Storm surges are an important coastal hazard component and it is unknown how they will evolve along Europe’s coastline in view of climate change. In the present contribution, the hydrodynamic model Delft3D-Flow was forced by surface wind and atmospheric pressure fields from a 8-member climate model ensemble in order to evaluate dynamics in storm surge levels (SSL) along the European coastline (1) for the baseline period 1970–2000; and (2) during this century under the Representative Concentration Pathways RCP4.5 and RCP8.5. Validation simulations, spanning from 2008 to 2014 and driven by ERA-Interim atmospheric forcing, indicated good predictive skill (0.06 m < RMSE < 0.29 m and 10 % < RMSE < 29 % for 110 tidal gauge stations across Europe). Peak-over-threshold extreme value analysis was applied to estimate SSL values for different return periods, and changes of future SSL were obtained from all models to obtain the final ensemble. Values for most scenarios and return periods indicate a projected increase in SSL at several locations along the North European coastline, which is more prominent for RCP8.5 and shows an increasing tendency towards the end of the century for both RCP4.5 and RCP8.5. Projected SSL changes along the European coastal areas south of 50°N show minimal change or even a small decrease, with the exception of RCP8.5 under which a moderate increase is projected towards the end of the century. The present findings indicate that the anticipated increase in extreme total water levels due to relative sea level rise (RSLR), can be further enforced by an increase of the extreme SSL, which can exceed 30 % of the RSLR, especially for the high return periods and pathway RCP8.5. This implies that the combined effect could increase even further anticipated impacts of climate change for certain European areas and highlights the necessity for timely coastal adaptation and protection measures. The dataset is publicly available under this link: http://data.jrc.ec.europa.eu/collection/LISCOAST.

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.

Decision Support Systems, and, more recently, participatory and collaborative modelling have emerged as a response to increased focus on stakeholder participation in modelling activities for certain fields like water resources management. Researchers and practitioners frequently use buzzwords such as participatory modelling and collaborative modelling. In some cases, both terms are used interchangeably, largely due to unclear distinction between them in literature. This article draws the line between participatory and collaborative modelling by using levels of participation and cooperation as conditioning dimensions. Based on this methodology, a new generic framework is presented. This framework can help identify determinant features of both modelling approaches currently used in water resources management. It permits analysis of these approaches in terms of context, specific use, information handling, stakeholder involvement, modelling team and means. The article concludes with an application of the framework to a collaborative modelling approach carried out for a groundwater study in the Netherlands. Participatory and collaborative modelling as a response to challenges encountered with traditional DSSs and stakeholder engagement methods.Participatory modelling differs from collaborative modelling by considering participation and cooperation as conditioning factors.A generic framework for participatory and collaborative modelling approaches in Water Resources Management is proposed.

Small Island Developing States (SIDS) have long been recognized as some of the planet’s most vulnerable areas to climate change, notably to rising sea levels and coastal extremes. They have been crucial in raising ambitions to keep global warming below 1.5 °C and in advancing the difficult debate on loss and damage. Still, quantitative estimates of loss and damage for SIDS under different mitigation targets are lacking. Here we carry out an assessment of future flood risk from slow-onset sea-level rise and episodic sea-level extremes along the coastlines of SIDS worldwide. We show that by the end of this century, without adaptation, climate change would amplify present direct economic damages from coastal flooding by more than 14 times under high-emissions scenarios. Keeping global warming below 1.5 °C could avoid almost half of unmitigated damage, depending on the region. Achieving this climate target, however, would still not prevent several SIDS from suffering economic losses that correspond to considerable shares of their GDP, probably leading to forced migration from low-lying coastal zones. Our results underline that investments in adaptation and sustainable development in SIDS are urgently needed, as well as dedicated support to assisting developing countries in responding to loss and damage due to climate change.

An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policymaking, and harmonization of climate change adaptation strategies. There is, however, limited insight into the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the coastal flood risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea level (ESL), (ii) the underlying uncertainty in the digital elevation model (DEM), (iii) flood defence information, (iv) the assumptions behind the use of depth–damage functions that express vulnerability, and (v) different climate change projections. The impact of these uncertainties on estimated expected annual damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal, and on the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, and their absolute and relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large datasets with sufficient resolution and accuracy, the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.

Nearshore slope, defined as the cross-shore gradient of the subaqueous profile, is an important input parameter which affects hydrodynamic and morphological coastal processes. It is used in both local and large-scale coastal investigations. However, due to unavailability of data, most studies, especially those that focus on continental or global scales, have historically adopted a uniform nearshore slope. This simplifying assumption could however have far-reaching implications for predictions/projections thus obtained. Here, we present the first global dataset of nearshore slopes with a resolution of 1 km at almost 620 000 points along the global coastline. To this end, coastal profiles were constructed using global topo-bathymetric datasets. The results show that the nearshore slopes vary substantially around the world. An assessment of coastline recession driven by sea level rise (SLR) (for an arbitrary 0.5 m SLR) with a globally uniform coastal slope of 1 : 100, as carried out in previous studies, and with the spatially variable coastal slopes computed herein shows that, on average, the former approach would underestimate coastline recession by about 40 %, albeit with significant spatial variation. The final dataset has been made publicly available at https://doi.org/10.4121/uuid:a8297dcd-c34e-4e6d-bf66-9fb8913d983d (Athanasiou, 2019).