On the time lag between sea-level rise and basin infilling at tidal inlets

Tidal inlets are a common feature along the world's coastline. Inlet-adjacent coastlines have for millennia supported communities and livelihoods, and therefore, projected climate change driven variations in catchment-estuary-coast (CEC) system drivers (e.g., sea-level rise (SLR)) are likely to lead to substantial socio-economic impacts. One important SLR-driven process that affects inlet-adjacent shoreline change is basin-infilling (i.e., sediment import to the estuary from the coast to satisfy the SLR-driven increase of estuarine accommodation space). Due to the slow morphological response to hydrodynamic forcing, however, there is a time lag between basin infilling and SLR, which, in numerical models that simulate century-scale evolution of CEC systems, is represented by a basin infilling lag factor (M). To date, an indicative M value has only been derived for small tidal inlet systems (M ~0.5), and due to the lack of M estimates for larger systems, studies have been using M ~0.5 indiscriminately. Here, for the first time, we derive indicative M values for small, medium, and large tidal inlet systems (M ~0.5, ~0.25 and ~0.15 respectively) via analytical considerations. Subsequently, to investigate the consequences of using sub-optimal M values on twenty-first century projections of inlet-adjacent shoreline change, we apply a probabilistic, reduced complexity model (G-SMIC), under four IPCC AR6 climate scenarios, to three CEC systems representing small, medium and large systems. Results show that, in general, shoreline change projections are substantially lower(higher) when M values smaller(larger) than the indicative M for a given system are used. When smaller-than-optimal M values (0.25 and 0.15) are used for the small tidal inlet, both mid- and end-century shoreline retreats are under-estimated by 50-75% (across the four climate scenarios), relative to projections obtained with the optimal M value. For the medium-sized inlet, shoreline retreats for both future periods are over-estimated by ~100% with the larger-than-optimal M value of 0.5, while they are under-estimated by ~40-75% (across climate scenarios) with the smaller-than-optimal M value of 0.15. When the two higher-than-optimal M values (0.25 and 0.5) are used for the large tidal inlet system, shoreline retreat is over-estimated by ~ 65-240% (across climate scenarios) for both future periods. In terms of absolute values, these under/over-estimations increase in time and with the severity of emission scenario.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-02-04
DOI
https://doi.org/10.1038/s41598-025-86699-0
Citations
2
Primary Topic
Coastal and Marine Dynamics
Type
article
Field-Weighted Citation Impact
3.34

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the time lag between sea-level rise and basin infilling at tidal inlets

Zheng Bing Wang, Trang Minh Duong, Janaka Bamunawala, Roshanka Ranasinghe
2 citations
Scientific Reports
Coastal and Marine Dynamics
3.34
article

On the time lag between sea-level rise and basin infilling at tidal inlets

Zheng Bing Wang, Trang Minh Duong, Janaka Bamunawala, Roshanka Ranasinghe
article en
2 citations

Abstract

Tidal inlets are a common feature along the world's coastline. Inlet-adjacent coastlines have for millennia supported communities and livelihoods, and therefore, projected climate change driven variations in catchment-estuary-coast (CEC) system drivers (e.g., sea-level rise (SLR)) are likely to lead to substantial socio-economic impacts. One important SLR-driven process that affects inlet-adjacent shoreline change is basin-infilling (i.e., sediment import to the estuary from the coast to satisfy the SLR-driven increase of estuarine accommodation space). Due to the slow morphological response to hydrodynamic forcing, however, there is a time lag between basin infilling and SLR, which, in numerical models that simulate century-scale evolution of CEC systems, is represented by a basin infilling lag factor (M). To date, an indicative M value has only been derived for small tidal inlet systems (M ~0.5), and due to the lack of M estimates for larger systems, studies have been using M ~0.5 indiscriminately. Here, for the first time, we derive indicative M values for small, medium, and large tidal inlet systems (M ~0.5, ~0.25 and ~0.15 respectively) via analytical considerations. Subsequently, to investigate the consequences of using sub-optimal M values on twenty-first century projections of inlet-adjacent shoreline change, we apply a probabilistic, reduced complexity model (G-SMIC), under four IPCC AR6 climate scenarios, to three CEC systems representing small, medium and large systems. Results show that, in general, shoreline change projections are substantially lower(higher) when M values smaller(larger) than the indicative M for a given system are used. When smaller-than-optimal M values (0.25 and 0.15) are used for the small tidal inlet, both mid- and end-century shoreline retreats are under-estimated by 50-75% (across the four climate scenarios), relative to projections obtained with the optimal M value. For the medium-sized inlet, shoreline retreats for both future periods are over-estimated by ~100% with the larger-than-optimal M value of 0.5, while they are under-estimated by ~40-75% (across climate scenarios) with the smaller-than-optimal M value of 0.15. When the two higher-than-optimal M values (0.25 and 0.5) are used for the large tidal inlet system, shoreline retreat is over-estimated by ~ 65-240% (across climate scenarios) for both future periods. In terms of absolute values, these under/over-estimations increase in time and with the severity of emission scenario.

Scientific ReportsVol. 15(1)
Tohoku University (JP), IHE Delft Institute for Water Education (NL), Deltares (NL), University of Twente (NL), Delft University of Technology (NL)
National Aeronautics and Space Administration, AXA Research Fund
Life below water
Openalex Percentile: Top 11%
Coastal and Marine Dynamics
3.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.