scholarly journals Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting

2015 ◽  
Vol 15 (10) ◽  
pp. 2413-2423 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
A. Rosi ◽  
G. Rossi ◽  
A. Battistini ◽  
...  
Keyword(s):  
Early Warning ◽  
State Of The Art ◽  
Test Site ◽  
Early Warning Systems ◽  
The Other ◽  
Threshold Models ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Landslide Forecasting ◽  
Validation Procedure

Abstract. This work proposes a methodology to compare the forecasting effectiveness of different rainfall threshold models for landslide forecasting. We tested our methodology with two state-of-the-art models, one using intensity–duration thresholds and the other based on cumulative rainfall thresholds. The first model identifies rainfall intensity–duration thresholds by means of a software program called MaCumBA (MAssive CUMulative Brisk Analyzer) (Segoni et al., 2014a) that analyzes rain gauge records, extracts intensity (I) and duration (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram and identifies the thresholds that define the lower bounds of the I–D values. A back analysis using data from past events is used to identify the threshold conditions associated with the least number of false alarms. The second model (SIGMA) (Sistema Integrato Gestione Monitoraggio Allerta) (Martelloni et al., 2012) is based on the hypothesis that anomalous or extreme values of accumulated rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the standard deviation (σ) are used as thresholds to discriminate between ordinary and extraordinary rainfall events. The name of the model, SIGMA, reflects the central role of the standard deviations. To perform a quantitative and objective comparison, these two models were applied in two different areas, each time performing a site-specific calibration against available rainfall and landslide data. For each application, a validation procedure was carried out on an independent data set and a confusion matrix was built. The results of the confusion matrixes were combined to define a series of indexes commonly used to evaluate model performances in natural hazard assessment. The comparison of these indexes allowed to identify the most effective model in each case study and, consequently, which threshold should be used in the local early warning system in order to obtain the best possible risk management. In our application, none of the two models prevailed absolutely over the other, since each model performed better in a test site and worse in the other one, depending on the characteristics of the area. We conclude that, even if state-of-the-art threshold models can be exported from a test site to another, their employment in local early warning systems should be carefully evaluated: the effectiveness of a threshold model depends on the test site characteristics (including the quality and quantity of the input data), and a validation procedure and a comparison with alternative models should be performed before its implementation in operational early warning systems.

scholarly journals Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting

2015 ◽  
Vol 3 (1) ◽  
pp. 891-917 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
A. Rosi ◽  
G. Rossi ◽  
A. Battistini ◽  
...  
Keyword(s):  
Early Warning ◽  
Extreme Values ◽  
Threshold Model ◽  
Confusion Matrix ◽  
Test Site ◽  
Early Warning Systems ◽  
The Other ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Forecasting

Abstract. This work proposes a methodology to compare the forecasting effectiveness of different rainfall threshold models for landslide forecasting. We tested our methodology with two state-of-the-art models, one using intensity-duration thresholds and the other based on cumulative rainfall thresholds. The first model identifies rainfall intensity-duration thresholds by means of a software called MaCumBA (MAssive CUMulative Brisk Analyzer) (Segoni et al., 2014a) that analyzes rain-gauge records, extracts the intensities (I) and durations (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram, and identifies thresholds that define the lower bounds of the I−D values. A back analysis using data from past events is used to identify the threshold conditions associated with the least amount of false alarms. The second model (SIGMA) (Sistema Integrato Gestione Monitoraggio Allerta) (Martelloni et al., 2012) is based on the hypothesis that anomalous or extreme values of rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the SD (σ) are used as thresholds to discriminate between ordinary and extraordinary rainfall events. The name of the model, SIGMA, reflects the central role of the SDs in the proposed methodology. To perform a quantitative and objective comparison, these two methodologies were applied in two different areas, each time performing a site-specific calibration against available rainfall and landslide data. After each application, a validation procedure was carried out on an independent dataset and a confusion matrix was build. The results of the confusion matrixes were combined to define a series of indexes commonly used to evaluate model performances in natural hazard assessment. The comparison of these indexes allowed assessing the most effective model in each case of study and, consequently, which threshold should be used in the local early warning system in order to obtain the best possible risk management. In our application, none of the two models prevailed absolutely on the other, since each model performed better in a test site and worse in the other one, depending on the physical characteristics of the area. This conclusion can be generalized and it can be assumed that the effectiveness of a threshold model depends on the test site characteristics (including the quality and quantity of the input data) and that a validation and a comparison with alternative models should be performed before the implementation in operational early warning systems.

