River deltas sustain dense human populations, major economic centres and vital ecosystems worldwide ^1,2 . Rising sea levels and subsiding land threaten the sustainability of these valuable landscapes with relative sea-level rise and associated flood, land loss and salinization hazards ^1–3 . Despite these risks, vulnerability assessments are impeded by the lack of contemporary, high-resolution, delta-wide subsidence observations ⁴ . Here we present spatially variable surface-elevation changes across 40 global deltas using interferometric synthetic aperture radar. Using this dataset, we quantify delta surface-elevation loss and show the prevalence and severity of subsidence in river deltas worldwide. Our analysis of three key anthropogenic drivers of delta elevation changes shows that groundwater storage has the strongest relative influence on vertical land motion in 10 of the 40 deltas. The other deltas are either influenced by multiple drivers or dominated by sediment flux or urban expansion. Furthermore, we find that contemporary subsidence surpasses absolute (geocentric) sea-level rise as the dominant driver of relative sea-level rise for most deltas over the twenty-first century. These findings suggest the need for targeted interventions addressing subsidence as an immediate and localized challenge, in parallel with broader efforts to mitigate and adapt to climate change-driven global sea-level rise.

Extreme water levels (EWLs) resulting from cyclones pose significant flood hazards and risks to coastal communities and interconnected ecosystems. To date, physically based models have enabled accurate prediction of EWLs despite their inherent high computational cost. However, the applicability of these models is limited to data‐rich sites with diverse characteristics. The dependence on high quality spatiotemporal data, which is often computationally expensive, hinders the applicability of these models to regions of either limited or data‐scarce conditions. To address this challenge, we present a Long Short‐Term Memory (LSTM) network framework to predict the evolution of EWLs beyond site‐specific training stations. The framework, named LSTM‐Station Approximated Models (LSTM‐SAM), consists of a collection of bidirectional LSTM models enhanced with a custom attention mechanism layer embedded in the architecture. LSTM‐SAM incorporates a transfer learning approach applicable to target (tide‐gage) stations along the U.S. Atlantic Coast. Importantly, LSTM‐SAM helps analyze: (a) the underlying limitations associated with transfer learning, (b) evaluate EWL predictions beyond training domains, and (c) capture the evolution of EWL caused by tropical and extratropical cyclones. The framework demonstrates satisfactory performance with “transferable” models achieving Kling‐Gupta Efficiency (KGE), Nash‐Sutcliffe Efficiency (NSE), and Root‐Mean Square Error (RMSE) ranging from 0.78 to 0.92, 0.90 to 0.97, and 0.09–0.18 m at the target stations, respectively. We show that LSTM‐SAM can accurately predict not only EWLs but also their evolution over time, that is, onset, peak, and dissipation, which could assist in operational flood forecasting in regions with limited resources to set up physically based models.

Studies on the variability in ocean wave climate provide engineers and policy makers with information to plan, develop, and control coastal and offshore activities. Ocean waves bear climatic imprints through which the global climate system can be better understood. Using the recently updated ERA5 dataset, this study evaluated the spatiotemporal distribution and variability in significant wave height (SWH) in the Eastern Tropical Atlantic (ETA). The short-term trends and rates of change were obtained using the Mann–Kendall trend test and the Theil–Sen slope estimator, respectively, and decadal trends were assessed using wavelet transformation. Significant, positive monthly and yearly trends and a prevailing decadal trend were observed across the domain. Observed trends suggest that stronger waves are getting closer to the coast and are modulated by the Southern and Northern Atlantic mid-latitude storm fields. These observations have implications for the increasing coastal erosion rates on the eastern coast of the Tropical Atlantic.

Due to challenging conditions of field survey techniques, it is difficult to measure the topography of tidal flats, an important parameter to understanding the evolution and dynamics of the constantly changing zone. This study used remotely sensed sediment moisture estimates to retrieve tidal flat elevation. The method is based on the observation that the intertidal zone is gradually exposed from land to sea at low tide, meaning that higher elevations contain less moisture. Here, we investigate the nature of the relationship between reflectance and moisture content from Landsat Enhanced Thematic Mapper Plus images and the study areas as a proxy for mapping the elevation of an exposed tidal flat surface. Statistical analysis confirmed a negative correlation between moisture and elevation; however, the correlation coefficient was relatively weak, and the slope of the intersecting tidal creek was found to be a crucial factor affecting this relationship. After segmenting the slope to correspond to areas of tidal flat and nontidal flat surfaces, the correlation coefficient of the moisture and elevation increased significantly. A retrieval model was then developed to generate the tidal flat elevations of different slope grades. After verification, the retrieval accuracy of the model was up to 17.3 cm. This research study demonstrated that the remotely sensed moisture method is suitable for monitoring the surface elevation of tidal flats.