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Water‐related disasters, such as fluvial floods and cyclonic storm surges, are a major concern in the world's mega‐delta regions. Furthermore, the simultaneous occurrence of extreme discharges from rivers and storm surges could exacerbate flood risk, compared to when they occur separately. Hence, it is of great importance to assess the compound risks of fluvial and coastal floods at a large scale, including mega‐deltas. However, most studies on compound fluvial and coastal flooding have been limited to relatively small scales, and global‐scale or large‐scale studies have not yet addressed both of them. The objectives of this study are twofold: to develop a global coupled river‐coast flood model; and to conduct a simulation of compound fluvial flooding and storm surges in Asian mega‐delta regions. A state‐of‐the‐art global river routing model was modified to represent the influence of dynamic sea surface levels on river discharges and water levels. We conducted the experiments by coupling a river model with a global tide and surge reanalysis data set. Results show that water levels in deltas and estuaries are greatly affected by the interaction between river discharge, ocean tides and storm surges. The effects of storm surges on fluvial flooding are further examined from a regional perspective, focusing on the case of Cyclone Sidr in the Ganges‐Brahmaputra‐Meghna Delta in 2007. Modeled results demonstrate that a >3 m storm surge propagated more than 200 km inland along rivers. We show that the performance of global river routing models can be improved by including sea level dynamics.

Large-scale flood events often show spatial correlation in neighbouring basins, and thus can affect adjacent basins simultaneously, as well as result in superposition of different flood peaks. Such flood events therefore need to be addressed with large-scale modelling approaches to capture these processes. Many approaches currently in place are based on either a hydrologic or a hydrodynamic model. However, the resulting lack of interaction between hydrology and hydrodynamics, for instance, by implementing groundwater infiltration on inundated floodplains, can hamper modelled inundation and discharge results where such interactions are important. In this study, the global hydrologic model PCR-GLOBWB at 30 arcmin spatial resolution was one-directionally and spatially coupled with the hydrodynamic model Delft 3D Flexible Mesh (FM) for the Amazon River basin at a grid-by-grid basis and at a daily time step. The use of a flexible unstructured mesh allows for fine-scale representation of channels and floodplains, while preserving a coarser spatial resolution for less flood-prone areas, thus not unnecessarily increasing computational costs. In addition, we assessed the difference between a 1-D channel/2-D floodplain and a 2-D schematization in Delft 3D FM. Validating modelled discharge results shows that coupling PCR-GLOBWB to a hydrodynamic routing scheme generally increases model performance compared to using a hydrodynamic or hydrologic model only for all validation parameters applied. Closer examination shows that the 1-D/2-D schematization outperforms 2-D for r2 and root mean square error (RMSE) whilst having a lower Kling–Gupta efficiency (KGE). We also found that spatial coupling has the significant advantage of a better representation of inundation at smaller streams throughout the model domain. A validation of simulated inundation extent revealed that only those set-ups incorporating 1-D channels are capable of representing inundations for reaches below the spatial resolution of the 2-D mesh. Implementing 1-D channels is therefore particularly of advantage for large-scale inundation models, as they are often built upon remotely sensed surface elevation data which often enclose a strong vertical bias, hampering downstream connectivity. Since only a one-directional coupling approach was tested, and therefore important feedback processes are not incorporated, simulated discharge and inundation extent for both coupled set-ups is generally overpredicted. Hence, it will be the subsequent step to extend it to a two-directional coupling scheme to obtain a closed feedback loop between hydrologic and hydrodynamic processes. The current findings demonstrating the potential of one-directionally and spatially coupled models to obtain improved discharge estimates form an important step towards a large-scale inundation model with a full dynamic coupling between hydrology and hydrodynamics.

