Natural lakes and reservoirs responses to drought across the conterminous United States

These datasets were created to quantify how natural lakes (NLs) and reservoirs (RSVRs) across the conterminous U.S. (CONUS) with different levels of hydrologic connectivity respond to drought and how natural and human factors operating at multiple spatial scales affect lake responses. The data used in this study were from the LAGOS-US research platform (Cheruvelil et al. 2021; https://doi.org/10.1002/lol2.10203) that includes lake and landscape data for 479950 lakes ≥ 1 ha across the CONUS. 

Project title: Sun, X., Cheruvelil, K.S., Hanly, P.J., &Soranno, P.A. Connectivity and ecological context affect natural lake and reservoir responses to drought across the conterminous United States.

Four datasets are included:

SPI.csv: Dataset contains the standard precipitation index (SPI) values from January 2009 to December 2018 for 479950 lakes across the conterminous U.S.

predictors_untransformed.csv: Dataset contains the untransformed data of 32 predictors (excluding lake maximum depth) for 62927 lakes across the conterminous U.S.

full_dataset_32predictors.csv: Dataset contains the response variables (ΔZ-score-median and directions of CHL responses) and 32 predictors (excluding lake maximum depth) for 62927 lakes across the conterminous U.S.

depth_subset_33predictors.csv: Dataset contains the response variables (ΔZ-score-median and directions of CHL responses) and 33 predictors (including lake maximum depth) for 8994 lakes across the conterminous U.S.

Some data were transformed and some variable names in the dataset are different from the names in the manuscript. Please see the Metadata file for the transformation and conversion of variable names. The 'lagoslakeid' (LAGOS-US unique identifier for each lake) is included in the dataset as a variable but was not used as a predictor in analyses.