwei; Ding, Juhua; Zhang, Jiang; Yang, Zhenan; Yang, Bin; Zhu, Qiuan; Peng, Changhui (Land, 2020)
The 2001–2012 MODIS MCD12Q1 land cover data and MOD17A3 NPP data were used to calculate changes in land cover in China and annual changes in net primary productivity (NPP) during a 12-year period and to quantitatively analyze the effects of land cover change on the NPP of China’s terrestrial ecosystems. The results revealed that during the study period, no changes in land cover type occurred in 7447.31 thousand km2 of China, while the area of vegetation cover increased by 160.97 thousand km2 in the rest of the country. Forest cover increased to 20.91%, which was mainly due to the conversion of large areas of savanna (345.19 thousand km2) and cropland (178.96 thousand km2) to forest. During the 12-year study period, the annual mean NPP of China was 2.70 PgC and increased by 0.25 PgC, from 2.50 to 2.75 PgC. Of this change, 0.21 PgC occurred in areas where there was no land cover change, while 0.04 PgC occurred in areas where there was land cover change. The contributions of forest and cropland to NPP exhibited increasing trends, while the contributions of shrubland and grassland to NPP decreased. Among these land cover types, the contributions of forest and cropland to the national NPP were the greatest, accounting for 40.97% and 27.95%, respectively, of the annual total NPP. There was no significant correlation between changes in forest area and changes in total annual NPP (R2 < 0.1), while the correlation coefficient for changes in cropland area and total annual NPP was 0.48. Additionally, the area of cropland converted to other land cover types was negatively correlated with the changes in NPP, and the loss of cropland caused a reduction in the national NPP.

Modeling Global Riverine DOC Flux Dynamics From 1951 to 2015 - Li, Mingxu; Peng, Changhui; Zhou, Xiaolu; Yang, Yanzheng; Guo, Yanrong; Shi, Guohua; Zhu, Qiuan (Journal of Advances in Modeling Earth Systems, 2019)
Abstract Climate change has a profound impact on the global carbon cycle, including effects on riverine carbon pools, which connect terrestrial, oceanic, and atmospheric carbon pools. Until now, terrestrial ecosystem models have rarely incorporated riverine carbon components into global carbon budgets. Here we developed a new process‐based model, TRIPLEX‐HYDRA (TRIPLEX‐hydrological routing algorithm), that considers the production, consumption, and transport processes of nonanthropogenic dissolved organic carbon (DOC) from soil to river ecosystems. After the parameter calibration, model results explained more than 50% of temporal variations in all but three rivers. Validation results suggested that DOC yield simulated by TRIPLEX‐HYDRA has a good fit ( R 2 = 0.61, n = 71, p < 0.001) with global river observations. And then, we applied this model for global rivers. We found that mean DOC yield of global river approximately 1.08 g C/m 2 year, where most high DOC yield appeared in the rivers from high northern or tropic regions. Furthermore, our results suggested that global riverine DOC flux appeared a significant decrease trend (average rate: 0.38 Pg C/year) from 1951 to 2015, although the variation patterns of DOC fluxes in global rivers are diverse. A decreasing trend in riverine DOC flux appeared in the middle and high northern latitude regions (30–90°N), which could be attributable to an increased flow path and DOC degradation during the transport process. Furthermore, increasing trend of DOC fluxes is found in rivers from tropical regions (30°S–30°N), which might be related to an increase in terrestrial organic carbon input. Many other rivers (e.g., Mississippi, Yangtze, and Lena rivers) experienced no significant changes under a changing environment. , Key Points Terrestrial ecosystem models rarely incorporate riverine DOC components into the global carbon cycle The TRIPLEX‐HYDRA model simulates the spatiotemporal variation in the DOC fluxes in global rivers The global riverine DOC flux simulated by the TRIPLEX‐HYDRA model has significantly decreased from 1951 to 2015

Modelling the impacts of climate and land use changes on soil water erosion: Model applications, limitations and future challenges - Guo, Yanrong; Peng, Changhui; Zhu, Qiuan; Wang, Meng; Wang, Han; Peng, Shushi; He, Honglin (Journal of Environmental Management, 2019)

The linkage between vegetation and soil nutrients and their variation under different grazing intensities in an alpine meadow on the eastern Qinghai-Tibetan Plateau - Yang, Zhenan; Zhu, Qiuan; Zhan, Wei; Xu, Yingyi; Zhu, Erxiong; Gao, Yongheng; Li, Shiqing; Zheng, Qunying; Zhu, Dan; He, Yixin; Peng, Changhui; Chen, Huai (Ecological Engineering, 2018)

The role of industrial nitrogen in the global nitrogen biogeochemical cycle - Gu, Baojing; Chang, Jie; Min, Yong; Ge, Ying; Zhu, Qiuan; Galloway, James N.; Peng, Changhui (Scientific Reports, 2013)

Allocation Mechanisms of Non-Structural Carbohydrates of Robinia pseudoacacia L. Seedlings in Response to Drought and Waterlogging - Yang, Bin; Peng, Changhui; Harrison, Sandy P.; Wei, Hua; Wang, Han; Zhu, Qiuan; Wang, Meng (Forests, 2018)
Climate change is likely to lead to an increased frequency of droughts and floods, both of which are implicated in large-scale carbon allocation and tree mortality worldwide. Non-structural carbohydrates (NSCs) play an important role in tree survival under stress, but how NSC allocation changes in response to drought or waterlogging is still unclear. We measured soluble sugars (SS) and starch in leaves, twigs, stems and roots of Robinia pseudoacacia L. seedlings that had been subjected to a gradient in soil water availability from extreme drought to waterlogged conditions for a period of 30 days. Starch concentrations decreased and SS concentrations increased in tissues of R. pseudoacacia seedlings, such that the ratio of SS to starch showed a progressive increase under both drought and waterlogging stress. The strength of the response is asymmetric, with the largest increase occurring under extreme drought. While the increase in SS concentration in response to extreme drought is the largest in roots, the increase in the ratio of SS to starch is the largest in leaves. Individual components of SS showed different responses to drought and waterlogging across tissues: glucose concentrations increased significantly with drought in all tissues but showed little response to waterlogging in twigs and stems; sucrose and fructose concentrations showed marked increases in leaves and roots in response to drought but a greater response to drought and waterlogging in stems and twigs. These changes are broadly compatible with the roles of individual SS under conditions of water stress. While it is important to consider the role of NSC in buffering trees against mortality under stress, modelling this behaviour is unlikely to be successful unless it accounts for different responses within organs and the type of stress involved.

