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Coastal deltas are extremely susceptible to flooding from sea, rivers, heavy rain and even more severe combinations thereof. Many coastal deltas are densely populated, and flood risk forms a serious threat that will likely increase in the future. There are two main mechanisms to reduce the devastating impacts of these floods; (1) adaptation to the increasing climate risks and (2) improved early warning and emergency response. We will present the Destination Earth digital twin on coastal compound flood inundation forecasting and climate adaptation.This digital replica of the delta will connect DE data to impact models and will generate user oriented actionable output. High-resolution meteorological forecasts and regular updated climate scenarios form the input for regional coastal and inland models that provide boundary conditions for local models. Local models are used for simulation of small-scale hydrological processes, high-resolution 2D compound flood inundation and impact on population and buildings. A flexible data post-processing routine, to be co-designed with the end-users, will be implemented to translates model results into actionable context-specific information. It will, for example, produce flood inundation and risk maps as well as other user-requested indicators. For the climate adaptation component, the system will allow users to run WhatIf scenarios to explore adaptation measures. The data products will be disseminated to the users via Web-API, WEB-mapping service or through (S)FTP, depending on the users’ preferences.We will outline and demonstrate our digital twin that serves both flood impact reduction mechanisms, i.e. climate adaptation and forecasting extremes. We will highlight end users’ needs and requirement that are involved through co-creation/design of the service.

The wealth of available global datasets at high spatial and temporal resolutions opens many opportunities for hydrological modelling and forecasting. It is now possible to provide high-resolution hydrological simulations for a river basin anywhere in the world, even in basins without in situ observations. Combined with the strength of global parameter estimations of the wflow_sbm concept (Imhoff et al, 2021), this allows us to build and run hydrological models without calibration. These models can ultimately be used to provide discharge forecasts for the seasonal time scale. Here we present a workflow that tests the interoperability, scalability, and performance of combining cloud and high-throughput compute and data resources. The workflow combines open source technologies, including containerization, to provide automated monthly river discharge forecasts for practically every basin on the globe on cloud, HTC and in the future HPC platforms. We leverage the global ERA5 and SEAS5 products from the Copernicus Climate Data store as input for the wflow hydrological model. The workflow automatically downloads the required input data for the model domain, resamples the data to the required model grids, and runs the simulations. The workflow is automatically triggered every month when new SEA5 forecasts become available. Prior to running the forecasts, the ERA5 files are used to update the hindcast model states in preparation for the forecast. Next, 50 wflow ensemble members, forced using the SEAS5 forecasts, are run in parallel to provide estimates on the probability of discharge events. The workflow is currently set up and running for the Rhine basin on SURF’s high-throughput computing platform, but can easily be deployed on different infrastructures and for different river basins.

A sufficient freshwater supply is vital for humans, ecosystems, and economies, but anticipated climate and socio-economic change are expected to substantially alter water availability. Across Europe, about 2/3 of the abstracted freshwater comes from rivers and streams. Various hydrological studies address the resulting need for projections on changes in river discharge. However, those assessments rarely account for the impact of various water withdrawal scenarios during low flow periods. We present here a novel, high-resolution hydrological modeling experiment using pseudo-global warming climate data to investigate the effects of changing water withdrawals under 2K global warming. We find substantial sensitivities in projected low flows to varying water withdrawal assumptions, especially across Western and Central Europe. Our results highlight the importance of accounting for future water withdrawals in low flow projections, showing that climate-focused impact assessments in near-natural catchments provide only one piece of the anticipated response and do not necessarily reflect changes in heavily managed river basins.

