Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing

. 2023 Sep 07 ; 10 (1) : 587. [epub] 20230907

Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu dataset, časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid37679357
Odkazy

PubMed 37679357
PubMed Central PMC10485062
DOI 10.1038/s41597-023-02473-9
PII: 10.1038/s41597-023-02473-9
Knihovny.cz E-zdroje

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

A N Severtsov Institute of Ecology and Evolution Russian Academy of Sciences 119071 Leninsky pr 33 Moscow Russia

Agrosphere Institute IBG 3 Forschungszentrum Jülich Jülich 52425 Germany

Center Agriculture Food Environment University of Trento Via Edmund Mach 1 1 38010 Trento San Michele all'Adige Italy

CESBIO Université de Toulouse CNES CNRS INRAE IRD UPS Toulouse France

Climate Change Dept Environmental Protection Agency of Aosta Valley Saint Christophe 1 11020 Italy

Climate System Research Finnish Meteorological Institute P O Box 503 FI 00101 Helsinki Finland

College of Resources and Environment University of Chinese Academy of Sciences Beijing 100049 China

Departement of Earth Sciences Gothenburg University Guldhedsgatan 5A Po Box 460 SE 405 30 Gothenburg Sweden

Department of Agroecology Aarhus University Tjele Denmark

Department of Biology University of Antwerp Wilrijk 2610 Belgium

Department of Computer Vision and Remote Sensing Technische Universität Berlin 10587 Berlin Germany

Department of Ecology and Genetics Limnology Uppsala University Norbyvägen 18 D 752 36 Uppsala Sweden

Department of Environmental and Biological Sciences University of Eastern Finland Joensuu campus P O Box 111 Joensuu FI 80101 Finland

Department of Environmental Engineering Technical University of Denmark Kgs Lyngby 2800 Denmark

Department of Environmental Systems Science Institute of Agricultural Sciences ETH Zürich 8092 Zürich Switzerland

Department of Geography and Environmental Management University of Port Harcourt PMB 5323 Choba East West Port Harcourt Nigeria

Department of Geography Ghent University Ghent 9000 Belgium

Department of Geosciences and Natural Resource Management University of Copenhagen Oester Voldgade 10 1350 Copenhagen K Denmark

Department of Matter and Energy Fluxes Global Change Research Institute CAS Bělidla 986 4a CZ 603 00 Brno Czech Republic

Earth and Life Institute Université Catholique de Louvain 1348 Louvain la Neuve Belgium

ECOSYS INRAE AgroParisTech Université Paris Saclay 22 place de l'agronomie 91120 Palaiseau France

Eurac research Institute for Alpine Environment Viale Druso 1 39100 Bolzano Italy

European Commission Joint Research Centre Ispra Italy

Fundación CEAM Parque Tecnológico C Charles R Darwin 14 Paterna 46980 Spain

GFZ German Research Centre for Geosciences Telegrafenberg 14473 Potsdam Germany

Grassland Ecosystem Research INRAE VetAgro Sup University of Clermont Auvergne 5 Chemin de Beaulieu 63000 Clermont Ferrand France

Institut für Ökologie Universität Innsbruck Innrain 52 6020 Innsbruck Austria

Institute for Biological Problems of Cryolithozone Siberian Branch of the Russian Academy of Sciences Yakutsk Russia

Institute of Ecology and School of Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China

Institute of Hydrology and Meteorology TUD Dresden University of Technology Pienner Str 23 01737 Tharandt Germany

Laboratory of Bioclimatology Department of Ecology and Environmental Protection Faculty of Environmental and Mechanical Engineering Poznan University of Life Sciences Piatkowska 94 60 649 Poznan Poland

Laboratory of Forest Hydrology Graduate School of Agriculture Kyoto University 606 8502 Kyoto Japan

Laboratory of Meteorology Department of Construction and Geoengineering Faculty of Environmental and Mechanical Engineering Poznan University of Life Sciences Piatkowska 94 60 649 Poznan Poland

