Inferring potential landslide damming using slope stability, geomorphic constraints, and run-out analysis: a case study from the NW Himalaya

Prediction of potential landslide damming has been a difficult process owing to the uncertainties related to landslide volume, resultant dam volume, entrainment, valley configuration, river discharge, material composition, friction, and turbulence associated with material. In this study, instability patterns of landslides, geomorphic indices, post-failure run-out predictions, and spatio-temporal patterns of rainfall and earthquakes are explored to predict the potential landslide damming sites. The Satluj valley, NW Himalaya, is chosen as a case study area. The study area has witnessed landslide damming in the past and incurred losses of USD ∼30 million and 350 lives in the last 4 decades due to such processes. A total of 44 active landslides that cover a total ∼4.81±0.05×106 m2 area and ∼34.1±9.2×106 m3 volume are evaluated to identify those landslides that may result in potential landslide damming. Out of these 44, a total of 5 landslides covering a total volume of ∼26.3±6.7×106 m3 are noted to form the potential landslide dams. Spatio-temporal variations in the pattern of rainfall in recent years enhanced the possibility of landslide triggering and hence of potential damming. These five landslides also revealed 24.8±2.7 to 39.8±4.0 m high debris flows in the run-out predictions.

Integrated Remote Sensing and GIS Analysis for Landslide Susceptibility Assessment along the Trenggalek–Ponorogo Road, East Java Province, Indonesia

Landslide, the second most common hazard in Indonesia, after an earthquake, is causing enormous losses of public infrastructures with subsequent economic disruptions. Roads are the most frequent public property which is affected by landslides. Due to the geomorphological condition of Indonesia, the construction of roads often intersects the mountainous topography. The Trenggalek–Ponorogo Road is one of the roads passing through mountainous terrains that are very susceptible affected by landslides. The road has an important role as the main transportation connector of some regencies in East Java Province. Landslide mitigation strategies along the Trenggalek–Ponorogo Road are needed to prevent enormous losses. This research was aimed to conduct a remote sensing-based assessment of landslide susceptibility areas along the Trenggalek–Ponorogo Road. The landslide susceptibility areas were assessed by considering landslide triggering parameters; those were topographic slope, distance to geological structure, distance to stream, lithology, and land use/land cover. The landslide triggering parameters were presented in spatial data and processed using Geographic Information System (GIS) technology. The Analytical Hierarchy Process (AHP) method was applied to integrate the landslide triggering parameters which have the degree of effect to determine Landslide Potential Index (LPI). The resulting LPI delineated the area into four susceptibility zones: very high, high, moderate, and low, which were presented as landslide susceptibility map. The susceptibility map was then validated by landslide occurrences inventory in the study area. The very high susceptibility zones, which are strongly predicted affecting the Trenggalek–Ponorogo Road, are located in Nglinggis and Grogol Village.

Integration of multiple observed and model-derived hydrological variables in landslide initiation threshold models in Rwanda

<p>This study was conducted using data collected from 3 catchments in North-Western region of Rwanda; Kivu, upper Nyabarongo and Mukungwa. We used two parsimonious  models, a transfer function noise time series model and a linear reservoir conceptual model, to simulate groundwater levels using rainfall and potential evapotranspiration as model inputs. The transfer function noise model was identified as the model with great explanatory predictive power to simulate groundwater levels as compared to the linear reservoir model. Hereafter, the modelled groundwater levels were used together with precipitation to explain the landslide occurrence in the studied catchments. These variables were categorized into landslide predisposing conditions which include the standardized groundwater level on the landslide day h<sub>t</sub> and prior to landslide triggering event h<sub>t-1</sub> and landslide triggering conditions which include the rainfall event, event intensity and duration.  Receiver operating characteristics curve and area under the curve metrics were used to test the discriminatory power of each landslide explanatory variable. The maximum true skill statistics and the minimum radial distance were used to highlight the most informative hydrological and meteorological threshold levels above which landslide are high likely to occur in each catchment. We will discuss our results of incorporation of groundwater information in the landslide predictions and compare these results with landslide prediction capacity which solely use of precipitation thresholds.Here we focus on at the same time on the practicalities of data availability for day-to-day landslide hazard management, both in terms of missed and false alarms</p>

Advancing rainfall-induced landslide detection using homogeneous slope units and distributed rainfall thresholds

<p>A better detection of landslide occurrence is critical for disaster prevention and mitigation, and a standing pursuit owing to increasing and widespread impact of slope failures on human activities and natural environment in a changing world. However, the detection of rainfall-induced landslide is limited in some areas by data scarcity and method applicability. In this study, we proposed distributed rainfall thresholds within homogeneous slope units, by considering the interaction of landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. Homogeneous slope units are extracted based on detailed terrain analysis. Various landforms are identified and used to obtain slope units with homogeneous slope traits. The concept behind the distributed rainfall threshold models is that rainfall threshold for landslide occurrence varies with geo-environmental conditions such as slope gradient. Thus, a link can be established between landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. We used elevation, slope, plan and profile curvature, mean annual precipitation and temperature, soil texture and land cover as independent variables. Rainfall duration and cumulated rainfall of landslide-triggering rainfall events are automatically calculated and used, the former as one of independent variables, and the latter as the dependent variable. A support vector regression (SVR) and a multiple linear regression (MLR) method are used. The error and correlation coefficient measurement indicate a better performance of SVR method. Compared with grid units, the model scores high accuracy for slope units. The models are implemented at a regional scale (Guangdong, China). The SVR model in slope units ran with error of 0.16 mm and correlation coefficient of 0.93.</p>

