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Wind damage is one of the major factors affecting forest ecosystem sustainability, especially in the coastal region. Typhoon Lekima is among the top five most devastating typhoons in China and caused economic losses totaling over USD 8 billion in Zhejiang Province alone during 9–12 August 2019. However, there still is no assessment of its impacts on forests. Here we detected forest damage and its spatial distribution caused by Typhoon Lekima by classifying Landsat 8 OLI images using the random forest (RF) machine learning algorithm and the univariate image differencing (UID) method on the Google Earth Engine (GEE) platform. The accuracy assessment indicated a high overall accuracy (>87%) and kappa coefficient (>0.75) for forest-damage detection, as evaluated against field-investigated plot data, with better performance using the RF method. The total affected forest area by Lekima was 4598.87 km2, accounting for 8.44% of the total forest area in Zhejiang Province. The light-, moderate- and severe-damage forest areas were 2106.29 km2, 2024.26 km2 and 469.76 km2, respectively. Considering the damage severity, the net forest canopy loss fraction was 2.57%. The affected forest area and damage severity exhibited large spatial variations, which were affected by elevation, slope, precipitation and forest type. Our study indicated a larger uncertainty for affected forest area and a smaller uncertainty for the proportion of damage severity, based on multiple assessment approaches. This is among the first studies on forest damage due to typhoons at a regional scale in China, and the methods can be extended to examine the impacts of other super-strong typhoons on forests. Our study results on damage severity, spatial distribution and controlling factors could help local governments, the forest sector and forest landowners make decision on tree-planting planning and sustainable management after typhoon strikes and could also raise public and governmental awareness of typhoons’ damage on China’s inland forests.
Xu Zhang; Guangsheng Chen; Lingxiao Cai; Hongbo Jiao; Jianwen Hua; Xifang Luo; Xinliang Wei. Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery. Sustainability 2021, 13, 4893 .
AMA StyleXu Zhang, Guangsheng Chen, Lingxiao Cai, Hongbo Jiao, Jianwen Hua, Xifang Luo, Xinliang Wei. Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery. Sustainability. 2021; 13 (9):4893.
Chicago/Turabian StyleXu Zhang; Guangsheng Chen; Lingxiao Cai; Hongbo Jiao; Jianwen Hua; Xifang Luo; Xinliang Wei. 2021. "Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery." Sustainability 13, no. 9: 4893.
Green development is a solution to achieve sustainable development, while tourism development is one of the best approaches to realize a green economy. As the most rapid economic development region in China, the Yangtze River Delta Urban Agglomeration (YRDUA) has also witnessed rapid changes in its tourism economy during 2001–2019. Here, we analyzed the spatiotemporal patterns of its tourism revenue, and further identified contributions from multiple socio-economic factors using spatial analysis tools and regression models. The total tourism revenue increased 14.35 fold, with an annual increase rate of 79.73% during 2001–2019. The proportion of tourism revenue to the GDP continuously increased from 11.57% in 2001 to 18.89% in 2019. Tourism revenue increased for all cities, with the least increasing rates in the metropolitan cities including Shanghai, Nanjing, Suzhou and Hangzhou, and the largest increase rates in Ma’anshan, Hefei, Huzhou and Zhoushan. A regression and causality test indicated that different socioeconomic factors controlled the spatiotemporal variation patterns in different cities. The economic structure in the YRDUA has undergone significant shifts, with an increasing importance of tourism revenue in the GDP for most cities and a reducing discrepancy of tourism revenue among cities. Our study can enable the policy makers to be aware of the magnitude, temporal variation patterns, differences among cities and controlling factors for tourism development, and thus take suitable measures to further promote green tourism development in the YRDUA region.
Gengying Jiao; Lin Lu; Guangsheng Chen; Zhiqiang Huang; Giuseppe Cirella; Xiaozhong Yang. Spatiotemporal Characteristics and Influencing Factors of Tourism Revenue in the Yangtze River Delta Urban Agglomeration Region during 2001–2019. Sustainability 2021, 13, 3658 .
AMA StyleGengying Jiao, Lin Lu, Guangsheng Chen, Zhiqiang Huang, Giuseppe Cirella, Xiaozhong Yang. Spatiotemporal Characteristics and Influencing Factors of Tourism Revenue in the Yangtze River Delta Urban Agglomeration Region during 2001–2019. Sustainability. 2021; 13 (7):3658.
Chicago/Turabian StyleGengying Jiao; Lin Lu; Guangsheng Chen; Zhiqiang Huang; Giuseppe Cirella; Xiaozhong Yang. 2021. "Spatiotemporal Characteristics and Influencing Factors of Tourism Revenue in the Yangtze River Delta Urban Agglomeration Region during 2001–2019." Sustainability 13, no. 7: 3658.
