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Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest ecosystems 2023年第2期10卷 141-152页

作者： Shuai Wang Bol Roland Kabindra Adhikari Qianlai Zhuang Xinxin Jin Chunlan Han Fengkui Qian College of Land and Environment Shenyang Agricultural UniversityShenyang110866China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchCASBeijing100101China Institute of Bio-and Geosciences Agrosphere(IBG-3)Forschungszentrum Jülich GmbHWilhelm-Johnen-Straße52428JülichGermany School of Natural Sciences Environment Centre WalesBangor UniversityBangorLL572UWUK USDA-ARS GrasslandSoil and Water Research LaboratoryTempleTX76502USA Department of Earth Atmosphericand Planetary SciencesPurdue UniversityWest LafayetteIN47907USA

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather *** study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and ***,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model *** selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient *** found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,*** maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in *** annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both *** stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 *** study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks *** spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C ***,our approach is likely to be useful in forestry management and ecologic

关键词： Soil organic carbon stocks Forest ecosystem Spatial-temporal variation Carbon sink Digital soil mapping

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China's current forest age structure will lead to weakened carbon sinks in the near future

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The Innovation 2023年第6期4卷 38-47页

作者： Rong Shang Jing M.Chen Mingzhu Xu Xudong Lin Peng Li Guirui Yu Nianpeng He Li Xu Peng Gong Liangyun Liu Han Liu Wenzhe Jiao Key Laboratory for Humid Subtropical Eco-Geogr aphical Processes of the Ministy of Education School of Geographical SciencesFujian Normal UniversityFuzhou 350117China Department of Geography and Planning University of TorontoTorontoOntario M5S 3G3Canada Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBejing 100101China Institute for Climate and Carbon Neutrality Department of Geography and Department of Earth SciencesUniversity of Hong KongHong Kong 999077China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Land Consolidation and Rehabilitation Center Ministry of Natural ResourcesBeijing 100035China Department of Civil and Environmental Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA

Forests are chiefly responsible for the terrestrial carbon sink that greatly re duces the buildup of CO_(2)concentrations in the atmosphere and alleviates climate *** predictions of terrestrial carbon sinks in the future have so far ignored the variation of forest carbon uptake with forest ***,we predict the role of China's current forest age in future carbon sink capacity by generating a high-resolution(30 m)forest age map in 2019 over China's landmass using satellite and forest inventory data and deriving forest growth curves using measurements of forest biomass and age in 3,121 *** China's forests currently have large proportions of young and middle-age stands,we project that China's forests will maintain high growth rates for about 15 ***,as the forests grow older,their net primary productivity will decline by 5.0%±1.4%in 2050,8.4%±1.6%in 2060,and 16.6%±2.8%in 2100,indicating weakened carbon sinks in the near *** weakening of forest carbon sinks can be potentially mitigated by optimizing forest age structure through selective logging and implementing new or improved *** finding is important not only for the global carbon cycle and climate projections but also for developing forest management strategies to enhance land sinks by alleviating the age effect.

关键词： forest carbon structure

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Relationship of minimum winter temperature and temperature seasonality to the northern range limit and species richness of trees in North America

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Journal of Geographical Sciences 2022年第2期32卷 280-290页

作者： Hong QIAN ZHANG Yangjian Robert E.RICKLEFS Xianli WANG Research and Collections Center Illinois State Museum1011 East Ash StreetSpringfieldIL 62703USA Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China Department of Biology University of Missouri–St.LouisSt.LouisMO 63121USA Northern Forestry Centre Canadian Forest ServiceNatural Resources Canada5320-122nd StreetEdmontonAB T6H 3S5Canada Department of Renewable Resources University of Alberta751 General Service Building EdmontonAB T6G 2H1Canada

Biologists have considered both winter coldness and temperature seasonality as major determinants of the northern limits of plants and animals in the Northern Hemisphere,which in turn drive the well-known latitudinal diversity ***,few studies have tested which of the two climate variables is the primary *** this study,we assess whether winter coldness or temperature seasonality is more strongly associated with the northern latitudinal limits of tree species and with tree species richness in North *** species were recorded in each of 1198 quadrats of 110 km×110 km in North *** used correlation and regression analyses to assess the relationship of the latitude of the northern boundary of each species,and of species richness per quadrat,with winter coldness and temperature *** richness was analyzed within 38 longitudinal,i.e.,north-south,bands(each being>1100 km long and 110 km wide).The latitudes of the northern range limits of tree species were three times better correlated with minimum temperatures at those latitudes than with temperature *** average,minimum temperature and temperature seasonality together explained 81.5%of the variation in the northern range limits of the tree species examined,and minimum temperature uniquely explained six-fold(33.7%versus 5.8%)more of this variation than did temperature *** of tree species richness with minimum temperatures were stronger than correlations with temperature seasonality for most of the longitudinal bands *** to temperature seasonality,winter coldness is more strongly associated with species distributions at high latitudes,and is likely a more important driver of the latitudinal diversity gradient.