Coastal flood risk is a serious global challenge facing current and future generations. Several disaster risk reduction (DRR) measures have been posited as ways to reduce the deleterious impacts of coastal flooding. On the global scale, however, efforts to model the effects of DRR measures (beyond structural) in the future are limited. In this paper, we use a global-scale flood risk model to estimate the risk of coastal flooding, and to assess and compare the effectiveness and economic performance of various DRR measures, namely: dykes and coastal levees, dry-proofing of urban assets, zoning restrictions in flood-prone areas, and management of foreshore vegetation. To assess the effectiveness of each DRR measure, we determine the extent to which they can limit future flood risk as a percentage of regional GDP to the same value as today (the so-called relative-risk constant objective). To assess their economic performance, we estimate the economic benefits and costs. If no DRR measures are taken in the future, we estimate expected annual damages to exceed $2 trillion USD by 2080, directly affecting an estimated 15 million people. Over 90 % of sub-national regions in the world can achieve their relative-risk constant targets if at least one of the investigated DRR measures is employed. At the global scale, we find the effectiveness of dykes and coastal levees in achieving the relative-risk constant objective to be 98 %, dry-proofing to be 49 %, zoning restrictions to be 11 %, and foreshore vegetation to be 6 %. In terms of direct costs, the overall figure is largest for dry-proofing ($151 billion) and dykes and coastal levees ($86 billion), much more than those of zoning restrictions ($27 million) and foreshore vegetation ($366 million). While zoning restrictions and foreshore vegetation achieve the highest global benefit-cost ratios, they also provide the least benefits overall. We show that there are large regional patterns in both the effectiveness and economic performance of modelled DRR measures. Future research could assess the indirect costs and benefits of these four and other DRR measures as well as their subsequent hybridisation.

Climate change will influence the severity and intensity of floods and droughts throughout Europe. Ensemble projections of future changes in discharge show large variation. Several methods for performance-based weighting exist that have the potential to increase the robustness of the change signal. Here we use future projections of an ensemble of three hydrological models forced with climate datasets from the Coordinated Downscaling Experiment - European Domain (EURO-CORDEX). The experiment is set-up for nine river basins spread over Europe that hold different climate and catchment characteristics. We evaluate the ensemble consistency and apply two weighting approaches; (1) the Climate model Weighting by Independence and Performance (ClimWIP) that focuses on meteorological variables and (2) the Reliability Ensemble Averaging (REA) that is in our study applied to discharge statistics per basin. For droughts we focus on the change in discharge during low flow periods of 7-days.For basins with a strong climate signal, in Southern and Northern Europe, the consistency in the set of projections is large. For rivers in Central Europe the differences between models become more pronounced. Both weighting methods favor projections from similar GCMs and assign high weights to a single or few best performing GCMs. The ClimWIP method results in ensemble mean weighted changes that differ only slightly from the non-weighted mean. The REA method influences the weighted mean more. The weights assigned to the CORDEX model simulations based on basin specific discharges highly vary from basin to basin.

An increasing number of countries and river basins worldwide are confronted with droughts. Inevitably, this leads to an increasing pressure on the available water resources and associated drought risks. In order to prevent severe drought impacts, such as food insecurity and possibly even conflicts on water use, timely and focused drought mitigation measures need to be taken. Here, early stage drought forecasts can be of great value, especially when they include local sector-specific information.Over the past years Deltares developed the Global Flood Forecasting and Information System (GLOFFIS). Up until recent its focus was on floods but now seasonal forecasts have been included to provide drought early warning information. GLOFFIS is based on the wflow framework for hydrological modelling which is embedded within the Delft-FEWS forecast production system that is applied in many countries worldwide.Wflow relies on so-called pedo-transfer functions that translate input base maps such as land use and soil type to estimate model parameter values. Herewith the model requires little calibration and can easily be implemented for additional basins. The set-up of GLOFFIS is global, yet for the drought forecasting component it was decided to include only high-resolution basin scale models since many global initiatives already exist and the resolution of global models generally lacks granularity to inform decision making. The system is flexible and contains workflows to be easily extended with hydrological models for new basins on demand. The drought forecasts are informed by means of standard drought indicators like discharge anomalies, soil moisture deficit, evaporation deficit indices. This list can be extended depending on the local need. So far hydrological models have been included for the Paraná, Lempa, Rhine and Niger river basins. The application for the Parana will be demonstrated as this basin in South-America currently experiences a severe drought.