After implementations of many economic and forestry policies during recent 30 years, forest cover in China has experienced significant changes. The fine scale and long-term information on forest temporary (disturbance) loss, persistent loss (deforestation) and gains (afforestation/reforestation) will provide a guide for forest management and policies at regional scale. Using Jiangxi Province as a pilot study region, here we assessed forest cover dynamics in the subtropical China during 1986-2019 by integrating time-series Landsat images, Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and Random Forest classifier on the Google Earth Engine (GEE) Platform. The accuracy assessment indicated a high overall accuracy (>90%) and Kappa coefficient (>0.9) for both forest gain and loss detections as evaluated against various sources of plot-level sample data and the existing global forest change product. The total forest loss area was 18,697.79 km2, with persistent loss area of 3,394.31 km2 due to land conversion during 1986-2019; while persistent (net) forest gain area was 45,656.96 km2, accounting for 57.70% of the forest area in 1986. Forest loss area exhibited large interannual variations, but showed a general increase trend from 1986 to 2019. The annual variation patterns of forest gain and loss area were associated with the changes in forestry policies and large disturbance events. Our assessments on the long-term and fine scale forest dynamic patterns will help evaluate the effectiveness of forest management practices and forestry polices on forest resource sustainability, and climate change and greenhouse gases mitigation in Jiangxi Province and China.
Jianwen Hua; Guangsheng Chen; Lin Yu; Qing Ye; Hongbo Jiao; Xifang Luo. Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 2754 -2768.
AMA StyleJianwen Hua, Guangsheng Chen, Lin Yu, Qing Ye, Hongbo Jiao, Xifang Luo. Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):2754-2768.
Chicago/Turabian StyleJianwen Hua; Guangsheng Chen; Lin Yu; Qing Ye; Hongbo Jiao; Xifang Luo. 2021. "Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 2754-2768.
Moso bamboo (Phyllostachys Pubescens) forests exhibit a great potential to sequestrate carbon dioxide from atmosphere and to mitigate global climate change. However, they were increasingly under abandoned (i.e., no fertilization, the low intensity and frequency of felling and bamboo shoot digging) due to decreasing economic values of bamboo-related products and increasing labor cost. So far, the changes in soil carbon (C) and nitrogen (N) pools in bamboo forests following abandonment are poorly addressed. In this study, Moso bamboo stands under intensively management and abandonment for different durations were sampled to explore the C and N pool dynamics at the top 40 cm soil. We classified abandonment durations into three categories: discarded or abandoned management for 1–6 years (DM-I), 7–12 years (DM-II) and 13–18 years (DM-III). Our results indicated that (1) soil organic carbon (SOC) storage was significantly increased with abandonment management compared with intensive management (Control, CK), but the durations of abandonment management had no significant effects on SOC. Microbial biomass carbon (MBC) concentration increased from DM-I to DM-III in the 0–40 cm soil layer (P < 0.01), and water-soluble organic carbon (WSOC) concentration decreased through DM-I (P < 0.01). (2) Abandonment management did not significantly affect soil total nitrogen (TN) storage at depth of 0–40 cm, with 9.54 Mg ha−1 for CK, 9.59 Mg ha−1 for DM-I, 9.89 Mg ha−1 for DM-II and 9.69 Mg ha−1 for DM-III. Water-soluble organic nitrogen (WSON) concentration significantly decreased from CK to DM-III. Ammonium nitrogen (NH4+-N) concentration increased from DM-I to DM-III (P < 0.01), and nitrate nitrogen (NO3−-N) concentration decreased from CK to DM-III (P < 0.01). The results of the effects of abandonment durations on soil properties in Moso bamboo forests provide valuable information for forest restoration and management.
Xu Deng; Jiayang Yin; Lin Xu; Yongjun Shi; Guomo Zhou; Yongfu Li; Guangsheng Chen; Yuzhu Ye; Fagen Zhang; Yufeng Zhou; Yulu Xiong. Effects of abandonment management on soil C and N pools in Moso bamboo forests. Science of The Total Environment 2020, 729, 138949 .
AMA StyleXu Deng, Jiayang Yin, Lin Xu, Yongjun Shi, Guomo Zhou, Yongfu Li, Guangsheng Chen, Yuzhu Ye, Fagen Zhang, Yufeng Zhou, Yulu Xiong. Effects of abandonment management on soil C and N pools in Moso bamboo forests. Science of The Total Environment. 2020; 729 ():138949.
Chicago/Turabian StyleXu Deng; Jiayang Yin; Lin Xu; Yongjun Shi; Guomo Zhou; Yongfu Li; Guangsheng Chen; Yuzhu Ye; Fagen Zhang; Yufeng Zhou; Yulu Xiong. 2020. "Effects of abandonment management on soil C and N pools in Moso bamboo forests." Science of The Total Environment 729, no. : 138949.
Fires play an important role in the terrestrial biosphere carbon cycle, not only through direct carbon release but also contributing to a potential long‐term storage as pyrogenic carbon (PyC). PyC is formed through fires, and, because it may resist further biological and chemical degradation, is more stable in soil and sediment than original biomass. At the global scale, contributions of fires to both atmospheric CO2 emissions and PyC accumulation are potentially large but difficult to estimate. Our analysis was based on existing simulation results from two different modeling approaches (Global Fire Emissions Database version 4 [GFED4s] and Terrestrial Ecosystem Model version 6 [TEM6]) that used global area burned data to provide recent, retrospective estimates of CO2 emissions from vegetation combustion, together with published, biome‐ and continental‐scale conversion ratios that relate CO2 emissions to PyC production (PyC/CO2) during combustion. The estimates of global CO2 emissions from fires differed substantially between the two models' results. GFED4s estimated 2,041 Tg C/year during the 2000–2016 time period, whereas the TEM6 estimate was considerably lower at 643 Tg C/year from 2000 to 2010. Global PyC production estimates from fires were 153.4 ± 18.7 and 49.5 ± 4.9 Tg C/year based on the emission estimates from GFED4s and TEM6, respectively. Our results suggest that African tropical savanna fires produced the largest amount of CO2 emissions and PyC among global biomes, the most significant interannual variations in CO2 emissions and PyC production were found in tropical forests, and the magnitude of PyC produced by fires each year represented a potentially significant long‐term sink of atmospheric CO2.
Xinyuan Wei; Daniel J. Hayes; Shawn Fraver; Guangsheng Chen. Global Pyrogenic Carbon Production During Recent Decades Has Created the Potential for a Large, Long‐Term Sink of Atmospheric CO 2. Journal of Geophysical Research: Biogeosciences 2018, 123, 3682 -3696.
AMA StyleXinyuan Wei, Daniel J. Hayes, Shawn Fraver, Guangsheng Chen. Global Pyrogenic Carbon Production During Recent Decades Has Created the Potential for a Large, Long‐Term Sink of Atmospheric CO 2. Journal of Geophysical Research: Biogeosciences. 2018; 123 (12):3682-3696.
Chicago/Turabian StyleXinyuan Wei; Daniel J. Hayes; Shawn Fraver; Guangsheng Chen. 2018. "Global Pyrogenic Carbon Production During Recent Decades Has Created the Potential for a Large, Long‐Term Sink of Atmospheric CO 2." Journal of Geophysical Research: Biogeosciences 123, no. 12: 3682-3696.
The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO2 at northern high latitudes since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO2 uptake since late 1990s. Here, based on in-situ atmospheric CO2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO2 amplitude from 1990s to mid-2000s. This phenomenon is associated with the paused decrease in the minimum CO2 concentration ([CO2]min), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models (TBMs). These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO2 concentration.
Zhao Li; Jianyang Xia; Anders Ahlström; Annette Rinke; Charles Koven; Daniel J Hayes; DuoYing Ji; Geli Zhang; Gerhard Krinner; Guangsheng Chen; Wanying Cheng; Jinwei Dong; Junyi Liang; John C. Moore; Lifen Jiang; Liming Yan; Philippe Ciais; Shushi Peng; Ying-Ping Wang; Xiangming Xiao; Zheng Shi; A David McGuire; Yiqi Luo; Anders Ahlstroem. Non-uniform seasonal warming regulates vegetation greening and atmospheric CO 2 amplification over northern lands. Environmental Research Letters 2018, 13, 124008 .
AMA StyleZhao Li, Jianyang Xia, Anders Ahlström, Annette Rinke, Charles Koven, Daniel J Hayes, DuoYing Ji, Geli Zhang, Gerhard Krinner, Guangsheng Chen, Wanying Cheng, Jinwei Dong, Junyi Liang, John C. Moore, Lifen Jiang, Liming Yan, Philippe Ciais, Shushi Peng, Ying-Ping Wang, Xiangming Xiao, Zheng Shi, A David McGuire, Yiqi Luo, Anders Ahlstroem. Non-uniform seasonal warming regulates vegetation greening and atmospheric CO 2 amplification over northern lands. Environmental Research Letters. 2018; 13 (12):124008.
Chicago/Turabian StyleZhao Li; Jianyang Xia; Anders Ahlström; Annette Rinke; Charles Koven; Daniel J Hayes; DuoYing Ji; Geli Zhang; Gerhard Krinner; Guangsheng Chen; Wanying Cheng; Jinwei Dong; Junyi Liang; John C. Moore; Lifen Jiang; Liming Yan; Philippe Ciais; Shushi Peng; Ying-Ping Wang; Xiangming Xiao; Zheng Shi; A David McGuire; Yiqi Luo; Anders Ahlstroem. 2018. "Non-uniform seasonal warming regulates vegetation greening and atmospheric CO 2 amplification over northern lands." Environmental Research Letters 13, no. 12: 124008.
Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this end, a new building detection method based on the morphological building index (MBI) is proposed in this study. First, the local feature points are detected from the VHR remote sensing imagery and they are optimized by the saliency index proposed in this study. Second, a voting matrix is calculated based on these optimized local feature points to extract built-up areas. Finally, buildings are detected from the extracted built-up areas using the MBI algorithm. Experiments confirm that our proposed method can effectively and accurately detect buildings in VHR remote sensing images captured in complex environments.
Yongfa You; Siyuan Wang; Yuanxu Ma; Guangsheng Chen; Bin Wang; Ming Shen; Weihua Liu. Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index. Remote Sensing 2018, 10, 1287 .
AMA StyleYongfa You, Siyuan Wang, Yuanxu Ma, Guangsheng Chen, Bin Wang, Ming Shen, Weihua Liu. Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index. Remote Sensing. 2018; 10 (8):1287.
Chicago/Turabian StyleYongfa You; Siyuan Wang; Yuanxu Ma; Guangsheng Chen; Bin Wang; Ming Shen; Weihua Liu. 2018. "Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index." Remote Sensing 10, no. 8: 1287.
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.
