Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy

Geomorphology ◽  
2015 ◽  
Vol 228 ◽  
pp. 653-665 ◽  
Author(s):  
S.L. Gariano ◽  
M.T. Brunetti ◽  
G. Iovine ◽  
M. Melillo ◽  
S. Peruccacci ◽  
...  
Keyword(s):  
Southern Italy ◽  
Shallow Landslide ◽  
Rainfall Thresholds ◽  
Calibration And Validation ◽  
Landslide Forecasting

scholarly journals Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy

2014 ◽  
Vol 14 (2) ◽  
pp. 317-330 ◽  
Author(s):  
C. Vennari ◽  
S. L. Gariano ◽  
L. Antronico ◽  
M. T. Brunetti ◽  
G. Iovine ◽  
...  
Keyword(s):  
Southern Italy ◽  
Central Italy ◽  
Mediterranean Area ◽  
Shallow Landslide ◽  
Google Earth ◽  
Shallow Landslides ◽  
Rainfall Thresholds ◽  
Rainfall Events ◽  
Landslide Occurrence ◽  
Hourly Rainfall

Abstract. In many areas, rainfall is the primary trigger of landslides. Determining the rainfall conditions responsible for landslide occurrence is important, and may contribute to saving lives and properties. In a long-term national project for the definition of rainfall thresholds for possible landslide occurrence in Italy, we compiled a catalogue of 186 rainfall events that resulted in 251 shallow landslides in Calabria, southern Italy, from January 1996 to September 2011. Landslides were located geographically using Google Earth®, and were given a mapping and a temporal accuracy. We used the landslide information, and sub-hourly rainfall measurements obtained from two complementary networks of rain gauges, to determine cumulated event vs. rainfall duration (ED) thresholds for Calabria. For this purpose, we adopted an existing method used to prepare rainfall thresholds and to estimate their associated uncertainties in central Italy. The regional thresholds for Calabria were found to be nearly identical to previous ED thresholds for Calabria obtained using a reduced set of landslide information, and slightly higher than the ED thresholds obtained for central Italy. We segmented the regional catalogue of rainfall events with landslides in Calabria into lithology, soil regions, rainfall zones, and seasonal periods. The number of events in each subdivision was insufficient to determine reliable thresholds, but allowed for preliminary conclusions about the role of the environmental factors in the rainfall conditions responsible for shallow landslides in Calabria. We further segmented the regional catalogue based on administrative subdivisions used for hydro-meteorological monitoring and operational flood forecasting, and we determined separate ED thresholds for the Tyrrhenian and the Ionian coasts of Calabria. We expect the ED rainfall thresholds for Calabria to be used in regional and national landslide warning systems. The thresholds can also be used for landslide hazard and risk assessments, and for erosion and landscape evolution studies, in the study area and in similar physiographic regions in the Mediterranean area.

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 Landslides, floods and sinkholes in a karst environment: the 1–6 September 2014 Gargano event, southern Italy

2017 ◽  
Vol 17 (3) ◽  
pp. 467-480 ◽  
Author(s):  
Maria Elena Martinotti ◽  
Luca Pisano ◽  
Ivan Marchesini ◽  
Mauro Rossi ◽  
Silvia Peruccacci ◽  
...  
Keyword(s):  
Southern Italy ◽  
Flash Floods ◽  
Temporal Information ◽  
Karst Area ◽  
Exceedance Probability ◽  
Karst Environment ◽  
Gargano Promontory ◽  
Landslide Forecasting ◽  
Landslide Early Warning ◽  
Torrential Rainfall

Abstract. In karst environments, heavy rainfall is known to cause multiple geohydrological hazards, including inundations, flash floods, landslides and sinkholes. We studied a period of intense rainfall from 1 to 6 September 2014 in the Gargano Promontory, a karst area in Puglia, southern Italy. In the period, a sequence of torrential rainfall events caused severe damage and claimed two fatalities. The amount and accuracy of the geographical and temporal information varied for the different hazards. The temporal information was most accurate for the inundation caused by a major river, less accurate for flash floods caused by minor torrents and even less accurate for landslides. For sinkholes, only generic information on the period of occurrence of the failures was available. Our analysis revealed that in the promontory, rainfall-driven hazards occurred in response to extreme meteorological conditions and that the karst landscape responded to the torrential rainfall with a threshold behaviour. We exploited the rainfall and the landslide information to design the new ensemble–non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the possible occurrence of rainfall-induced landslides and of related geohydrological hazards. The ensemble of the metrics produced by the E-NEP algorithm provided better diagnostics than the single metrics often used for landslide forecasting, including rainfall duration, cumulated rainfall and rainfall intensity. We expect that the E-NEP algorithm will be useful for landslide early warning in karst areas and in other similar environments. We acknowledge that further tests are needed to evaluate the algorithm in different meteorological, geological and physiographical settings.

