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Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming:Simulated Results from IAP-DGVM in CAS-ESM2

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Advances in Atmospheric Sciences 2022年第8期39卷 1285-1298,I0002-I0010页

作者： Xiaofei GAO Jiawen ZHU Xiaodong ZENG Minghua ZHANG Yongjiu DAI Duoying JI He ZHANG International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China School of Marine and Atmospheric Sciences Stony Brook UniversityNY 11790USA School of Atmospheric Sciences Sun Yat-Sen UniversityGuangzhou 510275China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Terrestrial ecosystems are an important part of Earth systems,and they are undergoing remarkable changes in response to global warming.This study investigates the response of the terrestrial vegetation distribution and carbon fluxes to global warming by using the new dynamic global vegetation model in the second version of the Chinese Academy of Sciences(CAS)Earth System Model(CAS-ESM2).We conducted two sets of simulations,a present-day simulation and a future simulation,which were forced by the present-day climate during 1981-2000 and the future climate during 2081-2100,respectively,as derived from RCP8.5 outputs in CMIP5.CO_(2)concentration is kept constant in all simulations to isolate CO_(2)-fertilization effects.The results show an overall increase in vegetation coverage in response to global warming,which is the net result of the greening in the mid-high latitudes and the browning in the tropics.The results also show an enhancement in carbon fluxes in response to global warming,including gross primary productivity,net primary productivity,and autotrophic respiration.We found that the changes in vegetation coverage were significantly correlated with changes in surface air temperature,reflecting the dominant role of temperature,while the changes in carbon fluxes were caused by the combined effects of leaf area index,temperature,and precipitation.This study applies the CAS-ESM2 to investigate the response of terrestrial ecosystems to climate warming.Even though the interpretation of the results is limited by isolating CO_(2)-fertilization effects,this application is still beneficial for adding to our understanding of vegetation processes and to further improve upon model parameterizations.

关键词： global warming vegetation distribution carbon flux leaf area index surface air temperature

Key variable for simulating critical nitrogen dilution curve of wheat:leaf area ratio-driven approach

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Pedosphere 2022年第3期32卷 463-474页

作者： Ke ZHANG Jifeng MA Yu WANG Weixing CAO Yan ZHU Qiang CAO Xiaojun LIU Yongchao TIAN National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095(China) Engineering Research Center of Smart Agriculture Ministry of EducationNanjing Agricultural UniversityNanjing 210095(China) Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture and Rural AffairsNanjing Agricultural UniversityNanjing 210095(China) Jiangsu Key Laboratory for Information Agriculture Nanjing Agricultural UniversityNanjing 210095(China) Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing Agricultural UniversityNanjing 210095(China)

Nitrogen(N)dilution curves,a pivotal tool for N nutrition diagnosis,have been developed using different winter wheat(Triticum aestivum L.)tissues.However,few studies have attempted to establish critical nitrogen(N_(c))dilution curves based on the leaf area ratio(LAR)to improve the monitoring accuracy of N status.In this study,three field experiments using eight N treatments and four wheat varieties were conducted in Jiangsu Province of China from 2013 to 2016.The empirical relationship of LAR with shoot biomass(expressed as dry matter)was developed under different N conditions.The results showed that LAR was a reliable index,which reduced the effects of wheat varieties and years compared with the traditional indicators.The N nutrition index(NNI)based on the LAR approach(NNI-LAR)produced equivalent results to that based on shoot biomass.Moreover,the NNI-LAR better predicted accumulated N deficit and best estimated the relative yield compared with the other two indicator-based NNI models.Therefore,the LAR-based approach improved the prediction accuracy of N_(c),accumulated N deficit,and relative yield,and it would be an optimal choice to conveniently diagnose the N status of winter wheat under field conditions.

关键词： agronomic parameter grain yield leaf area index nitrogen nutrition index nitrogen status nitrogen use efficiency

A better carbon-water flux simulation in multiple vegetation types by data assimilation

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Forest Ecosystems 2022年第1期9卷 131-145页

作者： Qiuyu Liu Tinglong Zhang Mingxi Du Huanlin Gao Qingfeng Zhang Rui Sun College of Natural Resources and Environment Northwest A&F UniversityYanglingShaanxi712100China Institute of Environment Sciences Department of Biology SciencesUniversity of Quebec at MontrealMontrealH3C 3P8Canada Department of Geography McGill UniversityMontrealQuebecH3A 0B9Canada Faculty of Geographical Science Beijing Normal UniversityBeijing100875China

