KMS Institute Of Geographic Sciences And Natural Resources Research,CAS
| Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets | |
| Wartenburger,Richard1,37; Seneviratne,Sonia I1; Hirschi,Martin1; Chang,Jinfeng24,30; Ciais,Philippe24; Deryng,Delphine2,3; Elliott,Joshua26; Folberth,Christian9; Gosling,Simon N20; Gudmundsson,Lukas1; Henrot,Alexandra-Jane14; Hickler,Thomas25,29; Ito,Akihiko23; Khabarov,Nikolay4; Kim,Hyungjun22; Leng,Guoyong8; Liu,Junguo4,12; Liu,Xingcai7; Masaki,Yoshimitsu18; Morfopoulos,Catherine28; Müller,Christoph17; Schmied,Hannes Müller5,6; Nishina,Kazuya16; Orth,Rene31,34; Pokhrel,Yadu13; Pugh,Thomas A M10,11; Satoh,Yusuke4; Schaphoff,Sibyll17; Schmid,Erwin19; Sheffield,Justin32,33; Stacke,Tobias15; Steinkamp,Joerg36; Tang,Qiuhong7; Thiery,Wim1,35; Wada,Yoshihide4; Wang,Xuhui24; Weedon,Graham P21; Yang,Hong27; Zhou,Tian8 | |
| 2018-06-21 | |
| Source Publication | Environmental Research Letters
 |
| ISSN | 1748-9326 |
| Volume | 13Issue:7 |
| Abstract | Abstract Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties. |
| Keyword | ISIMIP2a evapotranspiration uncertainty cluster analysis hydrological extreme events |
| DOI | 10.1088/1748-9326/aac4bb |
| Language | 英语 |
| WOS ID | IOP:1748-9326-13-7-aac4bb |
| Publisher | IOP Publishing |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.igsnrr.ac.cn/handle/311030/67775 |
| Collection | 中国科学院地理科学与资源研究所 |
| Affiliation | 1.Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, CH-8092 Zurich, Switzerland 2.Climate Analytics, 10969 Berlin, Germany 3.Columbia University Center for Climate Systems Research, New York, NY 10025, United States of America 4.International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria 5.Institute of Physical Geography, Goethe-University Frankfurt, Frankfurt, Germany 6.Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt, Germany 7.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China 8.Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99352, United States of America 9.Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria 10.School of Geography, Earth & Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, United Kingdom 11.Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany 12.School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen, People’s Republic of China 13.Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824 United States of America 14.Unité de Modèlisation du climat et des Cycles Biogéochimiques, UR SPHERES, Université de Liège, Quartier Agora, Liège, Belgium 15.Max Planck Institute for Meteorology, Hamburg, Germany 16.National Institute for Environmental Studies, Tsukuba, Japan 17.Potsdam Institute for Climate Impact Research (PIK), Telegraphenberg A31, 14473 Potsdam, Germany 18.Hirosaki University, Aomori, Japan 19.University of Natural Resources and Life Sciences, Department of Economics and Social Sciences, Feistmantelstrasse 4, A-1180 Vienna, Austria 20.School of Geography, University of Nottingham, Nottingham NG7 2RD, United Kingdom 21.Met Office (JCHMR), Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, United Kingdom 22.Institute of Industrial Science, The University of Tokyo, Tokyo, Japan 23.National Institute for Environmental Studies, Tsukuba, Japan 24.Laboratoire des Sciences du Climat et de l’Environnement, UMR8212, CEA-CNRS-UVSQ, Gif-sur-Yvette, France 25.Institute of Physical Geography, Geosciences, Goehte University, Frankfurt am Main, Germany 26.The University of Chicago, 5757 S. University Avenue, Chicago IL 60637, United States of America 27.Department of Systems Analysis, Integrated Assessment and Modelling, Eawag, 8600 Dübendorf, Switzerland 28.College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom 29.Senckenberg Biodiversity and Climate Research Centre (BiK-F) & Goethe-University Frankfurt, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany 30.Sorbonne Universités (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, LOCEAN/IPSL, Paris, France 31.Department of Physical Geography, Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden 32.Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, United States of America 33.Geography and Environment, University of Southampton, Southampton, United Kingdom 34.Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, D-07745 Jena, Germany 35.Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium 36.Zentrum für Datenverarbeitung, Johannes Gutenberg-Universit?t Mainz, Germany 37.Author to whom any correspondence should be addressed. |
| Recommended Citation GB/T 7714 | Wartenburger,Richard,Seneviratne,Sonia I,Hirschi,Martin,等. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets[J]. Environmental Research Letters,2018,13(7). |
| APA | Wartenburger,Richard.,Seneviratne,Sonia I.,Hirschi,Martin.,Chang,Jinfeng.,Ciais,Philippe.,...&Zhou,Tian.(2018).Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets.Environmental Research Letters,13(7). |
| MLA | Wartenburger,Richard,et al."Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets".Environmental Research Letters 13.7(2018). |
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