Application of the ecosystem model and Markov Chain Monte Carlo for parameter estimation and productivity prediction - Li, Weizhong; Peng, Changhui; Zhou, Xiaolu; Sun, Jianfeng; Zhu, Qiuan; Wu, Haibin; St-Onge, Benoit (Ecosphere, 2015)
It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process‐based ecological models and data in cohesive, systematic ways. In process‐based model applications, inherent spatial and temporal heterogeneities found within terrestrial ecosystems may lead to the uncertainties of model predictions. To reduce simulation uncertainties due to inaccurate model parameters, the Markov Chain Monte Carlo (MCMC) method was applied in this study to improve the estimations of four key parameters used in the process‐based ecosystem model of TRIPLEX‐FLUX. These four key parameters include a maximum photosynthetic carboxylation rate of 25°C (Vmax), an electron transport (Jmax) light‐saturated rate within the photosynthetic carbon reduction cycle of leaves, a coefficient of stomatal conductance (m), and a reference respiration rate of 10°C (R10). Seven forest flux tower sites located across North America were used to investigate and facilitate understanding of the daily variation in model parameters for three deciduous forests, three evergreen temperate forests, and one evergreen boreal forest. Eddy covariance CO 2 exchange measurements were assimilated to optimize the parameters in the year 2006. After parameter optimization and adjustment took place, net ecosystem production prediction significantly improved (by approximately 25%) compared to the CO 2 flux measurements taken at the seven forest ecosystem sites. Results suggest that greater seasonal variability occurs in broadleaf forests in respect to the selected parameters than in needleleaf forests. This study also demonstrated that the model‐data fusion approach by incorporating MCMC method is able to better estimate parameters and improve simulation accuracy for different ecosystems located across North America.

Structural and functional differentiation of the microbial community in the surface and subsurface peat of two minerotrophic fens in China - Wang, Meng; Tian, Jianqing; Bu, Zhaojun; Lamit, Louis J.; Chen, Huai; Zhu, Qiuan; Peng, Changhui (Plant and Soil, 2019)

Effects of future climate change, CO2 enrichment, and vegetation structure variation on hydrological processes in China - Zhu, Qiuan; Jiang, Hong; Peng, Changhui; Liu, Jinxun; Fang, Xiuqin; Wei, Xiaohua; Liu, Shirong; Zhou, Guomo (Global and Planetary Change, 2012)

Soil CH4 and CO2 dynamics and nitrogen transformations with incubation in mountain forest and meadow ecosystems - Zhang, Junjun; Peng, Changhui; Xue, Wei; Yang, Zhenan; Yang, Bin; Li, Peng; Zhu, Qiuan; Wang, Meng (CATENA, 2018)

Grazing alters vegetation phenology by regulating regional environmental factors on the Tibetan Plateau - Wang, Longjun; Li, Peng; Li, Tong; Zhou, Xiaolu; Liu, Zelin; Zou, Ziying; Zhu, Qiuan; Peng, Changhui (Agriculture, Ecosystems & Environment, 2023)

The carbon flux of global rivers: A re-evaluation of amount and spatial patterns - Li, Mingxu; Peng, Changhui; Wang, Meng; Xue, Wei; Zhang, Kerou; Wang, Kefeng; Shi, Guohua; Zhu, Qiuan (Ecological Indicators, 2017)

Simulated effects of nitrogen saturation on the global carbon budget using the IBIS model - Lu, Xuehe; Jiang, Hong; Liu, Jinxun; Zhang, Xiuying; Jin, Jiaxin; Zhu, Qiuan; Zhang, Zhen; Peng, Changhui (Scientific Reports, 2016)
Abstract Over the past 100 years, human activity has greatly changed the rate of atmospheric N (nitrogen) deposition in terrestrial ecosystems, resulting in N saturation in some regions of the world. The contribution of N saturation to the global carbon budget remains uncertain due to the complicated nature of C-N (carbon-nitrogen) interactions and diverse geography. Although N deposition is included in most terrestrial ecosystem models, the effect of N saturation is frequently overlooked. In this study, the IBIS (Integrated BIosphere Simulator) was used to simulate the global-scale effects of N saturation during the period 1961–2009. The results of this model indicate that N saturation reduced global NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) by 0.26 and 0.03 Pg C yr −1 , respectively. The negative effects of N saturation on carbon sequestration occurred primarily in temperate forests and grasslands. In response to elevated CO 2 levels, global N turnover slowed due to increased biomass growth, resulting in a decline in soil mineral N. These changes in N cycling reduced the impact of N saturation on the global carbon budget. However, elevated N deposition in certain regions may further alter N saturation and C-N coupling.

A global meta-analysis of changes in soil carbon, nitrogen, phosphorus and sulfur, and stoichiometric shifts after forestation - Shi, Shengwei; Peng, Changhui; Wang, Meng; Zhu, Qiuan; Yang, Gang; Yang, Yanzheng; Xi, Tingting; Zhang, Tinglong (Plant and Soil, 2016)

Estimating global natural wetland methane emissions using process modelling: spatio‐temporal patterns and contributions to atmospheric methane fluctuations - Zhu, Qiuan; Peng, Changhui; Chen, Huai; Fang, Xiuqin; Liu, Jinxun; Jiang, Hong; Yang, Yanzheng; Yang, Gang (Global Ecology and Biogeography, 2015)
Abstract Aim The fluctuations of atmospheric methane ( CH 4 ) that have occurred in recent decades are not fully understood, particularly with regard to the contribution from wetlands. The application of spatially explicit parameters has been suggested as an effective method for reducing uncertainties in bottom‐up approaches to wetland CH 4 emissions, but has not been included in recent studies. Our goal was to estimate spatio‐temporal patterns of global wetland CH 4 emissions using a process model and then to identify the contribution of wetland emissions to atmospheric CH 4 fluctuations. Location Global. Methods A process‐based model integrated with full descriptions of methanogenesis ( TRIPLEX‐GHG ) was used to simulate global wetland CH 4 emissions. Results Global annual wetland CH 4 emissions ranged from 209 to 245 T g CH 4 year −1 between 1901 and 2012, with peaks occurring in 1991 and 2012. There is a decreasing trend between 1990 and 2010 with a rate of approximately 0.48 T g CH 4 year −1 , which was largely caused by emissions from tropical wetlands showing a decreasing trend of 0.44 T g CH 4 year −1 since the 1970s. Emissions from tropical, temperate and high‐latitude wetlands comprised 59, 26 and 15% of global emissions, respectively. Main conclusion Global wetland CH 4 emissions, the interannual variability of which was primary controlled by tropical wetlands, partially drive the atmospheric CH 4 burden. The stable to decreasing trend in wetland CH 4 emissions, a result of a balance of emissions from tropical and extratropical wetlands, was a particular factor in slowing the atmospheric CH 4 growth rate during the 1990s. The rapid decrease in tropical wetland CH 4 emissions that began in 2000 was supposed to offset the increase in anthropogenic emissions and resulted in a relatively stable level of atmospheric CH 4 from 2000 to 2006. Increasing wetland CH 4 emissions, particularly after 2010, should be an important contributor to the growth in atmospheric CH 4 seen since 2007.

