Published June 18, 2025
| Version v2
Dataset
Open
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999)
Description
The data contains simulation results from 1975-1999, 25 years total.
You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:
Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245
Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254
Files
Files
(41.3 GB)
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md5:d99c25b09461cec3dee593d1c5d01fc7
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1.7 GB | Download |
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md5:b7d62396cedc671d93851f81abc21759
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1.7 GB | Download |
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md5:3ab6280258983f750fbe4c29b2fd5f8d
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1.7 GB | Download |
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md5:5c314a497ea61257c2e9c8bfd4e31982
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1.6 GB | Download |
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md5:cf4c4dc5b3a124d190deff1cb659db4f
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1.7 GB | Download |
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md5:21983e75e455d1659c2cb2427fe774bc
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1.7 GB | Download |
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md5:4cc8dc439e58d162cde2125e46334bf3
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1.6 GB | Download |
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md5:b2984b64005561dae655120e4798dd70
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1.7 GB | Download |
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md5:932e034ff2213775d7d780ade9f97506
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1.6 GB | Download |
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md5:8fe608b9733df435b8392954e1f06a9e
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1.6 GB | Download |
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md5:bad54bd9f25cc3b5e6a51de8df55400b
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1.6 GB | Download |
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md5:5f709ac978bffcfdd1d24020b3406d69
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1.7 GB | Download |
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md5:cdaeaae16d7688f0db443c7494c22f6d
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1.6 GB | Download |
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md5:45209ec526b79eb82c7226129847cda6
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1.7 GB | Download |
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md5:f64f0f1cbaec357fd3cdfa2444e123d2
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1.7 GB | Download |
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md5:61bf543e2250e74926f4488d5009af42
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1.6 GB | Download |
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md5:6a4f5495ae6baf4cf135356e92c9c4fb
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1.7 GB | Download |
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md5:bb300d1f335a7b0a8796776fbce273c8
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1.7 GB | Download |
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md5:2866012e4a222988593d227af7acd5e1
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1.7 GB | Download |
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md5:eac9d4d6103d11a8cdde819aa5a9b493
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1.7 GB | Download |
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md5:e8fe844a3bd189e82897628b3df26e46
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1.7 GB | Download |
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md5:a637b2bbd3ac101f61674bb13e494506
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1.7 GB | Download |
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md5:cd7475345716ad33f441087c94365586
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1.7 GB | Download |
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md5:89178ce04f44d2075900ff8a030682f7
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1.7 GB | Download |
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md5:41226e1d9840039d7dc8fa819cdd319b
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1.7 GB | Download |
Additional details
Related works
- Cites
- Publication: 10.1016/j.agrformet.2025.110645 (DOI)