Lhasa Station Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China

National Agriculture and Food Research Organization Tsukuba Ibaraki 305 8517 Japan

National Hulunber Grassland Ecosystem Observation and Research Station and Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China

National Research Council of Italy Institute for Agriculture and Forestry Systems in the Mediterranean Portici Naples Italy

Natural Resources Institute Finland Bioeconomy and environment 00790 Helsinki Finland

Natural Resources Institute Finland Joensuu Yliopistokatu 6 FI 80130 Joensuu Finland

Northwest Institute of Plateau Biology Chinese Academy of Sciences Qinghai Xining 810008 China

Research Faculty of Agriculture Hokkaido University Sapporo Hokkaido 060 8589 Japan

Research Institute for Nature and Forest Geraardsbergen 9500 Belgium

School of Earth Sciences and Engineering Hohai University Nanjing 211100 China

School of Engineering and Architecture University College Cork College Road Cork T12 K8AF Republic of Ireland

School of Forest Sciences University of Eastern Finland P O Box 111 FIN 80100 Joensuu Finland

School of Geographical Sciences Nanjing University of Information Science and Technology Nanjing 210044 China

School of Life Science Shanxi University Taiyuan 030006 China

School of life sciences Qufu Normal University 57 Jingxuan West Road Qufu 273165 Shandong China

School of Natural Sciences Botany Discipline Trinity College Dublin D2 Dublin Ireland

Sino Belgian Joint Laboratory for Geo Information Ghent Belgium

Sino Belgian Joint Laboratory for Geo Information Urumqi China

Sol Agro et hydrosystèmes Spatialisation UMR 1069 INRAE Institut Agro 35000 Rennes France

State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi Xinjiang 830011 China

State Key Laboratory of Vegetation and Environmental Change Institute of Botany Chinese Academy of Sciences Beijing 100093 China

Teagasc Johnstown Castle Wexford Ireland

Terra Teaching and Research Center University of Liège Gembloux Agro Bio Tech 5030 Gembloux Belgium

The National Key Laboratory of Ecological Security and Sustainable Development in Arid Region Chinese Academy of Sciences Urumqi China

Thünen Institute of Climate Smart Agriculture 38116 Braunschweig Germany

UK Centre for Ecology and Hydrology Bush Estate Penicuik EH26 0QB UK

University of Helsinki Institute for Atmospheric and Earth System Research Physics Faculty of Science Helsinki Finland

University of Padova DAFNAE Viale dell'Università 16 1 35020 Padova Legnaro Italy

Wageningen Univertsity Water Systems and Global change group PO bx 47 7700AA Wageningen Netherlands

Zobrazit více v PubMed

Jung M, et al. Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences. 2020;17:1343–1365.

Ciais P, et al. Five decades of northern land carbon uptake revealed by the interhemispheric CO2 gradient. Nature. 2019;568:221–225. PubMed

Jung M, et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data. 2019;6:1–14. PubMed PMC

Jung M, et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature. 2017;541:516–520. PubMed

Wang R, et al. Recent increase in the observation-derived land evapotranspiration due to global warming. Environ. Res. Lett. 2022;17:024020.

Reichstein M, et al. Deep learning and process understanding for data-driven Earth system science. Nature. 2019;566:195–204. PubMed

Li X, et al. Intercomparison of six upscaling evapotranspiration methods: From site to the satellite pixel. J. Geophys. Res.-Atmos. 2018;123:6777–6803.

Shi H, et al. Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis. Hydrol. Earth Syst. Sci. 2022;26:4603–4618.

Shi H, et al. Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation. Biogeosciences. 2022;19:3739–3756.

Xu T, et al. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. J. Geophys. Res.-Atmos. 2018;123:8674–8690.

Bzdok D, Nichols TE, Smith SM. Towards algorithmic analytics for large-scale datasets. Nat. Mach. Intell. 2019;1:296–306. PubMed PMC

Fatima M, Pasha M. Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 2017;9:1.

Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature. 2015;521:452–459. PubMed

Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349:255–260. PubMed

Zhu AX, Lu G, Liu J, Qin CZ, Zhou C. Spatial prediction based on Third Law of Geography. Ann. GIS. 2018;24:225–240.

Xie M. 2023. Flux station information. figshare. DOI

Che T, Xu Z, Ren Z, Tan J, Zhang Y. 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Zhangye wetland station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Huazhaizi desert steppe station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Huazhaizi station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Sidaoqiao superstation, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Zhangye wetland station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Sidaoqiao superstation, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Zhangye wetland station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, Che T, Xu Z, Ren Z, Zhang Y. 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Zhangye wetland station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of A’rou Superstation, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou Superstation, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Dashalong station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Dashalong station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Jingyangling station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Jingyangling station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Yakou station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Yakou station, 2019) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of A’rou Superstation, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou Superstation, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Jingyangling station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Jingyangling station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Yakou station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Yakou station, 2020) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Dashalong station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of Hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydro-meteorological observation network (automatic weather station of Huazhaizi Desert Steppe Station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Huazhaizi desert steppe station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert Station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of Sidaoqiao superstation, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of Sidaoqiao Superstation, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland station, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Huazhaizi desert steppe station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of Sidaoqiao Superstation, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Huazhaizi desert steppe station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of Hydrometeorological observation network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (automatic weather station of Huazhaizi desert steppe station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Huazhaizi station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (automatic weather station of Jingyangling station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of meteorological elements gradient of Sidaoqiao superstation, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Sidaoqiao superstation, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Zhangye wetland station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Zhangye wetland station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of A’rou Superstation, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of A’rou superstation, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of A’rou Superstation, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of A’rou Superstation, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (Dashalong automatic meteorological station, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Dashalong station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Dashalong station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of Hydrometeorological observation network (eddy covariance system of Dashalong station, 2013) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Yakou station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Yakou station, 2015) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydro-meteorological observation network (automatic weather station of Dashalong station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Yakou station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Yakou station, 2016) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of A’rou Superstation, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of A’rou Superstation, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydro-meteorological observation network (automatic weather station of Dashalong station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Yakou station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Yakou station, 2017) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of meteorological elements gradient of A’rou Superstation, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou superstation, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Dashalong station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Jingyangling station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Yakou station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Yakou station, 2018) National Tibetan Plateau Data Center. DOI

Liu S, Li X, Xu Z. 2016. HiWATER: Dataset of flux observation matrix (automatic meteorological station of No.1) of the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12) National Tibetan Plateau Data Center. DOI

Liu S, Li X, Xu Z. 2016. HiWATER: The multi-scale observation experiment on evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12)-dataset of flux observation matrix (No.1 eddy covariance system) National Tibetan Plateau Data Center. DOI

Liu S, Li X, Xu Z. 2016. HiWATER: Dataset of flux observation matrix (automatic meteorological station of No.17) of the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12) National Tibetan Plateau Data Center. DOI

Liu S, Li X, Xu Z. 2016. HiWATER: The multi-scale observation experiment on evapotranspiration over heterogeneous land surfaces (MUSOEXE-12)-dataset of flux observation matrix (No.17 eddy covariance system) from Mar to Sep, 2012. National Tibetan Plateau Data Center. DOI

Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2014) National Tibetan Plateau Data Center. DOI

Liu S, Xu Z. 2016. Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Daxing site-automatic weather station) (2008-2010) National Tibetan Plateau Data Center. DOI

Liu S, Xu Z. 2016. Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Daxing site - eddy covariance system) (2008-2010) National Tibetan Plateau Data Center. DOI

Che T, et al. Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China. Earth Syst. Sci. Data. 2019;11:1483–1499.

Jia Z, Liu S, Xu Z, Chen Y, Zhu M. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. J. Geophys. Res.-Atmos. 2012;117:D13113.

Liu S, et al. The heihe integrated observatory network: a basin‐scale land surface processes observatory in China. Vadose Zone J. 2018;17:1–21.

