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.

Nowadays, the development of high-resolution flood hazard models have become feasible at continental and global scale, and their application in developing countries and data-scarce regions can be extremely helpful to increase preparedness of population and reduce catastrophic impacts. The present work describes the development of a novel procedure for global flood hazard mapping, based on the most recent advances in large scale flood modelling. We derive a long-term dataset of daily river discharges from the global hydrological simulations of the Global Flood Awareness System (GloFAS). Streamflow data is downscaled on a high resolution river network and processed to provide the input for local flood inundation simulations, performed with a two-dimensional hydrodynamic model. All flood-prone areas identified along the river network are then merged to create continental flood hazard maps for different return periods at 30’’ resolution. We evaluate the performance of our methodology in several large river basins by comparing simulated flood maps with a mosaic of flooded areas detected from satellite images for the same reference period. We further investigate the sensitivity of the flood modelling framework to different parameters and modelling approaches and identify strengths, limitations and possible improvements of the methodology.JRC.H.7-Climate Risk Managemen

Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1^∘ at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling–Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions to the identification of hydroclimatic variability and change and as raw input for post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview (last access: 28 June 2020) with the following DOI: https://doi.org/10.24381/cds.a4fdd6b9 (C3S, 2019).

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 semi-dynamic 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 pan-European 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.