Bayesian integration of satellite geodetic data with models to separate land hydrology and surface deformation signals

Reliable quantification of water mass changes (or redistribution) within the different compartments of the water cycle is important for understanding processes and feedback loops within the Earth's climate system. This information is also essential in geodesy because it changes the Earth's orientation (importance for defining reference frames) and the Earth's gravity field, which is the physical shape of the Earth and is used for defining reference datum. The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) provide time-variable Earth's gravity fields that contain signals related to different processes such as non-steric sea level changes, Terrestrial Water Storage Changes (TWSC), ice sheet melting, and Post Glacial Rebound (PGR). Although GRACE(-FO) data represent an accurate superposition of these anomalies, separating this integrated signal into its contributors is desirable for many hydro-climatic and geophysical applications. In this thesis, three novel Bayesian data-model fusion frameworks are developed to separate land hydrology (surface and sub-surface) and surface deformation (due to PGR) from GRACE(-FO) data. The three main frameworks of this thesis include: 1- the Dynamic Model Data Averaging (DMDA), that is formulated to merge multi-model data with GRACE(-FO) data; 2- Markov Chain Monte Carlo-Data Assimilation (MCMC-DA), as an extension of DMDA, to recursively estimate components of the TWSC, while accounting for temporal dependencies between the storage compartments; and 3- the Constrained Bayesian-Data Assimilation (ConBay-DA) to use multi-sensor data for GRACE(-FO) signal separation. DMDA is used to compare several global hydrological models and merge them with GRACE data. The groundwater and soil water storage changes are extracted within the Conterminous United States (CONUS) by implementing the MCMC-DA approach. ConBay-DA is applied, based on the hierarchical MCMC optimization, to use GRACE data and the surface uplift rates from the Global Navigation Satellite System (GNSS) stations and separate hydrological and GIA deformation components over the Great Lakes (GL) area in North America

Making the best use of GRACE, GRACE‐FO and SMAP data through a constrained Bayesian data‐model integration

The 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%

Sustained water storage in Horn of Africa drylands dominated by seasonal rainfall extremes

Rural communities in the Horn of Africa Drylands (HAD) are increasingly vulnerable to multi-season droughts due to the strong dependence of livelihoods on seasonal rainfall. We analysed multiple observational rainfall datasets for recent decadal trends in mean and extreme seasonal rainfall, as well as satellite-derived terrestrial water storage and soil moisture trends arising from two key rainfall seasons across various subregions of HAD. We show that, despite decreases in total March-April-May rainfall, total water storage in the HAD has increased. This trend correlates strongly with seasonal totals and especially with extreme rainfall in the two dominant HAD rainy seasons between 2003 and 2016. We further show that high-intensity October-November-December rainfall associated with positive Indian Ocean Dipole events lead to the largest seasonal increases in water storage that persist over multiple years. These findings suggest that developing groundwater resources in HAD could offset or mitigate the impacts of increasingly common droughts

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5∘ standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003-2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections

Global groundwater droughts are more severe than they appear in hydrological models:an investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003–2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections