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Clay shrink-swell consists in volume changes of clayey, smectite-rich soils as a function of their soil water content. Building foundations can be affected by soil shrinkage during droughts, entailing what is called subsidence damage. This is the second costliest peril covered by the French national natural disaster compensation scheme, the losses amounting to more than 16B€ between 1989 and 2021 (CCR, 2021). As illustrated by the 2022 drought in France, these costs are likely to increase as a result of climate change and of the related amplification of annual soil moisture cycles.In this context, we investigate the relationship between drought and subsidence damage, using the ISBA land surface model developed by the French meteorological service (Météo-France), geotechnical data from the French geological survey (BRGM) and data from a national claim database operated by the French state-owned national reinsurance company (CCR). We compute several yearly drought indices based on multi-layer soil moisture time series simulated by the ISBA model. Different configurations of the indices are considered, varying in particular the ISBA model settings, and the soil drought definition through a threshold value accounting for a given temporal frequency, for each model soil layer. We assess a large range of configurations by using the Kendall rank correlation of the indices with yearly town-scale insurance claim data from 2000 to 2018, processed using the geotechnical data. The analysis is repeated for five sets of four towns with an important damage history distributed throughout France, in contrasting climate conditions.Highest rank correlation coefficients are obtained for soil layers deeper than 60 cm, and with temporal frequency threshold values corresponding to intense droughts. Under these conditions, the indices are able to fairly represent the occurrence of damages. The relationship between drought indices and the number of claims is non-linear. This study benefits from the latest improvements in land surface modeling and is a step forwards in climate risk modeling since the indices investigated can be considered as new predictors for subsidence damage. Climate change impact studies will be conducted in a next phase.[References] CCR: Les Catastrophes naturelles en France, Bilan 1982-2021, 2021.

PHUSICOS platform aims at gathering nature-based solutions (NBS) relevant to reduce hydro-geological risks in mountain landscapes. The platform can be accessed directly through a web portal. It is based on an Open Source CMS website, including a filter to store documents and a map server to bring ergonomic and powerful access.To design the platform, an in-depth review of 11 existing platforms has been performed. Furthermore, a list of metadata has been proposed to structure the information. These metadata have provided the baseline for database. The PHUSICOS platform currently references 176 NBS cases and 83 documents of interest (review articles, assessment papers…). It is continuously enriched through the contribution of NBS community.For that, a questionnaire based on relevant data, necessary for the definition and identification of the NBS (metadata, to be used for searching the NBSs within the platform) has been defined to enter new entries. A preliminary analysis of the cases has been realized. To characterize and analyse the current 152 solutions, we have worked on the following four categories: The nature of impacted ecosystems, The hazard(s) concerned, The other challenges treated by the NBS, The type of exposed assets.The platform also proposes a qualitative assessment of the NBSs collected according to 15 criteria related with five ambits: disaster risk reduction, technical and economical feasibility, environment, society, and local economy. The criteria level is sufficiently general to be analysed for the entire PHUSICOS platform NBSs whatever the type of work, the realized approaches, the problematic or the spatial or temporal scale.The structure of the platform and a first analysis of the qualitative NBS assessment are presented in this work.

In this work, we use a probabilistic approach for modelling rainfall thresholds (Caine 1980) triggering shallow landslides with a case study for the Alpes-Maritimes region (France).In particular, the CTRL-T algorithm (Melillo and al. 2018) is tested to output critical rainfall thresholds, based on the accumulated rainfall – duration parameters (E-D), for different exceedance probabilities from respectively a landslide and two climate datasets. The first climate dataset stores high resolution gridded rainfall data (1km resolution, hourly) and the second climate dataset contains lower resolution gridded rainfall, snow, temperature and evapotranspiration data (8km resolution, daily); the first dataset provides the rainfall records directly used for defining the rainfall events and then for the threshold construction; the second one enables to assess the region’s climate via parameters imported in CTRL-T. The thresholds are then validated using a method designed by Gariano and al. (2015).Several improvements are made to the method. First, potential evapotranspiration values approximated from temperatures and latitudes in one of the process’ steps are replaced by values from the second climate dataset, the result accounting best for the regional climate. Then, climate-specific duration values, used to split the raw rainfall records in events and sub-events, are computed for each mesh point. This second modification enables considering the heterogeneity of the Alpes-Maritimes climate.Rainfall thresholds are eventually obtained for different exceedance probabilities, first from a set of probable conditions (MRC), then from a set of highly probable conditions (MPRC). The validation process strengthens the analysis as well as enables to identify best performing thresholds. This work represents novel scientific progress towards landslide reliable warning systems by (a) making a case study of probabilistic rainfall thresholds for Alpes-Maritimes, (b) using for the first time high-resolution rainfall data and (c) adapting the method to climatically heterogeneous zones.