scholarly journals Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method

2015 ◽  
Vol 3 (5) ◽  
pp. 3487-3508
Author(s):  
J. Huang ◽  
N. P. Ju ◽  
Y. J. Liao ◽  
D. D. Liu
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Rainfall Threshold ◽  
Empirical Method ◽  
Shallow Landslides ◽  
Data Sets ◽  
Threshold Values ◽  
Mountainous Regions ◽  
Hourly Rainfall

Abstract. Rainfall-induced landslides not only cause property loss, but also kill and injure large numbers of people every year in mountainous areas in China. These losses and casualties may be avoided to some extent with rainfall threshold values used in an early warning system at a regional scale for the occurrence of landslides. However, the limited availability of data always causes difficulties. In this paper we present a method to calculate rainfall threshold values with limited data sets for the two rainfall parameters: maximum hourly rainfall intensity and accumulated precipitation. The method has been applied to the Huangshan region, in Anhui Province, China. Four early warning levels (Zero, Outlook, Attention, and Warning) have been adopted and the corresponding rainfall threshold values have been defined by probability lines. A validation procedure showed that this method can significantly enhance the effectiveness of a warning system, and finally reduce the risk from shallow landslides in mountainous regions.

scholarly journals Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method

2015 ◽  
Vol 15 (12) ◽  
pp. 2715-2723 ◽  
Author(s):  
J. Huang ◽  
N. P. Ju ◽  
Y. J. Liao ◽  
D. D. Liu
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Rainfall Threshold ◽  
Empirical Method ◽  
Shallow Landslides ◽  
Data Sets ◽  
Threshold Values ◽  
Mountainous Regions ◽  
Hourly Rainfall

Abstract. Rainfall-induced landslides not only cause property loss, but also kill and injure large numbers of people every year in mountainous areas in China. These losses and casualties may be avoided to some extent with rainfall threshold values used in an early warning system at a regional scale for the occurrence of landslides. However, the limited availability of data always causes difficulties. In this paper we present a method to calculate rainfall threshold values with limited data sets for two rainfall parameters: hourly rainfall intensity and accumulated precipitation. The method has been applied to the Huangshan region, in the province of Anhui, China. Four early warning levels (zero, outlook, attention, and warning) have been adopted and the corresponding rainfall threshold values have been defined by probability lines. A validation procedure showed that this method can significantly enhance the effectiveness of a warning system, and finally reduce and mitigate the risk of shallow landslides in mountainous regions.

scholarly journals Early warning system for shallow landslides using rainfall threshold and slope stability analysis

2018 ◽  
Vol 9 (6) ◽  
pp. 1871-1882 ◽  
Author(s):  
Shruti Naidu ◽  
K.S. Sajinkumar ◽  
Thomas Oommen ◽  
V.J. Anuja ◽  
Rinu A. Samuel ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Threshold ◽  
Slope Stability Analysis ◽  
Shallow Landslides

Shallow landslides predisposing and triggering factors in developing a regional early warning system

Landslides ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 235-251 ◽  
Author(s):  
Davide Tiranti ◽  
Gabriele Nicolò ◽  
Armando Riccardo Gaeta
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Shallow Landslides ◽  
Triggering Factors

scholarly journals Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1195 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Sai Kushal ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Early Warning ◽  
Sigma Model ◽  
Warning System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Integrated System ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Rainfall Thresholds

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

scholarly journals Study on the combined threshold for gully-type debris flow early warning

2019 ◽  
Vol 19 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Jian Huang ◽  
Theodoor Wouterus Johannes van Asch ◽  
Changming Wang ◽  
Qiao Li
Keyword(s):  
Debris Flow ◽  
Pore Pressure ◽  
Early Warning ◽  
Early Stage ◽  
Warning System ◽  
Early Warning Systems ◽  
Great Loss ◽  
Mountainous Regions ◽  
Pressure Threshold

