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Title

Multicriteria evaluation of discharge simulation in Dynamic Global Vegetation Models

Publication Year

2015

Author(s)
  • Yang, Hui
  • Piao, Shilong
  • Zeng, Zhenzhong
  • Ciais, Philippe
  • Yin, Yi
  • Friedlingstein, Pierre
  • Sitch, Stephen
  • Ahlstrom, Anders
  • Guimberteau, Matthieu
  • Huntingford, Chris
  • Levis, Sam
  • Levy, Peter E.
  • Huang, Mengtian
  • Li, Yue
  • Li, Xiran
  • Lomas, Mark R.
  • Peylin, Philippe
  • Poulter, Ben
  • Viovy, Nicolas
  • Zaehle, Soenke
  • Zeng, Ning
  • Zhao, Fang
  • Wang, Lei
Source
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES Volume: 120 Issue: 15 Pages: 7488-7505 Published: 2015
ISSN
2169-897X eISSN: 2169-8996
Abstract

In this study, we assessed the performance of discharge simulations by coupling the runoff from seven Dynamic Global Vegetation Models (DGVMs; LPJ, ORCHIDEE, Sheffield-DGVM, TRIFFID, LPJ-GUESS, CLM4CN, and OCN) to one river routing model for 16 large river basins. The results show that the seasonal cycle of river discharge is generally modeled well in the low and middle latitudes but not in the high latitudes, where the peak discharge (due to snow and ice melting) is underestimated. For the annual mean discharge, the DGVMs chained with the routing model show an underestimation. Furthermore, the 30year trend of discharge is also underestimated. For the interannual variability of discharge, a skill score based on overlapping of probability density functions (PDFs) suggests that most models correctly reproduce the observed variability (correlation coefficient higher than 0.5; i.e., models account for 50% of observed interannual variability) except for the Lena, Yenisei, Yukon, and the Congo river basins. In addition, we compared the simulated runoff from different simulations where models were forced with either fixed or varying land use. This suggests that both seasonal and annual mean runoff has been little affected by land use change but that the trend itself of runoff is sensitive to land use change. None of the models when considered individually show significantly better performances than any other and in all basins. This suggests that based on current modeling capability, a regional-weighted average of multimodel ensemble projections might be appropriate to reduce the bias in future projection of global river discharge.

Author Keyword(s)
  • river discharge
  • model evaluation
  • land use change
  • climate change impacts
KeyWord(s) Plus
  • LAND-SURFACE MODEL
  • CLIMATE-CHANGE
  • RIVER DISCHARGE
  • WATER AVAILABILITY
  • PLANT GEOGRAPHY
  • CARBON-DIOXIDE
  • RUNOFF
  • HYDROLOGY
  • CO2
  • PRECIPITATION
ESI Discipline(s)
  • Geosciences
Web of Science Category(ies)
  • Meteorology & Atmospheric Sciences
Adress(es)

[Yang, Hui; Piao, Shilong; Zeng, Zhenzhong; Ciais, Philippe; Huang, Mengtian; Li, Yue; Li, Xiran] Peking Univ, Coll Urban & Environm Sci, Sino French Inst Earth Syst Sci, Beijing 100871, Peoples R China; [Piao, Shilong; Wang, Lei] Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing, Peoples R China; [Ciais, Philippe; Yin, Yi; Guimberteau, Matthieu; Peylin, Philippe; Viovy, Nicolas] CEA, CNRS, UVSQ, Lab Sci Climat & Environm, F-91198 Gif Sur Yvette, France; [Friedlingstein, Pierre; Sitch, Stephen] Univ Exeter, Coll Engn Comp & Math, Exeter, Devon, England; [Ahlstrom, Anders] Stanford Univ, Dept Earth Syst Sci, Sch Earth Energy & Environm Sci, Stanford, CA 94305 USA; [Ahlstrom, Anders] Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden; [Huntingford, Chris] Ctr Ecol & Hydrol, Wallingford, Oxon, England; [Levis, Sam] Natl Ctr Atmospher Res, Boulder, CO 80307 USA; [Levis, Sam] Climate Corp, San Francisco, CA USA; [Levy, Peter E.] Ctr Ecol & Hydrol, Penicuik, Midlothian, Scotland; [Lomas, Mark R.] Univ Sheffield, Dept Anim & Plant Sci, Sheffield S10 2TN, S Yorkshire, England; [Poulter, Ben] Montana State Univ, Dept Ecol, Bozeman, MT 59717 USA; [Zaehle, Soenke] Max Planck Inst Biogeochem, D-07745 Jena, Germany; [Zeng, Ning; Zhao, Fang] Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA

Reprint Adress

Piao, SL (reprint author), Peking Univ, Coll Urban & Environm Sci, Sino French Inst Earth Syst Sci, Beijing 100871, Peoples R China.

Country(ies)
  • France
  • Germany
  • People's Republic of China
  • Sweden
  • United Kingdom
  • United States
CNRS - Adress(es)
  • Laboratoire des sciences du climat et de l'environnement (LSCE), UMR8212
Accession Number
WOS:000360501900012
uid:/Z2GV6K9T
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