Background:The accurate estimation of carbon-water flux is critical for understanding the carbon and water cycles of terrestrial ecosystems and further mitigating climate change.Model simulations and observations have been widely used to research water and carbon cycles of terrestrial ecosystems.Given the advantages and limitations of each method,combining simulations and observations through a data assimilation technique has been proven to be highly promising for improving carbon-water flux simulation.However,to the best of our knowledge,few studies have accomplished both parameter optimization and the updating of model state variables through data assimilation for carbon-water flux simulation in multiple vegetation types.And little is known about the variation of the performance of data assimilation for carbon-water flux simulation in different vegetation types.Methods:In this study,we assimilated leaf area index(LAI)time-series observations into a biogeochemical model(Biome-BGC)using different assimilation algorithms(ensemble Kalman filter algorithm(EnKF)and unscented Kalman filter(UKF))in different vegetation types(deciduous broad-leaved forest(DBF),evergreen broad-leaved forest(EBF)and grassland(GL))to simulate carbon-water flux.Results:The validation of the results against the eddy covariance measurements indicated that,overall,compared with the original simulation,assimilating the LAI into the Biome-BGC model improved the carbon-water flux simulations(R^(2)increased by 35%,root mean square error decreased by 10%;the sum of the absolute error decreased by 8%)but more significantly,improved the water flux simulations(R^(2)increased by 31%,root mean square error decreased by 18%;the sum of the absolute error decreased by 16%).Among the different forest types,the data assimilation techniques(both EnKF and UKF)achieved the best performance towards carbon-water flux in EBF(R^(2)increased by 44%,root mean square error decreased by 24%;the sum of the absolute error decreased by 28%),and the performances of EnKF an

关键词： Biome-BGC model leaf area index Evapotranspiration Net ecosystem CO_(2)exchange Ensemble Kalman filter algorithm Unscented Kalman filter

Droplets deposition and harvest-aid efficacy for UAV application in arid cotton areas in Xinjiang,China

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International Journal of Agricultural and Biological Engineering 2022年第5期15卷 9-18页

作者： Guobin Wang Jiahui Wang Pengchao Chen Xiaoqiang Han Shengde Chen Yubin Lan School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255049ShandongChina National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology(NPAAC) Ministry of Science and TechnologyCollege of Electronic EngineeringSouth China Agricultural UniversityGuangzhou 510642China College of Agricultural Shihezi UniversityShihezi 832000XinjiangChina Department of Biological and Agricultural Engineering Texas A&M UniversityCollege StationTX 77845

With the development of Unmanned Aerial Vehicle(UAV)sprayers,the application of low-volume spraying of harvest-aid and other agrochemicals to cotton using UAVs is becoming a new agronomic trend worldwide.The effect of spray volume and canopy density for UAV spraying is significant but was rarely studied.In this study,five representative spray volumes were explored to examine the effect of spray volume on deposition and harvest-aid efficacy for cotton using a UAV sprayer.To explore the effect of canopy density,similar tests were carried out in a field located nearby with a lower leaf area index(LAI).A conventional trailer boom sprayer was selected for comparison.Different spray volumes had a significant effect on defoliation,but had no significant effect on boll opening and fiber quality.A higher defoliation rate was achieved in the lower LAI field.The total rate of defoliation using the UAV was inferior to the boom sprayer in the high LAI field for lower deposition and defoliation rate in the lower canopy.Considering the deposition,defoliation rate,and working efficiency,a spray volume of 15.0 L/hm^(2) with an average droplet size of 150μm is recommended for UAV application.

关键词： UAV droplets deposition spray volume canopy density leaf area index defoliation boll opening rate

Transpiration rates of urban trees, Aesculus chinensis

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Journal of Environmental Sciences 2012年第7期24卷 1278-1287页

作者： Hua Wang Xiaoke Wang Ping Zhao Hua Zheng Yufen Ren Fuyuan Gao Zhiyun Ouyang State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Institute of Forestry and Pomology Beijing Academy of Agriculture and Forestry SciencesBeijing 100093China South China Institute of Botany Chinese Academy of SciencesGuangzhou 510650China Beijing Teaching Botanical Garden Beijing 100061China

Transpiration patterns of Aesculus chinensis in relation to explanatory variables in the microclimatic, air quality, and biological phenomena categories were measured in Beijing, China using the thermal dissipation method. The highest transpiration rate measured as the sap flux density of the trees took place from 10：00 am to 13：00 pm in the summer and the lowest was found during nighttime in the winter. To sort out co-linearity, principal component analysis and variation and hierarchical partitioning methods were employed in data analyses. The evaporative demand index （EDI） consisting of air temperature, soil temperature, total radiation, vapor pressure deficit, and atmospheric ozone （O3）, explained 68% and 80% of the hourly and daily variations of the tree transpiration, respectively. The independent and joint effects of EDI variables together with a three-variable joint effect exerted the greatest influences on the variance of transpiration rates. The independent effects of leaf area index and atmospheric 03 and their combined effect exhibited minor yet significant influences on tree transpiration rates.