Donghai Wu; Philippe Ciais; Nicolas Viovy; Alan K. Knapp; Kevin Wilcox; Michael Bahn; Melinda D. Smith; Sara Vicca; Simone Fatichi; Jakob Zscheischler; Yue He; Xiangyi Li; Akihiko Ito; Almut Arneth; Anna Harper; Anna Ukkola; Athanasios Paschalis; Benjamin Poulter; Changhui Peng; Daniel Ricciuto; David Reinthaler; Guangsheng Chen; Hanqin Tian; Hélène Genet; Jiafu Mao; Johannes Ingrisch; Julia E. S. M. Nabel; Julia Pongratz; Lena R. Boysen; Markus Kautz; Michael Schmitt; Patrick Meir; Qiuan Zhu; Roland Hasibeder; Sebastian Sippel; Shree R. S. Dangal; Stephen Sitch; Xiaoying Shi; Yingping Wang; Yiqi Luo; Yongwen Liu; Shilong Piao. Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites. Biogeosciences 2018, 15, 3421 -3437.
AMA StyleDonghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, Shilong Piao. Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites. Biogeosciences. 2018; 15 (11):3421-3437.
Chicago/Turabian StyleDonghai Wu; Philippe Ciais; Nicolas Viovy; Alan K. Knapp; Kevin Wilcox; Michael Bahn; Melinda D. Smith; Sara Vicca; Simone Fatichi; Jakob Zscheischler; Yue He; Xiangyi Li; Akihiko Ito; Almut Arneth; Anna Harper; Anna Ukkola; Athanasios Paschalis; Benjamin Poulter; Changhui Peng; Daniel Ricciuto; David Reinthaler; Guangsheng Chen; Hanqin Tian; Hélène Genet; Jiafu Mao; Johannes Ingrisch; Julia E. S. M. Nabel; Julia Pongratz; Lena R. Boysen; Markus Kautz; Michael Schmitt; Patrick Meir; Qiuan Zhu; Roland Hasibeder; Sebastian Sippel; Shree R. S. Dangal; Stephen Sitch; Xiaoying Shi; Yingping Wang; Yiqi Luo; Yongwen Liu; Shilong Piao. 2018. "Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites." Biogeosciences 15, no. 11: 3421-3437.
Nitrous oxide (N2O) 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 N2O fluxes and the key drivers of N2O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N2O 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 N2O 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 N2O 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 N2O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N2O dynamics; and 3) provide a benchmarking estimate of N2O 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 N2O estimation derived from the NMIP is a key component of the global N2O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.
Hanqin Tian; Jia Yang; Chaoqun Lu; Rongting Xu; Josep G. Canadell; Robert B. Jackson; Almut Arneth; Jinfeng Chang; Guangsheng Chen; Philippe Ciais; Stefan Gerber; Akihiko Ito; Yuanyuan Huang; Fortunat Joos; Sebastian Lienert; Palmira Messina; Stefan Olin; Shufen Pan; Changhui Peng; Eri Saikawa; Rona L. Thompson; Nicolas Vuichard; Wilfried Winiwarter; Sönke Zaehle; Bowen Zhang; Kerou Zhang; Qiuan Zhu. The Global N2O Model Intercomparison Project. Bulletin of the American Meteorological Society 2018, 99, 1231 -1251.
AMA StyleHanqin Tian, Jia Yang, Chaoqun Lu, Rongting Xu, Josep G. Canadell, Robert B. Jackson, Almut Arneth, Jinfeng Chang, Guangsheng Chen, Philippe Ciais, Stefan Gerber, Akihiko Ito, Yuanyuan Huang, Fortunat Joos, Sebastian Lienert, Palmira Messina, Stefan Olin, Shufen Pan, Changhui Peng, Eri Saikawa, Rona L. Thompson, Nicolas Vuichard, Wilfried Winiwarter, Sönke Zaehle, Bowen Zhang, Kerou Zhang, Qiuan Zhu. The Global N2O Model Intercomparison Project. Bulletin of the American Meteorological Society. 2018; 99 (6):1231-1251.
Chicago/Turabian StyleHanqin Tian; Jia Yang; Chaoqun Lu; Rongting Xu; Josep G. Canadell; Robert B. Jackson; Almut Arneth; Jinfeng Chang; Guangsheng Chen; Philippe Ciais; Stefan Gerber; Akihiko Ito; Yuanyuan Huang; Fortunat Joos; Sebastian Lienert; Palmira Messina; Stefan Olin; Shufen Pan; Changhui Peng; Eri Saikawa; Rona L. Thompson; Nicolas Vuichard; Wilfried Winiwarter; Sönke Zaehle; Bowen Zhang; Kerou Zhang; Qiuan Zhu. 2018. "The Global N2O Model Intercomparison Project." Bulletin of the American Meteorological Society 99, no. 6: 1231-1251.
We conducted a model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 projections between 2010 and 2299 for the northern permafrost region. All models simulating carbon represented soil with depth, a critical structural feature needed to represent the permafrost carbon–climate feedback, but that is not a universal feature of all climate models. Between 2010 and 2299, simulations indicated losses of permafrost between 3 and 5 million km2 for the RCP4.5 climate and between 6 and 16 million km2 for the RCP8.5 climate. For the RCP4.5 projection, cumulative change in soil carbon varied between 66-Pg C (1015-g carbon) loss to 70-Pg C gain. For the RCP8.5 projection, losses in soil carbon varied between 74 and 652 Pg C (mean loss, 341 Pg C). For the RCP4.5 projection, gains in vegetation carbon were largely responsible for the overall projected net gains in ecosystem carbon by 2299 (8- to 244-Pg C gains). In contrast, for the RCP8.5 projection, gains in vegetation carbon were not great enough to compensate for the losses of carbon projected by four of the five models; changes in ecosystem carbon ranged from a 641-Pg C loss to a 167-Pg C gain (mean, 208-Pg C loss). The models indicate that substantial net losses of ecosystem carbon would not occur until after 2100. This assessment suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of the permafrost carbon–climate feedback.