Flash Floods are damaging natural hazards which often occur in the European Alps. Precipitation patterns and intensity may change in a future climate affecting their occurrence and magnitude. For impact studies, flash floods can be difficult to simulate due the complex orography and limited extent & duration of the heavy rainfall events which trigger them. The new generation convection-permitting regional climate models (CP-RCMs) improve the representation of the intensity and frequency of heavy precipitation. Therefore, this study combines such simulations with high-resolution distributed hydrological modelling to assess changes in flash flood frequency over the Alpine domain. We use output from a state-of-the-art CP-RCM to drive a high-resolution distributed hydrological wflow_sbm model (0.0088333 degree, ~1km2) covering most of the Alpine mountain range on an hourly resolution. First, the hydrological model was validated by comparing ERA5 driven simulation with streamflow observations from 130 stations (across Rhone, Rhine, Po, Adige and Danube basins). Second, an hourly wflow_sbm simulation driven by the CP-RCM downscaled ERAInterim simulation was compared to databases of past flood events to evaluate if the model can accurately simulate flash floods and to determine a suitable threshold definition for flash flooding and to determine a suitable threshold definition for flash flooding. Finally, simulations of the future climate RCP 8.5 for the end-of-century (2096-2105) and current climate (1998-2007) are compared for which the CP-RCM is driven by a Global Climate Model. The simulations are compared to assess if there are changes in flash flood frequency and magnitude using a threshold approach. Results show a similar flash flood frequency for autumn in the future, but a decrease in summer. However, the Future Climate simulations indicate an increase in the maximal flash flood severity in both summer and autumn leading to more severe flash flood impacts.

Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning systems in data-scarce regions. In this study, we used statistical metrics to compare gauge-based and satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high-resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwandan weather station sites. Based on statistical indicators, rainfall data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM_IMERG) showed the highest skill in reproducing the main spatiotemporal precipitation patterns at the study sites in Rwanda. Similarly, the satellite- and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation: first, the maximum rainfall amount during a multi-day rainfall event, and second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, the top 5 cm by satellite-based technologies as well as the top 50 cm and 2 m by modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 d rainfall RD3 to be the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from no-landslide conditions (AUC ∼ 0.72), but also the resulting true positive alarms (TPRs) of ∼80 %. The modelled antecedent soil moisture in the 50 cm root zone Seroot(t−3) was the most informative hydrological variable for landslide hazard assessment (AUC ∼ 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t−3) and RD3 following the cause–trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by ∼20 % at the expense of a minor reduction in true alarms by ∼8 %.

Hydrologic variability is expected to change throughout Europe due to climate change. However, ensemble projections of future changes in discharge show large variation because of the uncertainty in climate projections. The robustness of the change signal can potentially be improved by performance-based weighting. Here we analyze future change projections from an ensemble of three hydrological models (CWatM, LISFLOOD and wflow_sbm) forced with climate datasets from the Coordinated Downscaling Experiment - European Domain (EURO-CORDEX). The experiment focusses on nine river basins spread over Europe. The basins have different climate and catchment characteristics that strongly influence the hydrological response. We evaluate the ensemble consistency, the geographical variation therein and apply two weighting approaches; (1) the Climate model Weighting by Independence and Performance (ClimWIP) that focuses on meteorological variables and (2) the Reliability Ensemble Averaging (REA) that is here applied to catchment specific discharge statistics.In Southern and Northern-Europe the ensemble consistency is high. There is a strong climate change signal. In Central Europe the differences between models are more pronounced. Analysis of the weighting method reveal that both weighting methods favor projections from similar GCMs and assign high weights to a single or few best performing GCMs.

Anticipatory action requires models that can accurately predict the impact of both the primary hazard and its consecutive events. In the Philippines, typhoons trigger 90% of landslides, causing a lot of fatalities and damage to infrastructure and agriculture. The lack of information on past landslides hampers the development of accurate forecasting models of landslide occurrence and impact. An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons and includes the possible impact due to landslides only via a static landslide susceptibility map. This study expands the impact-based forecasting model of 510, an initiative of the Netherlands Red Cross, with a dynamic landslide component via hybrid modeling for two typhoon events in the Bicol region in the Philippines.A hydrometeorological model to forecast landslide occurrences was successfully created, even with the limited data on landslide occurrences and rainfall available. The newly established regional event duration threshold was applied on the case study events with an increased impact boundary of 300 km compared to the typhoon impact boundary of 100 km. The dynamic multi-hazard model showed an improved impact forecast - compared to the model considering solely static input of landslides - both in geographical impact extent and accuracy: the True Positives doubled, whereas the False Negatives reduced by half. A separate landslide forecasting model as an extension of the existing ML model provided additional benefits as the models can be decoupled to optimize the performance and reliability of both models. This study resulted in a prototype of an impact-based multi-hazard or consecutive event model for the Philippines and demonstrated the importance of considering the impact from consecutive hazards.Keywords: Landslide, typhoon, consecutive hazards, impact-based forecasting, rainfall, machine learning, Philippines

