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| Type: | Resource | |
| Storage: | The size of this resource is 120.6 MB | |
| Created: | Jul 18, 2019 at 11:15 a.m. | |
| Last updated: | Dec 17, 2019 at 8:34 a.m. | |
| DOI: | 10.4211/hs.17c896843cf940339c3c3496d0c1c077 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 5458 |
| Downloads: | 1502 |
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Abstract
This repository contains drop-in replacements for the basin mean Maurer forcing data files of the CAMELS data set. Compared to the original files contained in the CAMELS data set, these files contain daily minimum and maximum temperature. In the original publications both of those variables contained the daily mean temperature. These files were generated for our HESS manuscript "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets" and will be merged into the official CAMELS data set in the next update.
The same TERMS OF USE apply as for the original CAMELS data set.
Subject Keywords
Coverage
Spatial
Content
README.md
Updated Maurer forcing data
This repository contains drop-in replacements for the basin mean Maurer forcing data files of the CAMELS data set. Compared to the original files contained in the CAMELS data set, these files contain daily minimum and maximum temperature. In the original publications both of those variables contained the daily mean temperature. These files were generated for our HESS manuscript "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets" and will be merged into the official CAMELS data set in the next update.
License / Terms of use
The same terms of use as of the original CAMELS data set apply here.
Contact
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Frederik Kratzert (kratzert@ml.jku.at)
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Andrew Newman (anewman@ucar.edu)
Related Resources
| This resource is described by | A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan: Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, doi:10.5194/hess-19-209-2015, 2015 |
| This resource is referenced by | Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 2019. |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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