An integrated model for prediction of shallow landslides at regional scale with the integration of satellite hydrological data

<p>Rainfall-induced shallow landslides are dangerous natural hazards, mainly due to their high temporal frequency, which causes fatalities and high economic damage worldwide. Early Warning Systems (EWS), generally based on definition of rainfall thresholds needed for landslides triggering, are useful tools for risks mitigation. Thresholds generally do not take into account soil hydrological conditions, which play an important role both in landslide triggering. Rainfall measures are also uncertain due to the limited spatial representativeness of ground sensors and the low density of currently available measuring networks. Moreover, in the last years, soil moisture data have become available over large areas (basin and regional scales), thanks to their measurement through satellite sensors.</p><p>The aim of this research is to develop a new integrated model to predict shallow landslides, based on a multidisciplinary approach involving physical models, data-driven methods and the implementation of satellite soil moisture and rainfall. The model is developing in Oltrepò Pavese (Northern Italy, Southern Lombardy), affected during the last 11 years by numerous events triggered by intense and frequent rainfalls, causing human fatalities, damaging/blocking roads and bridges, destructing cultivations (mainly vineyards).</p><p>To define satellite soil moisture (and rainfall) products, different remote sensing platform are investigating. A very new soil moisture product provided by Sentinel-1 images by ESA (European Space Agency) allows a fine spatial resolution (1 km) and a revisit time of ~7 days. Coarse resolution soil moisture products (~20 km) characterized by a daily temporal resolution and higher accuracy (e.g., SMAP–Soil Moisture Active and Passive, SMOS–Soil Moisture Ocean Salinity, ASCAT–Advanced SCATterometer) is used. These are validated through two hydrological monitoring stations already installed in two representative basins.</p><p>The prediction of shallow landslides are carried on by means of a model able to integrate spatial probability of occurrence and temporal occurrence, considering also satellite soil moisture and rainfall products. Empirical and physically-based thresholds considering different initial soil hydrological conditions on soil moisture, which seem the best indicators for shallow landslide triggering, are developing.</p><p>Predicition model is tested and validated with real cases, assessing its reliability, to build a prototypal Early Warning System for shallow landslide prediction, that will constitute a valuable tool for Civil Protection in attempt to mitigate the risk in the Oltrepò Pavese area. This work was made in the frame of the project ANDROMEDA, funded by Fondazione Cariplo.</p>

Exploring the potential of soil moisture reanalysis data for improving the identification of regional landslide triggering thresholds

<p>Landslide triggering thresholds provide the rainfall conditions that are likely to trigger landslides, therefore their derivation is key for prediction purposes. Different variables can be considered for the identification of thresholds, which commonly are in the form of a power-law relationship linking rainfall event duration and intensity or cumulated event rainfall. The assessment of such rainfall thresholds generally neglects initial soil moisture conditions at each rainfall event, which are indeed a predisposing factor that can be crucial for the proper definition of the triggering scenario. Thus, more studies are needed to understand whether and the extent to which the integration of the initial soil moisture conditions with rainfall thresholds could improve the conventional precipitation-based approach. Although soil moisture data availability has hindered such type of studies, yet now this information is increasingly becoming available at the large scale, for instance as an output of meteorological reanalysis initiatives. In particular, in this study, we focus on the use of the ERA5-Land reanalysis soil moisture dataset. Climate reanalysis combines past observations with models in order to generate consistent time series and the ERA5-Land data actually provides the volume of water in soil layer at different depths and at global scale. Era5-Land project is, indeed, a global dataset at 9 km horizontal resolution in which atmospheric data are at an hourly scale from 1981 to present. Volumetric soil water data are available at four depths ranging from the surface level to 289 cm, namely 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm. After collecting the rainfall and soil moisture data at the desired spatio-temporal resolution, together with the target data discriminating landslide and no-landslide events, we develop automatic triggering/non-triggering classifiers and test their performances via confusion matrix statistics. In particular, we compare the performances associated with the following set of precursors: a) event rainfall duration and depth (traditional approach), b) initial soil moisture at several soil depths, and c) event rainfall duration and depth and initial soil moisture at different depths. The approach is applied to the Oltrepò Pavese region (northern Italy), for which the historical observed landslides have been provided by the IFFI project (Italian landslides inventory). Results show that soil moisture may allow an improvement in the performances of the classifier, but that the quality of the landslide inventory is crucial.</p>