Using knowledge-guided machine learning to assess patterns of areal change in waterbodies across the contiguous U.S.: Data
Creators
- 1. Virginia Tech
- 2. University of California Davis
- 3. Dundalk Institute of Technology
- 4. McGill University
- 5. The University of Vermont
- 6. Rensselaer Polytechnic Institute
- 7. City University of New York
- 8. Northern Region Water Board
- 9. University of Minnesota
- 10. Los Alamos National Laboratory
- 11. University of Wisconsin - Madison
- 12. Cary Institute of Ecosystem Studies
Description
Data used for generating figures in the knowledge-guided machine learning manuscript and supplemental info by Wander et al.
This repository contains three folders:
- Results: csv file with the final KGML groups for each waterbody. Latitude, longitude, RealSAT (Khandelwal et al., 2022) waterbody id, and HydroLAKES (Messager et al., 2016) waterbody id are provided.
- Code_data: data used for generating figures in the knowledge-guided machine learning manuscript and supplemental info by Wander et al.
- prism: data from PRISM dataset (Matsuura and Willmott) used for preliminary driver analysis in Wander et al.
Khandelwal, A.; Karpatne, A.; Ravirathinam, P.; Ghosh, R.; Wei, Z.; Dugan, H.; Hanson, P. C.; Kumar, V. ReaLSAT, a global dataset of reservoir and lake surface area variations. Sci. Data 2022, 9, 356. https://doi.org/10.1038/s41597-022-01449-5
Messager, M. L.; Lehner, B.; Grill, G.; Nedeva, I.; Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 2016, 7, 1–11. https://doi.org/10.1038/ncomms13603
Matsuura, K.; Willmott, C. J. Terrestrial precipitation and air temperature: 1900–2014 gridded monthly time series. University of Delaware Dept. of Geography. [Available Online at https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html.]. [Accessed: Jan 2022]
Files
data.zip
Files
(164.4 MB)
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