Current global flood risk studies are based on either fluvial or coastal flood drivers; potentially underestimating total flood risk as the dependencies between these drivers are ignored. In this contribution a global impact-based assessment of riverine flooding from compound events is presented. These events occur when: 1) high sea water levels propagate up a river pushing up water levels in the river; or 2) the drainage of high river discharge is limited by elevated sea water levels. In order to assess the riverine flood impact of compound events we use a numerical experiment with a coupled state-of-the art global fluvial flood model (CaMa-Flood) and global tide and surge model (GTSM). For all rivers with an upstream area larger than 1000 km2, the downstream sea level boundary in CaMa-Flood is set based on time series of nearshore water levels from GTSM. In the experiment we simulate fluvial flooding based on discharge and tide (case 1); discharge climatology and tide and surge (case 2), and combined discharge and tide and surge (case 3). In case 1 the surge component in the sea level boundary is ignored while in case 2 the peak discharge is ignored in the CaMa-Flood forcing. Flood impact metrics for flood extent and volume as well as people and GDP affected are calculated for low lying delta areas and compared between the individual (1 and 2) and compound flood (3) cases to determine the additional impact from compound flooding. All runs are based on 35 years of consistent forcing data from the EartH2Observe and ERA-Interim datasets.The uncertainties in the model approach are assessed through validation against observations of the timing and magnitude of discharge and surge peaks as well as the dependence between both. Furthermore, the sensitivity of the coupled model to the timing of surge and discharge peaks is analyzed by looking at an ensemble of model forcings with a spread in discharge and surge peak timing.This analysis will identify the delta regions most affected by compound flood events.

This poster shows the methodology and technical background of GLOFRIM 2.0. GLOFRIM is a computational framework whcih can be applied to couple hydrologic and hydrodynamic models, especially with the aim to improve flood hazard simulation across scales. The added value of model coupling is showcased with an example in the Amazon River basin, showing differences in both simulated discharge and inundation extent.

Damage resulting from flood events is increasing world-wide, requiring the implementation of mitigation and adaption measures. To facilitate their implementation, it is essential to correctly model flood hazard at the large scale, yet fine spatial resolution. To reduce the computational load of models, flexible meshes are an efficient means compared to uniform regular grids. Yet, thus far they have been applied only for bespoke small-scale studies requiring a high level of a priori grid preparation. To better understand possible advantages as well as shortcomings of their application for large-scale riverine inundation simulations, three different flexible meshes were derived from Height Above Nearest Drainage (HAND) data and compared with regular grids under identical spatially explicit hydrologic forcing by using GLOFRIM, a framework for integrated hydrologic-hydrodynamic inundation modelling. By means of GLOFRIM, output from the global hydrologic model PCR-GLOBWB was passed to the hydrodynamic model Delft3D Flexible Mesh. Results show that applying flexible meshes can be beneficial depending on the envisaged purpose. For discharge simulations, similar model accuracy was obtained between flexible and regular grids, with the former generally having shorter run times. For inundation extent simulations, however, the coarser gridding of flexible meshes in upstream areas results in a poorer performance if assessed by contingency maps. Moreover, while the ratio between minimum and maximum spatial resolution of flexible meshes has limited impact on discharge simulations, water level estimates may be stronger influenced by the application of larger grid cells. . As this study presents only a small set of possible realizations, additional research needs to unravel how the data and methods used as well as the choices for discretizations influence model performance. Generally, the application and particularly discretization process of flexible meshes involves more options, bringing more responsibilities for the user. Once an a priori decision is made on the model purpose, flexible meshes can be a valuable addition to modelling approaches where short run times are essential, facilitating large-scale flood simulations, ensemble modelling or operational flood forecasting.