关键词： horse chestnut sap flux density microclimate air pollutants leaf area index

在显著变绿的地区叶面积指数控制总初级生产力的变化

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Journal of Geographical Sciences 2023年第8期33卷 1747-1764页

作者：陈鑫蔡安宁郭仁杰梁传壮李莹莹 School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China School of Tourism and Social Management Nanjing Xiaozhuang UniversityNanjing 211171China School of Geographical Sciences Nanjing Normal UniversityNanjing 210024China

The leaf area index(LAI)shows a significant increasing trend from global to regional scales,which is known as greening.Greening will further enhance photosynthesis,but it is unclear whether the contribution of greening has exceeded the CO_(2)fertilization effect and become the dominant factor in the gross primary productivity(GPP)variation.We took the Yangtze River Delta(YRD)of China,where cropland and natural vegetation are significantly greening,as an example.Based on the boreal ecosystem productivity simulator(BEPS)and Revised-EC-LUE models,the GPP in the YRD from 2001 to 2020 was simulated,and attribution analysis of the interannual variation in GPP was performed.In addition,the reliability of the GPP simulated by the dynamic global vegetation model(DGVM)in the area was further investigated.The research results showed that GPP in the YRD had three significant characteristics consistent with LAI:(1)GPP showed a significant increasing trend;(2)the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP;and(3)cropland GPP showed double-high peak characteristics.The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation(4.29 Tg C yr^(-1)for BEPS and 2.73 Tg C yr^(-1)for the Revised-EC-LUE model)determined the interannual variation in GPP,which was much higher than the CO_(2)fertilization effect(2.29 Tg C yr^(-1)for BEPS and 0.67 Tg C yr^(-1)for the Revised-EC-LUE model).The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models.The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon.Our study highlights that in significant greening areas,LAI has dominated GPP variation,both spatially and temporally,and DGVM can correctly simulate GPP only if it accurately simulates LAI variation.

关键词： leaf area index gross primary productivity dynamic global vegetation model Yangtze River Delta

Understanding the multi-seasonal spectral and biophysical characteristics of reedbed habitats in the UK

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Geo-Spatial Information Science 2016年第4期19卷 233-244页

作者： Alex O.Onojeghuo George A.Blackburn Department of Surveying and Geoinformatics Nnamdi Azikiwe UniversityAwkaNigeria Lancaster Environment Centre Lancaster UniversityLancasterUK

Reedbed in the UK has been classified as priority habitat for most regional Biodiversity Partnerships.However,critical information pertaining to the quality and spatial coverage of reedbed is currently lacking.This paper presents the results of a project conducted in collaboration with the Cumbria Wildlife Trust and Environment Agency aimed at monitoring and understanding variations in the spectral reflectance and biophysical properties of reedbed canopies across Leighton Moss Nature Reserve in Lancashire,northwest England.Throughout the seasonal phenological cycle of the reedbed habitats in the study area,optimal spectral indices required for quantifying its biophysical properties were determined using field spectroscopy and ground-based measurements.Results of the experiment showed that the narrow-bandderived Difference Vegetation index(DVI)and Renormalised Difference Vegetation index(RDVI),with the correlation coefficient R2 of 0.77 and 0.72,respectively,provided the most accurate estimates of the leaf area index for the reedbed canopies.

关键词： Vegetation index leaf area index biophysical measures

Spatial and temporal variation of global LAI during 1981-2006

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Journal of Geographical Sciences 2010年第3期20卷 323-332页

作者： LIU Siliang LIU Ronggao LIU Yang Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

Earth is always changing. Knowledge about where changes happened is the first step for us to understand how these changes affect our lives. In this paper, we use a long-term leaf area index data （LAI） to identify where changes happened and where has experienced the strongest change around the globe during 1981-2006. Results show that, over the past 26 years, LAI has generally increased at a rate of 0.0013 per year around the globe. The strongest increasing trend is around 0.0032 per year in the middle and northern high latitudes （north of 30°N）. LAI has prominently increased in Europe, Siberia, Indian Peninsula, America and south Canada, South region of Sahara, southwest corner of Australia and Kgalagadi Basin; while noticeably decreased in Southeast Asia, southeastern China, central Africa, central and southern South America and arctic areas in North America.

关键词： global change leaf area index spatiotemporal variation hot-spot areas

Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest

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International Journal of Digital Earth 2021年第1期14卷 106-120页

作者： Abebe Mohammed Ali Roshanak Darvishzadeh Andrew Skidmore Tawanda W.Gara Marco Heurich Faculty of Geo-Information Science and Earth Observation(ITC) University of TwenteEnschedeThe Netherlands Department of Geography and Environmental Studies Wollo UniversityDessieEthiopia Department of Environmental Science Macquarie UniversitySydneyAustralia Department of Geography and Environmental Science University of ZimbabweHarareZimbabwe Department of Visitor Management and National Park Monitoring Bavarian Forest National ParkGrafenauGermany Chair of Wildlife Ecology and Wildlife Management University of FreiburgFreiburgGermany

Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies.Here,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate forest.The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected contemporaneously.The RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function.The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

关键词： leaf area index leaf/canopy chlorophyll content radiative transfer model look-up table machine learning algorithms