Compounding effects of sea level rise and fluvial flooding.

Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.

Venice as a paradigm of coastal flooding under multiple compound drivers.

Full comprehension of the dynamics of hazardous sea levels is indispensable for assessing and managing coastal flood risk, especially under a changing climate. The 12 November 2019 devastating flood in the historical city of Venice (Italy) stimulated new investigations of the coastal flooding problem from different perspectives and timescales. Here Venice is used as a paradigm for coastal flood risk, due to the complexity of its flood dynamics facing those of many other locations worldwide. Spectral decomposition was applied to the long-term 1872-2019 sea-level time series in order to investigate the relative importance of different drivers of coastal flooding and their temporal changes. Moreover, a multivariate analysis via copulas provided statistical models indispensable for correctly understanding and reproducing the interactions between the variables at play. While storm surges are the main drivers of the most extreme events, tides and long-term forcings associated with planetary atmospheric waves and seasonal to inter-annual oscillations are predominant in determining recurrent nuisance flooding. The non-stationary analysis revealed a positive trend in the intensity of the non-tidal contribution to extreme sea levels in the last three decades, which, along with relative sea-level rise, contributed to an increase in the frequency of floods in Venice.

Multivariate sea storm hindcasting and design: the isotropic buoy-ungauged generator procedure.

The present work provides indications for assessment of wave climate and design of structures at sea at ungauged sites, both critical issues in Ocean sciences. The paper is of methodological nature and of global worldwide applicability. It shows how suitable wave hindcasting relations can be exploited in order to provide sea storm scenarios at an ungauged (Target) location useful for design purposes: in particular, only geographical information and the knowledge of another gauged (Source) buoy are used. Several are the novelties introduced. (i) New hindcasting relations are derived. (ii) A full statistical model is set up for the Target area, whereas traditional hindcasting simply transfers time series from a gauged to an ungauged site: this gives the possibility to appropriately deal with design and hazard assessment at the Target location. (iii) The multivariate behavior of non-independent random variables is properly modelled by using the Theory of Copulas. As an illustration, a number of case studies is investigated, involving four pairs of buoys which, given their positions and exposures, are representative of a wide variety of sea states and conditions, as well as of different wave generation mechanisms.

A distributional multivariate approach for assessing performance of climate-hydrology models.

One of the ultimate goals of climate studies is to provide projections of future scenarios: for this purpose, sophisticated models are conceived, involving lots of parameters calibrated via observed data. The outputs of such models are used to investigate the impacts on related phenomena such as floods, droughts, etc. To evaluate the performance of such models, statistics like moments/quantiles are used, and comparisons with historical data are carried out. However, this may not be enough: correct estimates of some moments/quantiles do not imply that the probability distributions of observed and simulated data match. In this work, a distributional multivariate approach is outlined, also accounting for the fact that climate variables are often dependent. Suitable statistical tests are described, providing a non-parametric assessment exploiting the Copula Theory. These procedures allow to understand (i) whether the models are able to reproduce the distributional features of the observations, and (ii) how the models perform (e.g., in terms of future climate projections and changes). The proposed methodological approach is appropriate also in contexts different from climate studies, to evaluate the performance of any model of interest: methods to check a model per se are sketched out, investigating whether its outcomes are (statistically) consistent.