Articles | Volume 13, issue 11
https://doi.org/10.5194/essd-13-5087-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5087-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
04 Nov 2021
Data description paper |
| 04 Nov 2021
| 04 Nov 2021
A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables
Diyang Cui
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
Dongdong Wang
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
Zheng Liu
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
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2 citations as recorded by crossref.
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- Estimation of soil moisture from remote sensing products using an ensemble machine learning model: a case study of Lake Urmia Basin, Iran S. Asadollah et al. 10.1007/s12145-023-01172-8
Latest update: 19 Apr 2024
Short summary
Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Large portions of the Earth's surface are expected to experience changes in climatic conditions....
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