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Déployant une méthodologie originale, cet article étudie la nature du couplage entre l’évolution climatique à l’échelle métropolitaine et les stratégies d’adaptation socioéconomique mises en œuvre pour y faire face. L’analyse située du système métropolitain grenoblois illustre notamment comment le rythme de l’organisation collective peine à suivre l’évolution climatique locale. Ce « climat de mauvais temps » trace, via la notion de régime de risque d’inondation, la fréquence des extrêmes hydroclimatiques, le rythme d’adaptation des territoires et les modalités concrètes d’organisation collective. Pour affronter ces enjeux temporels, il semble urgent qu’émergent des coalitions transdisciplinaires localisées. Structurées scientifiquement et politiquement, ces dernières sont appelées à coproduire une information climatique robuste et pertinente et à imaginer des stratégies soutenables pour le territoire. Afin de consolider analytiquement cette dynamique, nous invitons à un approfondissement de l’enjeu temporel au sein de la perspective des communs.

Intensity‐Duration‐Area‐Frequency (IDAF) models provide the mathematical link between precipitation intensities (I), durations (D), areas (A), and frequency of occurrence (F). They play a critical role in hydrological design, areal rainfall hazard quantification, storm characterization, and early warning system development. IDAF models extend the conventional Intensity‐Duration‐Frequency models by accounting for the spatial extent of precipitation (i.e., the area). In this study, we develop IDAF models using the entire non‐zero precipitation intensities, not only the extremes. We use the extended generalized Pareto distribution (EGPD) to model the precipitation intensities. To build the IDAF models, we adopt a data‐driven approach that allows the linkage of EGPD parameters with duration and area, based on empirically determined parametric relationships. The inference of model parameters is done using a global maximum likelihood estimation, and uncertainties are assessed by the bootstrap method. The study area is Switzerland, a topographically complex region of 42,000 km2 with regional precipitation variability and clear seasonality. The study utilizes 17 years of data from CombiPrecip, a radar‐reanalysis product developed by geostatistically merging radar and rain gauge data in an operational setting. We build the IDAF models for the spatiotemporal range of 1–72 hr and 1 to 1,089 km2 at each pixel in the study area. To the best of our knowledge, our study is the first attempt to use the EGPD in IDAF curve modeling. It discusses the use and limitations of CombiPrecip in extreme value analysis and highlights the challenges of modeling areal precipitation in a complex topographical environment. Key Points: We develop seasonal Intensity Duration Area Frequency models at every pixel location in SwitzerlandWe use all the non‐zero precipitation data and model the intensities using the extended generalized Pareto distributionWe highlight the complexity of modeling areal precipitation in mountainous regions [ABSTRACT FROM AUTHOR]

Following the projected increase in extreme precipitation, an increase in extreme snowfall may be expected in cold regions, e.g., for high latitudes or at high elevations. By contrast, in low- to medium-elevation areas, the probability of experiencing rainfall instead of snowfall is generally projected to increase due to warming conditions. Yet, in mountainous areas, despite the likely existence of these contrasted trends according to elevation, changes in extreme snowfall with warming remain poorly quantified. This paper assesses projected changes in heavy and extreme snowfall, i.e., in mean annual maxima and 100-year return levels, in the French Alps as a function of elevation and global warming level. We apply a recent methodology, based on the analysis of annual maxima with non-stationary extreme value models, to an ensemble of 20 adjusted general circulation model–regional climate model (GCM–RCM) pairs from the EURO-CORDEX experiment under the Representative Concentration Pathway 8.5 (RCP8.5) scenario. For each of the 23 massifs of the French Alps, maxima in the hydrological sense (1 August to 31 July) are provided from 1951 to 2100 and every 300 m of elevations between 900 and 3600 m. Results rely on relative or absolute changes computed with respect to current climate conditions (corresponding here to +1 ∘ C global warming level) at the massif scale and averaged over all massifs. Overall, daily mean annual maxima of snowfall are projected to decrease below 3000 m and increase above 3600 m, while 100-year return levels are projected to decrease below 2400 m and increase above 3300 m. At elevations in between, values are on average projected to increase until +3 ∘ C of global warming and then decrease. At +4 ∘ C, average relative changes in mean annual maxima and 100-year return levels, respectively, vary from -26% and -15% at 900 m to +3% and +8% at 3600 m. Finally, for each global warming level between +1.5 and +4 ∘ C, we compute the elevation threshold that separates contrasted trends, i.e., where the average relative change equals zero. This elevation threshold is shown to be lower for higher return periods, and it is projected to rise from 3000 m at +1.5 ∘ C to 3350 m at +4 ∘ C for mean annual maxima and from 2600 to 3000 m for 100-year return levels. These results have implications for the management of risks related to extreme snowfall. [ABSTRACT FROM AUTHOR]

The Alpine region is strongly affected by torrential floods, sometimes leading to severe negative impacts on society, economy, and the environment. Understanding such natural hazards and their drivers is essential to mitigate related risks. In this article we propose a statistical method based on a mere discriminative index to objectively isolate the atmospheric variables associated with torrential events with long return periods. The method is applied to the Grenoble Metropolitan area in the Northern French Alps using a list of dates of damaging torrential events since 1851. We consider seven atmospheric variables that describe the nature of the air masses involved and the possible triggers of precipitation, using both 20CRv2c and ERA5 reanalyses. Two spatial scales are considered - a local scale (the Grenoble conurbation) and a regional scale (the French Alps) -, in order to study the spatial variability of the atmospheric signature. This analysis is done conditionally on the main types of generating atmospheric circulation derived from Lamb weather classes, namely the North-West, Southeast-Southwest and Barometric Swamp classes. The results show that a simple discriminative index - the so-called silhouette index - is able to isolate the variables associated with torrential events, by objectively determining which of them differ particularly from the climatology at the dates of torrential events. All atmospheric variables turn out to be less discriminant for torrential events before 1950 according to 20CRv2c - this is likely linked to 20CRv2c issues over the remote past. For the post-1950 period, in the North-West class - which is both the most frequent class generating torrential events and the best discriminated - humidity and particularly humidity transport (IVT) plays the greatest role. In the Southeast-Southwest class - the second most frequent class generating torrential events-, instability potential (CAPE) is mostly at play. In the Barometric Swamp class both humidity (PWAT) and instability (CAPE) are discriminant -and even more at the local scale-, showing more mixed situations generating torrential events in this class. The gain in prediction provided by the discriminant variables is substantial. Depending on the class, torrential events are 4 to 14 times more likely when the respective discriminant variables are extreme (typically above their 0.95-quantile). Although the results are likely to be region-dependent worldwide, the methodology used in this article is generic and could be used elsewhere to find the most discriminating atmospheric variables - provided a list of flooding dates is available. It is also remarkable that the same atmospheric variables with the same discriminative power are found whether we consider them at local or regional scale. This means that, although torrential events are triggered by very local precipitation, the atmospheric signature for such events is actually much wider. Thus, although the present study applies to a small region of the Northern French Alps, it is reasonable to presume that similar results would apply to other torrential catchments in the French Alps. [ABSTRACT FROM AUTHOR]

Following the projected increase in extreme precipitation, an increase in extreme snowfall may be expected in cold regions, e.g. for high latitudes or at high elevations. By contrast, in low/medium elevation areas, the probability to experience rainfall instead of snowfall is generally projected to increase due to warming conditions. In mountainous areas, despite the likely existence of these contrasted trends according to elevation, changes in extreme snowfall with warming remain poorly quantified. This paper assesses projected changes in heavy and extreme snowfall, i.e. in mean annual maxima and 100-year return levels, in the French Alps as a function of elevation and global warming level using a recent methodology based on non-stationary extreme value models. This methodology is applied to an ensemble of 20 adjusted GCM-RCM pairs from the EURO-CORDEX experiment under the scenario RCP8.5. available for each of the 23 massifs of the French Alps from 1951 to 2100, and every 300 m of elevations. Results are provided as relative or absolute changes computed w.r.t. current climate conditions, at the massif scale and averaged over all available massifs. Overall, mean annual maxima are projected to decrease below 3000 m and increase above 3600 m, while 100-year return levels are projected to decrease below 2400 m and increase above 3300 m. At elevations in between, values are on average projected to increase until +3 °C of global warming, and then decrease. At +4 °C, average relative changes in mean annual maxima and 100-year return levels respectively vary from −26 % (−7 kg m−2) and −15 % (−11 kg m−2) at 900 m, to +3 % (+3 kg m−2) and +8 % (+13 kg m−2) at 3600 m. Finally, for each global warming level, we compute the elevation threshold that separates contrasted trends, i.e. where the average relative change equals zero. This elevation threshold is projected to rise between +1.5 °C and +4 °C: from 3000 m to 3350 m for mean annual maxima, and from 2600 m to 3000 m for 100-year return levels. These results have implications for the management of risks related to extreme snowfall. [ABSTRACT FROM AUTHOR]