A. David McGuire; David M. Lawrence; Charles Koven; Joy S. Clein; Eleanor Burke; Guangsheng Chen; Elchin Jafarov; Andrew H. MacDougall; Sergey Marchenko; Dmitry Nicolsky; Shushi Peng; Annette Rinke; Philippe Ciais; Isabelle Gouttevin; Daniel J. Hayes; DuoYing Ji; Gerhard Krinner; John C. Moore; Vladimir Romanovsky; Christina Schädel; Kevin Schaefer; Edward A. G. Schuur; Qianlai Zhuang. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proceedings of the National Academy of Sciences 2018, 115, 3882 -3887.
AMA StyleA. David McGuire, David M. Lawrence, Charles Koven, Joy S. Clein, Eleanor Burke, Guangsheng Chen, Elchin Jafarov, Andrew H. MacDougall, Sergey Marchenko, Dmitry Nicolsky, Shushi Peng, Annette Rinke, Philippe Ciais, Isabelle Gouttevin, Daniel J. Hayes, DuoYing Ji, Gerhard Krinner, John C. Moore, Vladimir Romanovsky, Christina Schädel, Kevin Schaefer, Edward A. G. Schuur, Qianlai Zhuang. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proceedings of the National Academy of Sciences. 2018; 115 (15):3882-3887.
Chicago/Turabian StyleA. David McGuire; David M. Lawrence; Charles Koven; Joy S. Clein; Eleanor Burke; Guangsheng Chen; Elchin Jafarov; Andrew H. MacDougall; Sergey Marchenko; Dmitry Nicolsky; Shushi Peng; Annette Rinke; Philippe Ciais; Isabelle Gouttevin; Daniel J. Hayes; DuoYing Ji; Gerhard Krinner; John C. Moore; Vladimir Romanovsky; Christina Schädel; Kevin Schaefer; Edward A. G. Schuur; Qianlai Zhuang. 2018. "Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change." Proceedings of the National Academy of Sciences 115, no. 15: 3882-3887.
Amazon droughts have major impacts on regional ecosystem functioning as well as global carbon cycling. The severe dry-season droughts in 2005 and 2010, driven by Atlantic sea surface temperature (SST) anomaly, have been widely investigated in terms of drought severity and impacts on ecosystems. Although the influence of Pacific SST anomaly on wet-season precipitation has been well recognized, it remains uncertain to what extent the droughts driven by Pacific SST anomaly could affect forest greenness and photosynthesis in the Amazon. Here we examined the monthly and annual dynamics of forest greenness and photosynthetic capacity when Amazon ecosystems experienced an extreme drought in 2015/2016 driven by a strong El Niño event. We found that the drought during August 2015 – July 2016 was one of the two most severe meteorological droughts since 1901. Due to the enhanced solar radiation during this drought, overall forest greenness showed a small increase, and 21.6% of forests even greened up (greenness index anomaly ≥ 1 standard deviation). In contrast, solar-induced chlorophyll fluorescence (SIF), an indicator of vegetation photosynthetic capacity, decreased by 8.2%. Responses of forest greenness and photosynthesis decoupled during this drought, indicating that forest photosynthesis could still be suppressed regardless of the variation of canopy greenness. If future El Niño frequency increases as projected by earth system models, droughts would result in persistent reduction in Amazon forest productivity, substantial changes in tree composition, and considerable carbon emissions from Amazon.
Jia Yang; Hanqin Tian; Shufen Pan; Guangsheng Chen; Bowen Zhang; Shree Dangal. Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Global Change Biology 2018, 24, 1919 -1934.
AMA StyleJia Yang, Hanqin Tian, Shufen Pan, Guangsheng Chen, Bowen Zhang, Shree Dangal. Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Global Change Biology. 2018; 24 (5):1919-1934.
Chicago/Turabian StyleJia Yang; Hanqin Tian; Shufen Pan; Guangsheng Chen; Bowen Zhang; Shree Dangal. 2018. "Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016." Global Change Biology 24, no. 5: 1919-1934.
Donghai Wu; Philippe Ciais; Nicolas Viovy; Alan K. Knapp; Kevin Wilcox; Michael Bahn; Melinda D. Smith; Sara Vicca; Simone Fatichi; Jakob Zscheischler; Yue He; Xiangyi Li; Akihiko Ito; Almut Arneth; Anna Harper; Anna Ukkola; Athanasios Paschalis; Benjamin Poulter; Changhui Peng; Daniel Ricciuto; David Reinthaler; Guangsheng Chen; Hanqin Tian; Hélène Genet; Jiafu Mao; Johannes Ingrisch; Julia E. S. M. Nabel; Julia Pongratz; Lena R. Boysen; Markus Kautz; Michael Schmitt; Patrick Meir; Qiuan Zhu; Roland Hasibeder; Sebastian Sippel; Shree R. S. Dangal; Stephen Sitch; Xiaoying Shi; Yingping Wang; Yiqi Luo; Yongwen Liu; Shilong Piao. Supplementary material to "Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites". 2018, 1 .
AMA StyleDonghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, Shilong Piao. Supplementary material to "Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites". . 2018; ():1.