A combination of extreme environmental conditions such as high soil moisture content and heavy or prolonged precipitation contribute to landslide initiation in mountainous areas worldwide. On-site soil moisture monitoring equipment and rain gauge have been widely used to record these variables despite the sparse spatial coverage. Satellite‐based technologies provide estimates of rainfall and soil moisture over large spatial areas sufficient to be explored for landslide hazard assessment in data scarce regions. In this study, we used statistical metrics to compare the gauge based to the satellite precipitation products: TRMM42, CHIRPS, PERSIANN-CDR, GLDAS-2.1, CFSV2, GPM-IMERG, and ERA-5 and assess their performance. Similarly, high resolution satellite and hydrological model derived soil moisture was compared to the automated soil moisture observations at Rwanda weather station sites to assess the usefulness in empirical landslide hazard assessment thresholds in Rwanda. Based on statistical indicators, the NASA GPM based IMERG showed the highest skill to reproduce the main spatiotemporal precipitation patterns. Similarly, the satellite and hydrological model derived soil moisture broadly reproduce the in situ measured soil moisture. The landslide explanatory variables from IMERG satellite precipitation; event rainfall volume E and Duration D in bilinear thresholds framework reveal promising results with improved landslide prediction capabilities in terms of true positive alarms ~80-90% and low rate of false alarms ~14-16%. However, the incorporation of satellite and model derived antecedent soil moisture to the empirical landslide hydro-meteorological thresholds showed no significant improvement. This may be attributed to the probable long and no constant timescale of the defined landslide triggering events that could be shortened to further improve the landslide prediction and support the early warning system development in Rwanda.

Coastal flood risk is a major global challenge facing current and future generations. Indeed, this risk is expected to increase over the next several decades due to the changing climate, increased urbanization within flood-prone areas, loss of natural coastal defenses, and underground resource extraction, among other global factors. A number of risk reduction strategies have been posited as methods of mitigating the deleterious impacts of coastal flooding and investigated thoroughly through several existing small-scale applications around the world.On the global scale, however, efforts to model the effects of such risk reduction strategies in the future are rather limited. Most global modeling efforts that have been undertaken typically examine the potential risk reductions of structural measures (e.g., dykes and levees). And while some initial progress has been made in recent years on assessing alternative risk reduction strategies, the majority of this work still looks to quantify the effects of individual strategies alone. In reality, the hybridization of risk reduction strategies may be the most cost effective or environmentally feasible pathway forward for different segments of society.We look to quantify the risk reductions expected from employing multiple strategies at once. Within the same global flood risk modeling framework, we model the dry-proofing of urban assets, restriction of future development within flood-prone zones, and conservation of foreshore vegetation. In addition to modeling these strategies individually, we determine what risk reductions are possible when they are hybridized, both with each other and also structural measures. By using a disaster risk framework – risk defined as a product of hazard, exposure, and vulnerability – and a benefit-cost analysis that measures financial and human impacts of each strategy, we determine expected levels of risk reduction for different regions of the world. These results are available for various points in the future under various representative concentration pathway and shared socioeconomic pathway combinations. Further, we demonstrate which (combinations of) strategies may be able to achieve similar levels of overall risk reduction that would be anticipated with solely structural measures. This work not only demonstrates how the envelope of potential options can be expanded for decision makers addressing coastal flood risk, but also can be used as the foundation of future risk flood reduction assessments that incorporate more options than those examined here.

Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning systems in data-scarce regions. In this study, we used statistical metrics to compare gauge-based and satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high-resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwandan weather station sites. Based on statistical indicators, rainfall data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM_IMERG) showed the highest skill in reproducing the main spatiotemporal precipitation patterns at the study sites in Rwanda. Similarly, the satellite- and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation: first, the maximum rainfall amount during a multi-day rainfall event, and second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, the top 5 cm by satellite-based technologies as well as the top 50 cm and 2 m by modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 d rainfall RD3 to be the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from no-landslide conditions (AUC ∼ 0.72), but also the resulting true positive alarms (TPRs) of ∼80 %. The modelled antecedent soil moisture in the 50 cm root zone Seroot(t−3) was the most informative hydrological variable for landslide hazard assessment (AUC ∼ 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t−3) and RD3 following the cause–trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by ∼20 % at the expense of a minor reduction in true alarms by ∼8 %.

