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| Type: | Resource | |
| Storage: | The size of this resource is 107.7 MB | |
| Created: | Dec 24, 2019 at 12:51 p.m. | |
| Last updated: | Dec 24, 2019 at 1:06 p.m. | |
| DOI: | 10.4211/hs.0a68bfd7ddf642a8be9041d60f40868c | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 2064 |
| Downloads: | 644 |
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Abstract
This repository contains drop-in replacements for the basin mean NLDAS 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 "Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning" and were derived from hourly NLDAS data.
The same TERMS OF USE apply as for the original CAMELS data set.
The same terms of use as of the original CAMELS data set apply here.
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.md
Updated NLDAS forcing data
This repository contains drop-in replacements for the basin mean NLDAS 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 "Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning" and were derived from hourly NLDAS data. The same TERMS OF USE apply as for the original CAMELS data set.
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 referenced 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, 2015A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodge |
| This resource is referenced by | Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S. ( 2019). Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065 |
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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