Monitoring the impact of aerosol contamination on the drought-induced decline of gross primary productivity - Zhang, Yao; Li, Weizhong; Zhu, Qiuan; Chen, Huai; Fang, Xiuqin; Zhang, Tinglong; Zhao, Pengxiang; Peng, Changhui (International Journal of Applied Earth Observation and Geoinformation, 2015)

Analysis of vegetation dynamics and climatic variability impacts on greenness across Canada using remotely sensed data from 2000 to 2009 - Fang, Xiuqin; Zhu, Qiuan; Chen, Huai; Ma, Zhihai; Wang, Weifeng; Song, Xinzhang; Zhao, Pengxiang; Peng, Changhui (Journal of Applied Remote Sensing, 2014)

Quantification of soil respiration in forest ecosystems across China - Song, Xinzhang; Peng, Changhui; Zhao, Zhengyong; Zhang, Zhiting; Guo, Baohua; Wang, Weifeng; Jiang, Hong; Zhu, Qiuan (Atmospheric Environment, 2014)

Monitoring and estimating drought-induced impacts on forest structure, growth, function, and ecosystem services using remote-sensing data: recent progress and future challenges - Zhang, Yao; Peng, Changhui; Li, Weizhong; Fang, Xiuqin; Zhang, Tinglong; Zhu, Qiuan; Chen, Huai; Zhao, Pengxiang (Environmental Reviews, 2013)
Alongside global warming, droughts are expected to increase in frequency, severity, and extent in the near future, which will likely result in significant impacts on forest growth, production, structure, composition, and ecosystem services. However, due to spatial and temporal characteristics, it is difficult to monitor and assess the potential effects of droughts. Remote sensing can provide an effective way to obtain real-time conditions of forests affected by drought and offer a range of spatial and temporal insights into drought-induced changes to forest ecosystem structure, function, and services. Remote sensing is rapidly developing as more satellites are launched. In situ and remotely sensed data fusion techniques have achieved notable success in assessing drought-induced damage to forests and carbon cycles. Even so, constraints still exist when using satellite data. The objectives of this review are to (1) briefly review existing data sources and methods of remote sensing; (2) synthesize current applications and contributions of remote sensing in monitoring and estimating impacts of droughts on forest ecosystems; and (3) highlight research gaps and future challenges.

Evaluation of Future Impacts of Climate Change, CO2, and Land Use Cover Change on Global Net Primary Productivity Using a Processed Model - Hu, Xiao; He, Yujie; Kong, Ze; Zhang, Jiang; Yuan, Minshu; Yu, Le; Peng, Changhui; Zhu, Qiuan (Land, 2021)
Few studies have focused on the combined impact of climate change, CO2, and land-use cover change (LUCC), especially the evaluation of the impact of LUCC on net primary productivity (NPP) in the future. In this study, we simulated the overall NPP change trend from 2010 to 2100 and its response to climatic factors, CO2 concentration, and LUCC conditions under three typical emission scenarios (Representative Concentration Pathway RCP2.6, RCP4.5, and RCP8.5). (1) Under the predicted global pattern, NPP showed an increasing trend, with the most prominent variation at the end of the century. The increasing trend is mainly caused by the positive effect of CO2 on NPP. However, the increasing trend of LUCC has only a small positive effect. (2) Under the RCP 8.5 scenario, from 2090 to 2100, CO2 has the most significant positive impact on tropical areas, reaching 8.328 Pg C Yr−1. Under the same conditions, climate change has the greatest positive impact on the northern high latitudes (1.175 Pg C Yr−1), but it has the greatest negative impact on tropical areas, reaching −4.842 Pg C Yr−1. (3) The average contribution rate of LUCC to NPP was 6.14%. Under the RCP8.5 scenario, LUCC made the largest positive contribution on NPP (0.542 Pg C Yr−1) globally from 2010 to 2020.

Combined control of multiple extreme climate stressors on autumn vegetation phenology on the Tibetan Plateau under past and future climate change - Li, Peng; Liu, Zelin; Zhou, Xiaolu; Xie, Binggeng; Li, Zhongwu; Luo, Yunpeng; Zhu, Qiuan; Peng, Changhui (Agricultural and Forest Meteorology, 2021)

Change in Autumn Vegetation Phenology and the Climate Controls From 1982 to 2012 on the Qinghai–Tibet Plateau - Li, Peng; Zhu, Qiuan; Peng, Changhui; Zhang, Jing; Wang, Meng; Zhang, Junjun; Ding, Juhua; Zhou, Xiaolu (Frontiers in Plant Science, 2020)

Dynamics of soil water extractable organic carbon and inorganic nitrogen and their environmental controls in mountain forest and meadow ecosystems in China - Zhang, Junjun; Peng, Changhui; Xue, Wei; Yang, Bin; Yang, Zhenan; Niu, Shuli; Zhu, Qiuan; Wang, Meng (CATENA, 2020)

High uncertainties detected in the wetlands distribution of the Qinghai–Tibet Plateau based on multisource data - Wang, Jieyi; Zhu, Qiuan; Yang, Yan; Zhang, Xian; Zhang, Jiang; Yuan, Minshu; Chen, Huai; Peng, Changhui (Landscape and Ecological Engineering, 2020)

Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes - Yang, Yan; Zhu, Qiuan; Liu, Jinxun; Li, Mingxu; Yuan, Minshu; Chen, Huai; Peng, Changhui; Yang, Zhenan (Soil Research, 2020)
Soil erosion by water affects soil organic carbon (SOC) migration and distribution, which are important processes for defining ecosystem carbon sources and sinks. Little has been done to quantify soil carbon erosion in the three major basins in China, the Yangtze River, Yellow River and Pearl River Basins, which contain the most eroded areas. This research attempts to quantify the lateral movement of SOC based on spatial and temporal patterns of water erosion rates derived from an empirical Unit Stream Power Erosion Deposition Model (USPED) model. The water erosion rates simulated by the USPED model agreed reasonably with observations (R2 = 0.43, P < 0.01). We showed that regional water erosion ranged within 23.3–50 Mg ha–1 year–1 during 1992–2013, inducing the lateral redistribution of SOC caused by erosion in the range of 0.027–0.049 Mg C ha–1 year–1, and that caused by deposition of 0.0079–0.015 Mg C ha–1 year–1, in the three basins. The total eroded SOC was 0.006, 0.002 and 0.001 Pg year–1 in the Yangtze River, Yellow River and Pearl River Basins respectively. The net eroded SOC in the three basins was ~0.0075 Pg C year–1. Overall, the annual average redistributed SOC rate caused by erosion was greater than that caused by deposition, and the SOC loss in the Yangtze River Basin was greatest among the three basins. Our study suggests that considering both processes of erosion and deposition – as well as effects of topography, rainfall, land use types and their interactions – on these processes are important to understand SOC redistribution caused by water erosion.

Weakening of the ‘enzymatic latch’ mechanism following long-term fertilization in a minerotrophic peatland - Li, Tong; Bu, Zhaojun; Liu, Wenyan; Zhang, Mingying; Peng, Changhui; Zhu, Qiuan; Shi, Shengwei; Wang, Meng (Soil Biology and Biochemistry, 2019)

An optimized water table depth detected for mitigating global warming potential of greenhouse gas emissions in wetland of Qinghai-Tibetan Plateau - Zhang, Jiang; Chen, Huai; Wang, Meng; Liu, Xinwei; Peng, Changhui; Wang, Le; Yu, Dongxue; Zhu, Qiuan (iScience, 2024)

Peatland degradation reduces methanogens and methane emissions from surface to deep soils - Yang, Gang; Tian, Jianqing; Chen, Huai; Jiang, Lin; Zhan, Wei; Hu, Ji; Zhu, Erxiong; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; He, Yixin; Li, Mingxu; et al. (Ecological Indicators, 2019)

Central Role of Nitrogen Fertilizer Relative to Water Management in Determining Direct Nitrous Oxide Emissions From Global Rice‐Based Ecosystems - Song, Hanxiong; Zhu, Qiuan; Blanchet, Jean‐Pierre; Chen, Zhi; Zhang, Kerou; Li, Tong; Zhou, Feng; Peng, Changhui (Global Biogeochemical Cycles, 2023)
Abstract The increasing atmospheric nitrous oxide (N 2 O) concentration stems from the development of agriculture. However, N 2 O emissions from global rice‐based ecosystems have not been explicitly and systematically quantified. Therefore, this study aims to estimate the spatiotemporal magnitudes of the N 2 O emissions from global rice‐based ecosystems and determine different contribution factors by improving a process‐based biogeochemical model, TRIPLEX‐GHG v2.0. Model validation suggested that the modeled N 2 O agreed well with field observations under varying management practices at daily, seasonal, and annual steps. Simulated N 2 O emissions from global rice‐based ecosystems exhibited significant increasing trends from 0.026 ± 0.0013 to 0.18 ± 0.003 TgN yr −1 from 1910 to 2020, with ∼69.5% emissions attributed to the rice‐growing seasons. Irrigated rice ecosystems accounted for a majority of global rice N 2 O emissions (∼76.9%) because of their higher N 2 O emission rates than rainfed systems. Regarding spatial analysis, Southern China, Northeast India, and Southeast Asia are hotspots for rice‐based N 2 O emissions. Experimental scenarios revealed that N fertilizer is the largest global rice‐N 2 O source, especially since the 1960s (0.047 ± 0.010 TgN yr −1 , 35.24%), while the impact of expanded irrigation plays a minor role. Overall, this study provides a better understanding of the rice‐based ecosystem in the global agricultural N 2 O budget; further, it quantitively demonstrated the central role of N fertilizer in rice‐based N 2 O emissions by including rice crop calendars, covering non‐rice growing seasons, and differentiating the effects of various water regimes and input N forms. Our findings emphasize the significance of co‐management of N fertilizer and water regimes in reducing the net climate impact of global rice cultivation. , Plain Language Summary Nitrous oxide (N 2 O) is a greenhouse gas with ∼300 times greater effect on climate warming than carbon dioxide. Global croplands represent the largest source of anthropogenic N 2 O emissions. However, the contribution of global rice‐based cropping ecosystems to the N 2 O budget remains largely uncertain because of inconsistent observed results. Inspired by the increasing availability of reliable global data sets, we improved and applied a process‐based biogeochemical model by describing the dynamics of various microbial activities to simulate N 2 O emissions from rice‐based ecosystems on a global scale. Model simulations showed that 0.18 million tons of N 2 O‐N were emitted from global rice‐based N 2 O emissions in the 2010s, which was five times larger than that in the 1910s. In the context of regional contribution, southern China, northern India, and Southeast Asia are responsible for more than 80% of the total emissions during 1910–2020. Results suggest that N fertilizer is the most important rice‐N 2 O source quantitively and that increasing irrigation exerts a buffering effect. This study confirmed the potential mitigating effect of co‐managing N fertilizer and irrigation on mitigating rice‐based N 2 O emissions globally. , Key Points N 2 O emissions from global rice‐based ecosystem increased from 0.026 to 0.18 TgN yr −1 between 1910 and 2020 Irrigated rice‐based ecosystems showed larger N 2 O fluxes than rainfed rice globally due to higher N fertilizer use and frequent aerations N fertilizer represents the largest N 2 O source, and co‐management of N fertilizer and flooding regimes is important for mitigation

Carbon dynamics of peatlands in China during the Holocene - Wang, Mei; Chen, Huai; Wu, Ning; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; Yang, Gang; Wu, Jianghua; He, Yixin; Gao, Yongheng; Tian, Jianqing; Zhao, Xinquan (Quaternary Science Reviews, 2014)

Regional drought-induced reduction in the biomass carbon sink of Canada's boreal forests - Ma, Zhihai; Peng, Changhui; Zhu, Qiuan; Chen, Huai; Yu, Guirui; Li, Weizhong; Zhou, Xiaolu; Wang, Weifeng; Zhang, Wenhua (Proceedings of the National Academy of Sciences, 2012)
The boreal forests, identified as a critical “tipping element” of the Earth's climate system, play a critical role in the global carbon budget. Recent findings have suggested that terrestrial carbon sinks in northern high-latitude regions are weakening, but there has been little observational evidence to support the idea of a reduction of carbon sinks in northern terrestrial ecosystems. Here, we estimated changes in the biomass carbon sink of natural stands throughout Canada's boreal forests using data from long-term forest permanent sampling plots. We found that in recent decades, the rate of biomass change decreased significantly in western Canada (Alberta, Saskatchewan, and Manitoba), but there was no significant trend for eastern Canada (Ontario and Quebec). Our results revealed that recent climate change, and especially drought-induced water stress, is the dominant cause of the observed reduction in the biomass carbon sink, suggesting that western Canada's boreal forests may become net carbon sources if the climate change–induced droughts continue to intensify.

Evaluating the effects of future climate change and elevated CO2 on the water use efficiency in terrestrial ecosystems of China - Zhu, Qiuan; Jiang, Hong; Peng, Changhui; Liu, Jinxun; Wei, Xiaohua; Fang, Xiuqin; Liu, Shirong; Zhou, Guomo; Yu, Shuquan (Ecological Modelling, 2011)

Estimates and Predictions of Methane Emissions from Wastewater in China from 2000 to 2020 - Du, Mingxi; Zhu, Qiuan; Wang, Xiaoge; Li, Peng; Yang, Bin; Chen, Huai; Wang, Meng; Zhou, Xiaolu; Peng, Changhui (Earth's Future, 2018)
Methane accounts for 20% of the global warming caused by greenhouse gases, and wastewater is a major anthropogenic source of methane. Based on the Intergovernmental Panel on Climate Change greenhouse gas inventory guidelines and current research findings, we calculated the amount of methane emissions from 2000 to 2014 that originated from wastewater from different provinces in China. Methane emissions from wastewater increased from 1349.01 to 3430.03 Gg from 2000 to 2014, and the mean annual increase was 167.69 Gg. The methane emissions from industrial wastewater treated by wastewater treatment plants ( E It ) accounted for the highest proportion of emissions. We also estimated the future trend of industrial wastewater methane emissions using the artificial neural network model. A comparison of the emissions for the years 2020, 2010, and 2000 showed an increasing trend in methane emissions in China and a spatial transition of industrial wastewater emissions from eastern and southern regions to central and southwestern regions and from coastal regions to inland regions. These changes were caused by changes in economics, demographics, and relevant policies. , Key Points Methane emission from wastewater from 2000 to 2014 was calculated to increase from 1349.01 Gg to 3430.03 Gg. Methane emission from wastewater from 2015 to 2020 was estimated to increase from 3875.30 Gg to 5212.75 Gg. A spatial transition of methane emission from wastewater was found and discussed in the present study.