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 Probabilistic Rainfall Thresholds for Landslide Occurrences in Bhutan

2019 ◽  
Vol 8 (2S10) ◽  
pp. 737-742 ◽  
Keyword(s):  
Bayesian Approach ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Trade Route

Several works have been conducted all across the globe determining rainfall thresholds in context of intensity-duration thresholds. Such results provide results in terms of occurrence or non-occurrence of landslides. This paper determines probabilities for various rainfall parameters causing landslides using a Bayesian approach along Phuentsholing Thimphu Highway in Chukha Dzongkhag of Bhutan. For Bhutan Himalayas, probabilistic rainfall thresholds for landslide initiation has not yet been attempted. The probabilities determined can be used for early warning systems considering it is a major trade route with India

Recent Advances in Early Warning Systems for Landslide Forecasting

2021 ◽  
pp. 249-260
Author(s):  
Ram Wanare ◽  
Kannan K. R. Iyer ◽  
Prathyusha Jayanthi
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Recent Advances ◽  
Landslide Forecasting

Defining Physically-Based Rainfall Thresholds for Early Warning Systems

2013 ◽  
pp. 651-657 ◽  
Author(s):  
Diana Salciarini ◽  
Claudio Tamagnini ◽  
Francesco Ponziani ◽  
Nicola Berni
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Physically Based

scholarly journals Satellite rainfall products outperform ground observations for landslide forecasting in India

2021 ◽  
Author(s):  
Maria Teresa Brunetti ◽  
Massimo Melillo ◽  
Stefano Luigi Gariano ◽  
Luca Ciabatta ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Early Warning ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Warning Systems ◽  
Landslide Occurrence ◽  
Landslide Forecasting ◽  
Satellite Rainfall ◽  
Global Precipitation ◽  
The Impact

Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important tool to forecast landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide forecasting, to reduce the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide forecasting in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (10 km vs 25 km) and temporal (hourly vs daily) resolution. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g., overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

scholarly journals Rainfall threshold calculation for debris flow early warning in areas with scarcity of data

2018 ◽  
Vol 18 (5) ◽  
pp. 1395-1409 ◽  
Author(s):  
Hua-Li Pan ◽  
Yuan-Jun Jiang ◽  
Jun Wang ◽  
Guo-Qiang Ou
Keyword(s):  
Debris Flow ◽  
Wenchuan Earthquake ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Warning Systems ◽  
Mountainous Areas ◽  
Rainfall Thresholds ◽  
Initiation Mechanism

Abstract. Debris flows are natural disasters that frequently occur in mountainous areas, usually accompanied by serious loss of lives and properties. One of the most commonly used approaches to mitigate the risk associated with debris flows is the implementation of early warning systems based on well-calibrated rainfall thresholds. However, many mountainous areas have little data regarding rainfall and hazards, especially in debris-flow-forming regions. Therefore, the traditional statistical analysis method that determines the empirical relationship between rainstorms and debris flow events cannot be effectively used to calculate reliable rainfall thresholds in these areas. After the severe Wenchuan earthquake, there were plenty of deposits deposited in the gullies, which resulted in several debris flow events. The triggering rainfall threshold has decreased obviously. To get a reliable and accurate rainfall threshold and improve the accuracy of debris flow early warning, this paper developed a quantitative method, which is suitable for debris flow triggering mechanisms in meizoseismal areas, to identify rainfall threshold for debris flow early warning in areas with a scarcity of data based on the initiation mechanism of hydraulic-driven debris flow. First, we studied the characteristics of the study area, including meteorology, hydrology, topography and physical characteristics of the loose solid materials. Then, the rainfall threshold was calculated by the initiation mechanism of the hydraulic debris flow. The comparison with other models and with alternate configurations demonstrates that the proposed rainfall threshold curve is a function of the antecedent precipitation index (API) and 1 h rainfall. To test the proposed method, we selected the Guojuanyan gully, a typical debris flow valley that during the 2008–2013 period experienced several debris flow events, located in the meizoseismal areas of the Wenchuan earthquake, as a case study. The comparison with other threshold models and configurations shows that the selected approach is the most promising starting point for further studies on debris flow early warning systems in areas with a scarcity of data.

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