Application of a physically based model to forecast shallow landslides at a regional scale

<p>In this work, we apply a physically based model, namely the HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the occurrence of shallow landslides at the regional scale. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions during a rainfall event. The modeling software is made up of two parts: hydrological and geotechnical. The hydrological model is based on an analytical solution from an approximated form of the Richards equation, while the geotechnical stability model is based on an infinite slope model that takes the unsaturated soil condition into account. The test area is a portion of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. on the Dora Baltea River's floodplain to 4810 m a.s.l. at Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features make the territory very prone to landslides, mainly shallow rapid landslides and rockfalls. In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslide formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed using 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model. In order to consider the effect of vegetation on slope stability, the soil reinforcement due to the presence of roots was also taken into account; this was done based on vegetation maps and literature values of root cohesion. The model was applied using back analysis for two past events that affected the Aosta Valley region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, provided good results and a good prediction accuracy for the HIRESSS model from both a temporal and spatial point of view.</p

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010-2021)

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (similar to 301 x 10(3) km(2)) - a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides similar to 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010-31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30-60 cm and 90-300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain-snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 +/- 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March +/- 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endeavor in a warming and drier climate

Snow multivariable data assimilation for hydrological predictions in Alpine sites

EGU General Assembly 2017, Vienna, AUT, 23-/04/2017 - 28/04/2017International audienceSeveral snowmodels have been developed with different degrees of complexity (Essery et al. 2013).Goal: physically based snowmodel designed to be:&amp;#8226;suited to real time hydrological applications;&amp;#8226;coupled with a multivariable DA scheme &amp;#8594; limited modelcomplexity

Complex system modelling, an innovative concept for asset management of electricity networks

RTE et The CoSMo Company développent actuellement un nouvel outil appelé MONA (Management and Optimization of Network Assets). MONA est un outil de gestion stratégique des actifs à destination des gestionnaires de réseau de transport d’électricité, basé sur une analyse des risques.RTE and The CoSMo Company have been developing a new tool called MONA (Management and Optimization of Network Assets). MONA is a strategic asset management tool for transmission system operators based on risk analysis

Distinctive features of Drosophila alternative splicing factor RS domain: implication for specific phosphorylation, shuttling, and splicing activation

The human splicing factor 2, also called human alternative splicing factor (hASF), is the prototype of the highly conserved SR protein family involved in constitutive and regulated splicing of metazoan mRNA precursors. Here we report that the Drosophila homologue of hASF (dASF) lacks eight repeating arginine-serine dipeptides at its carboxyl-terminal region (RS domain), previously shown to be important for both localization and splicing activity of hASF. While this difference has no effect on dASF localization, it impedes its capacity to shuttle between the nucleus and cytoplasm and abolishes its phosphorylation by SR protein kinase 1 (SRPK1). dASF also has an altered splicing activity. While being competent for the regulation of 5' alternative splice site choice and activation of specific splicing enhancers, dASF fails to complement S100-cytoplasmic splicing-deficient extracts. Moreover, targeted overexpression of dASF in transgenic flies leads to higher deleterious developmental defects than hASF overexpression, supporting the notion that the distinctive structural features at the RS domain between the two proteins are likely to be functionally relevant in vivo