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Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The threshold and tail scale parameter are estimated at each location and used to calculate estimates of the 10- and 200-year return levels. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and modulating the threshold, with a less detectable influence on rarer extremes and the tail scale parameter. The length of historical data required to have the lowest error in estimating return levels is quantified using both observed and simulated time series of the historical NAO. For reducing errors in estimating 200-year return levels of an independent 10-year period, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels, and so there is more variability in estimates. Using theoretical estimates of future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model. [ABSTRACT FROM AUTHOR]

Aim: Invasive alien species are a growing problem worldwide due to their ecological, economic and human health impacts. The "killer shrimp" Dikerogammarus villosus is a notorious invasive alien amphipod from the Ponto‐Caspian region that has invaded many fresh and brackish waters across Europe. Understandings of large‐scale population dynamics of highly impactful invaders such as D. villosus are lacking, inhibiting predictions of impact and efficient timing of management strategies. Hence, our aim was to assess trends and dynamics of D. villosus as well as its impacts in freshwater rivers and streams. Location: Europe. Methods: We analysed 96 European time series between 1994 and 2019 and identified trends in the relative abundance (i.e. dominance %) of D. villosus in invaded time series, as well as a set of site‐specific characteristics to identify drivers and determinants of population changes and invasion dynamics using meta‐regression modelling. We also looked at the spread over space and time to estimate the invasion speed (km/year) of D. villosus in Europe. We investigated the impact of D. villosus abundance on recipient community metrics (i.e. abundance, taxa richness, temporal turnover, Shannon diversity and Pielou evenness) using generalized linear models. Results: Population trends varied across the time series. Nevertheless, community dominance of D. villosus increased over time across all time series. The frequency of occurrences (used as a proxy for invader spread) was well described by a Pareto distribution, whereby we estimated a lag phase (i.e. the time between introduction and spatial expansion) of approximately 28 years, followed by a gradual increase before new occurrences declined rapidly in the long term. D. villosus population change was associated with decreased taxa richness, community turnover and Shannon diversity. Main Conclusion: Our results show that D. villosus is well‐established in European waters and its abundance significantly alters ecological communities. However, the multidecadal lag phase prior to observed spatial expansion suggests that initial introductions by D. villosus are cryptic, thus signalling the need for more effective early detection methods. [ABSTRACT FROM AUTHOR]

• A larger intensification of rarer extreme precipitation events is projected. • A greater change is projected for shorter-duration extreme precipitation events. • Frequency of extreme events will be tripled under the high-end scenario. • Climate change will lead to a large uplift and steepening of IDF curves. To build future climate-proof water systems and to reduce the failure risk of infrastructure, short-duration extreme precipitation design storms must be updated. However, a robust future projection of such storms on a continental scale has not been presented due to the scarcity of long, high quality sub-daily data both for observations and model simulations. To address this need, we develop current and future precipitation intensity–duration–frequency (IDF) curves for Europe for 0.5-, 1-, 3-, 6-, 12- and 24-hour durations and for different return periods ranging between 1 and 100 years derived from a Generalized Pareto Distribution (GPD). Future IDF curves are developed in the framework of the quantile perturbation downscaling method by applying climate change signals from the EURO-CORDEX RCMs on current IDF curves obtained from high-resolution remote sensing based products. Here we show that rarer sub-daily extreme precipitation events will intensify more than less rare events and that changes may depend on precipitation duration. The frequency of sub-daily extreme precipitation events of 50- and 100-year return periods will be tripled under the high-end RCP8.5 scenario. Such intensification will lead to a substantial uplift (16–27% depending on duration and return period) and steepening (17–25% depending on return period) of the IDF curves under future climate change, calling attention to the vulnerability of the existing water infrastructure systems. [ABSTRACT FROM AUTHOR]

Urban water infrastructure and flood control structures are typically designed based on the properties of extreme precipitation in the form of intensity-duration-frequency (IDF) curves. The IDF curves are currently based on historical precipitation statistics with a temporal stationarity assumption for these statistics. However, climate change has been altering extreme precipitation properties, leading to the violation of the stationarity assumption and an increase in the probability of structure failure designed based on the current IDF curves. Therefore, to facilitate damage limitation, current design standards based on IDF curves need to be updated considering climate change impact on extreme precipitation properties. To take into account the climate change impact and the spatial pattern of extreme precipitation, this study presents Europe-wide IDF curves for current and future climates. The current IDF curves over Europe are developed using 3-hourly precipitation data from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset with 12 km spatial resolution and 30-min precipitation product of Climate Prediction Center Morphing technique (CMORPH) with 8 km spatial resolution. The developed current IDF curves based on the gridded datasets are validated using sub-daily raingauge precipitation at different locations in Europe. The precipitation with different return periods (ranging between 1 and 100 yr) is calculated by fitting a Generalized Pareto Distribution (GPD) to the extreme values of different aggregation levels (ranging between 0.5 and 24 hr). The future IDF curves are derived by applying climate change signals from a large ensemble of CMIP5 GCMs and EURO-CORDEX RCMs on the current IDF curves in the framework of the quantile perturbation downscaling method. The uncertainty in projected IDF relationships arising from the choice of climate models, representative concentration pathways (RCPs) and initial conditions is quantified, corresponding to model, scenario and internal variability uncertainties, respectively. The results show that making a temporal stationarity assumption for the climate system will lead to a noticeable spatially-varying underestimation of precipitation quantiles over Europe. The uncertainty analysis reveals that the model uncertainty is the dominant source of uncertainty for all aggregation levels and return periods. [ABSTRACT FROM AUTHOR]