Chicago/Turabian StyleDonghai Wu; Philippe Ciais; Nicolas Viovy; Alan K. Knapp; Kevin Wilcox; Michael Bahn; Melinda D. Smith; Sara Vicca; Simone Fatichi; Jakob Zscheischler; Yue He; Xiangyi Li; Akihiko Ito; Almut Arneth; Anna Harper; Anna Ukkola; Athanasios Paschalis; Benjamin Poulter; Changhui Peng; Daniel Ricciuto; David Reinthaler; Guangsheng Chen; Hanqin Tian; Hélène Genet; Jiafu Mao; Johannes Ingrisch; Julia E. S. M. Nabel; Julia Pongratz; Lena R. Boysen; Markus Kautz; Michael Schmitt; Patrick Meir; Qiuan Zhu; Roland Hasibeder; Sebastian Sippel; Shree R. S. Dangal; Stephen Sitch; Xiaoying Shi; Yingping Wang; Yiqi Luo; Yongwen Liu; Shilong Piao. 2018. "Supplementary material to "Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites"." , no. : 1.
Changes in precipitation variability are known to influence grassland growth. Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation may occur. Under normally variable precipitation regimes, 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 ecosystem models that couple carbon-water system in grasslands are capable of simulating these non-symmetrical 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 fourteen 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) Gross primary productivity (GPP), NPP, ANPP and belowground NPP (BNPP) showed concave-down nonlinear response curves to altered precipitation in all the models, but with different curvatures and mean values. (2) The slopes of spatial relationships (across sites) between modeled primary productivity and precipitation were steeper than the temporal slopes obtained from inter-annual variations, consistent with empirical data. (3) The asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation variability differed among models, and the median of the model-ensemble suggested a negative asymmetry across the three sites, in contrast to empirical studies. (4) The median sensitivity of modeled productivity to rainfall consistently suggested greater negative impacts with reduced precipitation than positive effects with increased precipitation under extreme conditions. This study indicates that most models overestimate the extent of negative drought effects and/or underestimate the impacts of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco-hydrological processes in models.
Donghai Wu; Philippe Ciais; Nicolas Viovy; Alan K. Knapp; Kevin Wilcox; Michael Bahn; Melinda D. Smith; Sara Vicca; Simone Fatichi; Jakob Zscheischler; Yue He; Xiangyi Li; Akihiko Ito; Almut Arneth; Anna Harper; Anna Ukkola; Athanasios Paschalis; Benjamin Poulter; Changhui Peng; Daniel Ricciuto; David Reinthaler; Guangsheng Chen; Hanqin Tian; Hélène Genet; Jiafu Mao; Johannes Ingrisch; Julia E. S. M. Nabel; Julia Pongratz; Lena R. Boysen; Markus Kautz; Michael Schmitt; Patrick Meir; Qiuan Zhu; Roland Hasibeder; Sebastian Sippel; Shree R. S. Dangal; Stephen Sitch; Xiaoying Shi; Yingping Wang; Yiqi Luo; Yongwen Liu; Shilong Piao. Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites. 2018, 15, 3421 -3437.
AMA StyleDonghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, Shilong Piao. Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites. . 2018; 15 (11):3421-3437.
Chicago/Turabian StyleDonghai Wu; Philippe Ciais; Nicolas Viovy; Alan K. Knapp; Kevin Wilcox; Michael Bahn; Melinda D. Smith; Sara Vicca; Simone Fatichi; Jakob Zscheischler; Yue He; Xiangyi Li; Akihiko Ito; Almut Arneth; Anna Harper; Anna Ukkola; Athanasios Paschalis; Benjamin Poulter; Changhui Peng; Daniel Ricciuto; David Reinthaler; Guangsheng Chen; Hanqin Tian; Hélène Genet; Jiafu Mao; Johannes Ingrisch; Julia E. S. M. Nabel; Julia Pongratz; Lena R. Boysen; Markus Kautz; Michael Schmitt; Patrick Meir; Qiuan Zhu; Roland Hasibeder; Sebastian Sippel; Shree R. S. Dangal; Stephen Sitch; Xiaoying Shi; Yingping Wang; Yiqi Luo; Yongwen Liu; Shilong Piao. 2018. "Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites." 15, no. 11: 3421-3437.
Bowen Zhang; Hanqin Tian; Chaoqun Lu; Guangsheng Chen; Shufen Pan; Christopher Anderson; Benjamin Poulter. Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets. Atmospheric Environment 2017, 165, 310 -321.
AMA StyleBowen Zhang, Hanqin Tian, Chaoqun Lu, Guangsheng Chen, Shufen Pan, Christopher Anderson, Benjamin Poulter. Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets. Atmospheric Environment. 2017; 165 ():310-321.
Chicago/Turabian StyleBowen Zhang; Hanqin Tian; Chaoqun Lu; Guangsheng Chen; Shufen Pan; Christopher Anderson; Benjamin Poulter. 2017. "Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets." Atmospheric Environment 165, no. : 310-321.
Plantation forest area in the conterminous United States (CONUS) ranked second among the world's nations in the land area apportioned to forest plantation. As compared to the naturally regenerated forests, plantation forests demonstrate significant differences in biophysical characteristics, and biogeochemical and hydrological cycles as a result of more intensive management practices. Inventory data have been reported for multiple time periods on plot, state, and regional scales across the CONUS, but the requisite annual and spatially explicit plantation data set over a long-term period for analysis of the role of plantation management on regional or national scales is lacking. Through synthesis of multiple inventory data sources, this study developed methods to spatialize the time series plantation forest and tree species distribution data for the CONUS over the 1928–2012 time period. According to this new data set, plantation forest area increased from near zero in the 1930s to 268.27 thousand km2 in 2012, accounting for 8.65 % of the total forestland area in the CONUS. Regionally, the South contained the highest proportion of plantation forests, accounting for about 19.34 % of total forestland area in 2012. This time series and gridded data set developed here can be readily applied in regional Earth system modeling frameworks for assessing the impacts of plantation management practices on forest productivity, carbon and nitrogen stocks, and greenhouse gases (e.g., CO2, CH4, and N2O) and water fluxes on regional or national scales. The gridded plantation distribution and tree species maps, and the interpolated state-level annual tree planting area and plantation area during 1928–2012, are available from https://doi.org/10.1594/PANGAEA.873558.
Guangsheng Chen; Shufen Pan; Daniel J. Hayes; Hanqin Tian. Spatial and temporal patterns of plantation forests in the United States since the 1930s: an annual and gridded data set for regional Earth system modeling. Earth System Science Data 2017, 9, 545 -556.
AMA StyleGuangsheng Chen, Shufen Pan, Daniel J. Hayes, Hanqin Tian. Spatial and temporal patterns of plantation forests in the United States since the 1930s: an annual and gridded data set for regional Earth system modeling. Earth System Science Data. 2017; 9 (2):545-556.
Chicago/Turabian StyleGuangsheng Chen; Shufen Pan; Daniel J. Hayes; Hanqin Tian. 2017. "Spatial and temporal patterns of plantation forests in the United States since the 1930s: an annual and gridded data set for regional Earth system modeling." Earth System Science Data 9, no. 2: 545-556.
Shufen Pan; Guangsheng Chen; Wei Ren; Shree R. S. Dangal; Kamaljit Banger; Jia Yang; Bo Tao; Hanqin Tian. Responses of global terrestrial water use efficiency to climate change and rising atmospheric CO2 concentration in the twenty-first century. International Journal of Digital Earth 2017, 11, 558 -582.
AMA StyleShufen Pan, Guangsheng Chen, Wei Ren, Shree R. S. Dangal, Kamaljit Banger, Jia Yang, Bo Tao, Hanqin Tian. Responses of global terrestrial water use efficiency to climate change and rising atmospheric CO2 concentration in the twenty-first century. International Journal of Digital Earth. 2017; 11 (6):558-582.
Chicago/Turabian StyleShufen Pan; Guangsheng Chen; Wei Ren; Shree R. S. Dangal; Kamaljit Banger; Jia Yang; Bo Tao; Hanqin Tian. 2017. "Responses of global terrestrial water use efficiency to climate change and rising atmospheric CO2 concentration in the twenty-first century." International Journal of Digital Earth 11, no. 6: 558-582.
Burn area and the frequency of extreme fire events have been increasing during recent decades in North America, and this trend is expected to continue over the 21st century. While many aspects of the North American carbon budget have been intensively studied, the net contribution of fire disturbance to the overall net carbon flux at the continental scale remains uncertain. Based on national scale, spatially explicit and long-term fire data, along with the improved model parameterization in a process-based ecosystem model, we simulated the impact of fire disturbance on both direct carbon emissions and net terrestrial ecosystem carbon balance in North America. Fire-caused direct carbon emissions were 106.55 ± 15.98 Tg C/yr during 1990–2012; however, the net ecosystem carbon balance associated with fire was −26.09 ± 5.22 Tg C/yr, indicating that most of the emitted carbon was resequestered by the terrestrial ecosystem. Direct carbon emissions showed an increase in Alaska and Canada during 1990–2012 as compared to prior periods due to more extreme fire events, resulting in a large carbon source from these two regions. Among biomes, the largest carbon source was found to be from the boreal forest, primarily due to large reductions in soil organic matter during, and with slower recovery after, fire events. The interactions between fire and environmental factors reduced the fire-caused ecosystem carbon source. Fire disturbance only caused a weak carbon source as compared to the best estimate terrestrial carbon sink in North America owing to the long-term legacy effects of historical burn area coupled with fast ecosystem recovery during 1990–2012.
Guangsheng Chen; Daniel J. Hayes; Anthony McGuire. Contributions of wildland fire to terrestrial ecosystem carbon dynamics in North America from 1990 to 2012. Global Biogeochemical Cycles 2017, 31, 878 -900.
AMA StyleGuangsheng Chen, Daniel J. Hayes, Anthony McGuire. Contributions of wildland fire to terrestrial ecosystem carbon dynamics in North America from 1990 to 2012. Global Biogeochemical Cycles. 2017; 31 (5):878-900.
Chicago/Turabian StyleGuangsheng Chen; Daniel J. Hayes; Anthony McGuire. 2017. "Contributions of wildland fire to terrestrial ecosystem carbon dynamics in North America from 1990 to 2012." Global Biogeochemical Cycles 31, no. 5: 878-900.
Wenquan Zhu; Nan Jiang; Guangsheng Chen; Donghai Zhang; Zhoutao Zheng; Deqin Fan. Divergent shifts and responses of plant autumn phenology to climate change on the Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology 2017, 239, 166 -175.
