ALBERT

Description: Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness – the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution.

Description: Reliable hazard analysis is crucial in the flood risk management of river basins. For the floodplains of large, developed rivers, flood hazard analysis often needs to account for the complex hydrology of multiple tributaries and the potential failure of dikes. Estimating this hazard using deterministic methods ignores two major aspects of large-scale risk analysis: the spatial–temporal variability of extreme events caused by tributaries, and the uncertainty of dike breach development. Innovative stochastic methods are here developed to account for these uncertainties and are applied to the Po River in Italy. The effects of using these stochastic methods are compared against deterministic equivalents, and the methods are combined to demonstrate applications for an overall stochastic hazard analysis. The results show these uncertainties can impact extreme event water levels by more than 2 m at certain channel locations, and also affect inundation and breaching patterns. The combined hazard analysis allows for probability distributions of flood hazard and dike failure to be developed, which can be used to assess future flood risk management measures.

Description: Levee failures due to floods often cause considerable economic damage and life losses in inundated dike-protected areas, and significantly change flood hazard upstream and downstream the breach location during the event. We present a new extension for the LISFLOOD-FP hydrodynamic model which allows levee breaching along embankments in fully two-dimensional (2D) mode. Our extension allows for breach simulations in 2D structured grid hydrodynamic models at different scales and for different hydraulic loads in a computationally efficient manner. A series of tests performed on synthetic and historic events of different scale and magnitude show that the breaching module is numerically stable and reliable. We simulated breaches on synthetic terrain using unsteady flow as an upstream boundary condition and compared the outcomes with an identical setup of a full-momentum 2D solver. The synthetic tests showed that differences in the maximum flow through the breach between the two models were less than 1%, while for a small-scale flood event on the Secchia River (Italy), it was underestimated by 7% compared to a reference study. A large scale extreme event simulation on the Po River (Italy) resulted in 83% accuracy (critical success index).

Description: Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.

Description: The increasing frequency of cloudbursts and extreme rainstorms combined with high population densities and soil sealing make urban areas particularly vulnerable to pluvial flooding. An accurate spatial representation of these natural phenomena is of paramount importance. The number of privately owned weather stations is steadily increasing, sometimes significantly higher than the number of weather services sensors, and their spatial distribution roughly follows population density. Therefore, private rain gauging networks represent interesting data sources (crowdsourcing) with huge untapped potential for a high-resolution representation of rainfall fields over urban areas. Our contribution is twofold. First, we assess the accuracy of hourly rainfall data collected by Netatmo private sensors relative to data from the official gauging network of Norway, Sweden, Finland and Denmark. Second, we focus on Oslo and two recent pluvial flood events that occurred on Jun. 2019, and Aug. 2019; we feed simplified DEM-based and 2D physically-based numerical inundation models with rainfall fields generated by combining data from three different sources (a) official raingauges, (b) Netatmo private sensors, (c) weather radars. Our results show that (1) private sensors have very good skills in rain detection but tend to underestimate the reference value up to ∼25%, (2) concerning the simulated inundation maps, rainfall fields derived from bias-corrected crowdsourced rainfall data may be significantly more accurate than those generated from official raingauges, and as accurate as the fields resulting from the combination radar and official raingauges data.

Description: The scientific literature reports on various approaches to regional frequency analysis (RFA) of rainfall extremes. Traditional regional models usually refer to a single time-aggregation interval. They are developed for medium-to-small and climatically and morphologically homogeneous regions, which leads to higher accuracy, yet limits the models' exportability. We propose a different and innovative approach to RFA of rainfall extremes; it consists of ensembles of unsupervised artificial neural networks (ANNs), predicting the parameters of a Gumbel distribution for dimensionless (i.e. standardized by the local value of the mean annual maximum rainfall depth) annual maximum rainfall depths at any location in the study area and for any time-aggregation interval in the 1-24 hours range. Predictions are based on several morpho-climatic descriptors, used as covariates of precipitation extremes, and the applicability of the models extends to a large and morphologically as well as climatically heterogeneous region (Western and Central Northern Italy). ANNs are trained on over 2300 Annual Maximum Series of rainfall depths collected between 1928 and 2011 and associated with five hourly time-aggregation intervals. Based on a comprehensive analysis of predictions obtained at 100 randomly selected validation points, we discuss the potential and drawbacks of ensembles of unsupervised ANNs for integrated (i.e., across various time-aggregation intervals) and multivariate (i.e., considering numerous morphoclimatic covariates) RFA of sub-daily rainfall extremes.

Description: Megafloods that far exceed previously observed records often take citizens and experts by surprise, resulting in extremely severe damage and loss of life. Existing methods based on local and regional information rarely go beyond national borders and cannot predict these floods well because of limited data on megafloods, and because flood generation processes of extremes differ from those of smaller, more frequently observed events. Here we analyse river discharge observations from over 8,000 gauging stations across Europe and show that recent megafloods could have been anticipated from those previously observed in other places in Europe. Almost all observed megafloods (95.5%) fall within the envelope values estimated from previous floods in other similar places on the continent, implying that local surprises are not surprising at the continental scale. This holds also for older events, indicating that megafloods have not changed much in time relative to their spatial variability. The underlying concept of the study is that catchments with similar flood generation processes produce similar outliers. It is thus essential to transcend national boundaries and learn from other places across the continent to avoid surprises and save lives.