Abstract

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 21st 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.

Highlights

  • There are thousands small tidal inlets (STIs), known as “bar-built” or “barrier” estuaries, along the world’s coastline

  • Using the process-based Delft3D model, Duong et al (2017, 2018) investigated how climate change might affect the stability of the three main STI Types: Type 1 – permanently open, locationally stable inlets, Type 2 – permanently open, alongshore migrating inlets, and Type 3 – seasonally/intermittently open, locationally stable inlets (Duong et al, 2016). These results showed that in general none of the three STI Types will change Types by the year 2100, but that the inlet stability level, represented by the widely used Bruun inlet stability criterion r = P/M [where P = ebb tidal prism which takes into account the riverflow effect (m3), M = annual longshore sediment transport (LST) rate (m3/year)] (Bruun, 1978), will change due to climate change

  • The alongshore variability in coastline position appears to differ significantly from the contemporary condition (i.e., T1_PS) in the T1_C5 simulation, when M increases due to a climate change driven southerly rotation in mean wave direction

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Summary

Introduction

There are thousands small tidal inlets (STIs), known as “bar-built” or “barrier” estuaries, along the world’s coastline. These are most commonly found along wave-dominated, microtidal mainland coasts, comprising about 50% of the world’s coastline (Ranasinghe et al, 2013; Duong et al, 2016, 2017, 2018). STI environs, including the inlet-adjacent coast, have historically supported a number of human activities, including navigation, sand mining, fishing, tourism, and waterfront developments (Kjerfve, 1994; Nicholls et al, 2007; Ranasinghe et al, 2013; Bamunawala et al, 2020a). Any changes in inlet dynamics might negatively affect some or all of these human activities, with associated socioeconomic losses (Ranasinghe et al, 2013; Bamunawala et al, 2020a,b)

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