关键词： cold tolerance latitudinal diversity gradient minimum temperature temperature seasonality temperature variation trees tropical conservatism hypothesis

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Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

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Science of the Total Environment 2024年 955卷 177169页

作者： Zhang, Jiang Chen, Huai Peng, Changhui Huang, Yuanyuan Wang, Yike Liu, Xinwei Wang, Meng Wang, Le Yu, Dongxue Zhu, Qiuan Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Chengdu Institute of Biology Chinese Academy of Sciences Chengdu610041 China Institute of Environment Sciences Department of Biology Sciences University of Quebec at Montreal Case Postale 8888 Succursale Centre-Ville MontrealQCH3C 3P8 Canada School of Geography Science Hunan Normal University Changsha410081 China Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Changchun130024 China Key Laboratory of Soil and Water Processes in Watershed College of Geography and Remote Sensing Hohai University Nanjing210098 China National Earth System Science Data Center National Science & Technology Infrastructure of China Beijing100101 China

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios. © 2024 Elsevier B.V.

关键词： Greenhouse gas emissions

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Model bias in calculating factor importance of climate on vegetation growth

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Global and Planetary Change 2023年第1期228卷

作者： Liang, Boyi Liu, Hongyan Wang, Shaopeng Cressey, Elizabeth L. Dahlsjö, Cecilia A.L. Xu, Chongyang Wang, Jia Wang, Zong Liu, Feng Feng, Siwen Shi, Liang Dai, Jingyu Cao, Jing Li, Fufu Quine, Timothy A. College of Forestry Precision Forestry Key Laboratory of Beijing Beijing Forestry University Beijing100083 China College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface Processes Peking University Beijing100871 China Geography College of Life and Environmental Sciences University of Exeter ExeterEX4 4RJ United Kingdom School of Geography and the Environment University of Oxford OxfordOX1 3QY United Kingdom Faculty of Agriculture Food and Environment The Hebrew University of Jerusalem Rehovot7610001 Israel Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China National Ecosystem Science Data Center Beijing100101 China State Key Laboratory of Environmental Criteria and Risk Assessment State Environmental Protection Key Laboratory of Regional Eco-process and Function Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China

Machine learning is increasingly used to study vegetation growth, however, more often than not, predicting and simulating functions are prioritized over quantitative estimates of the drivers of vegetation growth such as climate. In this paper, we, for the first time, systematically investigate the model bias in calculating factor importance of climate on vegetation growth, especially when various kinds of machine learning models are considered. We undertake two case studies to simulate research in remote sensing and ground-based scenarios from which the difference in quantitative relationships between climate and vegetation is evaluated across multiple models. We found that model complexity increased the determination coefficient (R2) but reduced the absolute importance of the preselected independent variables. As the fitting accuracy increases, the absolute factor importance of dominant factor and all the other influencing factors decreases simultaneously, and factor importance calculated by different models tended to be more normally distributed across the study region. The reduction in factor importance was accompanied by the increased effect of model selection;e.g. the model that was used to estimate vegetation growth played a larger role in producing the factor importance (shown by variance analysis, remote sensing scenario, F-statistic = 555.2;ground based scenario, F-statistic = 30.8) than the climate variables (variance analysis, remote sensing scenario, F = 460.8;ground based scenario, F = 28.8). Critically, for those machine learning models with highest fitting accuracy, the resultant factor importance of climate factors had smaller difference with that of random factor. In contrast, the relative factor influence among the selected climate factor is more robust and reliable (in variation analysis, model was detected no significant impact on the resultant factor importance). For 5 of 8 models, the dominant factor (temperature) has relative influence over 0.

关键词： Machine learning

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Degradable film mulching increases soil carbon sequestration in major Chinese dryland agroecosystems

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Nature communications 2025年第1期16卷 5029页

作者： Zihan Liu Chenxu Zhao Nanhai Zhang Jing Wang Zhaoyang Li Yves Uwiragiye Nyumah Fallah Thomas W Crowther Yuanyuan Huang Yi Xu Sheng Zhang Yakov Kuzyakov Kadambot H M Siddique Zhikuan Jia Zucong Cai Scott X Chang Minggang Xu Christoph Müller Yi Cheng School of Geography Nanjing Normal University 210023 Nanjing China. College of Agronomy Northwest A&F University 712100 Yangling China. Co-Innovation Center for Sustainable Forestry in Southern China College of Forestry Nanjing Forestry University 210037 Nanjing China. Department of Environmental Systems Science Institute of Integrative Biology ETH Zurich Zurich 8092 Switzerland. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences 100101 Beijing China. College of Agronomy and Biotechnology China Agricultural University 100193 Beijing China. Geo-Biosphere Interactions Department of Geosciences University of Tübingen Tübingen 72076 Germany. Institute of Soil Science Chinese Academy of Sciences 210008 Nanjing China. Department of Soil Science of Temperate Ecosystems Department of Agricultural Soil Science University of Göttingen Göttingen 37077 Germany. Peoples Friendship University of Russia (RUDN University) Moscow 117198 Russia. The UWA Institute of Agriculture The University of Western Australia Perth WA 6001 Australia. College of Agronomy Northwest A&F University 712100 Yangling China. jiazhk@***. Department of Renewable Resources University of Alberta Edmonton AB T6G 2E3 Canada. Shanxi Province Key Laboratory of Soil Environment and Nutrient Resources Engineer and Technology Academy of Ecology and Environment Shanxi Agricultural University 030031 Taiyuan China. State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China Key Laboratory of Arable Land Quality Monitoring and Evaluation Ministry of Agriculture and Rural Affairs Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences 100081 Beijing China. Liebig Centre of Agroecology and Climate Impact Research Justus Liebig University Giessen Giessen 35392 Germany. Institute of Plant Ecology Justus Liebig Univer

Plastic film mulching (PM), which contributes to nearly half of the increased crop yields in dryland agroecosystems, exacerbates environmental burdens due to its non-degradable nature. Globally, there is a growing demand to replace non-degradable PM with degradable film mulching (DM), yet its impacts on soil organic carbon (SOC) in dryland agroecosystems remains unknown. Here, using multi-field studies and mesocosm experiments, we found that DM strongly increased but PM reduced SOC storage (0-1 m). This difference is likely attributable to the higher microbial C use efficiency in soil under DM, leading to increased microbial-derived C compared to PM. Under the high roading scenario for 2100, DM could reduce the decomposition of SOC (0-1 m) in China's drylands by 9.0 ± 1.0 Mg ha year (one standard error) compared with PM. Our findings highlight that DM is a promising alternative to PM for sequestrating SOC and alleviating C loss under climate change in dryland agroecosystems.

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Satellite-based Estimation of Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau:A Multi-model Comparison

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Journal of Resources and Ecology 2017年第1期8卷 57-66页

作者： NIU Ben ZHANG Xianzhou HE Yongtao SHI Peili FU Gang DU Mingyuan ZHANG Yangjian ZONG Ning ZHANG Jing WU Jianshuang Lhasa Plateau Ecosystem Research Station Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China Institute for Agro-Environmental Sciences National Agriculture and Food Research Organization 3-1-3 Kannondai Tsukuba Ibaraki 305-8604 Japan College of Global Change and Earth System Sciences Beijing Normal University Beijing 100875 China

Alpine swamp meadows on the Tibetan Plateau,with the highest soil organic carbon content across the globe,are extremely vulnerable to climate *** accurately and continually quantify the gross primary production（GPP） is critical for understanding the dynamics of carbon cycles from site-scale to global *** covariance technique（EC） provides the best approach to measure the site-specific carbon flux,while satellite-based models can estimate GPP from local,small scale sites to regional and global ***,the suitability of most satellite-based models for alpine swamp meadow is *** we tested the performance of four widely-used models,the MOD17 algorithm（MOD）,the vegetation photosynthesis model（VPM）,the photosynthetic capacity model（PCM）,and the alpine vegetation model（AVM）,in providing GPP estimations for a typical alpine swamp meadow as compared to the GPP estimations provided by EC-derived *** results indicated that all these models provided good descriptions of the intra-annual GPP patterns（R〉20.89,P〈0.0001）,but hardly agreed with the inter-annual GPP *** strongly underestimated the GPP of alpine swamp meadow,only accounting for 54.0% of GPP_***,the other three satellite-based GPP models could serve as alternative tools for tower-based GPP *** estimated from AVM captured 94.5% of daily GPP_EC with the lowest average RMSE of 1.47 g C m^（-2）.PCM slightly overestimated GPP by 12.0% while MODR slightly underestimated by 8.1% GPP compared to the daily GPP_*** results suggested that GPP estimations for this alpine swamp meadow using AVM were superior to GPP estimations using the other relatively complex models.

关键词： satellite remote sensing eddy covariance technique（EC） gross primary production（GPP） model performance alpine swamp meadow Tibetan Plateau

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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modeling EVAPOTRANSPIRATION AND ITS COMPONENTS IN SUBTROPICAL CONIFEROUS FOREST BASED ON REMOTE SENSING AND METEOROLOGICAL DATA

MODELING EVAPOTRANSPIRATION AND ITS COMPONENTS IN SUBTROPICA...

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2010年亚洲通量网会议(Asiaflux workshop2010)

作者： Huanqi Wei Honglin He Min Liu Li Zhang Guirui Yu Hui-min Wang Ying Liu 3Graduate University of Chinese Academy of Sciences BeijingChina College of Geography and EnvironmentJiangxi Normal UniversityNanchangChina Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina College of Geography and EnvironmentJiangxi Normal UniversityNanchangChina Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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IMPACTS OF EXTREME PRECIPITATION ON TREE PLANTATION CARBON CYCLE

IMPACTS OF EXTREME PRECIPITATION ON TREE PLANTATION CARBON C...

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2010年亚洲通量网会议(Asiaflux workshop2010)

作者： Mei Huang Jinjun Ji Feng Deng Hongsheng Liu Institute of Atmospheric Physics CASBeijing100029China Department of GeographyUniversity of TorontoTorontoOntarioM5S 3G3Canada Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China

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