Using knowledge-guided machine learning to assess patterns of areal change in waterbodies across the contiguous U.S.: Data

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]