We present an automatic P‐ and S‐wave onset‐picking algorithm, using kurtosis‐derived characteristic functions (CF) and eigenvalue decompositions on three‐component seismic data. We modified the kurtosis CF (Saragiotis et al., 2002) to improve pick precision by computing the CF over several frequency bandwidths, window sizes, and smoothing parameters. Once phases are picked, our algorithm determines the onset type (P or S) using polarization parameters, removes bad picks using a clustering procedure and the signal‐to‐noise ratio (SNR) and assigns a pick quality index based on the SNR.

Significance: This work provides a robust estimate of water-level (WL) changes in the Ganges–Brahmaputra–Meghna delta, driven by continental freshwater dynamics, vertical land motion, and sea-level rise. Through an unprecedented set of 101 gauges, we reconstruct WL variations since the 1970s and show that the WL across the delta increased slightly faster, ∼3 mm/y, than the global mean sea-level rise (∼2 mm/y). By combining satellite altimetry and WL reconstructions, we estimate that maximum expected rates of delta subsidence since the 1990s range from 1 to 7 mm/y. By 2100, even under a greenhouse gas emission mitigation scenario (RCP4.5), the subsidence could double the projected sea-level rise, making it reach 85 to 140 cm across the delta.

Summary. The combined effects of stretching and magmatism permanently modify crustal structure in continental rifts and volcanic passive margins. The Red Sea-Gu

A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.