Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

Hobeichi, Sanaa; Abramowitz, Gab; Evans, Jason; Ukkola, Anna

doi:10.5194/hess-22-1317-2018

Title: Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

Journal Article · Wed Feb 21 00:00:00 EST 2018 · Hydrology and Earth System Sciences (Online)

DOI:https://doi.org/10.5194/hess-22-1317-2018· OSTI ID:1511441

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Univ. of New South Wales, Sydney, NSW (Australia)

Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

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Research Organization:: Oregon State Univ., Corvallis, OR (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: FG02-04ER63911; FG02-04ER63917

OSTI ID:: 1511441

Journal Information:: Hydrology and Earth System Sciences (Online), Vol. 22, Issue 2; ISSN 1607-7938

Publisher:: European Geosciences Union (EGU)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 34 works

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