Estimating water-level in High Plains Aquifer combining satellite data with groundwater observations

Music City Center 

The High Plains Aquifer (HPA) is a critical water resource in the Central United States, yet its depletion remains a major concern. Satellite data from GRACE (Gravity Recovery and Climate Experiment) provides large-scale estimates of Liquid Water Equivalent Thickness (LWET), but its coarse resolution (∼ 24 km) limits local inference. In contrast, groundwater well observations from NGWMN (National Groundwater Monitoring Network) offer sparse, site-specific depth-to-groundwater measurements. We develop a downscaling framework that integrates these datasets using a latent variable model with a Gaussian Markov Random Field (GMRF) prior. Additional covariates, including irrigation intensity and population density, help refine spatial predictions. Our approach enables high-resolution (∼ 10 km) estimates of groundwater variations from 2002 to 2022. The resulting fine-scale inference provides valuable insights into groundwater depletion, land use impacts, and long-term water sustainability, potentially supporting informed policy decisions.

Groundwater Modeling

Spatial Downscaling

Gaussian Markov Random Field

GRACE Satellite Data

High Plains Aquifer

Bayesian Inference 

Section on Statistics and the Environment