Lake floods occur when the water level in the lake exceeds a threshold causing inundation of neighbouring shorelines. Despite the potential impacts of this type of flood on neighbouring settlements, the mechanisms and drivers that govern when lake floods occur, and particularly how they result from compound factors, remains poorly understood. Here we compile and analyze meteorological and historical data on lake floods at Lake Como (northern Italy) between 1980 and 2020. We identify seven modes of lake floods with climate-based drivers. In 70% of cases, floods are associated with a temporal clustering of rainfall. This was also the predominant trigger of the seven most severe floods. To a lesser extent, floods were driven by a single rainfall event over a water level previously increased by rainfall and/or melting. We conclude that lake floods represent a clear example of the potential for compound mechanisms to govern and exacerbate hazards.

The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size.

Abstract. The regulating role of glaciers in catchment run-off is of fundamental importance in sustaining people living in low-lying areas. The reduction in glacierized areas under the effect of climate change disrupts the distribution and amount of run-off, threatening water supply, agriculture and hydropower. The prediction of these changes requires models that integrate hydrological, nivological and glaciological processes. In this work we propose a local model that combines the nivological and glaciological scales. The model describes the formation and evolution of the snowpack and the firn below it, under the influence of temperature, wind speed and precipitation. The model has been implemented in two versions: (1) a multi-layer one that considers separately each firn layer and (2) a single-layer one that models firn and underlying glacier ice as a single layer. The model was applied at the site of Colle Gnifetti (Monte Rosa massif, 4400–4550 ma.s.l.). We obtained an average reduction in annual snow accumulation due to wind erosion of 2×103 kgm-2yr-1 to be compared with a mean annual precipitation of about 2.7×103 kgm-2yr-1. The conserved accumulation is made up mainly of snow deposited between April and September, when temperatures above the melting point are also observed. End-of-year snow density, instead, increased an average of 65 kg m−3 when the contribution of wind to snow compaction was added. Observations show a high spatial and interannual variability in the characteristics of snow and firn at the site and a correlation of net balance with radiation and the number of melt layers. The computation of snowmelt in the model as a sole function of air temperature may therefore be one of the reasons for the observed mismatch between model and observations.

<p>Compound climate-related events are impactful extreme events in which the interactions between multiple variables amplify the final impact. They may be classified depending on the types of interaction and the scales involved. For example, temporal compounding events are characterized by the occurrence of subsequent events in time, as in case of a temporal clustering of precipitation. This last trigger is of great importance when the antecedent soil saturation shapes the intensity or occurrence of a given natural hazard, like for floods or deep landslides. Here, we focus on the characteristics of temporal clustering of precipitation over the Italian territory and its link with landslides occurrence. First, we investigate the spatial and temporal distribution of temporal clustering and the synoptic conditions more prone to it, using Era5-Land dataset. Second, we link the identified clusters with the occurrence of different movements’ types (complex, debris flow, fall, flow, and sliding), using a shuffling procedure to assess the significance. Regarding the first point, clear differences emerged between the Italian regions and the four seasons. Clusters were more widespread in autumn and spring and more localized in winter and summer. During winter, we observed a negative link between the number of clusters and the Mediterranean oscillation index in south-central Italy. Regarding the second point, differences were found between the five landslide typologies: fall events were mostly preceded by an intense precipitation event, debris flow by a temporal clustering over small windows and complex, flow, and sliding with a temporal clustering over long windows.</p>

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