Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis

B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P.A. Dirmeyer, A.J. Dolman, J.B. Fisher, M. Jung, F. Ludwig, F. Maignan, D.G. Miralles, Matthew McCabe, M. Reichstein, J. Sheffield, K. Wang, E.F. Wood, Y. Zhang, S.I. Seneviratne

Research output: Contribution to journalArticlepeer-review

291 Scopus citations


Land evapotranspiration (ET) estimates are available from several global data sets.Here, Monthly Global Land et Synthesis Products, Merged from These Individual Data Sets over the Time Periods 1989-1995 (7 Yr) and 1989-2005 (17 Yr), Are Presented. the Merged Synthesis Products over the Shorter Period Are Based on A Total of 40 Distinct Data Sets while Those over the Longer Period Are Based on A Total of 14 Data Sets. in the Individual Data Sets, et Is Derived from Satellite And/or in Situ Observations (Diagnostic Data Sets) or Calculated Via Land-surface Models (LSMs) Driven with Observations-based Forcing or Output from Atmospheric Reanalyses. Statistics for Four Merged Synthesis Products Are Provided, One Including All Data Sets and Three Including only Data Sets from One Category Each (Diagnostic, LSMs, and Reanalyses). the Multi-annual Variations of et in the Merged Synthesis Products Display Realistic Responses. They Are Also Consistent with Previous Findings of A Global Increase in et between 1989 and 1997 (0.13 Mm yr-2 in Our Merged Product) Followed by A Significant Decrease in This Trend (-0.18 Mm yr-2), although These Trends Are Relatively Small Compared to the Uncertainty of Absolute et Values. the Global Mean et from the Merged Synthesis Products (Based on All Data Sets) Is 493 Mm yr-1 (1.35 Mm d-1) for Both the 1989-1995 and 1989-2005 Products, Which Is Relatively Low Compared to Previously Published Estimates. We Estimate Global Runoff (Precipitation Minus ET) to 263 Mm yr -1 (34 406 km3 yr-1) for A Total Land Area of 130 922 000 km2. Precipitation, Being An Important Driving Factor and Input to Most Simulated et Data Sets, Presents Uncertainties between Single Data Sets As Large As Those in the et Estimates. in Order to Reduce Uncertainties in Current et Products, Improving the Accuracy of the Input Variables, Especially Precipitation, As Well As the Parameterizations of ET, Are Crucial. 2013 Author(s).
Original languageEnglish (US)
Pages (from-to)3707-3720
Number of pages14
JournalHydrology and Earth System Sciences
Issue number10
StatePublished - Oct 1 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Water Science and Technology


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