Abstract. Gully-type debris flow induced by high-intensity and short-duration rainfall frequently causes great loss of properties and causalities in mountainous regions of southwest China. In order to reduce the risk by geohazards, early warning systems have been provided. A triggering index can be detected in an early stage by the monitoring of rainfall and the changes in physical properties of the deposited materials along debris flow channels. Based on the method of critical pore pressure for slope stability analysis, this study presents critical pore pressure threshold in combination with rainfall factors for gully-type debris flow early warning. The Wenjia gully, which contains an enormous amount of loose material, was selected as a case study to reveal the relationship between the rainfall and pore pressure by field monitoring data. A three-level early warning system (zero, attention, and warning) is adopted and the corresponding judgement conditions are defined in real time. Based on this threshold, there are several rainfall events in recent years have been validated in Wenjia gully, which prove that such a combined threshold may be a reliable approach for the early warning of gully-type debris flow to safeguard the population in the mountainous areas.

scholarly journals Application of FLaIR model for early warning system in Chibo Pashyor, Kalimpong, India for rainfall-induced landslides

2017 ◽  
Author(s):  
Abhirup Dikshit ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Risk Mitigation ◽  
Warning System ◽  
Landslide Risk ◽  
Rainfall Forecasting ◽  
Landslide Occurrence ◽  
Mobility Function ◽  
Hourly Rainfall ◽  
Rainfall Model

Abstract. The development of an early warning system for landslides due to rainfall has become an indispensable part for landslide risk mitigation. This paper explains the application of the hydrological FLaIR (Forecasting of Landslides Induced by Rainfall) model, correlating rainfall amount and landslide events. The FLaIR model comprises of two modules: RL (Rainfall-Landslide) which correlates rainfall and landslide occurrence and RF (Rainfall-Forecasting) which allows simulation of future rainfall events. The model can predetermine landslides based on identification of mobility function Y(.) which links actual rainfall and incidence of landslide occurrence. The critical value of mobility function was analyzed using 1st July 2015 event and applying it to 2016 monsoon to validate the results. These rainfall thresholds presented can be improved with intense hourly rainfall and landslide inventory data. This paper describes the details of the model and its performance for the study area.

Updating and tuning a regional-scale landslide early warning system

Landslides ◽  
2012 ◽  
Vol 10 (1) ◽  
pp. 91-97 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Landslide Early Warning

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 Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems

2013 ◽  
Vol 17 (3) ◽  
pp. 1229-1240 ◽  
Author(s):  
G. Martelloni ◽  
S. Segoni ◽  
D. Lagomarsino ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Snow Accumulation ◽  
Point Of View ◽  
Warning Systems ◽  
Temperature Index ◽  
Landslide Early Warning ◽  
Optimal Values

Abstract. We propose a simple snow accumulation/melting model (SAMM) to be applied at regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimisation algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing about the additional benefit of a relatively easy implementation. After performing a cross validation and a comparison with two simpler temperature index models, we simulated an operational employment in a regional scale landslide early warning system (EWS) and we found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

A fire weather index as a basis for an early warning system in Spain

2014 ◽  
Vol 23 (4) ◽  
pp. 510 ◽  
Author(s):  
A. Mestre ◽  
M. I. Manta
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Economic Value ◽  
Warning System ◽  
Fire Weather ◽  
Decision Threshold ◽  
Threshold Values ◽  
Weather Index ◽  
Fire Weather Index ◽  
Binary Decision

This research focuses on the evaluation of the economic value that can be achieved by the use of decision threshold values for the Canadian Fire Weather Index for the projection of different specific wildfire events, as a basis for wildfire prevention activities. The study was done in two testing areas in Spain, corresponding to La Coruña and Alicante provinces. The applied methodology makes use of a simple binary decision model, which has been widely applied for the verification of quantitative meteorological forecasts. Meteorological and wildfire data from a 10-year period (1997–2006) were used. The results show that use of the Fire Weather Index results in potential economic savings, especially in the case of La Coruña. Therefore, the use of the Canadian Fire Weather Index as a basis to develop an early warning system for wildfire is recommended in the two provinces under study.

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