Predicting the impact of sea-level rise on intertidal rocky shores with remote sensing

Schaefer, Nina; Mayer‐Pinto, Mariana; Griffin, Kingsley J.; Johnston, Emma L.; Glamore, William; Dafforn, Katherine A.

doi:10.1016/j.jenvman.2020.110203

Cited by 11 publications

(9 citation statements)

References 47 publications

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“…There has been an unprecedented rise in global sea levels, with devastating physical and socioeconomical effects on the coastal communities (Nicholls and Cazenave 2010;Nidhinarangkoon et al 2020;Ranjbar et al 2020;Schaefer et al 2020;Shi et al 2020;Stammer et al 2013). Since the start of the 20th century, the Global Mean Sea Level (GMSL) has risen by about 16-21cm, with more than 7 cm of this occurring since 1993 (Global Change Research Program 2019;Jevrejeva et al 2016;Yi et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

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This study aims to integrate a broad spectrum of ocean-atmospheric variables to predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep learning techniques. 4 scenarios of different combinations of variables such as sea surface temperature, sea surface salinity, sea surface density, surface atmospheric pressure, wind speed, total cloud cover, precipitation and sea level data were used to train ARIMA, SVR and LSTM neural network models. Results show that atmospheric processes have more influence on prediction accuracy than ocean processes. Combining ocean and atmospheric variables improves the model prediction at all stations by 1-9% for both SVR and LSTM. The means of R accuracy of optimal performing LSTM, SVR and ARIMA models at all stations are 0.853, 0.748 and 0.710, respectively. Comparison of model performance shows that the LSTM model trained with ocean and atmospheric variables is optimal for predicting sea level variation at all stations except Pulua Langkawi where ARIMA model trained without ocean-atmospheric variables performed best due to the dominating tide influence. This suggests that performance and suitability of prediction models vary across regions and selecting an optimal prediction model depends on the dominant physical processes governing sea level variability in the area of investigation.

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Section: Introductionmentioning

confidence: 99%

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

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“…While much remains unknown regarding the realized effects of SLR along the coast of southern California, our results provide an Our findings are generally consistent with those of other studies that have attempted to forecast the effects of SLR on future intertidal area and coastal profile of rocky shores. For example , Jackson & Mcilvenny (2011), , Hollenbeck, Olsen & Haig (2014), and Schaefer et al (2020) all estimated that SLR will significantly reduce intertidal habitat area. In addition, Vaselli et al (2008) and Jackson & Mcilvenny (2011) suggest that as seas rise, the intertidal zone will generally steepen.…”

Section: Discussionmentioning

confidence: 99%

“…When backed by steep cliffs or anthropogenic structures, as is the case on the majority of coastlines globally (Emery & Kuhn, 1982), many rocky shores are expected to experience ''coastal squeeze''-a general narrowing of the extent of the intertidal zone (Doody, 2013;Pontee, 2013) and a steepening of the coastal profile (Vaselli et al, 2008). Whether SLR will lead to losses or gains in habitat at a given site is highly dependent upon geomorphology, rates of erosion, underlying stratigraphy, and alteration in sedimentation processes at local and regional scales (Emery & Kuhn, 1980;Young & Ashford, 2006;Masselink et al, 2020;Schaefer et al, 2020). Studies investigating durable coastlines with low erosion rates have suggested that SLR may cause substantial rocky intertidal habitat loss.…”

Section: Sea-level Rise Projections and Potential Impacts To The Rockmentioning

confidence: 99%

“…found that with 0.3-1.0 m of SLR on five rocky headlands in New South Wales, Australia, impacts will be variable but will largely result in substantial habitat loss. Most recently, Schaefer et al (2020) suggested that along 50 km of rocky shoreline in eastern Australia, SLR will generally reduce habitat availability, though impacts will show high spatial variability, with gently sloped shores that lack the ability to migrate landward experiencing the greatest losses of habitat. Because habitat loss is among the greatest threats to global biodiversity (Brooks et al, 2002;Mantyka-Pringle, Martin & Rhodes, 2012), it is critical to evaluate the current state of rocky intertidal ecosystems and assess how SLR may affect these important communities in coming years.…”

Section: Sea-level Rise Projections and Potential Impacts To The Rockmentioning

confidence: 99%

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Future sea-level rise drives rocky intertidal habitat loss and benthic community change

Kaplanis

Edwards

Eynaud

et al. 2020

PeerJ

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The impacts of sea-level rise (SLR) are likely to be the greatest for ecosystems that exist at the land-sea interface, where small changes in sea-level could result in drastic changes in habitat availability. Rocky intertidal ecosystems possess a number of characteristics which make them highly vulnerable to changes in sea-level, yet our understanding of potential community-scale responses to future SLR scenarios is limited. Combining remote-sensing with in-situ large-area imaging, we quantified habitat extent and characterized the biological community at two rocky intertidal study locations in California, USA. We then used a model-based approach to estimate how a range of SLR scenarios would affect total habitat area, areal extent of dominant benthic space occupiers, and numerical abundance of invertebrates. Our results suggest that SLR will reduce total available rocky intertidal habitat area at our study locations, leading to an overall decrease in areal extent of dominant benthic space occupiers, and a reduction in invertebrate abundances. As large-scale environmental changes, such as SLR, accelerate in the next century, more extensive spatially explicit monitoring at ecologically relevant scales will be needed to visualize and quantify their impacts to biological systems.

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“…Relevant habitat modifications included changes in the coastal slope and related communities (7) and general habitat loss due to mean sea-level increase. (8)(9)(10)(11) However, minor attention was given to the supralittoral zone, particularly in areas where the tidal seawater input is negligible (e.g.,Western Mediterranean Sea).…”

Section: Introductionmentioning

confidence: 99%

Sea-level change and the supralittoral environment: Potential impact on a splashpool habitat on the Ligurian coast (NW Mediterranean)

Bonello¹,

Carpi²,

Mucerino³

et al. 2022

J Biol Res

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Climate change represents one of the major drivers of habitat modification that is affecting a wide variety of environments. In coastal environments, great effort is being put in trying to understand and forecast the possible effects of such processes, and the Sea-Level Rise (SLR) is one of the most investigated phenomena. This paper describes the possible effects of different 2100 sea-level scenarios related to greenhouse gas mitigation policies (Representative Concentration Pathways - RCPs). This work was conducted on a supralittoral habitat situated in Genova (Ligurian Sea), and has covered an eventual change of environmental conditions driven by SLR, which might impact the Culicid Acartomyiamariae, a resident species. The wave run-up stemming from the different RCPs was simulated using the XBeach model, and to infer SLR effects on A. mariae life cycle; the results were coupled with data obtained from field surveys. The model outputs highlighted a variation in the wave run-up oscillations under common wave conditions, which might affect the supralittoral area in terms of water input and hydric balance, and the A. mariae life cycle, which is highly dependent on temperature and salinity.

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Predicting the impact of sea-level rise on intertidal rocky shores with remote sensing

Cited by 11 publications

References 47 publications

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Future sea-level rise drives rocky intertidal habitat loss and benthic community change

Sea-level change and the supralittoral environment: Potential impact on a splashpool habitat on the Ligurian coast (NW Mediterranean)

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