Spatial patterns of leaf δ13 C and its relationship with plant functional groups and environmental factors in China: Factors Dominate Plant Traits - Li, Mingxu; Peng, Changhui; Wang, Meng; Yang, Yanzheng; Zhang, Kerou; Li, Peng; Yang, Yan; Ni, Jian; Zhu, Qiuan (Journal of Geophysical Research: Biogeosciences, 2017)

Temperature sensitivity of soil carbon dioxide and nitrous oxide emissions in mountain forest and meadow ecosystems in China - Zhang, Junjun; Peng, Changhui; Zhu, Qiuan; Xue, Wei; Shen, Yan; Yang, Yanzheng; Shi, Guohua; Shi, Shengwei; Wang, Meng (Atmospheric Environment, 2016)

Spatial and temporal variations of N2O emissions from global forest and grassland ecosystems - Zhang, Kerou; Zhu, Qiuan; Liu, Jinxun; Wang, Meng; Zhou, Xiaolu; Li, Mingxu; Wang, Kefeng; Ding, Juhua; Peng, Changhui (Agricultural and Forest Meteorology, 2019)

Changes in soil organic carbon and microbial carbon storage projected during the 21st century using TRIPLEX-MICROBE - Wang, Kefeng; Peng, Changhui; Zhu, Qiuan; Wang, Meng; Wang, Gangsheng; Zhou, Xiaolu; Yang, Yanzheng; Ding, Juhua; Wei, Hua (Ecological Indicators, 2019)

Simulation of dissolved organic carbon concentrations and fluxes in Chinese monsoon forest ecosystems using a modified TRIPLEX-DOC model - Shi, Shengwei; Yang, Mingxia; Hou, Yue; Peng, Changhui; Wu, Haibin; Zhu, Qiuan; Liang, Qiong; Xie, Junfei; Wang, Meng (Science of The Total Environment, 2019)

Global responses of wetland methane emissions to extreme temperature and precipitation - Xu, Min; Zhang, Jiang; Zhang, Zhen; Wang, Meng; Chen, Huai; Peng, Changhui; Yu, Dongxue; Zhan, Hao; Zhu, Qiuan (Environmental Research, 2024)

Higher recent peat C accumulation than that during the Holocene on the Zoige Plateau - Wang, Mei; Yang, Gang; Gao, Yongheng; Chen, Huai; Wu, Ning; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; Wu, Jianghua; He, Yixin; Tian, Jianqing; Zhao, Xinquan; et al. (Quaternary Science Reviews, 2015)

Soil methane uptake by grasslands and forests in China - Wang, Yanfen; Chen, Huai; Zhu, Qiuan; Peng, Changhui; Wu, Ning; Yang, Gang; Zhu, Dan; Tian, Jianqing; Tian, Liuxi; Kang, Xiaoming; He, Yixin; Gao, Yongheng; et al. (Soil Biology and Biochemistry, 2014)

Model prediction of biome‐specific global soil respiration from 1960 to 2012 - Zhao, Zhengyong; Peng, Changhui; Yang, Qi; Meng, Fan‐Rui; Song, Xinzhang; Chen, Shutao; Epule, Terence Epule; Li, Peng; Zhu, Qiuan (Earth's Future, 2017)
Abstract Biome‐specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome‐specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network ( ANN ) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome‐specific global annual Rs was the one that applied mean annual temperature ( MAT ), mean annual precipitation ( MAP ), and biome type as inputs ( r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT ( r 2 = 0.87) in the savannah biome. The developed biome‐specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle. , Key Points Predict biome‐specific global soil respiration from 1960 to 2012 using an artificial neural network model Prediction determined an average global soil respiration of 93.3 ± 6.1 Pg C yr −1 and an increasing trend of 0.04 Pg C yr −1 The 10 biome‐specific soil respiration estimates made it possible to trace different responses to global climate change in each biome

Quantification and uncertainty of global upland soil methane sinks: Processes, controls, model limitations, and improvements - Song, Hanxiong; Peng, Changhui; Zhu, Qiuan; Chen, Zhi; Blanchet, Jean-Pierre; Liu, Qiuyu; Li, Tong; Li, Peng; Liu, Zelin (Earth-Science Reviews, 2024)

A drought-induced pervasive increase in tree mortality across Canada's boreal forests - Peng, Changhui; Ma, Zhihai; Lei, Xiangdong; Zhu, Qiuan; Chen, Huai; Wang, Weifeng; Liu, Shirong; Li, Weizhong; Fang, Xiuqin; Zhou, Xiaolu (Nature Climate Change, 2011)

Evaluating the spatiotemporal variations of water budget across China over 1951–2006 using IBIS model - Zhu, Qiuan; Jiang, Hong; Liu, Jinxun; Wei, Xiaohua; Peng, Changhui; Fang, Xiuqing; Liu, Shirong; Zhou, Guomo; Yu, Shuquan; Ju, Weiming (Hydrological Processes, 2010)
Abstract The Integrated Biosphere Simulator is used to evaluate the spatial and temporal patterns of the crucial hydrological variables [run‐off and actual evapotranspiration (AET)] of the water balance across China for the period 1951–2006 including a precipitation analysis. Results suggest three major findings. First, simulated run‐off captured 85% of the spatial variability and 80% of the temporal variability for 85 hydrological gauges across China. The mean relative errors were within 20% for 66% of the studied stations and within 30% for 86% of the stations. The Nash–Sutcliffe coefficients indicated that the quantity pattern of run‐off was also captured acceptably except for some watersheds in southwestern and northwestern China. The possible reasons for underestimation of run‐off in the Tibetan plateau include underestimation of precipitation and uncertainties in other meteorological data due to complex topography, and simplified representations of the soil depth attribute and snow processes in the model. Second, simulated AET matched reasonably with estimated values calculated as the residual of precipitation and run‐off for watersheds controlled by the hydrological gauges. Finally, trend analysis based on the Mann–Kendall method indicated that significant increasing and decreasing patterns in precipitation appeared in the northwest part of China and the Yellow River region, respectively. Significant increasing and decreasing trends in AET were detected in the Southwest region and the Yangtze River region, respectively. In addition, the Southwest region, northern China (including the Heilongjiang, Liaohe, and Haihe Basins), and the Yellow River Basin showed significant decreasing trends in run‐off, and the Zhemin hydrological region showed a significant increasing trend. Copyright © 2009 John Wiley & Sons, Ltd.

Nitrous oxide fluxes from three forest types of the tropical mountain rainforests on Hainan Island, China - Bai, Zhenzhi; Yang, Gang; Chen, Huai; Zhu, Qiuan; Chen, Dexiang; Li, Yide; Wang, Xu; Wu, Zhongmin; Zhou, Guangyi; Peng, Changhui (Atmospheric Environment, 2014)

Corrigendum: Extrapolation and Uncertainty Evaluation of Carbon Dioxide and Methane Emissions in the Qinghai-Tibetan Plateau Wetlands Since the 1960s - Zhang, Jiang; Zhu, Qiuan; Yuan, Minshu; Liu, Xinwei; Chen, Huai; Peng, Changhui; Wang, Meng; Yang, Zhenan; Jiang, Lin; Zhao, Pengxiang (Frontiers in Earth Science, 2021)

Global response of terrestrial gross primary productivity to climate extremes - Yuan, Minshu; Zhu, Qiuan; Zhang, Jiang; Liu, Jinxun; Chen, Huai; Peng, Changhui; Li, Peng; Li, Mingxu; Wang, Meng; Zhao, Pengxiang (Science of The Total Environment, 2021)

