Climate change (CC) is likely to affect the thousands of bar-built or barrier estuaries (here referred to as Small tidal inlets - STIs) around the world. Any such CC impacts on the stability of STIs, which governs the dynamics of STIs as well as that of the inlet-adjacent coastline, can result in significant socio-economic consequences due to the heavy human utilisation of these systems and their surrounds. This article demonstrates the application of a process based snap-shot modelling approach, using the coastal morphodynamic model Delft3D, to 3 case study sites representing the 3 main STI types; Permanently open, locationally stable inlets (Type 1), Permanently open, alongshore migrating inlets (Type 2) and Seasonally/Intermittently open, locationally stable inlets (Type 3). The 3 case study sites (Negombo lagoon – Type 1, Kalutara lagoon – Type 2, and Maha Oya river – Type 3) are all located along the southwest coast of Sri Lanka.

The sediment budget associated with future coastline change in the vicinity of tidal inlets consists of four components; sea level rise-driven landward movement of the coastline (i.e., the Bruun effect), basin infilling effect due to sea level rise-induced increase in accommodation space, basin volume change due to variation in river discharge, and coastline change caused by change in fluvial sediment supply. These four components are affected by climate change and/or anthropogenic impacts. Despite this understanding, holistic modelling techniques that account for all the aforementioned processes under both climate change and anthropogenic influences are lacking. This manuscript presents the applications of a newly-developed reduced complexity modelling approach that accounts for both climate change and anthropogenically-driven impacts on future coastline changes. Modelled results corresponding to the year 2100 indicate considerable coastline recessions at Wilson Inlet (152 m) and the Swan River system (168 m) in Australia and Tu Hien Inlet (305 m) and Thuan An Inlet (148 m) in Vietnam. These results demonstrate that coastline models should incorporate both climate change and anthropogenic impacts to quantify future changes in fluvial sediment supply to coasts to achieve better estimates of total coastline changes at tidal inlets. Omission of these impacts is one of the major drawbacks in all the existing coastline models that simulate future coastline changes at tidal inlets. A comparison of these modelled future coastline changes with the predictions made by a relevant existing modelling technique (Scale Aggregated Model for Inlet-interrupted Coasts (SMIC)) indicates that the latter method overestimates total coastline recessions at the Swan River system, and the Tu Hien and Thuan An Inlets by 7%, 10%, and 30%, respectively, underlining the significance of integrating both climate change and anthropogenic impacts to assess future coastline changes at tidal inlets.

The many thousands of small tidal inlets (STIs), and their adjacent coastlines, are almost certain to be affected by climate change in multiple ways, due to their behaviour being closely linked to both oceanic and terrestrial drivers such as riverflow, sea level, and ocean waves, all which are projected to change over the 21^st century. Development of risk informed adaptation strategies for these highly utilized and inhabited inlet-interrupted coast zones requires projections of both alongshore average coastline recession and alongshore variability in coastline position along the coast under future forcing conditions, the latter being an aspect that has not received much attention to date. Here, a combination of a process-based morphodynamic model (Delft3D) and the reduced complexity coastline model (SMIC), concurrently forced with tides, waves, riverflows, and sea level rise, is used to investigate both of these phenomena at STI-interrupted coasts. The models are here applied to schematised conditions representing two systems in Sri Lanka, representing two of the three main Types of STIs: Negombo lagoon – permanently open, locationally stable inlet (Type 1), and Kalutara lagoon – permanently open, alongshore migrating inlet (Type 2). Results indicate that, under a high emissions climate scenario following RCP 8.5, by end-century, the coastline adjacent to the Type 1 STI may experience an alongshore average recession as large as 200 m, and that the alongshore variability in coastline position may be up to twice that at present. The Type 2 STI is projected to experience an alongshore average coastline recession of about 120 m, and up to a 75% increase in alongshore variability in coastline position by end-century, relative to the present. Thus, both the alongshore average coastline recession and the increase in the alongshore variability in coastline position are greater at the Type 1 STI, compared to at the Type 2 STI. These findings highlight the importance of accounting for both alongshore average coastline recession and future changes in alongshore variability in coastline position when assessing coastal hazards and risk on inlet-interrupted coasts to adequately inform climate adaptation strategies.

Coastal flooding is projected to become more severe over the 21^st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).

Climate change is widely expected to affect the thousands of small tidal inlets (STIs) dotting the global coastline. To properly inform effective adaptation strategies for the coastal areas in the vicinity of these inlets, it is necessary to know the temporal evolution of inlet stability over climate change time scales (50–100 years). As available numerical models are unable to perform continuous morphodynamic simulations at such time scales, here we develop and pilot a fast, probabilistic, reduced complexity model (RAPSTA – RAPid assessment tool of inlet STAbility) that can also quantify forcing uncertainties. RAPSTA accounts for the key physical processes governing STI stability and for climate change driven variations in system forcing. The model is very fast, providing a 100 year projection in less than 3 seconds. RAPSTA is demonstrated here at 3 STIs, representing the 3 main Types of STIs; Permanently open, locationally stable inlet (Type 1); Permanently open, alongshore migrating inlet (Type 2); Seasonally/Intermittently open, locationally stable inlet (Type 3). Model applications under a high greenhouse gas emissions scenario (RCP 8.5), accounting for forcing uncertainties, show that while the Type 1 STI will not change type over the twenty-first century, the Type 2 inlet may change into a more unstable Type 3 system around mid-century, and the Type 3 STI may change into a less unstable Type 2 system in about 20 years from now, further changing into a stable Type 1 STI around mid-century. These projections underscore the need for future adaptation strategies to remain flexible.

Storm waves and rising sea levels pose significant threats to low-lying coastal areas, particularly sandy beaches, which are especially vulnerable. The research on the long-time-scale changes in sandy coasts, especially the identification of tipping points in the shoreline-retreat rate, is limited. Vung Tau beach, characterized by its low terrain and rapid tourism-driven economic growth, was selected as a typical study area to quantify the shoreline retreat throughout the 21st century under various sea-level rise (SLR) scenarios, and to identify the existence of tipping points by investigating the projected annual change in shoreline retreat (m/yr). This study employs the Probabilistic Coastline Recession (PCR) model, a physics-based tool specifically designed for long-term coastline change assessments. The results indicate that shoreline retreat accelerates over time, particularly after a tipping point is reached around 2050 in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Under the SSP5-8.5 scenario, the median retreat distance is projected to increase from 19 m in 2050 to 89 m by 2100, nearly a fourfold rise. In comparison, the retreat distances are smaller under the SSP1-2.6 and SSP2-4.5 scenarios, but the same accelerating trend is observed beyond 2050. These findings highlight the growing risks associated with sea-level rise, especially the rapid increase in exceedance probabilities for retreat distances by the end of the century. By 2100, the probability of losing the entire beach at Vung Tau is projected to be 22% under SSP5-8.5. The approach of identifying tipping points based on the PCR model presented here can be applied to other sandy coastal regions, providing critical references for timely planning and the implementation of adaptation measures.