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

Nooshin Mehrnegar; Owen Jones; Michael Bliss Singer; Maike Schumacher; Thomas Jagdhuber; Bridget R Scanlon; Ashraf Rateb; Ehsan Forootan

doi:10.1016/j.scitotenv.2020.143579

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

Sci Total Environ. 2021 Mar 1:758:143579. doi: 10.1016/j.scitotenv.2020.143579. Epub 2020 Nov 19.

Authors

Nooshin Mehrnegar¹, Owen Jones², Michael Bliss Singer³, Maike Schumacher⁴, Thomas Jagdhuber⁵, Bridget R Scanlon⁶, Ashraf Rateb⁶, Ehsan Forootan⁷

Affiliations

¹ School of Earth and Environmental Sciences, Cardiff University, CF103AT Cardiff, UK. Electronic address: mehrnegarN@cardiff.ac.uk.
² School of Mathematics, Cardiff University, CF244AG Cardiff, UK.
³ School of Earth and Environmental Sciences, Cardiff University, CF103AT Cardiff, UK; Water Research Institute, Cardiff University, CF103AX Cardiff, UK; Earth Research Institute, University of California Santa Barbara, 91306 Santa Barbara, USA.
⁴ Institute of Physics and Meteorology (IPM), University of Hohenheim, 70593 Stuttgart, Germany.
⁵ Microwaves and Radar Institute, German Aerospace Center, 82234 Wessling, Germany.
⁶ Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, TX, 78758 Austin, USA.
⁷ School of Earth and Environmental Sciences, Cardiff University, CF103AT Cardiff, UK; Geodesy and Earth Observation Group, Institute of Planning, Aalborg University, Rendburggade 14, 9000 Aalborg, Denmark.

PMID: 33257057
DOI: 10.1016/j.scitotenv.2020.143579

Abstract

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

Keywords: CONUS; GRACE TWSC; Groundwater storage; Markov Chain Monte Carlo (MCMC); Soil water storage; W3RA.