Development of Landslide Early Warning Using Rainfall Thresholds and Field Monitoring: A Case Study from Kalimpong

2021 ◽  
pp. 153-173
Author(s):  
Neelima Satyam ◽  
Minu Treesa Abraham
Keyword(s):  
Early Warning ◽  
Field Monitoring ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

scholarly journals Definition of 3D rainfall thresholds to increase operative landslide early warning system performances

Landslides ◽  
2020 ◽  
Author(s):  
Ascanio Rosi ◽  
Samuele Segoni ◽  
Vanessa Canavesi ◽  
Antonio Monni ◽  
Angela Gallucci ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Thresholds ◽  
Landslide Early Warning ◽  
Definition Of

Landslide Early Warning Systems Considering Impulse Waves – a Case Study of the Jiuxianping Landslide in the Three Gorges Reservoir Area, China

2020 ◽  
Author(s):  
Yu Zhang ◽  
Yunkong Yin ◽  
Frederic Evers ◽  
Xin Liang
Keyword(s):  
Early Warning ◽  
Three Gorges Reservoir ◽  
Disaster Risk ◽  
Secondary Process ◽  
Three Gorges ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Landslide Early Warning ◽  
Impulse Waves

<p>Landslides along river, lake, reservoir and ocean shorelines may trigger impulse waves when they slide into the water body with a high velocity. This secondary process can extremely expand the area threatened by the landslide beyond its primary impact zone. Since the impoundment of the Three Gorges Reservoir in 2003, several landslides have caused huge property damage and several casualties due to an insufficient understanding of and reaction to impulse waves as a secondary process in landslide disaster risk management. This contribution aims to provide an integrative approach for risk perception and mitigation of a local landslide considering impulse waves as a secondary disaster risk.</p><p>Jiuxianping landslide is located in the middle part of the Three Gorges Reservoir in China. Featuring a large thick layer of rock slope, the elevation of the landslide ranges from 95 to 385 m a.s.l., and the volume is approximately 5.7×10<sup>7</sup> m<sup>3</sup>. The trailing edge of the landslide appeared as a more than 100 meters transverse tensile crack with an opening width of at least 25 cm in 2008, leading to damaged housing. The landslide stability is strongly influenced by rainfall and the reservoir water level. More than 300 people still live at the landslide site and there is a shipyard in operation at its toe.</p><p>As a new perspective to detect secondary disasters, the areas with the highest risk and probability of damage under different conditions were estimated using an auto search function in GeoStudio and the Morgenstern-Price method. Then, we simulated the landslide runout as well as wave generation and propagation using Tsunami Squares to predict the risk intensity and impact area of the generated impulse waves. Lastly, we evaluated the warning levels for different scenarios and proposed the area restricted for navigation at corresponding warning levels. Our case study demonstrates the necessity and the importance of considering secondary disaster risks such as impulse waves in landslide early warning system.</p>

scholarly journals Landslide early warning based on failure forecast models: the example of Mt. de La Saxe rockslide, northern Italy

2015 ◽  
Vol 3 (2) ◽  
pp. 1511-1525 ◽  
Author(s):  
A. Manconi ◽  
D. Giordan
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Warning Systems ◽  
Surface Displacements ◽  
Forecast Models ◽  
Landslide Early Warning ◽  
Critical Phases ◽  
Temporal Windows

Abstract. We investigate the use of landslide failure forecast models by exploiting near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here we describe the main concepts of our method, and show an example of application to a real emergency scenario, the La Saxe rockslide, Aosta Valley region, northern Italy. Based on the herein presented case study, we identify operational thresholds based on the reliability of the forecast models, in order to support the management of early warning systems in the most critical phases of the landslide emergency.

scholarly journals Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system

2018 ◽  
Vol 18 (3) ◽  
pp. 807-812 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Daniela Lagomarsino ◽  
Riccardo Fanti ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Preliminary Investigation ◽  
Regional Scale ◽  
Back Analysis ◽  
Warning System ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

Abstract. We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

scholarly journals Landslide early warning based on failure forecast models: the example of the Mt. de La Saxe rockslide, northern Italy

2015 ◽  
Vol 15 (7) ◽  
pp. 1639-1644 ◽  
Author(s):  
A. Manconi ◽  
D. Giordan
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Surface Displacements ◽  
Forecast Models ◽  
Landslide Early Warning ◽  
Unstable Slope ◽  
Critical Phases

Abstract. We apply failure forecast models by exploiting near-real-time monitoring data for the La Saxe rockslide, a large unstable slope threatening Aosta Valley in northern Italy. Starting from the inverse velocity theory, we analyze landslide surface displacements automatically and in near real time on different temporal windows and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here, we present the result obtained for the La Saxe rockslide, a large unstable slope located in Aosta Valley, northern Italy. Based on this case study, we identify operational thresholds that are established on the reliability of the forecast models. Our approach is aimed at supporting the management of early warning systems in the most critical phases of the landslide emergency.

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