RESUMEN
Future warming in the Mediterranean is expected to significantly exceed global values with unpredictable implications on the sea-level rise rates in the coming decades. Here, we apply an empirical-Bayesian spatio-temporal statistical model to a dataset of 401 sea-level index points from the central and western Mediterranean and reconstruct rates of sea-level change for the past 10,000 years. We demonstrate that the mean rates of Mediterranean industrial-era sea-level rise have been significantly faster than any other period since ~4000 years ago. We further highlight a previously unrecognized variability in Mediterranean sea-level change rates. In the Common Era, this variability correlates with the occurrence of major regional-scale cooling/warming episodes. Our data show a sea-level stabilization during the Late Antique Little Ice Age cold event, which interrupted a general rising trend of ~0.45 mm a-1 that characterized the warming episodes of the Common Era. By contrast, the Little Ice Age cold event had only minor regional effects on Mediterranean sea-level change rates.
RESUMEN
The SEAMIS database (Mendeley data repository; https://doi.org/10.17632/wp4ctb4667.1) contains 546 relative sea-level indicators from 31 different studies within the broader Southeast Asian region including the Maldives, India and Sri Lanka. Here we compare quality-controlled and site-specific relative sea-level data from 23 studies from the SEAMIS database to a suite of ICE-5G glacial isostatic adjustment models. The relation between robust and, if applicable, tectonically corrected relative sea-level data with the broad predictions of glacial isostatic adjustment models is interpreted and discussed in the article "Holocene sea levels in Southeast Asia, Maldives, India and Sri Lanka: The SEAMIS database" [1] in Quaternary Science Reviews.
RESUMEN
From 2000 to 2015, tsunamis and storms killed more than 430,000 people worldwide and affected a further >530 million, with total damages exceeding US$970 billion. These alarming trends, underscored by the tragic events of the 2004 Indian Ocean catastrophe, have fueled increased worldwide demands for assessments of past, present, and future coastal risks. Nonetheless, despite its importance for hazard mitigation, discriminating between storm and tsunami deposits in the geological record is one of the most challenging and hotly contended topics in coastal geoscience. To probe this knowledge gap, we present a 4500-year reconstruction of "tsunami" variability from the Mediterranean based on stratigraphic but not historical archives and assess it in relation to climate records and reconstructions of storminess. We elucidate evidence for previously unrecognized "tsunami megacycles" with three peaks centered on the Little Ice Age, 1600, and 3100 cal. yr B.P. (calibrated years before present). These ~1500-year cycles, strongly correlated with climate deterioration in the Mediterranean/North Atlantic, challenge up to 90% of the original tsunami attributions and suggest, by contrast, that most events are better ascribed to periods of heightened storminess. This timely and provocative finding is crucial in providing appropriately tailored assessments of coastal hazard risk in the Mediterranean and beyond.
RESUMEN
The upper portion of the meadows of the protected Mediterranean seagrass Posidonia oceanica occurs in the region of the seafloor mostly affected by surf-related effects. Evaluation of its status is part of monitoring programs, but proper conclusions are difficult to draw due to the lack of definite reference conditions. Comparing the position of the meadow upper limit with the beach morphodynamics (i.e. the distinctive type of beach produced by topography and wave climate) provided evidence that the natural landwards extension of meadows can be predicted. An innovative model was therefore developed in order to locate the region of the seafloor where the meadow upper limit should lie in natural conditions (i.e. those governed only by hydrodynamics, in absence of significant anthropogenic impact). This predictive model was validated in additional sites, which showed perfect agreement between predictions and observations. This makes the model a valuable tool for coastal management.
