One of the major error sources affecting vertical land motion (VLM) estimations are nonlinearities, such as discontinuities or changing velocities over differing periods. Here, we present a novel Bayesian approach to automatically and simultaneously detect such events, together with the commonly estimated motion signatures in coastal Global Navigation Satellite System (GNSS) data. Next to GNSS observations, for the first time, we directly estimate discontinuities and piecewise VLM derived from altimetry and tide-gauge differences (SATTG). We show that, compared to estimating a single linear trend, accounting for time series discontinuities and trend changes significantly increases the agreement of SATTG with GNSS estimates (by 0.44 mm/year) at 387 globally distributed station pairs.
The Bayesian change point detection is applied to 606 SATTG and 457 GNSS time series. Observed VLM, which is identified as linear (i.e. is not affected by discontinuities), has a substantially higher consistency with large scale VLM effects of Glacial Isostatic Adjustment (GIA) and contemporary mass redistribution (CMR). The standard deviation of SATTG (and GNSS) trend differences with respect to GIA+CMR trends, is by 38% (and 51%) lower for VLM which is categorized as linear compared to VLM, where discontinuities or trend changes are detected. Given that in more than a third of the SATTG time series nonlinearities are detected, the results underpin the importance to account for such features, in particular to avoid extrapolation biases of coastal VLM and its influence on relative sea level change. The Bayesian approach uncovers the potential for a better characterization of SATTG VLM changes on much longer periods than for GNSS observations and is widely applicable to geophysical time series.
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One of the major error sources affecting vertical land motion (VLM) estimations are nonlinearities, such as discontinuities or changing velocities over differing periods. Here, we present a novel Bayesian approach to automatically and simultaneously detect such events, together with the commonly estimated motion signatures in coastal Global Navigation Satellite System (GNSS) data. Next to GNSS observations, for the first time, we directly estimate discontinuities and piecewise VLM derived from a...
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