Dataset

Derived Optimal Linear Combination Evapotranspiration - DOLCE v2.0

Also known as: DOLCE_v2.0
CLEX
Sanaa Hobeichi (Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25914/5eab8f533aeae&rft.title=Derived Optimal Linear Combination Evapotranspiration - DOLCE v2.0&rft.identifier=10.25914/5eab8f533aeae&rft.publisher=CLEX&rft.description=DOLCE V2 is a new hybrid Evapotranspiration (ET) dataset derived by merging 11 available global ET datasets. These include BACI, FLUXCOM RS, FLUXCOM MET, ERA5-land, GLEAM v3.3a, GLEAM v3.3b,  PML-CSIRO, PLSH, MOD16, SEBS, and SRB-GEWEX. The contribution of each dataset to DOLCE V2 is based on its ability to match field observations as well as its dependence to the other parent datasets. DOLCE V2 provides time-variant estimates of its uncertainty errors, which are consistent with its agreement with field observations. DOLCE V2 and its previous version DOLCE V1 use the same merging technique and provide monthly ET estimates and associated uncertainties over the global land. There are several improvements implemented in DOLCE V2, these include (DOLCE V2 vs DOLCE V1): Employing a wider range of parent datasets (11 parents vs 6 parent datasets) Incorporating more field observations to constrain the merging technique (260 sites vs 160 sites) Finer spatial resolution (0.25° vs 0.5°). Longer temporal coverage (1980-2018 vs 2000-2009).   Datasets employed to derive DOLCE V2: BACI FLUXCOM RS FLUXCOM MET ERA5-land GLEAM v3.3a GLEAM v3.3b PML-CSIRO PLSH MOD16 SEBS SRB-GEWEX. Field observations from flux tower networks including: FLUXNET2015-tier1 and tier2 LaThuile Free Fair Use  CarboEurope AmeriFlux ARM AsiaFlux Oak Ridge Ozflux   The final output is a monthly, 0.25-degree dataset of terrestrial Evapotranspiration and its error estimates over 1980-2018. The dataset is provided as yearly NETCDF4 files We used RStudio with R version 3.6.1 (2019-07-05) Copyright (C) 2019 The R Foundation for Statistical ComputingPlatform: x86_64-w64-mingw32/x64 (64-bit)   The validity of the script and the outputs were tested by: Visual assessment of the results Comparison with results from similar studies Validation with in-situ observations Using statistical metrics (detailed in the related manuscript).&rft.creator=Sanaa Hobeichi&rft.date=2020&rft.relation=in preparation&rft.coverage=-180.0,-90.0 180.0,-90.0 180.0, 90.0 -180.0, 90.0 -180.0,-90.0&rft_rights=Users are free to share, copy and redistribute the material in any medium or format; adapt, remix, transform, and build upon the material for any purpose, even commercially. Users must give appropriate credit, provide a link to the license, and indicate if changes were made. They may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use. They may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.&rft_rights=Creative Commons - Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/legalcode&rft_subject=Surface Processes&rft_subject=EARTH SCIENCES&rft_subject=PHYSICAL GEOGRAPHY AND ENVIRONMENTAL GEOSCIENCE&rft_subject=Hydrogeology&rft_subject=Hydrology&rft_subject=Climate Change Processes&rft_subject=ATMOSPHERIC SCIENCES&rft_subject=Energy Budget&rft_subject=Water Budget&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
CC-BY

Creative Commons - Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/legalcode

Users are free to share, copy and redistribute the material in any medium or format; adapt, remix, transform, and build upon the material for any purpose, even commercially. Users must give appropriate credit, provide a link to the license, and indicate if changes were made. They may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use. They may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Access:

Open view details

Dataset is available online via NCI thredds catalogue

Full description

DOLCE V2 is a new hybrid Evapotranspiration (ET) dataset derived by merging 11 available global ET datasets. These include BACI, FLUXCOM RS, FLUXCOM MET, ERA5-land, GLEAM v3.3a, GLEAM v3.3b,  PML-CSIRO, PLSH, MOD16, SEBS, and SRB-GEWEX. The contribution of each dataset to DOLCE V2 is based on its ability to match field observations as well as its dependence to the other parent datasets. DOLCE V2 provides time-variant estimates of its uncertainty errors, which are consistent with its agreement with field observations.

DOLCE V2 and its previous version DOLCE V1 use the same merging technique and provide monthly ET estimates and associated uncertainties over the global land. There are several improvements implemented in DOLCE V2, these include (DOLCE V2 vs DOLCE V1):

Employing a wider range of parent datasets (11 parents vs 6 parent datasets)

Incorporating more field observations to constrain the merging technique (260 sites vs 160 sites)

Finer spatial resolution (0.25° vs 0.5°).

Longer temporal coverage (1980-2018 vs 2000-2009).

 

Datasets employed to derive DOLCE V2: BACI

FLUXCOM RS

FLUXCOM MET

ERA5-land

GLEAM v3.3a

GLEAM v3.3b

PML-CSIRO

PLSH

MOD16

SEBS

SRB-GEWEX.

Field observations from flux tower networks including:

FLUXNET2015-tier1 and tier2

LaThuile Free Fair Use 

CarboEurope

AmeriFlux

ARM

AsiaFlux

Oak Ridge

Ozflux

 

The final output is a monthly, 0.25-degree dataset of terrestrial Evapotranspiration and its error estimates over 1980-2018. The dataset is provided as yearly NETCDF4 files

We used RStudio with R version 3.6.1 (2019-07-05) Copyright (C) 2019 The R Foundation for Statistical ComputingPlatform: x86_64-w64-mingw32/x64 (64-bit)

 

The validity of the script and the outputs were tested by:

Visual assessment of the results

Comparison with results from similar studies

Validation with in-situ observations

Using statistical metrics (detailed in the related manuscript).

Created: 2020-04-01

Data time period: 1980-01-01 to 2018-12-31

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Spatial Coverage And Location

kmlPolyCoords: -180.0,-90.0 180.0,-90.0 180.0, 90.0 -180.0, 90.0 -180.0,-90.0

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