Trends in global land hydrology are not the primary focus of this study but they are, nonetheless, important in two respects: (i) for constraining the ocean mass contribution to global mean sea level and (ii) its regional variability.
There is considerable debate and controversy over the magnitude of both the natural and anthropogenic hydrology influence on global mean sea level . A recent inversion of GRACE and satellite altimetry estimated that, for the period 2002-09, the contribution was -0.2 ± 0.04 mm/yr . While this might be considered minor at a global scale, ENSO events are strongly correlated with significant sea level variability and this is likely due to land hydrology . In addition, there can be pronounced regional sea level changes, particularly in coastal areas close to large catchments .
It is, therefore, necessary to include land hydrology as a latent process in the Bayesian Hierarchical Model (BHM), as it affects both the spatial and temporal characteristics of sea level that are used to aid source separation. We have estimated the large-scale land hydrology signal from GRACE alone , but a joint inversion of GRACE and altimetry  that employed a so-called fingerprinting approach achieved improved source separation and resolution. The addition of further observational and model constraints will further improve the separation and resolution of the land hydrology signal.
The key task in work package 5 is to ensure that the mean influence of land hydrology is correctly represented in the observation layer of the BHM and that its spatio-temporal properties from both observations and simulators (such as the Total Runoff Integrating Pathways (TRIP) model) are incorporated as priors.
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