Rising global temperature has put increasing pressure on understanding the linkage between atmospheric warming and the occurrence of natural hazards. While the Paris Agreement has set the ambitious target to limiting global warming to 1.5 ∘ C compared to preindustrial levels, scientists are urged to explore scenarios for different warming thresholds and quantify ranges of socioeconomic impact. In this work, we present a framework to estimate the economic damage and population affected by river floods at global scale. It is based on a modeling cascade involving hydrological, hydraulic and socioeconomic impact simulations, and makes use of state-of-the-art global layers of hazard, exposure and vulnerability at 1-km grid resolution. An ensemble of seven high-resolution global climate projections based on Representative Concentration Pathways 8.5 is used to derive streamflow simulations in the present and in the future climate. Those were analyzed to assess the frequency and magnitude of river floods and their impacts under scenarios corresponding to 1.5 ∘ C, 2 ∘ C, and 4 ∘ C global warming. Results indicate a clear positive correlation between atmospheric warming and future flood risk at global scale. At 4 ∘ C global warming, countries representing more than 70% of the global population and global gross domestic product will face increases in flood risk in excess of 500%. Changes in flood risk are unevenly distributed, with the largest increases in Asia, U.S., and Europe. In contrast, changes are statistically not significant in most countries in Africa and Oceania for all considered warming levels.

River floods are among some of the costliest natural disasters [1], but their socioeconomic impacts under contrasting warming levels remain little explored [2]. Here, using a multi-model framework, we estimate human losses, direct economic damage, and subsequent indirect impacts (welfare losses) under a range of temperature (1.5°C, 2°C, and 3°C [3]) and socioeconomic scenarios, assuming current vulnerability levels and in absence of future adaptation. At 1.5°C, depending on the socioeconomic scenario, it is found that human losses from flooding could rise by 70 to 83%, direct flood damage by 160 to 240%, with a relative welfare reduction between 0.23 to 0.29%. In a 2°C world, by contrast, the death toll is 50% higher, direct economic damage doubles, and welfare losses grow to 0.4%. Impacts are notably higher under 3C warming, but at the same time, variability between ensemble members also increases, leading to greater uncertainty regarding flood impacts at higher warming levels. Flood impacts are further shown to have uneven regional distribution, with greatest losses observed over the Asian continent at all specific warming levels. It is clear that increased adaptation and mitigation effortsperhaps through infrastructural investment [4]is needed to offset increasing river flood risk in the future.

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Keywords:EURO-CORDEX climate projections RCP 8.5 Shared socioeconomic pathways (SSP) Flood risk Potential flood damage A B S T R A C T At the current rate of global warming, the target of limiting it within 2 degrees by the end of the century seems more and more unrealistic. Policymakers, businesses and organizations leading international negotiations urge the scientific community to provide realistic and accurate assessments of the possible consequences of so called "high end" climate scenarios.This study illustrates a novel procedure to assess the future flood risk in Europe under high levels of warming. It combines ensemble projections of extreme streamflow for the current century based on EURO-CORDEX RCP 8.5 climate scenarios with recent advances in European flood hazard mapping. Further novelties include a threshold-based evaluation of extreme event magnitude and frequency, an alternative method to removing bias in climate projections, the latest pan-European exposure maps, and an improved flood vulnerability estimation.Estimates of population affected and direct flood damages indicate that by the end of the century the socio-economic impact of river floods in Europe is projected to increase by an average 220% due to climate change only. When coherent socio-economic development pathways are included in the assessment, central estimates of population annually affected by floods range between 500,000 and 640,000 in 2050, and between 540,000 and 950,000 in 2080, as compared to 216,000 in the current climate. A larger range is foreseen in the annual flood damage, currently of 5.3 Bs, which is projected to rise at 20-40 Bs in 2050 and 30-100 Bs in 2080, depending on the future economic growth. 2015 Z. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract. Coastal flooding related to marine extreme events has severe socioeconomic impacts, and even though the latter are projected to increase under the changing climate, there is a clear deficit of information and predictive capacity related to coastal flood mapping. The present contribution reports on efforts towards a new methodology for mapping coastal flood hazard at European scale, combining (i) the contribution of waves to the total water level; (ii) improved inundation modeling; and (iii) an open, physics-based framework which can be constantly upgraded, whenever new and more accurate data become available. Four inundation approaches of gradually increasing complexity and computational costs were evaluated in terms of their applicability to large-scale coastal flooding mapping: static inundation (SM); a semidynamic method, considering the water volume discharge over the dykes (VD); the flood intensity index approach (Iw); and the model LISFLOOD-FP (LFP). A validation test performed against observed flood extents during the Xynthia storm event showed that SM and VD can lead to an overestimation of flood extents by 232 and 209 %, while Iw and LFP showed satisfactory predictive skill. Application at panEuropean scale for the present-day 100-year event confirmed that static approaches can overestimate flood extents by 56 % compared to LFP; however, Iw can deliver results of reasonable accuracy in cases when reduced computational costs are a priority. Moreover, omitting the wave contribution in the extreme total water level (TWL) can result in a ∼ 60 % underestimation of the flooded area. The present findings have implications for impact assessment studies, since combination of the estimated inundation maps with population exposure maps revealed differences in the estimated number of people affected within the 20-70 % range.

Quantifying flood hazard is an essential component of resilience planning, emergency response, and mitigation, including insurance. Traditionally undertaken at catchment and national scales, recently, efforts have intensified to estimate flood risk globally to better allow consistent and equitable decision making. Global flood hazard models are now a practical reality, thanks to improvements in numerical algorithms, global datasets, computing power, and coupled modelling frameworks. Outputs of these models are vital for consistent quantification of global flood risk and in projecting the impacts of climate change. However, the urgency of these tasks means that outputs are being used as soon as they are made available and before such methods have been adequately tested. To address this, we compare multi-probability flood hazard maps for Africa from six global models and show wide variation in their flood hazard, economic loss and exposed population estimates, which has serious implications for model credibility. While there is around 30%-40% agreement in flood extent, our results show that even at continental scales, there are significant differences in hazard magnitude and spatial pattern between models, notably in deltas, arid/semi-arid zones and wetlands. This study is an important step towards a better understanding of modelling global flood hazard, which is urgently required for both current risk and climate change projections.

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[1] New survey techniques provide a large amount of high-resolution data, which can be extremely precious for flood inundation modeling. Such data availability raises the issue as to how to exploit their information content to effectively improve flood risk mapping and predictions. In this paper, we will discuss a number of important issues which should be taken into account in works related to flood modeling. These include the large number of uncertainty sources in model structure and available data; the difficult evaluation of model results, due to the scarcity of observed data; computational efficiency; false confidence that can be given by high-resolution outputs, as accuracy is not necessarily increased by higher precision. Finally, we briefly review and discuss a number of existing approaches, such as subgrid parameterization and roughness upscaling methods, which can be used to incorporate high detailed data into flood inundation models, balancing efficiency and reliability.