Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

2021 ◽  
Author(s):  
Samuele Segoni ◽  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan
Keyword(s):  
Sigma Model ◽  
Confusion Matrix ◽  
Regional Scale ◽  
Geographical Location ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Integrated System ◽  
Rainfall Thresholds ◽  
Skill Scores ◽  
Landslide Forecasting

<p>SIGMA (Sistema Integrato Gestione Monitoraggio Allerta – integrated system for management, monitoring and alerting) is a landslide forecasting model at regional scale which is operational in Emilia Romagna (Italy) for more than 20 years. It was conceived to be operated with a sparse rain gauge network with coarse (daily) temporal resolution and to account for both shallow landslides (typically triggered by short and intense rainstorms) and deep seated landslides (typically triggered by long and less intense rainfalls). SIGMA model is based on the statistical distribution of cumulative rainfall values (calculated over varying time windows), and rainfall thresholds are defined as the multiples of standard deviation of the same, to identify anomalous rainfalls with the potential of triggering landslides.</p><p>In this study, SIGMA model is applied for the first time in a geographical location outside of Italy, i.e. Kalimpong town in India. The SIGMA algorithm is customized using the historical rainfall and landslide data of Kalimpong from 2010 to 2015 and has been validated using the data from 2016 to 2017. The model was validated by building a confusion matrix and calculating statistical skill scores, which were compared with those of the state-of-the-art intensity-duration rainfall thresholds derived for the region.</p><p>Results of the comparison clearly show that SIGMA performs much better than the other models in forecasting landslides: all instances of the validation confusion matrix are improved, and all skill scores are higher than I-D thresholds, with an efficiency of 92% and a likelihood ratio of 11.28. We explain this outcome mainly with technical characteristics of the site: when only daily rainfall measurements from a spare gauge network are available, SIGMA outperforms other approaches based on peak measurements, like intensity – duration thresholds, which cannot be captured adequately by daily measurements. SIGMA model thus showed a good potential to be used as a part of the local Landslide Early Warning System (LEWS).</p>

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 Regional rainfall thresholds for landslide occurrence using a centenary database

2017 ◽  
Author(s):  
Teresa Vaz ◽  
José Luís Zêzere ◽  
Susana Pereira ◽  
Sérgio C. Oliveira ◽  
Ricardo A. C. Garcia ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Period Analysis ◽  
Landslide Occurrence ◽  
Landslide Event ◽  
Landslide Early Warning ◽  
Regional Rainfall

Abstract. This work proposes a comprehensive methodology to assess rainfall thresholds for landslide initiation, using a centenary landslide database associated with a single centenary daily rainfall dataset. The methodology is applied to the Lisbon region and include the rainfall return period analysis that was used to identify the critical rainfall combination (quantity-duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds is assessed and validated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, such thresholds may be used with acceptable confidence up to 50 km distance from the rain gauge. The obtained rainfall thresholds using linear and potential regression have a good performance in ROC metrics. However, the intermediate thresholds based on the probability of landslide events, established in the zone between the lower limit threshold and the upper limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

scholarly journals A Regional-Scale Landslide Warning System Based on 20 Years of Operational Experience

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1297 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Riccardo Fanti ◽  
Angela Gallucci ◽  
Antonio Monni ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Development Process ◽  
Regional Scale ◽  
Warning System ◽  
Operational Experience ◽  
Complex Task ◽  
Warning Systems ◽  
Research Activity ◽  
Rainfall Thresholds ◽  
Landslide Forecasting

SIGMA is a regional landslide warning system based on statistical rainfall thresholds that operates in Emilia Romagna (Italy). In this work, we depict its birth and the continuous development process, still ongoing, after two decades of operational employ. Indeed, a constant work was carried out to gather and incorporate in the modeling new data (extended rainfall recordings, updated landslides inventories, temperature and soil moisture data). The use of these data allowed for regular updates of the model and some conceptual improvements, which consistently increased the forecasting effectiveness of the warning system through time. Landslide forecasting at regional scale is a very complex task, but this paper shows that, as time passes by, the systematic gathering and analysis of new data and the continuous progresses of research activity, uncertainties can be progressively reduced. Thus, by the setting up of forward-looking research programs, the performances and the reliability of regional scale warning systems can be increased with time.

