Synthesis of global actual evapotranspiration from 1982 to 2019
As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets avail...
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Published in | Earth system science data Vol. 13; no. 2; pp. 447 - 480 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Katlenburg-Lindau
Copernicus GmbH
16.02.2021
Copernicus Publications |
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Abstract | As a linkage among water, energy, and carbon cycles,
global actual evapotranspiration (ET) plays an essential role in
agriculture, water resource management, and climate change. Although it is
difficult to estimate ET over a large scale and for a long time, there are
several global ET datasets available with uncertainty associated with
various assumptions regarding their algorithms, parameters, and inputs. In
this study, we propose a long-term synthesized ET product at a kilometer
spatial resolution and monthly temporal resolution from 1982 to 2019.
Through a site-pixel evaluation of 12 global ET products over different time
periods, land surface types, and conditions, the high-performing products
were selected for the synthesis of the new dataset using a high-quality flux
eddy covariance (EC) covering the entire globe. According to the study results,
Penman–Monteith–Leuning (PML), the operational Simplified Surface Energy Balance
(SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105),
and the Numerical Terradynamic Simulation Group (NTSG) ET products were
chosen to create the synthesized ET set. The proposed product agreed well
with flux EC ET over most of the all comparison levels, with a maximum relative mean error
(RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm
(47.45 %). Furthermore, the product performed better than local ET
products over China, the United States, and the African continent and
presented an ET estimation across all land cover classes. While no product
can perform best in all cases, the proposed ET can be used without looking
at other datasets and performing further assessments. Data are available on
the Harvard Dataverse public repository through the following Digital Object
Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED
(Elnashar et al., 2020), as well as on the Google Earth
Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021). |
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AbstractList | As a linkage among water, energy, and carbon cycles,
global actual evapotranspiration (ET) plays an essential role in
agriculture, water resource management, and climate change. Although it is
difficult to estimate ET over a large scale and for a long time, there are
several global ET datasets available with uncertainty associated with
various assumptions regarding their algorithms, parameters, and inputs. In
this study, we propose a long-term synthesized ET product at a kilometer
spatial resolution and monthly temporal resolution from 1982 to 2019.
Through a site-pixel evaluation of 12 global ET products over different time
periods, land surface types, and conditions, the high-performing products
were selected for the synthesis of the new dataset using a high-quality flux
eddy covariance (EC) covering the entire globe. According to the study results,
Penman–Monteith–Leuning (PML), the operational Simplified Surface Energy Balance
(SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105),
and the Numerical Terradynamic Simulation Group (NTSG) ET products were
chosen to create the synthesized ET set. The proposed product agreed well
with flux EC ET over most of the all comparison levels, with a maximum relative mean error
(RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm
(47.45 %). Furthermore, the product performed better than local ET
products over China, the United States, and the African continent and
presented an ET estimation across all land cover classes. While no product
can perform best in all cases, the proposed ET can be used without looking
at other datasets and performing further assessments. Data are available on
the Harvard Dataverse public repository through the following Digital Object
Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED
(Elnashar et al., 2020), as well as on the Google Earth
Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021). As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with uncertainty associated with various assumptions regarding their algorithms, parameters, and inputs. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel evaluation of 12 global ET products over different time periods, land surface types, and conditions, the high-performing products were selected for the synthesis of the new dataset using a high-quality flux eddy covariance (EC) covering the entire globe. According to the study results, Penman–Monteith–Leuning (PML), the operational Simplified Surface Energy Balance (SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105), and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum relative mean error (RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED (Elnashar et al., 2020), as well as on the Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021). As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with uncertainty associated with various assumptions regarding their algorithms, parameters, and inputs. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel evaluation of 12 global ET products over different time periods, land surface types, and conditions, the high-performing products were selected for the synthesis of the new dataset using a high-quality flux eddy covariance (EC) covering the entire globe. According to the study results, Penman–Monteith–Leuning (PML), the operational Simplified Surface Energy Balance (SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105), and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum relative mean error (RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): 10.7910/DVN/ZGOUED (Elnashar et al., 2020), as well as on the Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021). As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with uncertainty associated with various assumptions regarding their algorithms, parameters, and inputs. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel evaluation of 12 global ET products over different time periods, land surface types, and conditions, the high-performing products were selected for the synthesis of the new dataset using a high-quality flux eddy covariance (EC) covering the entire globe. According to the study results, Penman-Monteith-Leuning (PML), the operational Simplified Surface Energy Balance (SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105), and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum relative mean error (RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): |
Audience | Academic |
Author | Wu, Bingfang Elnashar, Abdelrazek Zeng, Hongwei Zhu, Weiwei Wang, Linjiang |
Author_xml | – sequence: 1 givenname: Abdelrazek orcidid: 0000-0001-8008-5670 surname: Elnashar fullname: Elnashar, Abdelrazek – sequence: 2 givenname: Linjiang surname: Wang fullname: Wang, Linjiang – sequence: 3 givenname: Bingfang surname: Wu fullname: Wu, Bingfang – sequence: 4 givenname: Weiwei surname: Zhu fullname: Zhu, Weiwei – sequence: 5 givenname: Hongwei surname: Zeng fullname: Zeng, Hongwei |
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Snippet | As a linkage among water, energy, and carbon cycles,
global actual evapotranspiration (ET) plays an essential role in
agriculture, water resource management,... As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management,... |
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SubjectTerms | Agriculture Algorithms Basins Carbon cycle Climate change Climatic changes Covariance Data assimilation Datasets Eddy covariance Energy Energy balance Evapotranspiration Evapotranspiration estimates Force and energy Hydrology Land cover Management Mathematical models Parameter uncertainty Precipitation Remote sensing Resolution Resource management Simplified surfaces Spatial discrimination Spatial resolution Spectroradiometers Surface energy Surface energy balance Surface properties Synthesis Temporal resolution Water Water resources Water resources management |
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Title | Synthesis of global actual evapotranspiration from 1982 to 2019 |
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