Differential single-frequency GPS monitoring of the La Valette landslide (French Alps)

Recently, Global Positioning System (GPS) surveying techniques have been increasingly employed to monitor landslide movement. Here we present an application of GPS to monitor the La Valette landslide, located in the Ubaye Valley in the southern French Alps. This complex landslide is composed by an upper rotational part, a central part with generally translational movement and a lower part, which occasionally transforms into mud flows during intense rainfall events. Displacement rates average a few centimeters per month, with velocity peaks of some centimeters per day during periods of strong activity. GPS data presented in this study were acquired with two single-frequency GPS receivers Magellan connected to multipath-resistant antennas. The data were processed with the Magellan software MSTAR. Nine points have been set in the studied area, seven of them in the moving area, one in a stable area near the landslide and one on the facing slope, which is used as reference point. For each observation, one GPS receiver is placed on the reference point for the whole day and the second one is placed on each monitored point for 1-h sessions. The distance between the base and monitored point ranges from 480 to 1660 m. Eight survey campaigns were made between October 2000 and October 2002, to follow the evolution of the surface displacements. The maximum cumulative 3-D displacement observed in the area was about 21 m during the period in the center part of the landslide, corresponding to an average rate of movement of about 3 cm/day. The accuracy achieved during the GPS measurements has been evaluated to be about 2.4 cm in E–W direction, about 11 cm in N–S direction and about 7.4 cm in elevation in the worst case. The GPS results have been compared with traditional surveying techniques (EDM) carried out on the same site by RTM Service (Restauration des Terrains en Montagne). The velocities obtained by the two methods are similar. The advantage of the GPS technique is the collection of data for the three spatial coordinates (x, y, z) of each point, which allow to calculate the 3-D displacement vector. These measurements have been combined with SAR interferometric data in order to analyse the temporal evolution of the different landslide sectors.     

Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)

Landslide susceptibility mapping is one of the most critical issues in Turkey. At present, geotechnical models appear to be useful only in areas of limited extent, because it is difficult to collect geotechnical data with appropriate resolution over larger regions. In addition, many of the physical variables that are necessary for running these models are not usually available, and their acquisition is often very costly. Conversely, statistical approaches are currently pursued to assess landslide hazard over large regions. However, these approaches cannot effectively model complicated landslide hazard problems, since there is a non-linear relationship between nature-based problems and their triggering factors. Most of the statistical methods are distribution-based and cannot handle multisource data that are commonly collected from nature. In this respect, logistic regression and neural networks provide the potential to overcome drawbacks and to satisfy more rigorous landslide susceptibility mapping requirements. In the Hendek region of Turkey, a segment of natural gas pipeline was damaged due to landslide. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map. For this purpose, logistic regression analysis and neural networks are applied to prepare landslide susceptibility map of the problematic segment of the pipeline. At the end, comparative analysis is conducted on the strengths and weaknesses of both techniques. Based on the higher percentages of landslide bodies predicted in very high and high landslide susceptibility zones, and compatibility between field observations and the important factors obtained in the analyses, the result found by neural network is more realistic.     

Seismic hazard analysis for critical infrastructures in California

California is in a highly seismically active region, and structures must be designed and constructed to withstand earthquakes. Seismic hazard analysis to estimate realistic earthquake ground motions and surface fault rupture offsets is done for various mitigation measures. The best policy is to avoid constructing structures crossing seismogenic faults. Because earthquake timings are unpredictable within our current understanding, the best method is time-invariant deterministic seismic hazard analysis (DHSA) to assess effects from the largest single earthquake called Maximum Credible Earthquake (MCEs) expected from seismogenic faults. Time-dependent hazard estimates such as those arrived at through probabilistic seismic hazard analysis (PSHA) are inherently unreliable. Hazard analyses based on MCEs have been in continuous use for the design and construction of highways and bridges in California for over 30 years.