scholarly journals Deriving rainfall thresholds for landsliding at the regional scale: daily and hourly resolutions, normalisation, and antecedent rainfall

2020 ◽  
Vol 20 (11) ◽  
pp. 2905-2919
Author(s):  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Regional Scale ◽  
Predictive Performance ◽  
Rainfall Threshold ◽  
Rain Gauge ◽  
Mean Annual Precipitation ◽  
Threshold Estimation ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Hourly Rainfall ◽  
The Impact

Abstract. Rainfall thresholds are a simple and widely used method to forecast landslide occurrence. We provide a comprehensive data-driven assessment of the effects of rainfall temporal resolution (hourly versus daily) on rainfall threshold performance in Switzerland, with sensitivity to two other important aspects which appear in many landslide studies – the normalisation of rainfall, which accounts for local climatology, and the inclusion of antecedent rainfall as a proxy of soil water state prior to landsliding. We use an extensive landslide inventory with over 3800 events and several daily and hourly, station, and gridded rainfall datasets to explore different scenarios of rainfall threshold estimation. Our results show that although hourly rainfall did show the best predictive performance for landslides, daily data were not far behind, and the benefits of hourly resolutions can be masked by the higher uncertainties in threshold estimation connected to using short records. We tested the impact of several typical actions of users, like assigning the nearest rain gauge to a landslide location and filling in unknown timing, and we report their effects on predictive performance. We find that localisation of rainfall thresholds through normalisation compensates for the spatial heterogeneity in rainfall regimes and landslide erosion process rates and is a good alternative to regionalisation. On top of normalisation by mean annual precipitation or a high rainfall quantile, we recommend that non-triggering rainfall be included in rainfall threshold estimation if possible. Finally, while antecedent rainfall threshold approaches used at the local scale are not successful at the regional scale, we demonstrate that there is predictive skill in antecedent rain as a proxy of soil wetness state, despite the large heterogeneity of the study domain.

scholarly journals Empirical Rainfall Thresholds for Landslide Occurrence in Serra do Mar, Angra dos Reis, Brazil

2021 ◽  
Author(s):  
Daniel Germain ◽  
Sébastien Roy ◽  
Antonio Jose Teixera Guerra
Keyword(s):  
Temporal Dynamics ◽  
Regional Scale ◽  
Daily Rainfall ◽  
Wet Season ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Serra Do Mar ◽  
Temporal Prediction ◽  
Northern Regions ◽  
Highly Correlated

In the tropical environment such as Brazil, the frequency of rainfall-induced landslides is particularly high because of the rugged terrain, heavy rainfall, increasing urbanization, and the orographic effect of mountain ranges. Since such landslides repeatedly interfere with human activities and infrastructures, improved knowledge related to spatial and temporal prediction of the phenomenon is of interest for risk management. This study is an analysis of empirical rainfall thresholds, which aims to establish local and regional scale correlations between rainfall and the triggering of landslides in Angra dos Reis in the State of Rio de Janeiro. A statistical analysis combining quantile regression and binary logistic regression was performed on 1640 and 526 landslides triggered by daily rainfall over a 6-year period in the municipality and the urban center of Angra dos Reis, in order to establish probabilistic rainfall duration thresholds and assess the role of antecedent rainfall. The results show that the frequency of landslides is highly correlated with rainfall events, and surprisingly the thresholds in dry season are lower than those in wet season. The aspect of the slopes also seems to play an important role as demonstrated by the different thresholds between the southern and northern regions. Finally, the results presented in this study provide new insight into the spatial and temporal dynamics of landslides and rainfall conditions leading to their activation in this tropical and mountainous environment.

scholarly journals Combining landslide susceptibility maps and rainfall thresholds using a matrix approach