Extrapolation and Uncertainty Evaluation of Carbon Dioxide and Methane Emissions in the Qinghai-Tibetan Plateau Wetlands Since the 1960s - Zhang, Jiang; Zhu, Qiuan; Yuan, Minshu; Liu, Xinwei; Chen, Huai; Peng, Changhui; Wang, Meng; Yang, Zhenan; Jiang, Lin; Zhao, Pengxiang (Frontiers in Earth Science, 2020)
Wetlands are important modulators of atmospheric greenhouse gas (GHGs) concentrations. However, little is known about the magnitudes and spatiotemporal patterns of GHGs fluxes in wetlands on the Qinghai-Tibetan Plateau (QTP), the world’s largest and highest plateau. In this study, we measured soil temperature and the fluxes of carbon dioxide (CO 2 ) and methane (CH 4 ) in an alpine wetland on the QTP from April 2017 to April 2019 by the static chamber method, and from January 2017 to December 2017 by the eddy covariance (EC) method. The CO 2 and CH 4 emission measurements from both methods showed different relationships to soil temperature at different timescales (annual and seasonal). Based on such relationship patterns and soil temperature data (1960–2017), we extrapolated the CO 2 and CH 4 emissions of study site for the past 57 years: the mean CO 2 emission rate was 91.38 mg C m –2 h –1 on different measurement methods and timescales, with the range of the mean emission rate from 35.10 to 146.25 mg C m –2 h –1 , while the mean CH 4 emission rate was 2.75 mg C m –2 h –1 , with the ranges of the mean emission rate from 1.41 to 3.85 mg C m –2 h –1 . The estimated regional CO 2 and CH 4 emissions from permanent wetlands on the QTP were 94.29 and 2.37 Tg C year –1 , respectively. These results indicate that uncertainties caused by measuring method and timescale should be fully considered when extrapolating wetland GHGs fluxes from local sites to the regional level. Moreover, the results of global warming potential showed that CO 2 dominates the GHG balance of wetlands on the QTP.

Effects of increasing aerosol optical depth on the gross primary productivity in China during 2000–2014 - Zhang, Jing; Ding, Juhua; Zhang, Jiang; Yuan, Minshu; Li, Peng; Xiao, Zhongyong; Peng, Changhui; Chen, Huai; Wang, Meng; Zhu, Qiuan (Ecological Indicators, 2020)

The effects of persistent drought and waterlogging on the dynamics of nonstructural carbohydrates of Robinia pseudoacacia L. seedlings in Northwest China - Yang, Bin; Peng, Changhui; Zhu, Qiuan; Zhou, Xiaolu; Liu, Weiguo; Duan, Min; Wang, Hui; Liu, Zhihao; Guo, Xinyi; Wang, Meng (Forest Ecosystems, 2019)

A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China - Yang, Yanzheng; Zhu, Qiuan; Peng, Changhui; Wang, Han; Xue, Wei; Lin, Guanghui; Wen, Zhongming; Chang, Jie; Wang, Meng; Liu, Guobin; Li, Shiqing (Scientific Reports, 2016)
Abstract Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-N mass -LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

The Spatial and Temporal Distribution of Dissolved Organic Carbon Exported from Three Chinese Rivers to the China Sea - Shi, Guohua; Peng, Changhui; Wang, Meng; Shi, Shengwei; Yang, Yanzheng; Chu, Junyao; Zhang, Junjun; Lin, Guanghui; Shen, Yan; Zhu, Qiuan (PLOS ONE, 2016)

Quantification of Ecosystem-Scale Methane Sinks Observed in a Tropical Rainforest in Hainan, China - Liu, Zhihao; Li, Hong; Wu, Fangtao; Wang, Hui; Chen, Huai; Zhu, Qiuan; Yang, Gang; Liu, Weiguo; Chen, Dexiang; Li, Yide; Peng, Changhui (Land, 2022)
Tropical rainforest ecosystems are important when considering the global methane (CH4) budget and in climate change mitigation. However, there is a lack of direct and year-round observations of ecosystem-scale CH4 fluxes from tropical rainforest ecosystems. In this study, we examined the temporal variations in CH4 flux at the ecosystem scale and its annual budget and environmental controlling factors in a tropical rainforest of Hainan Island, China, using 3 years of continuous eddy covariance measurements from 2016 to 2018. Our results show that CH4 uptake generally occurred in this tropical rainforest, where strong CH4 uptake occurred in the daytime, and a weak CH4 uptake occurred at night with a mean daily CH4 flux of −4.5 nmol m−2 s−1. In this rainforest, the mean annual budget of CH4 for the 3 years was −1260 mg CH4 m−2 year−1. Furthermore, the daily averaged CH4 flux was not distinctly different between the dry season and wet season. Sixty-nine percent of the total variance in the daily CH4 flux could be explained by the artificial neural network (ANN) model, with a combination of air temperature (Tair), latent heat flux (LE), soil volumetric water content (VWC), atmospheric pressure (Pa), and soil temperature at −10 cm (Tsoil), although the linear correlation between the daily CH4 flux and any of these individual variables was relatively low. This indicates that CH4 uptake in tropical rainforests is controlled by multiple environmental factors and that their relationships are nonlinear. Our findings also suggest that tropical rainforests in China acted as a CH4 sink during 2016–2018, helping to counteract global warming.

Contrasting responses of soil exoenzymatic interactions and the dissociated carbon transformation to short- and long-term drainage in a minerotrophic peatland - Li, Tong; Ge, Leming; Huang, Jingjing; Yuan, Xin; Peng, Changhui; Wang, Shengzhong; Bu, Zhaojun; Zhu, Qiuan; Wang, Zucheng; Liu, Weiguo; Wang, Meng (Geoderma, 2020)

Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China - Yang, Yanzheng; Zhao, Jun; Zhao, Pengxiang; Wang, Hui; Wang, Boheng; Su, Shaofeng; Li, Mingxu; Wang, Liming; Zhu, Qiuan; Pang, Zhiyong; Peng, Changhui (Frontiers in Plant Science, 2019)

Detecting One-Hundred-Year Environmental Changes in Western China Using Seven-Year Repeat Photography - Chen, Huai; Yin, Kaipu; Wang, Haiyan; Zhong, Shenxian; Wu, Ning; Shi, Fusun; Zhu, Dan; Zhu, Qiuan; Wang, Weifeng; Ma, Zhihai; Fang, Xiuqin; Li, Weizhong; et al. (PLoS ONE, 2011)

The carbon stock of alpine peatlands on the Qinghai–Tibetan Plateau during the Holocene and their future fate - Chen, Huai; Yang, Gang; Peng, Changhui; Zhang, Yao; Zhu, Dan; Zhu, Qiuan; Hu, Ji; Wang, Mei; Zhan, Wei; Zhu, Erxiong; Bai, Zengzhi; Li, Wei; et al. (Quaternary Science Reviews, 2014)

Carbon and nitrogen cycling on the Qinghai–Tibetan Plateau - Chen, Huai; Ju, Peijun; Zhu, Qiuan; Xu, Xingliang; Wu, Ning; Gao, Yongheng; Feng, Xiaojuan; Tian, Jianqing; Niu, Shuli; Zhang, Yangjian; Peng, Changhui; Wang, Yanfen (Nature Reviews Earth & Environment, 2022)