This paper presents an alternative to other methods of analysis, e.g., Abrahamson (2000) [Abrahamson, N.A., 2000. State of the practice of seismic hazard evaluation. Melbourne: proceedings of GeoEng, 2000].     

A feasible methodology for landslide susceptibility assessment in developing countries: A case-study of NW Nicaragua after Hurricane Mitch

In October 1998, Hurricane Mitch triggered a large number of landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. In recent years, a number of risk assessment methodologies have been devised to mitigate natural disasters. However, due to scarcity of funds and lack of specialised personnel few of these methodologies are accessible to developing countries. To explore the potential application of relatively simple and affordable landslide susceptibility methodologies in such countries, we focused on a region in NW Nicaragua which was among the most severely hit during the Mitch event. Our study included (1) detailed field work to produce a high-resolution inventory landslide map at 1 : 10,000 scale, and (2) a selection of the relevant instability factors from a Terrain Units Map which had previously been generated in a project for rural development. Based on the combination of these two datasets and using GIS tools we developed a comparative analysis of failure-zones and terrain factors in an attempt to classify the land into zones according to the propensity to landslides triggered by heavy rainfalls. The resulting susceptibility map was validated by using a training and a test zone, providing results comparable to those reached in studies based in more sophisticated methodologies. Thus, we provide an example of a methodology which is simple enough to be fully comprehended by non-specialised technicians and which could be of help in landslide risk mitigation through implementation of non-structural measures, such as land planning or emergency measures.     

Large earthquake-triggered landslides and mountain belt erosion: The Tsaoling case,Taiwan

The 1999 Chi-Chi earthquake triggered the catastrophic Tsaoling landslide in central Taiwan. We mapped the landslide area and estimated the landslide volume, using high-resolution digital elevation model from airborne LiDAR (Light Detection and Ranging), satellite images, aerial photographs and topographic maps. The comparison between cut and fill volumes, about 0.126 and 0.150 km³, respectively, suggests a volume increase of 19% due to decompaction during landsliding. In April 2002, the cut and fill volumes were about 0.137 and 0.116 km³, respectively. These estimates suggest that 2.5 years after the event, the volume of landslide debris removed by river erosion was nearly 0.045 km³. Such a large value highlights the importance of landslide processes for erosion and long-term denudation in the Taiwan mountain belt. To cite this article: R.-F. Chen et al., C. R. Geoscience 337 (2005).     

Field monitoring of the Corvara landslide (Dolomites, Italy) and its relevance for hazard assessment

The Corvara landslide is an active slow moving rotational earth slide - earth flow, located uphill of the village of Corvara in Badia, one of the main tourist centres in the Alta Badia valley in the Dolomites (Province of Bolzano, Italy). Present-day movements of the Corvara landslide cause National Road 244 and other infrastructures to be damaged on a yearly basis. The movements also give rise to more serious risk scenarios for some buildings located in front the toe of the landslide. For these reasons, the landslide has been under observation since 1997 with various field devices that enable slope movements to be monitored for hazard assessment purposes. Differential GPS measurements on a network of 47 benchmarks has shown that horizontal movements at the surface of the landslide have ranged from a few centimetres to more than 1 m between September 2001 and September 2002. Over the same period, vertical movements ranged from a few centimetres to about 10 cm, with the maximum displacement rate being recorded in the track zone and in the uppermost part of the accumulation lobe of the landslide. Borehole systems, such as inclinometers and TDR cables, have recorded similar rates of movement, with the depths of the major active shear surfaces ranging from 48 m to about 10 m. From these data, it is estimated that the active component of the landslide has a volume of about 50 million m³. In this paper the monitoring data collected so far are presented and discussed in detail to prove that the hazard for the Corvara landslide, considered as the product of yearly probability of occurrence and magnitude of the phenomenon, can be regarded has as medium or high if the velocity or alternatively the volume involved is considered. Finally, it is also concluded that the monitoring results obtained provide a sound basis on which to develop and validate numerical models, manage hazard and support the identification of viable passive and active mitigation measures.     

