Chronic flooding events due to sea-level rise in French Guiana.

As sea levels are rising, the number of chronic flooding events at high tide is increasing across the world coastlines. Yet, many events reported so far either lack observational evidence of flooding, or relate to coastal areas where ground subsidence or oceanic processes often enhance climate change-induced sea-level rise (SLR). Here we present observational and modelling evidence of high-tide flooding events that are unlikely to occur without SLR in French Guiana, where sea-level rise rates are close to the global average and where there is no significant ground subsidence. In particular, on 16 October 2020, a well-documented flooding event happened in Cayenne under calm weather conditions. Our probabilistic assessment of daily maximum water levels superimposed on SLR shows that this event can be modelled and is a consequence of SLR. As sea levels will continue to rise, we show that the number, severity and extent of such high-tide flooding events will increase across several urban areas of French Guiana, with an evolution depending on the topography. As concerns are growing regarding the economic impacts and adaptation challenges of high-tide chronic events across the world, our study provides new evidence that this early impact of SLR is emerging now.

Blind testing of shoreline evolution models.

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Author Correction: Quantifying uncertainties of sandy shoreline change projections as sea level rises.

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Quantifying uncertainties of sandy shoreline change projections as sea level rises.

Sandy shorelines are constantly evolving, threatening frequently human assets such as buildings or transport infrastructure. In these environments, sea-level rise will exacerbate coastal erosion to an amount which remains uncertain. Sandy shoreline change projections inherit the uncertainties of future mean sea-level changes, of vertical ground motions, and of other natural and anthropogenic processes affecting shoreline change variability and trends. Furthermore, the erosive impact of sea-level rise itself can be quantified using two fundamentally different models. Here, we show that this latter source of uncertainty, which has been little quantified so far, can account for 20 to 40% of the variance of shoreline projections by 2100 and beyond. This is demonstrated for four contrasting sandy beaches that are relatively unaffected by human interventions in southwestern France, where a variance-based global sensitivity analysis of shoreline projection uncertainties can be performed owing to previous observations of beach profile and shoreline changes. This means that sustained coastal observations and efforts to develop sea-level rise impact models are needed to understand and eventually reduce uncertainties of shoreline change projections, in order to ultimately support coastal land-use planning and adaptation.