Flash Floods are damaging natural hazards which often occur in the European Alps. Precipitation patterns and intensity may change in a future climate affecting their occurrence and magnitude. For impact studies, flash floods can be difficult to simulate due the complex orography and limited extent & duration of the heavy rainfall events which trigger them. The new generation convection-permitting regional climate models improve the intensity and frequency of heavy precipitation (Ban et al., 2021).Therefore, this study combines such simulations with high-resolution distributed hydrological modelling to assess changes in flash flood frequency and occurrence over the Alpine terrain. We use the state-of-the-art Unified Model (Berthou et al., 2018) to drive a high-resolution distributed hydrological wflow_sbm model (e.g. Imhoff et al., 2020) covering most of the Alpine mountain range on an hourly resolution. Simulations of the future climate RCP 8.5 for the end-of-century (2096-2105) and current climate (1998-2007) are compared.First, the wflow_sbm model was validated by comparing ERA5 driven simulation with streamflow observations (across Rhone, Rhine, Po, Adige and Danube). Second, the wflow_sbm simulation driven by UM simulation of the current climate was compared to a dataset of historical flood occurrences (Paprotny et al., 2018, Earth Syst. Sci. Data) to validate if the model can accurately simulate the location of the flash floods and to determine a suitable threshold for flash flooding. Finally, the future run was used to asses changes in flash flood frequency and occurrence. Results show an increase in flash flood frequency for the Upper Rhine and Adige catchments. For the Rhone the increase was less pronounced. The locations where the flash floods occur did not change much.This research is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to support climate adaptation and mitigation decisions for the coming decades by developing a regional climate prediction and projection system based on high-resolution climate models for Europe. N. Ban, E. Brisson, C. Caillaud, E. Coppola, E. Pichelli, S. Sobolowski, …, M.J. Zander (2021): “The first multi-model ensemble of regional climate simulations at the kilometer-scale resolution, Part I: Evaluation of precipitation”, manuscript accepted for publication in Climate Dynamics.S. Berthou, E.J. Kendon, S. C. Chan, N. Ban, D. Leutwyler, C. Schär, and G. Fosser, 2018, “Pan-european climate at convection-permitting scale: a model intercomparison study.” Climate Dynamics, pages 1–25, DOI: 10.1007/s00382-018-4114-6Imhoff, R.O., W. van Verseveld, B. van Osnabrugge, A.H. Weerts, 2020. “Scaling point-scale pedotransfer functions parameter estimates for seamless large-domain high-resolution distributed hydrological modelling: An example for the Rhine river.” Water Resources Research, 56. Doi: 10.1029/2019WR026807Paprotny, D., Morales Napoles, O., & Jonkman, S. N., 2018. "HANZE: a pan-European database of exposure to natural hazards and damaging historical floods since 1870". Earth System Science Data, 10, 565–581, https://doi.org/10.5194/essd-10-565-2018

