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)

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

Nitrous oxide fluxes from the littoral zone of a lake on the Qinghai-Tibetan Plateau - Chen, Huai; Wang, Meng; Wu, Ning; Wang, Yanfen; Zhu, Dan; Gao, Yongheng; Peng, Changhui (Environmental Monitoring and Assessment, 2011)

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)

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)

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.

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)

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)

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 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)

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)

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)

Leaf respiration/photosynthesis relationship and variation: an investigation of 39 woody and herbaceous species in east subtropical China - Chu, Ziyan; Lu, Yijun; Chang, Jie; Wang, Meng; Jiang, Hong; He, Jinsheng; Peng, Changhui; Ge, Ying (Trees, 2011)

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)

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)

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)

Variation in Soil Methane Fluxes and Comparison between Two Forests in China - Wei, Hua; Peng, Changhui; Liu, Shirong; Liu, Xiaojing; Li, Peng; Song, Hanxiong; Yuan, Minshu; Wang, Meng (Forests, 2018)
Methane (CH4) is a vital greenhouse gas with a 28-fold higher global warming potential than carbon dioxide when considering a molar basis for the time horizon of 100 years. Here, we investigated the variation of soil CH4 fluxes, soil physiochemical properties, and CH4-related bacteria community composition of two forests in China. We measured CH4 fluxes using static chambers and analyzed soil bacterial communities using next-generation high-throughput sequencing in a temperate broad-leaved deciduous forest at Baotianman Nature Reserve (TBDF-BTM) and a tropical rainforest at Jianfengling National Natural Reserve (TRF-JFL). Our results showed that the soils from both sites were CH4 sinks. Significant variation in soil CH4 fluxes was found at TBDF-BTM exclusively, while no seasonal variation in the CH4 uptake was observed at TRF-JFL. The CH4 fluxes at TBDF-BTM were substantially higher than those at TRF-JFL during all seasons. One genus of methanotrophs and three genera of methylotrophs were detected at both sites, though they had no direct relationship with soil CH4 fluxes. Water-filled pore space and soil total carbon content are the main factors controlling the soil CH4 fluxes at TBDF-BTM. At TRF-JFL, the soil CH4 fluxes showed no significant correlations with any of the soil properties. This study improves our understanding of soil CH4 fluxes and their influencing factors in forests in different climatic zones and provides a reference for future investigation of forest soil CH4 fluxes, the forest ecosystem carbon cycle, and the forest CH4 model.

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.

Spatial patterns of leaf δ^13 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)

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)

Contrasting Soil Bacterial Community, Diversity, and Function in Two Forests in China - Wei, Hua; Peng, Changhui; Yang, Bin; Song, Hanxiong; Li, Quan; Jiang, Lin; Wei, Gang; Wang, Kefeng; Wang, Hui; Liu, Shirong; Liu, Xiaojing; Chen, Dexiang; et al. (Frontiers in Microbiology, 2018)

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)

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.