AMA StyleWenquan Zhu, Nan Jiang, Guangsheng Chen, Donghai Zhang, Zhoutao Zheng, Deqin Fan. Divergent shifts and responses of plant autumn phenology to climate change on the Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology. 2017; 239 ():166-175.
Chicago/Turabian StyleWenquan Zhu; Nan Jiang; Guangsheng Chen; Donghai Zhang; Zhoutao Zheng; Deqin Fan. 2017. "Divergent shifts and responses of plant autumn phenology to climate change on the Qinghai-Tibetan Plateau." Agricultural and Forest Meteorology 239, no. : 166-175.
Plantation forest area in the conterminous United States (CONUS) ranked second among the world’s nations in the land area apportioned to forest plantation management. As compared to the naturally-regenerated forests, plantation forests demonstrate significant differences in biophysical characteristics, and biogeochemical and hydrological cycles as a result of more intensive management practices. Inventory data have been reported for multiple time periods at plot, state and regional scales across the CONUS, but there lacks the requisite annual and spatially-explicit plantation data set over a long-term period for analysis of the role of plantation management at regional or national scale. Through synthesizing multiple inventory data sources, this study developed methods to spatialize the time series plantation forest and tree species distribution data for the CONUS over the 1928–2012 time period. According to this new data set, plantation forest area increased from near zero in the 1930s to 268.27 thousand km2 by 2012, accounting for 8.65 % of the total forest land area in the CONUS. Regionally, the South contained the highest proportion of plantation forests, accounting for about 19.34 % of total forest land area in 2012. This time series and gridded data set developed here can be readily applied in regional Earth system modeling frameworks for assessing the impacts of plantation management practices on forest productivity, carbon and nitrogen stocks, and greenhouse gas (e.g., CO2, CH4 and N2O) and water fluxes at regional or national scales. The gridded plantation distribution and tree species maps, the state-level tree planting area and plantation distribution area during 1928–2012 are available from doi:10.1594/PANGAEA.873558.
Guangsheng Chen; Shufen Pan; Daniel J. Hayes; Hanqin Tian. Spatial and temporal patterns of plantation forests in the United States since the 1930s: An annual and gridded data set for regional Earth system modeling. 2017, 1 -38.
AMA StyleGuangsheng Chen, Shufen Pan, Daniel J. Hayes, Hanqin Tian. Spatial and temporal patterns of plantation forests in the United States since the 1930s: An annual and gridded data set for regional Earth system modeling. . 2017; ():1-38.
Chicago/Turabian StyleGuangsheng Chen; Shufen Pan; Daniel J. Hayes; Hanqin Tian. 2017. "Spatial and temporal patterns of plantation forests in the United States since the 1930s: An annual and gridded data set for regional Earth system modeling." , no. : 1-38.
Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.
Jianyang Xia; A. David McGuire; David Lawrence; Eleanor Burke; Guangsheng Chen; Xiaodong Chen; Christine Delire; Charles Koven; Andrew MacDougall; Shushi Peng; Annette Rinke; Kazuyuki Saito; Wenxin Zhang; Ramdane Alkama; Theodore J. Bohn; Philippe Ciais; Bertrand Decharme; Isabelle Gouttevin; Tomohiro Hajima; Daniel J. Hayes; Kun Huang; DuoYing Ji; Gerhard Krinner; Dennis P. Lettenmaier; Paul A. Miller; John C. Moore; Benjamin Smith; Tetsuo Sueyoshi; Zheng Shi; Liming Yan; Junyi Liang; Lifen Jiang; Qian Zhang; Yiqi Luo. Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region. Journal of Geophysical Research: Biogeosciences 2017, 122, 430 -446.
AMA StyleJianyang Xia, A. David McGuire, David Lawrence, Eleanor Burke, Guangsheng Chen, Xiaodong Chen, Christine Delire, Charles Koven, Andrew MacDougall, Shushi Peng, Annette Rinke, Kazuyuki Saito, Wenxin Zhang, Ramdane Alkama, Theodore J. Bohn, Philippe Ciais, Bertrand Decharme, Isabelle Gouttevin, Tomohiro Hajima, Daniel J. Hayes, Kun Huang, DuoYing Ji, Gerhard Krinner, Dennis P. Lettenmaier, Paul A. Miller, John C. Moore, Benjamin Smith, Tetsuo Sueyoshi, Zheng Shi, Liming Yan, Junyi Liang, Lifen Jiang, Qian Zhang, Yiqi Luo. Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region. Journal of Geophysical Research: Biogeosciences. 2017; 122 (2):430-446.
Chicago/Turabian StyleJianyang Xia; A. David McGuire; David Lawrence; Eleanor Burke; Guangsheng Chen; Xiaodong Chen; Christine Delire; Charles Koven; Andrew MacDougall; Shushi Peng; Annette Rinke; Kazuyuki Saito; Wenxin Zhang; Ramdane Alkama; Theodore J. Bohn; Philippe Ciais; Bertrand Decharme; Isabelle Gouttevin; Tomohiro Hajima; Daniel J. Hayes; Kun Huang; DuoYing Ji; Gerhard Krinner; Dennis P. Lettenmaier; Paul A. Miller; John C. Moore; Benjamin Smith; Tetsuo Sueyoshi; Zheng Shi; Liming Yan; Junyi Liang; Lifen Jiang; Qian Zhang; Yiqi Luo. 2017. "Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region." Journal of Geophysical Research: Biogeosciences 122, no. 2: 430-446.