In this study a methodology is developed and tested to delineate homogeneous regions of extreme rainfall around a city of interest using meteorological indices from reanalysis data.Scenarios of future climate change established with numerical climate models are well-established tools to help inform climate adaptation policy. The latest generation of regional climate models is now employed at a grid resolution of 2 to 3 kilometers. This enables the simulation of convection; whereby intensive convective rainfall is better represented (Kendon et al., 2017). However, the associated large computational burden limits the simulation length, which poses a challenge for estimating future rainfall statistics.Rainfall return periods are a commonly used indicator in the planning, design and evaluation of urban water systems and urban water management. In order to estimate potential future rainfall for return periods larger than the length of the simulation length, regional frequency analysis (RFA) can be applied (Li et al., 2017). For applying RFA, time series from nearby locations are pooled, the locations considered should fall within the same hydroclimatic climate. This is a region which can be assumed statistically homogeneous for extreme rainfall (Hosking & Wallis, 2009).Traditionally, these homogeneous regions are defined on geographical region characteristics and rain gauge statistics (Hosking & Wallis, 2009). To make the methodology less dependent on rain gauge record availability, Gabriele & Chiaravalloti (2013) used meteorological indices derived from reanalysis data to delineate the homogeneous regions.Here we evaluate the methodology to delineate homogeneous regions around cities. Meteorological indices are calculated from the ERA-5 reanalysis dataset (Hersbach et al., 2018) for days with extreme rainfall. The variation herein is used as a measure of homogeneity. The derived homogeneous regions will in future work be used for data pooling of convection-permitting regional climate model simulations datasets to enable the derivation of future extreme rainfall statistics.This study is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to develop a regional climate prediction and projection system based on high-resolution climate models for Europe, to support climate adaptation and mitigation decisions for the coming decades.References:Gabriele, S., & Chiaravalloti, F. (2013). “Searching regional rainfall homogeneity using atmospheric fields”. Advances in Water Resources, 53, 163–174. https://doi.org/https://doi.org/10.1016/j.advwatres.2012.11.002Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., …, Zuo, H. (2018). “Operational global reanalysis: progress, future directions and synergies with NWP”, ECMWF.Hosking, J. R. M., & Wallis, J. R. (2009). “Regional Frequency Analysis: An Approach Based on L-Moments”. The Edinburgh Building, Cambridge CB2 2RU, UK: Cambridge University Press. ISBN: 9780511529443.Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., … Wilkinson, J. M. (2017). “Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?” BAMS, 98(1), 79–93. https://doi.org/10.1175/BAMS-D-15-0004.1 Li, J., Evans, J., Johnson, F., & Sharma, A. (2017). “A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM.” Journal of Hydrology, 547(Supplement C), 413–427. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.02.019

Flash floods are a significant natural hazard in the Alpine region (FOEN, 2010). With changing rainfall regimes and decreased snow accumulation due to climate change, the risk of flash flood occurrence and timing thereof could change as well (Etchevers et al., 2002).In this study the frequency and occurrence of flash floods in the Alpine region is estimated for current and future climate (RCP8.5) using state-of-the-art high-resolution convection permitting climate models (CP-RCMs). For the historical period and far future (2100), data from an ensemble of convection permitting climate models (Ban et al., submitted 2019) was used to drive a high-resolution distributed hydrological model, i.e. the wflow_sbm model (Imhoff et al., 2019, Verseveld et al., 2020). The model domains cover the mountainous parts of the Danube, Rhone, Rhine and Po located in the Alps. The CP-RCM time-series available are of limited length due to computational constrains. At the same time the locations of flash floods vary per year therefore a regional scale analysis is made to assess whether in general the severity, frequency and timing of flash floods in the Alps will likely change under changing climate conditions.This research is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to support climate adaptation and mitigation decisions for the coming decades by developing a regional climate prediction and projection system based on high-resolution climate models for Europe.References:Etchevers, P., Golaz, C., Habets, F., and Noilhan, J., Impact of a climate change on the Rhone river catchment hydrology, J. Geophys. Res., 107( D16), doi:, 2002. Federal office for the environment FOEN (2010) Environment Switzerland 2011, Bern and Neuchatel 2011. Retrieved from www.environment-stat.admin.chImhoff, R.O., W. van Verseveld, B. van Osnabrugge, A.H. Weerts, 2019. Scaling point-scale pedotransfer functions parameter estimates for seamless large-domain high-resolution distributed hydrological modelling: An example for the Rhine river. Submitted to Water Resources Research, 2019.N. Ban, E. Brisson, C. Caillaud, E. Coppola, E. Pichelli, S. Sobolowski, …, M.J. Zander (submitted 2019): “The first multi-model ensemble of regional climate simulations at the kilometer-scale resolution, Part I: Evaluation of precipitation”, manuscript submitted for publication.