2017 ◽  
Vol 19 (1) ◽  
pp. 58-74
Author(s):  
THIEBES Benni ◽  
BAI Shibiao ◽  
XI Yanan ◽  
GLADE Thomas ◽  
BELL Rainer
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Landslide Susceptibility Mapping ◽  
Matrix Approach ◽  
Hazard Maps ◽  
Rainfall Thresholds ◽  
Rainfall Model ◽  
Simple Matrix

On the regional scale, investigations on future landslide can broadly be distinguished in spatial or temporal analyses, i.e. landslide susceptibility or hazard maps, and landslide triggering rainfall thresholds. Even though both approaches have its uses e.g. in spatial planning, risk management and early warning, they also have limitations. Susceptibility and hazard maps do not contain information on when landslides will be triggered, while rainfall thresholds give no detailed indication on where a landslide might take place. The combination of spatial and temporal landslide research remains a complex issue and no ready-to-use methodology for combined spatiotemporal landslide analyses is presently available. In our study, we present a simple matrix approach to combine spatial and temporal landslide probabilities and highlight its application for a case study in the Wudu region, China. Landslide susceptibility mapping is based on a previous study involving logistic regression; the analysis of rainfall threshold was carried out applying the daily rainfall model. A 4x4 matrix was used to combine and reclassify the spatial and temporal landslide information. The results are then plotted on a map to highlight the susceptibility for rainfall events with varying likelihood of triggering landslides.

scholarly journals Regional rainfall thresholds for landslide occurrence using a centenary database

2018 ◽  
Vol 18 (4) ◽  
pp. 1037-1054 ◽  
Author(s):  
Teresa Vaz ◽  
José Luís Zêzere ◽  
Susana Pereira ◽  
Sérgio Cruz Oliveira ◽  
Ricardo A. C. Garcia ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Data Set ◽  
Landslide Occurrence ◽  
Comprehensive Method ◽  
Landslide Event ◽  
Landslide Early Warning

Abstract. This work proposes a comprehensive method to assess rainfall thresholds for landslide initiation using a centenary landslide database associated with a single centenary daily rainfall data set. The method is applied to the Lisbon region and includes the rainfall return period analysis that was used to identify the critical rainfall combination (cumulated rainfall duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds are assessed and calibrated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, these thresholds may be used with acceptable confidence up to 50 km from the rain gauge. The rainfall thresholds obtained using linear and potential regression perform well in ROC metrics. However, the intermediate thresholds based on the probability of landslide events established in the zone between the lower-limit threshold and the upper-limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

scholarly journals Reanalysis of Soil Moisture Used for Rainfall Thresholds for Rainfall-Induced Landslides: The Italian Case Study

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1977
Author(s):  
Nejc Bezak ◽  
Mateja Jemec Auflič ◽  
Matjaž Mikoš
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Rainfall Threshold ◽  
Economic Damage ◽  
Rainfall Thresholds ◽  
Forecasting Accuracy ◽  
Soil Moisture Data ◽  
Italian Case ◽  
Landslide Forecasting

Landslides are one of the most frequent natural disasters that can endanger human lives and property. Therefore, prediction of landslides is essential to reduce economic damage and save human lives. Numerous methods have been developed for the prediction of landslides triggering, ranging from simple methods that include empirical rainfall thresholds, to more complex ones that use sophisticated physically- or conceptually-based models. Reanalysis of soil moisture data could be one option to improve landslide forecasting accuracy. This study used the publicly available FraneItalia database hat contains almost 9000 landslide events that occurred in the 2010–2017 period in Italy. The Copernicus Uncertainties in Ensembles of Regional Reanalyses (UERRA) dataset was used to obtain precipitation and volumetric soil moisture data. The results of this study indicated that precipitation information is still a much better predictor of landslides triggering compared to the reanalyzed (i.e., not very detailed) soil moisture data. This conclusion is valid both for local (i.e., grid) and regional (i.e., catchment-based) scales. Additionally, at the regional scale, soil moisture data can only predict a few landslide events (i.e., on average around one) that are not otherwise predicted by the simple empirical rainfall threshold approach; however, this approach on average, predicted around 18 events (i.e., 55% of all events). Despite this, additional investigation is needed using other (more complete) landslide databases and other (more detailed) soil moisture products.

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 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.

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