Groundwater levels in urban areas are irregularly sampled and not well understood. Using a separable space–time Bayesian Hierarchical Model, we obtain multiple imputations of the missing values to analyze spatial and temporal groundwater level fluctuations in Los Angeles, CA.
Bibliographical noteKAUST Repository Item: Exported on 2022-06-09
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: Kimberly F. Manago and Terri S. Hogue were supported, in part, by the National Science Foundation (NSF) Water Sustainability and Climate Grant (EAR-12040235) and the NSF Engineering Research Center for Reinventing the Nation's Urban Water Infrastructure (ReNUWIt.org; EEC-1028968). Amanda S. Hering was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty