|Constraint of glacial isostatic adjustment in the North Sea with geological relative sea-level and GNSS vertical land motion data|Simon, K.M.; Riva, R.E.M.; Vermeersen, L.L.A (2021). Constraint of glacial isostatic adjustment in the North Sea with geological relative sea-level and GNSS vertical land motion data. Geophys. J. Int. 227(2): 1168-1180. https://dx.doi.org/10.1093/gji/ggab261
In: Geophysical Journal International. Wiley: Oxford. ISSN 0956-540X; e-ISSN 1365-246X, meer
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- NIOZ: NIOZ Open Repository - postprints 368104 [ beschikbaar vanaf 07/01/2022 ]
- NIOZ: NIOZ files 367614
loading of the Earth; sea level change; satellite geodesy; Europe
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- Simon, K.M., meer
- Riva, R.E.M.
- Vermeersen, L.L.A, meer
In this study, we focus on improved constraint of the glacial isostatic adjustment (GIA) signal at present-day, and its role as a contributor to present-day sea-level budgets. The main study area extends from the coastal regions of northwestern Europe to northern Europe. Both Holocene relative sea level (RSL) data as well as vertical land motion (VLM) data are incorporated as constraints in a semi-empirical GIA model. 71 geological rates of GIA-driven RSL change are inferred from Holocene proxy data and 108 rates of vertical land motion from GNSS provide an additional measure of regional GIA deformation. Within the study area, the geological RSL data complement the spatial gaps of the VLM data and vice versa. Both datasets are inverted in a semi-empirical GIA model to yield updated estimates of regional present-day GIA deformations. A regional validation using tide gauges is presented for the North Sea, where the GIA signal may be complicated by lateral variations in Earth structure and existing predictions of regional and global GIA models show discrepancies. The model validation in the North Sea region suggests that geological data are needed to fit independent estimates of GIA-related RSL change inferred from tide gauge rates, indicating that geological rates from Holocene data do provide an important additional constraint for data-driven approaches to GIA estimation.