Forecasting the behaviour of complex landslides with a spatially distributed hydrological model

International audienceThe relationships between rainfall, hydrology and landslide movement are often difficult to establish. In this context, ground-water flow analyses and dynamic modelling can help to clarify these complex relations, simulate the landslide hydrological behaviour in real or hypothetical situations, and help to forecast future scenarios based on environmental change. The primary objective of this study is to investigate the possibility of including more temporal and spatial information in landslide hydrology forecasting, by using a physically based spatially distributed model. Results of the hydrological and geomorphological investigation of the Super-Sauze earthflow, one of the persistently active landslide occurring in clay-rich material of the French Alps, are presented. Field surveys, continuous monitoring and interpretation of the data have shown that, in such material, the groundwater level fluctuates on a seasonal time scale, with a strong influence of the unsaturated zone. Therefore a coupled unsaturated/saturated model, incorporating Darcian saturated flow, fissure flow and meltwater flow is needed to adequately represent the landslide hydrology. The conceptual model is implemented in a 2.5-D spatially distributed hydrological model. The model is calibrated and validated on a multi-parameters database acquired on the site since 1997. The complex time-dependent and three-dimensional groundwater regime is well described, in both the short- and long-term. The hydrological model is used to forecast the future hydrological behaviour of the earthflow in response to potential environmental changes

Physical Model of Hydrological Behavior of Permeable Pavements Using FlexPDE

International audienceThe increase of urbanization combined with the increase in impervious surfaces has led to an increasing frequency of flooding events in urban catchments. This context highlights the inadequacy of traditional urban drainage systems and the need for alternative solutions. Permeable pavements have proven to be a valuable low-impact development (LID) technique. They are able to retain surface runoff volumes by increasing the infiltration and evaporation processes. Even though their benefits are well known, the lack of adequate modeling tools limits their adoption. This paper presents the development of a physically based model to describe their hydrological behavior. The particularity of this partial differential equation (PDE) model is to base the parameter estimations on basic measurements. The model is calibrated and validated using measurements from a laboratory permeable pavement rig. Results demonstrate a high reliability of the model with a Nash-Sutcliffe model efficiency (NSE) value of 0.969 and 0.891, respectively, for calibration and validation. Finally, a sensitivity analysis was conducted and highlighted the influence of the hydraulic conductivity of the base layer on the performance of the structure

Assessment of coastal landslide hazard: the Villerville-Cricqueboeuf landslides (Normandy coast, France).

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Assessment of coastal landslide hazard: the Villerville-Cricqueboeuf landslides (Normandy coast, France).

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Landslide susceptibility assessment in the pays d’Auge plateau (Normandy, France): application at different scales

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Landslide susceptibility assessment by logistic regression in the pays d’Auge plateau (Normandy, France): improvement of a procedure in a hilly valley context

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Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

International audienceThis paper aims at assessing the impact of the data set quality for landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted on the Pays d'Auge plateau (Normandy, France) with a scale objective of 1 / 10 000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, and geomorphological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlight that only high-quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formation maps) can predict a satisfying proportion of landslides in the study area