Global river flood risk is expected to increase substantially over coming decades due to both climate change and socioeconomic development. Model-based projections suggest that southeast Asia and Africa are at particular risk, highlighting the need to invest in adaptation measures. Understanding global future river flood risk is a prerequisite for the quantification of climate change impacts and planning effective adaptation strategies1. Existing global flood risk projections fail to integrate the combined dynamics of expected socio-economic development and climate change. We present the first global future river flood risk projections that separate the impacts of climate change and socio-economic development. The projections are based on an ensemble of climate model outputs2, socio-economic scenarios3, and a state-of-the-art hydrologic river flood model combined with socio-economic impact models4,5. Globally, absolute damage may increase by up to a factor of 20 by the end of the century without action. Countries in Southeast Asia face a severe increase in flood risk. Although climate change contributes significantly to the increase in risk in Southeast Asia6, we show that it is dwarfed by the effect of socio-economic growth, even after normalization for gross domestic product (GDP) growth. African countries face a strong increase in risk mainly due to socio-economic change. However, when normalized to GDP, climate change becomes by far the strongest driver. Both high- and low-income countries may benefit greatly from investing in adaptation measures, for which our analysis provides a basis.

Managing future flood risk is necessary to minimize costs and achieve maximum benefit from investment. This study presents a framework to assess urban structural protection under climate change and socio-economic development. Floods cause billions of dollars of damage each year1, and flood risks are expected to increase due to socio-economic development, subsidence, and climate change2,3,4. Implementing additional flood risk management measures can limit losses, protecting people and livelihoods5. Whilst several models have been developed to assess global-scale river-flood risk2,4,6,7,8, methods for evaluating flood risk management investments globally are lacking9. Here, we present a framework for assessing costs and benefits of structural flood protection measures in urban areas around the world. We demonstrate its use under different assumptions of current and future climate change and socio-economic development. Under these assumptions, investments in dykes may be economically attractive for reducing risk in large parts of the world, but not everywhere. In some regions, economically efficient investments could reduce future flood risk below today’s levels, in spite of climate change and economic growth. We also demonstrate the sensitivity of the results to different assumptions and parameters. The framework can be used to identify regions where river-flood protection investments should be prioritized, or where other risk-reducing strategies should be emphasized.

The world's mega-delta regions and estuaries are susceptible to various water-related disasters, such as river flooding and storm surge. Moreover, simultaneous occurrence of them would be more devastating than a situation where they occur in isolation. Therefore, it is important to provide information about compound risks of fluvial and coastal floods at a large scale, both their statistical dependency as well as their combined resulting flooding in delta regions. Here we report on a first attempt to address this issue globally by developing a method to couple a global river model (CaMa-Flood) and a global tide and surge reanalysis (GTSR) dataset. A state-of-the-art global river routing model, CaMa-Flood, was modified to represent varying sea levels due to tides and storm surges as downstream boundary condition, and the GTSR dataset was post-processed to serve as inputs to the CaMa-Flood river routing simulation and a long-term simulation was performed to incorporate the temporal dependency between coastal tide and surge on the one hand, and discharge on the other. The coupled model was validated against observations, showing better simulation results of water levels in deltaic regions than simulation without GTSR. For example in the Ganges Delta, correlation coefficients were increased by 0.06, and root mean square errors were reduced by 0.22 m. Global coupling simulations revealed that storm surges affected river water levels in coastal regions worldwide, especially in low-lying flat areas with increases in water level larger than 0.5 m. By employing enhanced storm surge simulation with tropical storm tracks, we also applied the model to examine impacts of past hurricane and cyclone storm events on river flood inundation.

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of −0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39–59% higher estimate of population exposure.

Extreme sea levels, caused by storm surges and high tides, can have devastating societal impacts. To effectively protect our coasts, global information on coastal flooding is needed. Here we present the first global reanalysis of storm surges and extreme sea levels (GTSR data set) based on hydrodynamic modelling. GTSR covers the entire world's coastline and consists of time series of tides and surges, and estimates of extreme sea levels. Validation shows that there is good agreement between modelled and observed sea levels, and that the performance of GTSR is similar to that of many regional hydrodynamic models. Due to the limited resolution of the meteorological forcing, extremes are slightly underestimated. This particularly affects tropical cyclones, which requires further research. We foresee applications in assessing flood risk and impacts of climate change. As a first application of GTSR, we estimate that 1.3% of the global population is exposed to a 1 in 100-year flood.