Landslides triggered by the 23 November 2000 rainfall event in the Imperia Province, Western Liguria, Italy

From mid-October to 22 November 2000, the western Liguria Region of Italy experienced prolonged and intense rainfall, with cumulative values exceeding 1000 mm in 45 days. The severe rainfall sequence ended on November 23 with a high-intensity storm that dumped more than 180 mm of rain in 24 h. The high-intensity event caused flooding and triggered more than 1000 soils slips and debris flows and a few large, complex landslides. Slope failures caused three fatalities and severe damage to roads, private homes, and agriculture. Large (1:13,000) and very large (1:5000) scale colour aerial photographs were taken 45 days after the event over the areas most affected by the landslides. Through the interpretation of the 334 photographs covering an area of 500 km², we prepared a landslide inventory map that shows 1204 landslides, for a total landslide area of 1.6 km². We identified the rainfall conditions that triggered landslides in the Armea valley using cumulative- and continuous-rainfall data, combined with detailed information on the time of landslide occurrence. Landslide activity initiated 8 to 10 h after the beginning of the storm, and the most abundant activity occurred in response to rainfall intensities of 8 to 10 mm per hour. For the Ceriana Municipality, an area where the landslides were numerous in November 2000, we also collected information about a historical event that occurred on 8–11 December 1910 and triggered abundant landslides resulting in severe economic damage. A comparison of the damage caused by the historical and the recent landslide events indicated that damage caused by the 1910 historical event was more diffused but less costly than the damage caused by the 2000 event.     

Identification of hazard conditions for mudflow occurrence by hydrological model: Application of FLaIR model to Sarno warning system

Mathematical models for forecasting landslides and mudflow movements triggered by heavy rainfalls are useful tools to develop warning systems and hazard mitigation strategy for loss reduction.

In the present paper, an application of Forecasting of Landslides Induced by Rainfalls (FLaIR) hydrological model, correlating the rainfall amount and landslide or mudflow movement occurrences, will be performed. Model application presented here refers to the mudflows of Sarno, Southern Italy, and is based on hourly precipitation data available from a real-time rain gauge installed immediately after the catastrophic event that occurred on May 1998.

The application is extended from October 1998 to May 2002. The main objective is to perform a backanalysis in order to verify the reliability of the proposed scheme for use in a warning system.

Among the most interesting results of the application, the relatively few false alarms for populations given by the model may be highlighted.

The FLaIR model is more useful when it is integrated with a probabilistic model for forecasting precipitation depths during a storm event at an hourly scale. By stochastic modelling of hourly precipitation, it is possible to estimate the probability of reaching the alarm threshold before allowing civil protection actions.     

Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)

Preparation of landslide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, many procedures have been used to produce such maps. In this study, a new attempt is tried to produce landslide susceptibility map of a part of West Black Sea Region of Turkey. To obtain the fuzzy relations for producing the susceptibility map, a landslide inventory database is compiled by both field surveys and airphoto studies. A total of 266 landslides are identified in the study area, and dominant mode of failure is rotational slide while the other mode of failures are soil flow and shallow translational slide. The landslide inventory and the parameter maps are analyzed together using a computer program (FULLSA) developed in this study. The computer program utilizes the fuzzy relations and produces the landslide susceptibility map automatically. According to this map, 9.6% of the study area is classified as very high susceptibility, 10.3% as high susceptibility, 8.9% as moderate susceptibility, 27.5% as low susceptibility and 43.8% as very low susceptibility or nonsusceptible areas. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. For this purpose, strength of the relation (r_ij) and the root mean square error (RMSE) values are calculated as 0.867 and 0.284, respectively. These values show that the produced landslide susceptibility map in the present study has a sufficient reliability. It is believed that the approach employed in this study mainly prevents the subjectivity sourced from the parameter selection and provides a support to improve the landslide susceptibility mapping studies.