In the version of this Article originally published, in the sentence beginning "Here, we quantify global-mean relative sea-level rise... " in the Abstract, the value '2.5 mm yr -1 ' should have been '2.6 mm yr -1 ' . Furthermore, the sentence "In 2015, this floodplain population is approximately 235 million people. " should have made clear that the value of the floodplain population was for the scenario without subsidence and climate-induced sea-level rise (SLR); it has now been amended to "Without subsidence and climate-induced SLR, the global floodplain population in 2015 would have been approximately 235 million people. " Also, the beginning of the subsequent sentence "Assuming no subsidence and no climate-induced SLR... " has been amended to "Still assuming no subsidence and no climate-induced SLR... " The online versions of the Article have been corrected.

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The effectiveness of stringent climate stabilization scenarios for coastal areas in terms of reduction of impacts/adaptation needs and wider policy implications has received little attention. Here we use the Warming Acidification and Sea Level Projector Earth systems model to calculate large ensembles of global sea-level rise (SLR) and ocean pH projections to 2300 for 1.5°C and 2.0°C stabilization scenarios, and a reference unmitigated RCP8.5 scenario. The potential consequences of these projections are then considered for global coastal flooding, small islands, deltas, coastal cities and coastal ecology. Under both stabilization scenarios, global mean ocean pH (and temperature) stabilize within a century. This implies significant ecosystem impacts are avoided, but detailed quantification is lacking, reflecting scientific uncertainty. By contrast, SLR is only slowed and continues to 2300 (and beyond). Hence, while coastal impacts due to SLR are reduced significantly by climate stabilization, especially after 2100, potential impacts continue to grow for centuries. SLR in 2300 under both stabilization scenarios exceeds unmitigated SLR in 2100. Therefore, adaptation remains essential in densely populated and economically important coastal areas under climate stabilization. Given the multiple adaptation steps that this will require, an adaptation pathways approach has merits for coastal areas.This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels’.

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This study explores the uncertainty introduced in global assessments of coastal flood exposure and risk when not accounting for water-level attenuation due to landsurface characteristics. We implement a range of plausible water-level attenuation values for characteristic land-cover classes in the flood module of the Dynamic and Integrated Vulnerability Assessment (DIVA) modelling framework and assess the sensitivity of flood exposure and flood risk indicators to differences in attenuation rates. Results show a reduction of up to 44 % in area exposure and even larger reductions in population exposure and expected flood damages when considering water-level attenuation. The reductions vary by country, reflecting the differences in the physical characteristics of the floodplain as well as in the spatial distribution of people and assets in coastal regions. We find that uncertainties related to not accounting for water attenuation in global assessments of flood risk are of similar magnitude to the uncertainties related to the amount of sea-level rise expected over the 21st century. Despite using simplified assumptions to account for the process of water-level attenuation, which depends on numerous factors and their complex interactions, our results strongly suggest that an improved understanding and representation of the temporal and spatial variation of water levels across floodplains is essential for future impact modelling.

Key Points• We present the first comparison of uncertainties in global to world-regional scale assessments of current and future coastal flood risk. • The largest uncertainty relates to future coastal adaptation, which can influence future coastal flood risk by factors of 20-27. • Uncertainties in socioeconomic development, elevation data, defense levels, emissions and ice sheets can affect risks by factors of 2-6.

China experiences frequent coastal flooding, with nearly US$ 77 billion of direct economic losses and over 7,000 fatalities reported from 1989 to 2014. Flood damages are likely to grow due to climate change induced sea-level rise and increasing exposure if no further adaptation measures are taken. This paper quantifies potential damage and adaptation costs of coastal flooding in China over the 21 st Century, including the effects of sea-level rise. It develops and utilises a new, detailed coastal database of China developed within the Dynamic Interactive Vulnerability Assessment (DIVA) model framework. The refined database provides a more realistic spatial representation of coasts, with more than 2,700 coastal segments, covering 28,966 km of coastline. Over 50% of China's coast is artificial, representing defended coast and/or claimed land. Coastal flood damage and adaptation costs for China are assessed for different Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathways (SSP) combinations representing climate change and socioeconomic change and two adaptation strategies: no upgrade of currently existing defences and maintaining current protection levels. By 2100, 0.7-20.0 million people may be flooded/yr and US$ 67-3,308 billion damages/yr are projected without upgrade to defences. In contrast, maintaining the current protection level would reduce those numbers to 0.2-0.4 million people flooded/yr and US$ 22-60 billion/yr flood costs by 2100, with a protection investment costs of US$ 8-17 billion/yr. In 2100, maintaining current protection levels, dikes costs are two orders of magnitude smaller than flood costs across all scenarios, even without accounting for indirect damages. This research improves on earlier 4 national assessments of China by generating a wider range of projections, based on improved datasets. The information delivered in this study will help governments, policy-makers, insurance companies and local communities in China understand risks and design appropriate strategies to adapt to increasing coastal flood risk in an uncertain world.

Large-area coastal exposure and impact analysis has focussed on using sea-level rise (SLR) scenarios and has placed little emphasis on socioeconomic scenarios, while neglecting spatial variations of population dynamics. We use the Dynamic Interactive Vulnerability Assessment (DIVA) Framework to assess the population exposed to 1 in 100-year coastal flood events under different population scenarios, that are consistent with the Shared Socioeconomic Pathways (SSPs); and different SLR scenarios, derived from the Representative Concentration Pathways (RCPs); and analyse the effect of accounting for regionalised population dynamics on population exposure until 2100. In a reference approach, we use homogeneous population growth on national level. In the regionalisation approaches, we test existing spatially explicit projections that also account for urbanisation, coastal migration and urban sprawl. Our results show that projected global exposure in 2100 ranges from 100 million to 260 million, depending on the combination of SLR and population scenarios and method used for regionalising the population projections. The assessed exposure based on the regionalised approaches is higher than that derived from the reference approach by up to 60 million people (39%). Accounting for urbanisation and coastal migration leads to an increase in exposure, whereas considering urban sprawl leads to lower exposure. Differences between the reference and the regionalised approaches increase with higher SLR. The regionalised approaches show highest exposure under SSP5 over most of the 21 st century, although total population in SSP5 is the second lowest overall. All methods project the largest absolute growth in exposure for Asia and relative growth for Africa.