In this contribution we present the wflow_sbm hydrologic model concept, which is a conceptual bucket-style hydrologic model based on simplified physical relationships including kinematic wave routing for surface and subsurface lateral flow. The model maximizes the use of global data for local applications and allows us to automatically setup a high resolution (~1km2) wflow_sbm model for any basin in the world. For most discharge gauging stations in selected basins from different climate zones, wflow_sbm showed promising results without further calibration. Depending on the geographical area of interest two model parameters, besides anthropogenic interference like reservoir and lake management, show most sensitivity: rooting depth and horizontal saturated hydraulic conductivity.We extended the parameter estimation of the wflow_sbm hydrological model for the Rhine basin (Imhoff et al, 2019) with point-scale (pedo)transfer-functions (PTFs) in conjunction with scaling operators as applied in Multiscale Parameter Regionalization (MPR) to the global scale at high resolution (~1km2). The state-of-the-art hydro-MERIT dataset at 3 arcsec resolution (Yamazaki et al. (2019)) is scaled to model resolution whilst conserving the drainage network using a newly developed extended Effective Area Method (EAM) for flow direction scaling which builds on the original EAM (Yamazaki et al. 2009). Compared to EAM and the double maximum method, the extended EAM method shows improved skill. The automated model setup derives subgrid information about land slope, river slope and length. River widths are derived from power law relationships between hydro-MERIT river widths and global discharge estimates through multiple linear regression based on GRDC data, precipitation and upstream area with clustering on climate zones. Soil hydraulic parameters are derived from the 250m ISRIC SoilGrids product using PTFs. Furthermore, parameters for interception and rooting depth are derived and upscaled using global or regional land cover maps. Monthly LAI profiles are derived from MODIS (500m) and upscaled. Lake and reservoir parameters are derived from HydroLAKES and GRanD, respectively. The models are run using forcing from globally available data sets like ERA5 and CHIRPS. Imhoff, R., van Verseveld, W., Osnabrugge, B., A. Weerts, Scaling point-scale pedotransfer functions to seamless large-domain parameter estimates for high-resolution distributed hydrological modelling: An example for the Rhine river, submitted to WRR, 2019.Yamazaki D., D. Ikeshima, J. Sosa, P.D. Bates, G.H. Allen, T.M. Pavelsky, MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets, Water Resources Research, 2019, doi: 10.1029/2019WR024873.Yamazaki, D., T. Oki., and S. Kanae, Deriving a global river network map and its sub‐grid topographic characteristics from a fine‐resolution flow direction map, Hydrol. Earth Syst. Sci., 13, 2241– 2251, 2009.

The hydrologic regime of a river is one of the factors determining its ecological status. This paper tries to indicate the present hydrologic stress occurring across European rivers on the basis of model integration. This results in a pan-European assessment at the resolution of the functional elementary catchment (FEC), based on simulated daily time-series of river flows from the model PCR-GLOBWB. To estimate proxies of the present hydrologic stress, two datasets of river flow were simulated under the same climate, one from a hypothetic least disturbed condition scenario and the second from the anthropogenic scenario with the actual water management occurring. Indicators describing the rivers&rsquo, hydrologic regime were calculated with the indicators of hydrologic alteration (IHA) software package and the river total mean flow and the relative baseflow magnitude over the total flow were used to express the deviations between the two scenarios as proxy metrics of rivers&rsquo, hydrologic alteration or hydrologic stress. The alteration results on Europe&rsquo, s FEC-level background showed that Southern Europe is more hydrologically stressed than the rest of Europe, with greater potential for hydrology to be clearly associated with river segments of unreached good ecological status and high basin management needs.

Current and future urban flooding is influenced by changes in short-duration rainfall intensities. Conventional approaches to projecting rainfall extremes are based on precipitation projections taken from General Circulation Models (GCM) or Regional Climate Models (RCM). However, these and more complex and reliable climate simulations are not yet available for many locations around the world. In this work, we test an approach that projects future rainfall extremes by scaling the empirical relation between dew-point temperature and hourly rainfall and projected changes in dew-point temperature from the EC-Earth GCM. These projections are developed for the RCP 8.5 scenario and are applied to a case study in the Netherlands. The shift in intensity-duration-frequency (IDF) curves shows that a 100-year (hourly) rainfall event today could become a 73-year event (GCM), but could become as frequent as a 30-year (temperature-scaling) in the period 2071&ndash, 2100. While more advanced methods can help to further constrain future changes in rainfall extremes, the temperature-scaling approach can be of use in practical applications in urban flood risk and design studies for locations where no high-resolution precipitation projections are available.