Liu S, et al. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agric. For. Meteorol. 2016;230-231:97–113.

Liu S, et al. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrol. Earth Syst. Sci. 2011;15:1291–1306.

Liu S, Xu Z, Zhu Z, Jia Z, Zhu M. Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China. J. Hydrol. 2013;487:24–38.

Xu Z, et al. Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE. J. Geophys. Res.-Atmos. 2013;118:13140–13157.

Dušek J, Faußer A, Stellner S, Kazda M. Stems of Phragmites australis are buffering methane and carbon dioxide emissions. Sci. Total Environ. 2023;882:163493. PubMed

Foltýnová L, Fischer M, McGloin RP. Recommendations for gap-filling eddy covariance latent heat flux measurements using marginal distribution sampling. Theor. Appl. Climatol. 2020;139:677–688.

Granier A, Bréda N, Longdoz B, Gross P, Ngao J. Ten years of fluxes and stand growth in a young beech forest at Hesse, North-eastern France. Ann. For. Sci. 2008;65:704.

Kivalov SN, et al. Addressing effects of environment on eddy-covariance flux estimates at a Temperate Sedge-Grass Marsh. Bound.-Layer Meteor. 2023;186:217–250.

Wutzler T, et al. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences. 2018;15:5015–5030.

Aurela M, Laurila T, Hatakka J, Tuovinen J-P, Rainne J. 2016. FLUXNET2015 RU-Tks Tiksi. FLUXNET. DOI

Bernhofer C, 2016. FLUXNET2015 DE-Spw Spreewald. FLUXNET. DOI

Bernhofer C, 2016. FLUXNET2015 DE-Tha Tharandt. FLUXNET. DOI

Chen S. 2016. FLUXNET2015 CN-Du2 Duolun_grassland. FLUXNET. DOI

Dong G. 2016. FLUXNET2015 CN-Cng Changling. FLUXNET. DOI

Ibrom A, Pilegaard K. 2016. FLUXNET2015 DK-Sor Soroe. FLUXNET. DOI

Klatt J, Schmid H, Mauder M, Steinbrecher R. 2016. FLUXNET2015 DE-SfN Schechenfilz Nord. FLUXNET. DOI

Kosugi Y, Takanashi S. 2016. FLUXNET2015 MY-PSO Pasoh Forest Reserve (PSO) FLUXNET. DOI

Kotani A. 2016. FLUXNET2015 JP-MBF Moshiri Birch Forest Site. FLUXNET. DOI

Kotani A. 2016. FLUXNET2015 JP-SMF Seto Mixed Forest Site. FLUXNET. DOI

Li Y. 2016. FLUXNET2015 CN-Ha2 Haibei Shrubland. FLUXNET. DOI

Maximov T. 2016. FLUXNET2015 RU-SkP Yakutsk Spasskaya Pad larch. FLUXNET. DOI

Olesen J. 2016. FLUXNET2015 DK-Fou Foulum. FLUXNET. DOI

Pastorello G, et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data. 2020;7:225. doi: 10.1038/s41597-020-0534-3. PubMed DOI PMC

Pilegaard K, Ibrom A. 2016. FLUXNET2015 DK-Eng Enghave. FLUXNET. DOI

Sachs T, Wille C, Larmanou E, Franz D. 2016. FLUXNET2015 DE-Zrk Zarnekow. FLUXNET. DOI

Schneider K, Schmidt M. 2016. FLUXNET2015 DE-Seh Selhausen. FLUXNET. DOI

Shao C. 2016. FLUXNET2015 CN-Du3 Duolun Degraded Meadow. FLUXNET. DOI

Shao C. 2016. FLUXNET2015 CN-Sw2 Siziwang Grazed (SZWG) FLUXNET. DOI

Shi P, Zhang X, He Y. 2016. FLUXNET2015 CN-Dan Dangxiong. FLUXNET. DOI

Tang Y, Kato T, Du M. 2016. FLUXNET2015 CN-HaM Haibei Alpine Tibet site. FLUXNET. DOI

Wang H, Fu X. 2016. FLUXNET2015 CN-Qia Qianyanzhou. FLUXNET. DOI

Zhang J, Han S. 2016. FLUXNET2015 CN-Cha Changbaishan. FLUXNET. DOI

Zhou G, Yan J. 2016. FLUXNET2015 CN-Din Dinghushan. FLUXNET. DOI

Anapalli SS, et al. Quantifying soybean evapotranspiration using an eddy covariance approach. Agric. Water Manage. 2018;209:228–239.

Xie M, et al. Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China. Hydrol. Process. 2021;35:e14444.

Zhang C, et al. A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach. J. Hydrol. 2021;603:127047.

Liang S, et al. The global land surface satellite (GLASS) product suite. Bull. Amer. Meteorol. Soc. 2020;102:1–37.

Zhang X, Liang S, Zhou G, Wu H, Zhao X. Generating Global Land Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sens. Environ. 2014;152:318–332.

Tang W. 2019. Dataset of high-resolution (3 hour, 10 km) global surface solar radiation (1983–2018) National Tibetan Plateau Data Center. https://cstr.cn/18406.11.Meteoro.tpdc.270112

Tang W, Yang K, Qin J, Li X, Niu X. A 16-year dataset (2000-2015) of high-resolution (3 h, 10 km) global surface solar radiation. Earth Syst. Sci. Data. 2019;11:1905–1915.

Myneni R, Knyazikhin Y, Park T. 2015. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI

Vermote E, Wolfe R. 2015. MOD09GA MODIS/Terra Surface Reflectance Daily L2G Global 1kmand 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI

Fang B, Lei H, Zhang Y, Quan Q, Yang D. Spatio-temporal patterns of evapotranspiration based on upscaling eddy covariance measurements in the dryland of the North China Plain. Agric. For. Meteorol. 2020;281:107844.

Yamazaki D, et al. A high-accuracy map of global terrain elevations. Geophys. Res. Lett. 2017;44:5844–5853.

FAO/IIASA/ISRIC/ISSCAS/JRC. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austriahttps://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML (2012).

Friedl M, Sulla-Menashe D. 2019. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI

Sorensen, L. A spatial analysis approach to the global delineation of dryland areas of relevance to the CBD Programme of Work on Dry and Subhumid Lands. UNEP-WCMChttps://www2.unep-wcmc.org/resources-and-data/a-spatial-analysis-approach-to-the-global-delineation-of-dryland-areas-of-relevance-to-the-cbd-programme-of-work-on-dry-and-subhumid-lands (2007).

Xie M. 2023. Division of flux stations. figshare. DOI

Lopatin J, Dolos K, Hernández H, Galleguillos M, Fassnacht F. Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sens. Environ. 2016;173:200–210.

Biau G. Analysis of a random forests model. J. Mach. Learn. Res. 2012;13:1063–1095.

Bergstra J, Komer B, Eliasmith C, Yamins D, Cox DD. Hyperopt: a python library for model selection and hyperparameter optimization. Comput. Sci. Discov. 2015;8:014008.

Ploton P, et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 2020;11:1–11. PubMed PMC

Patel SP, Upadhyay SH. Euclidean distance based feature ranking and subset selection for bearing fault diagnosis. Expert Syst. Appl. 2020;154:113400.

Lever J, Krzywinski M, Altman N. Points of significance: Principal component analysis. Nat. Methods. 2017;14:641–643.

Xie M. 2022. Carbon-water flux datasets of Eurasian meteorological stations. figshare. DOI

Xie M. 2023. Meteorological station information. figshare. DOI

Xie M. 2023. RSMs information. figshare. DOI

Jiang F, et al. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO 2 retrievals (GCAS2021) Earth Syst. Sci. Data. 2022;14:3013–3037.

Running S, Mu Q, Zhao M. 2017. MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI

Xie M. 2022. Code for mining carbon-water flux information at Eurasian meteorological stations. figshare. DOI

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