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)

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.

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.

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.

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)

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.

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)

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.

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)

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

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)

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)

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)

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)

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.

Characterizing Performance of Freshwater Wetland Methane Models Across Time Scales at FLUXNET‐CH_4 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.

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).

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.