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/ncomms13603Willmott, C. J.; Matsuura, K. Terrestrial Air Temperature and Precipitation: 1900-2014 Gridded Monthly Time Series: NOAA Physical Sciences Laboratory Terrestrial Air Temperature and Precipitation: 1900-2014 Gridded Monthly Time Series, 2015. https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html. (Accessed Jan 2022).
Danielson, J. J.; Gesch, D. B. TEMIS -- GMTED2010 Elevation Data at Different Resolutions, 2011. https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-multi-resolution-terrain-elevation. (Accessed Jan 2022).

本数据集用于Wander等人撰写的知识引导机器学习（knowledge-guided machine learning）论文及补充材料中的图表生成。本仓库包含三个文件夹：
1. Results：包含各水体最终KGML组的CSV文件，其中提供了纬度、经度、RealSAT（Khandelwal等，2022）水体ID以及HydroLAKES（Messager等，2016）水体ID。
2. Code_data：用于生成Wander等人知识引导机器学习论文及补充材料中图表的相关数据。
3. prism：取自PRISM数据集（PRISM dataset）的数据，由Matsuura与Willmott提供，用于Wander等人研究中的初步驱动因子分析。

参考文献：
Khandelwal, A.; Karpatne, A.; Ravirathinam, P.; Ghosh, R.; Wei, Z.; Dugan, H.; Hanson, P. C.; Kumar, V. ReaLSAT：全球水库与湖泊表面积变化数据集。《科学数据》（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. 基于地质统计方法估算全球湖泊储水量与储水时长。《自然·通讯》（Nat. Commun.）2016, 7, 1–11。https://doi.org/10.1038/ncomms13603
Willmott, C. J.; Matsuura, K. 《地面气温与降水：1900-2014年网格化月时间序列》，NOAA物理科学实验室，2015。https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html。（2022年1月访问）
Danielson, J. J.; Gesch, D. B. TEMIS——不同分辨率下的GMTED2010高程数据，2011。https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-multi-resolution-terrain-elevation。（2022年1月访问）