Interspecific difference in N:P stoichiometric homeostasis drives nutrient release and soil microbial community composition during decomposition - Song, Hanxiong; Huang, Jingjing; Ge, Leming; Peng, Changhui; Zhao, Pengxiang; Guo, Xinyi; Li, Tong; Shen, Xiaojuan; Zhu, Qiuan; Liu, Weiguo; Wei, Hua; Wang, Meng (Plant and Soil, 2020)

Estimating natural nitrous oxide emissions from the Qinghai–Tibetan Plateau using a process-based model: Historical spatiotemporal patterns and future trends - Zhang, Kerou; Peng, Changhui; Zhu, Qiuan; Li, Mingxu; Yan, Zhongqing; Li, Meng; Yan, Liang; Zhang, Xiaodong; Wang, Jinzhi; Li, Yong; Kang, Enze; Song, Hanxiong; et al. (Ecological Modelling, 2022)

An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau - Zhu, Qiuan; Chen, Huai; Peng, Changhui; Liu, Jinxun; Piao, Shilong; He, Jin-Sheng; Wang, Shiping; Zhao, Xinquan; Zhang, Jiang; Fang, Xiuqin; Jin, Jiaxin; Yang, Qi-En; et al. (Nature Communications, 2023)
Abstract 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 CO 2 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.

The impacts of climate change and human activities on biogeochemical cycles on the Q inghai‐ T ibetan P lateau - Chen, Huai; Zhu, Qiuan; Peng, Changhui; Wu, Ning; Wang, Yanfen; Fang, Xiuqing; Gao, Yongheng; Zhu, Dan; Yang, Gang; Tian, Jianqing; Kang, Xiaoming; Piao, Shilong; et al. (Global Change Biology, 2013)
Abstract With a pace of about twice the observed rate of global warming, the temperature on the Qinghai‐Tibetan Plateau (Earth's ‘third pole’) has increased by 0.2 °C per decade over the past 50 years, which results in significant permafrost thawing and glacier retreat. Our review suggested that warming enhanced net primary production and soil respiration, decreased methane ( CH 4 ) emissions from wetlands and increased CH 4 consumption of meadows, but might increase CH 4 emissions from lakes. Warming‐induced permafrost thawing and glaciers melting would also result in substantial emission of old carbon dioxide ( CO 2 ) and CH 4 . Nitrous oxide ( N 2 O ) emission was not stimulated by warming itself, but might be slightly enhanced by wetting. However, there are many uncertainties in such biogeochemical cycles under climate change. Human activities (e.g. grazing, land cover changes) further modified the biogeochemical cycles and amplified such uncertainties on the plateau. If the projected warming and wetting continues, the future biogeochemical cycles will be more complicated. So facing research in this field is an ongoing challenge of integrating field observations with process‐based ecosystem models to predict the impacts of future climate change and human activities at various temporal and spatial scales. To reduce the uncertainties and to improve the precision of the predictions of the impacts of climate change and human activities on biogeochemical cycles, efforts should focus on conducting more field observation studies, integrating data within improved models, and developing new knowledge about coupling among carbon, nitrogen, and phosphorus biogeochemical cycles as well as about the role of microbes in these cycles.

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades - Zhu, Qiuan; Peng, Changhui; Liu, Jinxun; Jiang, Hong; Fang, Xiuqin; Chen, Huai; Niu, Zhenguo; Gong, Peng; Lin, Guanghui; Wang, Meng; Wang, Han; Yang, Yanzheng; et al. (Scientific Reports, 2016)
Abstract Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH 4 ) emissions in China is important for improving our knowledge on CH 4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH 4 model to quantify the human and climate change induced contributions to natural wetland CH 4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH 4 emissions reduction (0.92 TgCH 4 ), and climate change contributed 20.4% to the CH 4 emissions increase (0.31 TgCH 4 ), suggesting that decreasing CH 4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH 4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH 4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH 4 emissions estimation.

Interannual variation in methane emissions from tropical wetlands triggered by repeated El Niño Southern Oscillation - Zhu, Qiuan; Peng, Changhui; Ciais, Philippe; Jiang, Hong; Liu, Jinxun; Bousquet, Philippe; Li, Shiqin; Chang, Jie; Fang, Xiuqin; Zhou, Xiaolu; Chen, Huai; Liu, Shirong; et al. (Global Change Biology, 2017)
Abstract Methane (CH 4 ) emissions from tropical wetlands contribute 60%–80% of global natural wetland CH 4 emissions. Decreased wetland CH 4 emissions can act as a negative feedback mechanism for future climate warming and vice versa. The impact of the El Niño–Southern Oscillation (ENSO) on CH 4 emissions from wetlands remains poorly quantified at both regional and global scales, and El Niño events are expected to become more severe based on climate models’ projections. We use a process‐based model of global wetland CH 4 emissions to investigate the impacts of the ENSO on CH 4 emissions in tropical wetlands for the period from 1950 to 2012. The results show that CH 4 emissions from tropical wetlands respond strongly to repeated ENSO events, with negative anomalies occurring during El Niño periods and with positive anomalies occurring during La Niña periods. An approximately 8‐month time lag was detected between tropical wetland CH 4 emissions and ENSO events, which was caused by the combined time lag effects of ENSO events on precipitation and temperature over tropical wetlands. The ENSO can explain 49% of interannual variations for tropical wetland CH 4 emissions. Furthermore, relative to neutral years, changes in temperature have much stronger effects on tropical wetland CH 4 emissions than the changes in precipitation during ENSO periods. The occurrence of several El Niño events contributed to a lower decadal mean growth rate in atmospheric CH 4 concentrations throughout the 1980s and 1990s and to stable atmospheric CH 4 concentrations from 1999 to 2006, resulting in negative feedback to global warming.

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005 - Shao, Junjiong; Zhou, Xuhui; Luo, Yiqi; Zhang, Guodong; Yan, Wei; Li, Jiaxuan; Li, Bo; Dan, Li; Fisher, Joshua B.; Gao, Zhiqiang; He, Yong; Huntzinger, Deborah; et al. (Journal of Geophysical Research: Biogeosciences, 2016)
Abstract Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature‐based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr −1 , respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time‐varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr −1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models. , Key Points Large uncertainty exists in estimates of terrestrial NPP and NBP in China Methodological differences greatly contribute to the uncertainty in NPP and NBP Uncertainty in the interannual pattern of NBP is greater than that of NPP

Characterizing Performance of Freshwater Wetland Methane Models Across Time Scales at FLUXNET‐CH4 Sites Using Wavelet Analyses - Zhang, Zhen; Bansal, Sheel; Chang, Kuang‐Yu; Fluet‐Chouinard, Etienne; Delwiche, Kyle; Goeckede, Mathias; Gustafson, Adrian; Knox, Sara; Leppänen, Antti; Liu, Licheng; Liu, Jinxun; Malhotra, Avni; et al. (Journal of Geophysical Research: Biogeosciences, 2023)
Abstract Process‐based land surface models are important tools for estimating global wetland methane (CH 4 ) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site‐level patterns of freshwater wetland CH 4 fluxes (FCH 4 ) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model‐observation disagreements are mainly at multi‐day time scales (<15 days); (b) most of the models can capture the CH 4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH 4 production). Our evaluation suggests the need to accurately replicate FCH 4 variability, especially at short time scales, in future wetland CH 4 model developments. , Plain Language Summary Land surface models are useful tools to estimate and predict wetland methane (CH 4 ) flux but there is no evaluation of modeled CH 4 flux error at different time scales. Here we use a statistical approach and observations from eddy covariance sites to evaluate the performance of seven wetland models for different wetland types. The results suggest models have captured CH 4 flux variability at monthly or seasonal time scales for boreal and Arctic tundra wetlands but failed to capture the observed seasonal variability for temperate and tropical/subtropical wetlands. The analysis suggests that improving modeled flux at short time scale is important for future model development. , Key Points Significant model‐observation disagreements were found at multi‐day and weekly time scales (<15 days) Models captured variability at monthly and seasonal time (42–142 days) scales for boreal and Arctic tundra sites but not for temperate and tropical sites The model errors show that biases at multi‐day time scales may contribute to persistent systematic biases on longer time scales

