There is an increasing interest in the broad-scale implementation of coastal erosion early warning systems (EWS) with the goal of enhancing community preparedness to extreme coastal storm wave events. These emerging systems typically rely on process-based models to predict the storm-induced morphological change. A key challenge with incorporating these models in EWSs is the need for up-to-date nearshore and surf zone bathymetry data, which is difficult to measure routinely, but potentially important for accurate erosion forecasting. This study evaluates the degree to which up-to-date bathymetry is required for accurate coastal erosion predictions using the morphodynamic model XBeach and, subsequently, whether a range of “representative” and/or “synthetic” bathymetries can be used for the bottom boundary, when a survey of the immediate pre-storm bathymetry is not available. Twelve storm events at two contrasting sites were modelled using six different bathymetry scenarios, including the expected “best case” bathymetry surveyed immediately pre-storm. These results indicate that alternative bathymetries can be used to obtain sub-aerial erosion predictions that are similar (and in some cases better) than those resulting from the use of an immediately pre-storm surveyed bathymetry, provided that rigorous model calibration is undertaken prior. This generalized finding is attributed to specific parametrizations in the XBeach model structure that are optimized during the calibration process to match the particular bottom boundary condition used. This study provides practical guidance for the selection of suitable nearshore bathymetry for use in operational coastal erosion EWSs.

Environmental data collected independently for research or development projects presents a valuable opportunity for Least Developed Countries and dispersed Small Island Nations (LDCs/SINs) to obtain regular, long-term, information about their ecosystems without the typical financial and logistical challenges. However, effectively utilizing this independently collected environmental data requires a robust data governance approach which establishes the principles of data sovereignty. This study evaluates the data governance principles and data management mechanisms needed to gather, host, and disseminate de-centrally collected environmental data so that it can be used for sustainable development and informed decision-making. Using the 1200+ dispersed islands of the Maldives as a case study, this research examines the existing national environmental regulatory framework of the country within the context of environmental data management, to better understand the challenges associated with governance of de-centrally collected data and thereby derive recommendations for improvement. Limited technical infrastructure and capacity to securely store and host environmental data is identified as potentially being at the root of a lack of enforcement of existing regulations that govern the consolidation of data currently collected by third parties in the Maldives. Moreover, where data is available, conflicting mandates within the legal framework of the roles of different levels of government create barriers to collaboration and data sharing, leading to duplication of collection and management efforts. These challenges appear to be commonly faced by other LDCs/SINs. Recommendations for improvement included legally defining government roles, developing a national data management system, and reforming the research permit system. The results of this study provide a reference for other LDCs/ SINs on mechanisms to efficiently gather, host, and make accessible data collected within national borders by independent researchers or during development activities, so that it can be used to effectively manage the environment and safeguard their environment, culture, and economy.

Coastal storms pose a threat to livelihoods and assets along Australia’s coastlines. By delivering timely information about approaching coastal storms, early warning systems (EWSs) can enhance community preparedness and inform risk-reduction measures, with the goal of reducing potential impacts to property, critical infrastructure, and loss of life. Worldwide, existing coastal hazard EWSs primarily center around the forecasting of coastal flooding risks, which predominantly occur along surge-dominated coastlines. However, many of Australia’s densely populated coastlines are wavedominated, where erosion hazards feature more prominently. This pilot project has developed a multiscale, coastal hazard EWS capability for Australia that uses state-of-the-art scientific methods for predicting both erosion and flooding impacts caused by coastal storms.

Understanding beach morphodynamics is essential for effective erosion mitigation and coastal management. In monsoon-dominated regions of the Indian Ocean, seasonal reversals in wave direction can drive beach rotation, producing oscillatory shoreline change within embayed systems. Despite observations of this phenomenon, monsoon-driven seasonal beach rotation has not been systematically examined across contrasting embayed beaches of the basin. This study analyses nine years (2016-2025) of shoreline data from three diverse sites to quantify the contribution of seasonal beach rotation to shoreline variability. Empirical Orthogonal Functional analysis reveals a dominant oscillating mode of shoreline variability at all three sites with a mean periodicity of approximately 3.5 months - consistent with the average regional monsoonal season duration, and are interpreted as rotation driven primarily by monsoon-induced shifts in wave direction. This rotational signal is consistent across sites spanning both hemispheres of the Indian Ocean Basin, despite marked differences in wave climate (storminess, significant wave height, period, directional regimes) and littoral cell configurations (artificially constrained vs natural headlands). However, key characteristics of rotation – such as the timing of the direction reversals, magnitude, and influence of regional climate modes such as the Indian Ocean Dipole - varies between sites, underscoring the importance of site-specific controls. While these findings should not be broadly generalized to all embayed beaches of the Indian Ocean, they advance understanding of monsoon-driven shoreline dynamics and demonstrates that seasonal beach rotation can be a dominant control on intra-annual shoreline variability, with implications for coastal management across this densely populated and economically significant region.

Coastal zones host some of the planet’s most biodiverse and productive ecosystems, including mangroves, seagrasses, and salt marshes, “blue carbon” habitats that provide essential ecosystem services such as shoreline protection, fisheries support, and substantial carbon sequestration. Yet these ecosystems are under increasing pressure from climate change, sea-level rise, pollution, and habitat degradation. Effective biodiversity monitoring and management therefore require approaches that integrate ecological processes with the complex physical dynamics of coastal environments.The COASTS (Coastal Observation Advances Leveraging Space Technology Services) project addresses this challenge by combining multi-scale Earth observation (EO) data, advanced modelling, and process-based morphodynamic simulations to map, model, and monitor coastal biodiversity and blue carbon ecosystem dynamics. COASTS brings together European partners to close knowledge gaps through the integration of EO, ecological surveying, and digital analysis tools. Working closely with local stakeholders at three pilot sites in Germany, Jersey/Chausey, and the Maldives, the project co-designs fit-for-purpose solutions tailored to regional management needs.A central goal of COASTS is the development of a downstream service that leverages Copernicus Marine data and other EO initiatives to provide decision-ready environmental information. This includes spatial-temporal mapping of blue carbon habitats, assessments of ecosystem services, and quantification of coastal resilience functions such as erosion protection. The project further examines how EO-derived indicators can support identify priority areas for restoration and conservation.A key component of COASTS is its nested hydro-morphodynamic modelling framework, which captures coastal processes across scales relevant to biodiversity and ecosystem functioning. The workflow downscales Copernicus Marine global or regional data to local, high-resolution simulations through a sequence of nested models. Delft3D Flexible Mesh (FM) provides detailed hydrodynamics and wave–flow interactions, incorporating bathymetry, seabed roughness, wind forcing and sediment transport. These outputs drive site-specific XBeach models that resolve nearshore morphodynamics, sediment exchange and storm-driven change at meter-scale resolution. Using satellite-derived bathymetry, seabed classification, ERA5 winds and CMEMS ocean products, this modelling chain reconstructs past coastal behaviour and generates future scenarios relevant for habitat stability, erosion risk and blue carbon ecosystem resilience.The project will deliver a web-based decision-support tool enabling stakeholders to visualise, analyse, and report on coastal ecosystem dynamics.