Evolution of high-filling loess slope under long-term seasonal fluctuation of groundwater

Bao, Han; Liu, Li; Lan, Hengxing; Peng, Jianbing; Yan, Changgen; Tang, Ming; Guo, Guanmiao; Zheng, Han

doi:10.1016/j.catena.2024.107898

Cited by 8 publications

(1 citation statement)

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“…This model has been implemented, revised and extended by other authors [48][49][50] trying to overcome its limitations regarding the steady-state hydrological model and including a more complete slope stability routine. However, these solutions have not always considered the influence of the terrain's partial saturation conditions on slope stability which is a determinant for accurate failure prediction [51,52].…”

Section: Introductionmentioning

confidence: 99%

SLEM (Shallow Landslide Express Model): A Simplified Geo-Hydrological Model for Powerlines Geo-Hazard Assessment

Abbate,

Mancusi

2024

Water

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Powerlines are strategic infrastructures for the Italian electro-energetic network, and natural threats represent a potential risk that may influence their operativity and functionality. Geo-hydrological hazards triggered by heavy rainfall, such as shallow landslides, have historically affected electrical infrastructure networks, causing pylon failures and extensive blackouts. In this work, an application of the reworked version of the model proposed by Borga et al. and Tarolli et al. for rainfall-induced shallow landslide hazard assessment is presented. The revised model is called SLEM (Shallow Landslide Express Model) and is designed to merge in a closed-from equation the infinite slope stability with a simplified hydrogeological model. SLEM was written in Python language to automatise the parameter calculations, and a new strategy for evaluating the Dynamic Contributing Area (DCA) and its dependence on the initial soil moisture condition was included. The model was tested for the case study basin of Trebbia River, in the Emilia-Romagna region (Italy) which in the recent past experienced severe episodes of geo-hydrological hazards. The critical rainfall ratio (rcrit) able to trigger slope instability prediction was validated against the available local rainfall threshold curves, showing good performance skills. The rainfall return time (TR) was calculated from rcrit identifying the most hazardous area across the Trebbia basin with respect to the position of powerlines. TR was interpreted as an index of the magnitude of the geo-hydrological events considering the hypothesis of iso-frequency with precipitation. Thanks to its fast computing, the critical rainfall conditions, the temporal recurrence and the location of the most vulnerable powerlines are identified by the model. SLEM is designed to carry out risk analysis useful for defining infrastructure resilience plans and for implementing mitigation strategies against geo-hazards.

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