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates - Chang, Kuang‐Yu; Riley, William J.; Collier, Nathan; McNicol, Gavin; Fluet‐Chouinard, Etienne; Knox, Sara H.; Delwiche, Kyle B.; Jackson, Robert B.; Poulter, Benjamin; Saunois, Marielle; Chandra, Naveen; Gedney, Nicola; et al. (Global Change Biology, 2023)
Abstract The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 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 CH 4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 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 CH 4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.

The Global N2O Model Intercomparison Project - Tian, Hanqin; Yang, Jia; Lu, Chaoqun; Xu, Rongting; Canadell, Josep G.; Jackson, Robert B.; Arneth, Almut; Chang, Jinfeng; Chen, Guangsheng; Ciais, Philippe; Gerber, Stefan; Ito, Akihiko; et al. (Bulletin of the American Meteorological Society, 2018)
Abstract Nitrous oxide (N 2 O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N 2 O fluxes and the key drivers of N 2 O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N 2 O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N 2 O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N 2 O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N 2 O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N 2 O dynamics; and 3) provide a benchmarking estimate of N 2 O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N 2 O estimation derived from the NMIP is a key component of the global N 2 O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.

Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region - Cui, Erqian; Huang, Kun; Arain, Muhammad Altaf; Fisher, Joshua B.; Huntzinger, Deborah N.; Ito, Akihiko; Luo, Yiqi; Jain, Atul K.; Mao, Jiafu; Michalak, Anna M.; Niu, Shuli; Parazoo, Nicholas C.; et al. (Global Biogeochemical Cycles, 2019)
Abstract Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time ( τ veg ), leaf C fraction (F leaf ), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (P LAI ), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τ veg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems. , Key Points A GPP‐traceability framework is established to diagnose the uncertainty sources of modeled GPP Large intermodel differences of modeled GPP result from their different representation of vegetation functional properties Positive bias in simulated GPP over the East Asian monsoon region could be attributed to the higher simulated CUE and SLA comparing with observations

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends - Mao, Jiafu; Fu, Wenting; Shi, Xiaoying; Ricciuto, Daniel M; Fisher, Joshua B; Dickinson, Robert E; Wei, Yaxing; Shem, Willis; Piao, Shilong; Wang, Kaicun; Schwalm, Christopher R; Tian, Hanqin; et al. (Environmental Research Letters, 2015)

Global vegetation biomass production efficiency constrained by models and observations - He, Yue; Peng, Shushi; Liu, Yongwen; Li, Xiangyi; Wang, Kai; Ciais, Philippe; Arain, M. Altaf; Fang, Yuanyuan; Fisher, Joshua B.; Goll, Daniel; Hayes, Daniel; Huntzinger, Deborah N.; et al. (Global Change Biology, 2020)
Abstract 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).

Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics - Poulter, Benjamin; Bousquet, Philippe; Canadell, Josep G; Ciais, Philippe; Peregon, Anna; Saunois, Marielle; Arora, Vivek K; Beerling, David J; Brovkin, Victor; Jones, Chris D; Joos, Fortunat; Gedney, Nicola; et al. (Environmental Research Letters, 2017)
Abstract Increasing atmospheric methane (CH 4 ) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH 4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999–2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH 4 emissions from wetlands, the largest natural global CH 4 source, for 2000–2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000–2012, boreal wetland CH 4 emissions increased by 1.2 Tg yr −1 (−0.2–3.5 Tg yr −1 ), tropical emissions decreased by 0.9 Tg yr −1 (−3.2−1.1 Tg yr −1 ), yet globally, emissions remained unchanged at 184 ± 22 Tg yr −1 . Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH 4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH 4 emissions have not contributed significantly to the period of renewed atmospheric CH 4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH 4 emissions, and a decrease in the atmospheric oxidative sink.

Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization - Liu, Yongwen; Piao, Shilong; Gasser, Thomas; Ciais, Philippe; Yang, Hui; Wang, Han; Keenan, Trevor F.; Huang, Mengtian; Wan, Shiqiang; Song, Jian; Wang, Kai; Janssens, Ivan A.; et al. (Nature Geoscience, 2019)

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand? - Paschalis, Athanasios; Fatichi, Simone; Zscheischler, Jakob; Ciais, Philippe; Bahn, Michael; Boysen, Lena; Chang, Jinfeng; De Kauwe, Martin; Estiarte, Marc; Goll, Daniel; Hanson, Paul J.; Harper, Anna B.; et al. (Global Change Biology, 2020)
Abstract 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.

Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites - Wu, Donghai; Ciais, Philippe; Viovy, Nicolas; Knapp, Alan K.; Wilcox, Kevin; Bahn, Michael; Smith, Melinda D.; Vicca, Sara; Fatichi, Simone; Zscheischler, Jakob; He, Yue; Li, Xiangyi; et al. (Biogeosciences, 2018)
Abstract. Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation (P) may occur. Under normal range of precipitation variability, wet years typically result in ANPP gains being larger than ANPP declines in dry years (positive asymmetry), whereas increases in ANPP are lower in magnitude in extreme wet years compared to reductions during extreme drought (negative asymmetry). Whether the current generation of ecosystem models with a coupled carbon–water system in grasslands are capable of simulating these asymmetric ANPP responses is an unresolved question. In this study, we evaluated the simulated responses of temperate grassland primary productivity to scenarios of altered precipitation with 14 ecosystem models at three sites: Shortgrass steppe (SGS), Konza Prairie (KNZ) and Stubai Valley meadow (STU), spanning a rainfall gradient from dry to moist. We found that (1) the spatial slopes derived from modeled primary productivity and precipitation across sites were steeper than the temporal slopes obtained from inter-annual variations, which was consistent with empirical data; (2) the asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation variability differed among models, and the mean of the model ensemble suggested a negative asymmetry across the three sites, which was contrary to empirical evidence based on filed observations; (3) the mean sensitivity of modeled productivity to rainfall suggested greater negative response with reduced precipitation than positive response to an increased precipitation under extreme conditions at the three sites; and (4) gross primary productivity (GPP), net primary productivity (NPP), aboveground NPP (ANPP) and belowground NPP (BNPP) all showed concave-down nonlinear responses to altered precipitation in all the models, but with different curvatures and mean values. Our results indicated that most models overestimate the negative drought effects and/or underestimate the positive effects of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco-hydrological processes in those models in the future.