Articles | Volume 14, issue 11
https://doi.org/10.5194/essd-14-4949-2022
© Author(s) 2022. 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-14-4949-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Nov 2022
Data description paper |
| 11 Nov 2022
Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA
Dipankar Dwivedi
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA
James B. Brown
Environmental Genomics and System Biology, Lawrence Berkeley National
Laboratory, Berkeley, CA 94720, USA
Department of Statistics, University of California, Berkeley, CA 94720, USA
Carl I. Steefel
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA
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Pia Ebeling, Rohini Kumar, Stefanie R. Lutz, Tam Nguyen, Fanny Sarrazin, Michael Weber, Olaf Büttner, Sabine Attinger, and Andreas Musolff
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Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an indicator of climate change. By combining the strengths of satellites and models, we produced an integrated dataset on daily LSWT of 160 large lakes across the Tibetan Plateau (TP) for the period 1978–2017. LSWT increased significantly at a rate of 0.01–0.47° per 10 years. The dataset can contribute to research on water and heat balance changes and their ecological effects in the TP.
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Short summary
We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning framework that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets and constitutes a valuable resource to model the water cycle in the mountains of Colorado, western United States.
We present a new dataset that estimates small-scale variations in precipitation and temperature...
