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CAMELS Extended NLDAS Forcing Data


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Resource type: Composite 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
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Sharing Status: Published
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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.

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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
48.2950°
East Longitude
-62.2497°
South Latitude
24.6232°
West Longitude
-127.1129°

Temporal

Start Date:
End Date:

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

  • Frederik Kratzert (kratzert@ml.jku.at)

  • Andrew Newman (anewman@ucar.edu)

References

Related Resources

The content of this resource is part of: 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
The content of this resource is part of: 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

Kratzert, F. (2019). CAMELS Extended NLDAS Forcing Data, HydroShare, https://doi.org/10.4211/hs.0a68bfd7ddf642a8be9041d60f40868c

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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