Environmental research that involves assessment of climate impact or extreme events like drought, floods, or other risks often requires the combination of geospatial data, distributed models, and weather forecast products. The Delft-FEWS forecasting platform, used by operational flood warning organizations around the world, offers the opportunity to carry out this type of research within a state-of-the-art forecasting environment. This paper shows opportunities Delft-FEWS offers in dealing with geospatial data like satellite measurements, weather, and climate forecasts. It also shows that Delft-FEWS offers the possibility to include distributed hydrological models using an embedded version of PCRASTER. This is illustrated by two case studies that combine different geospatial data, satellite data, and AOGCM climate data, with two different types of models, a groundwater model and a global hydrological model, within the Delft-FEWS system.

El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability and has a strong influence on climate over large parts of the world. In turn, it strongly influences many natural hazards (such as hurricanes and droughts) and their resulting socioeconomic impacts, including economic damage and loss of life. However, although ENSO is known to influence hydrology in many regions of the world, little is known about its influence on the socioeconomic impacts of floods (i.e., flood risk). To address this, we developed a modeling framework to assess ENSO's influence on flood risk at the global scale, expressed in terms of affected population and gross domestic product and economic damages. We show that ENSO exerts strong and widespread influences on both flood hazard and risk. Reliable anomalies of flood risk exist during El Niño or La Niña years, or both, in basins spanning almost half (44%) of Earth's land surface. Our results show that climate variability, especially from ENSO, should be incorporated into disaster-risk analyses and policies. Because ENSO has some predictive skill with lead times of several seasons, the findings suggest the possibility to develop probabilistic flood-risk projections, which could be used for improved disaster planning. The findings are also relevant in the context of climate change. If the frequency and/or magnitude of ENSO events were to change in the future, this finding could imply changes in flood-risk variations across almost half of the world's terrestrial regions.

[1] Recent studies suggest the increasing contribution of groundwater depletion to global sea-level rise. Groundwater depletion has more than doubled during the last decades, primarily due to increase in water demand, while the increase in water impoundments behind dams has been tapering off since the 1990s. As a result, the contribution of groundwater depletion to sea-level rise is likely to dominate over those of other terrestrial water sources in the coming decades. Yet, no projections into the 21st century are available. Here we present a reconstruction of past groundwater depletion and its contribution to global sea-level variation, as well as 21st century projections based on three combined socio-economic and climate scenarios (SRES) with transient climate forcing from three General Circulation Models (GCMs). We validate and correct estimated groundwater depletion with independent local and regional assessments, and place our results in context of other terrestrial water contributions to sea-level variation. Our results show that the contribution of groundwater depletion to sea-level increased from 0.035 (±0.009) mm yr−1 in 1900 to 0.57 (±0.09) mm yr−1 in 2000, and is projected to increase to 0.82 (±0.13) mm yr−1 by the year 2050. We estimate the net contribution of terrestrial sources to be negative of order −0.15 (±0.09) mm yr−1 over 1970–1990 as a result of dam impoundment. However, we estimate this to become positive of order +0.25 (±0.09) mm yr−1 over 1990–2000 due to increased groundwater depletion and decreased dam building. We project the net terrestrial contribution to increase to +0.87 (±0.14) mm yr−1 by 2050. As a result, the cumulative contribution will become positive by 2015, offsetting dam impoundment (maximum −31 ± 3.1 mm in 2010), and resulting in a total rise of +31 (±11) mm by 2050.

During dry years, such as 2003 and the early summer of 2011, the Netherlands faces water shortages, salt intrusion, navigation limitations and problems with availability of cooling water for power plants. Considering the changing climate, the frequency of these problems is expected to increase. A policy analysis using an integrated set of models has been launched by the government: the Dutch Delta Programme. The Delta model is the consistent set of models for analysing the decisions related to the long-term fresh water supply and flood risk management of the Netherlands. The country-wide SOBEK-1D hydrodynamic surface water model (LSM) was developed as part of the Delta model and forms the link between the Netherlands Hydrological Instrument (NHI) and impact assessment models for salinity, temperature, water quality, aquatic ecology and navigation. The Delta model provides a computational facility that automates the workflow of running sets of interconnected models for the national policy analysis on fresh water and various regional programmes within the Delta Programme. The Delta model, although being computationally demanding, enabled analyses of the present situation, future scenarios (2050 and 2100) and possible adaptation measures. Moreover, the Delta model has supported consistency between the national analyses and regional analyses in the Delta Programme.