HIRESSS: a physically based slope stability simulator for HPC applications

Abstract. Located in a mountainous area of south-eastern Brazil, the municipality of Campos do Jordão has been hit by several landslides in recent history. Among those events, the landslides of early 2000 were significant in terms of the number of deaths (10), the population affected and the destruction of infrastructure that was caused. The purpose of this study is to assess the relative contribution of natural and human factors to triggering the landslides of the 2000 event. To achieve this goal, a detailed geotechnical survey was conducted in three representative slopes of the area to obtain geotechnical parameters needed for slope stability analysis. Then, a set of numerical experiments with GEO-SLOPE software was designed, including separate natural and anthropic factors. Results showed that natural factors, that is, high-intensity rainfall and geotechnical conditions, were not severe enough to trigger landslides in the study area and that human disturbance was entirely responsible for the landslide events of 2000. Since the anthropic effects used in the simulations are typical of hazardous urban areas in Brazil, we concluded that the implementation of public policies that constrain the occupation of landslide susceptible areas are urgently needed.

Section: Introductionmentioning

confidence: 99%

Understanding shallow landslides in Campos do Jordão municipality – Brazil: disentangling the anthropic effects from natural causes in the disaster of 2000

Mendes

Andrade

Tomasella

et al. 2018

“…Snow has less impact on animal husbandry in developed countries due to better infrastructure in grassland and livestock industry, in which the studying emphasis has been particularly focused on risk assessment and early warning of traffic, landslides and avalanches related to heavy snowfall events (Jones and Jamieson, 2001;Williamson et al, 2002;Martelloni et al, 2013;Mercogliano et al, 2013;Rossi et al, 2013). As for snow disasters in pastoral areas in China, the emphasis has been placed on monitoring the change of snow distribution and livestock loss evaluation post-disasters.…”

Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

This study develops a model for early warning of snow-caused livestock disasters on a county basis and proposes a method of qualitative risk assessment of snow disasters at 500 m resolution for pastoral areas on the Tibetan Plateau (TP). Data used for the model development include remote sensing data, statistical data of weather, livestock, and social economy, and 45 typical snow disaster cases from 2000 to 2010. The principal component analysis (PCA) approach is used to choose 7 crucial factors that contribute over 85% of information for early warning snow disasters on the TP. They are mean annual probability of snow disaster, number of snow-covered days, livestock stocking rate, continual days of mean daily temperature below −10 °C, grassland burial index, rate of snow-covered grassland, and per livestock gross domestic product. The chosen 411 cases from 2008 to 2010 are used to validate the prediction results from the developed early warning model, with an overall accuracy of 85.64% in predicting snow disasters and no disasters. This suggests that the early warning approach developed in the study has operational potential for predicting snow disasters on the TP

“…However, in recent years computational power has become available at reasonable costs, and the area size, associated with a high-resolution DTM, steadily increased over time for physically based applications and today exceeds thousands of square kilometers (e.g., Tofani et al, 2017;Alvioli and Baum, 2016). Recent landslide model development aims towards featuring multithreading and parallelization, and significant reductions of simulation durations of almost 90 % have been reported (Rossi et al, 2013). Since high-resolution DTMs are available in many parts of the world, the computational demands increased significantly, especially when applied in a dynamic and timedependent modeling framework.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Canli

Mergili

Thiebes

et al. 2018

Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslidetriggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.