Hydrometeorological features ans surface velocity measurements for the Viella landslide

The Viella datasets are based on a multi-sensor monitoring network installed and maintained by the French Landslide Observatory OMIV (Observatoire Multidisciplinaire des Instabilités de Versants; ano-omiv.cnrs.fr). They include daily surface-displacement measurements from seven monitoring plots (tacheometric targets; cm d⁻¹), together with local precipitation (mm) acquired at the Saint-Gatien-des-Bois Météo-France station, located ~1.5 km to the southwest. Groundwater-level record (m) is collected by the PZ8 piezometer representative of the local hydrogeological behaviour. Effective rainfall (mm) is estimated as precipitation minus potential evapotranspiration, with evapotranspiration computed using the Oudin formulation (Oudin et al., 2005). Daily temperature (°C) required for this calculation is derived from the MF weather stations, and the maximum soil available water capacity (SAWCMAX) is set to 50 mm.

Raw displacement records are processed using a sequential data-preparation workflow to mitigate instrumental errors that can affect model performance and interpretation. This workflow includes position-bias correction, reconstruction of cumulative displacement and velocity by differencing, outlier detection, and adaptive smoothing. Technical details of the pre-processing steps are provided in the associated publication.

Hydrometeorological predictors computed from the forcing series are designed to be physics-informed and non-site-specific. They represent three complementary aspects of water-driven forcing: hydrological state (e.g. saturation versus dryness), hydrological memory (e.g. cumulative rainfall and groundwater trends/extrema), and short-term hydrological transients (e.g. intense rainfall events and rapid groundwater rises or drawdown). Predictors are computed over multiple time windows (1–90 days), enabling the model to resolve short-, intermediate- and long-term hydromechanical responses. To account for delayed kinematic responses, groundwater descriptors are additionally computed with site-specific time lags. This results in a set of 248 predictors derived from three forcing series (R, ER, GWL). No-data cells occur when values from the underlying forcing series are missing; the resulting gap length increases with the predictor time window (i.e. longer windows produce longer no-data segments). Predictor names follow a consistent convention that encodes the data source, descriptor type and time window (and lag, where applicable). For example, cumulative rainfall over 60 days is denoted R_60, and a 10-day groundwater-level difference with a 5-day lag is denoted GWL_l5_diff10. Full predictor definitions are provided in the associated publication. The code used to generate the predictor set is available from the certified IN2P3 GitLab repository: [to be completed]. This work contributes to a PhD thesis supported by the Université Marie et Louis Pasteur (Doctoral School ED554) – Béjean-Maillard O. (2025).