English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Accurate global gridded estimates of evapotranspiration (ET) are keyto understanding water and energy budgets, in addition to being requiredfor model evaluation. Several gridded ET products have already beendeveloped which differ in their data requirements, the approachesused to derive them and their estimates, yet it is not clear whichprovides the most reliable estimates. This paper presents a newglobal ET dataset and associated uncertainty with monthly temporalresolution for 2000–2009. Six existing gridded ET products arecombined using a weighting approach trained by observationaldatasets from 159 FLUXNET sites. The weighting method is based ona technique that provides an analytically optimal linear combinationof ET products compared to site data and accounts for both theperformance differences and error covariance between theparticipating ET products. We examine the performance of theweighting approach in several in-sample and out-of-sample tests thatconfirm that point-based estimates of flux towers provideinformation on the grid scale of these products. We also provideevidence that the weighted product performs better than its sixconstituent ET product members in four common metrics. Uncertaintyin the ET estimate is derived by rescaling the spread ofparticipating ET products so that their spread reflects the abilityof the weighted mean estimate to match flux tower data. While issuesin observational data and any common biases in participating ETdatasets are limitations to the success of this approach, futuredatasets can easily be incorporated and enhance the derived product.

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