Making the Best Use of GRACE, GRACE‐FO and SMAP Data Through a Constrained Bayesian Data‐Model Integration

Abstract

AbstractThe Gravity Recovery and Climate Experiment (GRACE, 2003–2017) and its Follow‐On mission GRACE‐FO (2018‐now) provide global estimates of the vertically integrated Terrestrial Water Storage Changes (TWSC). Since 2015, the Soil Moisture Active Passive (SMAP) radiometer observes global L‐band brightness temperatures, which are sensitive to near‐surface soil moisture. In this study, we introduce our newly developed Constrained Bayesian (ConBay) optimization approach to merge the TWSC of GRACE/GRACE‐FO along with SMAP soil moisture data into the ∼10 km resolution W3RA water balance model. ConBay is formulated based on two hierarchical multivariate state‐space models to (I) separate land hydrology compartments from GRACE/GRACE‐FO TWSC, and (II) constrain the estimation of surface soil water storage based on the SMAP data. The numerical implementation is demonstrated over the High Plain (HP) aquifer in the United States between 2015 and 2021. The implementation of ConBay is compared with an unconstrained Bayesian formulation, and our validations are performed against in‐situ USGS groundwater level observations and the European Space Agency (ESA)'s Climate Change Initiative (CCI) soil moisture data. Our results indicate that the single GRACE/GRACE‐FO assimilation improves particularly the groundwater compartment. Adding SMAP data to the ConBay approach controls the updates assigned to the surface storage compartments. For example, correlation coefficients between the ESA CCI and the ConBay‐derived surface soil water storage (0.8) that are considerably higher than those derived from the unconstrained experiment (−0.3) in the North HP. The percentage of updates introduced to the W3RA groundwater storage is also decreased from 64% to 57%.