Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into W3RA

Climate variability and change along with anthropogenic water use have affected the (re)distribution of storage fluxes across Contiguous United States (CONUS). Available hydrological models, however, do not represent recent changes in cycle. Therefore, this study, a novel Bayesian Markov Chain Monte Carlo-based Data Assimilation (MCMC-DA) approach is formulated to integrate Terrestrial Water Storage (TWSC) from Gravity Recovery Experiment (GRACE) satellite mission into W3RA balance model. The benefit integration its dynamic solution that uses GRACE TWSC update W3RA's individual estimates while rigorously accounting for uncertainties. It also down-scales data provides groundwater soil at ~12.5 km resolution CONUS covering 2003–2017. Independent validations are performed against in-situ (from USGS) Change Initiative (CCI) moisture products European Space Agency (ESA). Our results indicate MCMC-DA introduces trends, which exist TWSC, mostly lesser extent storage. Higher similarity found between estimation those USGS southeastern CONUS. We show stronger linear trend CONUS, compared (changing ±0.5 mm/yr ±2 mm/yr), closer independent ESA CCI. improves regions high forest intensity, where CCI models difficulties capturing soil-vegetation-atmosphere continuum. representation El Niño Southern Oscillation (ENSO)-related be considerably improved after integrating W3RA. This new hybrid shows promise understanding links climate over broad regions.