scholarly journals Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides Occurrence

2020 ◽  
Author(s):  
Maurizio Lazzari ◽  
Marco Piccarreta ◽  
Ram L. Ray ◽  
Salvatore Manfreda
Keyword(s):  
Soil Moisture ◽  
Rainfall Intensity ◽  
Shallow Landslide ◽  
Rainfall Thresholds ◽  
Antecedent Soil Moisture ◽  
Correct Definition ◽  
Triggering Conditions ◽  
Definition Of ◽  
Moisture Conditions ◽  
Traditional Approaches

Rainfall-triggered shallow landslide events have caused losses of human lives and millions of euros in damage to property in all parts of the world. The need to prevent such hazards combined with the difficulty of describing the geomorphological processes over regional scales led to the adoption of empirical rainfall thresholds derived from records of rainfall events triggering landslides. These rainfall intensity thresholds are generally computed, assuming that all events are not influenced by antecedent soil moisture conditions. Nevertheless, it is expected that antecedent soil moisture conditions may provide critical support for the correct definition of the triggering conditions. Therefore, we explored the role of antecedent soil moisture on critical rainfall intensity-duration thresholds to evaluate the possibility of modifying or improving traditional approaches. The study was carried out using 326 landslide events that occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e., rainstorm intensity and duration), we also derived the antecedent soil moisture conditions using a parsimonious hydrological model. These data have been used to derive the rainfall intensity thresholds conditional on the antecedent saturation of soil quantifying the impact of such parameters on rainfall thresholds.

Comparison of statistical methods and multi-time validation for the determination of the shallow landslide rainfall thresholds

Landslides ◽  
2017 ◽  
Vol 15 (5) ◽  
pp. 937-952 ◽  
Author(s):  
Yuri Galanti ◽  
Michele Barsanti ◽  
Andrea Cevasco ◽  
Giacomo D’Amato Avanzi ◽  
Roberto Giannecchini
Keyword(s):  
Statistical Methods ◽  
Shallow Landslide ◽  
Rainfall Thresholds

Spatio-temporal landslide forecasting based on combination of rainfall thresholds and landslide susceptibility maps: a test in the Northern Apennines (Italy).

2020 ◽  
Author(s):  
Ascanio Rosi ◽  
Samuele Segoni ◽  
Veronica Tofani ◽  
Filippo Catani
Keyword(s):  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
End Users ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Coarse Spatial Resolution ◽  
Landslide Forecasting ◽  
Spatio Temporal ◽  
Landslide Susceptibility Maps

<p>Landslide forecasting and early warning at regional scale are difficult task and they are usually accomplished by the mean of statistical approaches aimed to define rainfall thresholds and landslide susceptibility maps.<br> Landslide susceptibility maps are based on the analysis of predisposing factors to assess the spatial probability of landslide occurrence, while rainfall thresholds are based on the correlation, valid on a wide area, between landslide occurrence and triggering factors, which usually are a couple of rainfall parameters, such as rainfall duration and intensity.<br>Susceptibility maps are static map that can be used for the spatial prediction of the most landslide prone areas, nut cannot be used to predict the temporal occurrence of a landslide triggering.<br>Rainfall thresholds can be used for temporal prediction, but with a coarse spatial resolution (usually some hundreds or thousands of km<sup>2</sup>), and the reference areas could contains both plains and hillslopes, so the alerts could involves both areas, even if landslides are improbable in river plains; this means that rainfall thresholds are not very suitable to identify the most probable triggering sites.<br>Rainfall thresholds and susceptibility maps can be therefore conveniently combined into dynamic hazard matrixes to obtain spatio-temporal forecasts of landslide hazard.<br>To combine these inputs, they are combined in a purposely-built hazard matrix, where each parameter is classified into 3 classes: landslide susceptibility map has been classified in S1 (low susceptibility), S2 (medium susceptibility) and S3 (high susceptibility), while rainfall rate has been classified in the classes R1, R2 and R3, by the definition of 2 rainfall thresholds.<br>The combination of the aforementioned classes allowed to define a matrix with 5 hazard classes, from H0 (null hazard) to H4 (high hazard), which was calibrated so that there was not any landslide in the H0 class and that the 90% of the landslide were in H2-H4 classes.<br>The result of this procedure is a dynamic hazard map, where the hazard, which is calculated for each pixel, can change over the time, based on rainfall rate variations.<br>For operational purposes, such a map cannot be used, since the pixel based resolution is too fine to be used during an emergency or to plan any activity in the planification phase, so the results have been aggregated at municipality scale, which is more easily readable for the end-users as local administrators and decision makers.<br>In this way it is possible to overcome the issues due to the stillness of susceptibility maps and to the coarse spatial resolution of rainfall thresholds, also avoiding results which could be hardly understandable outside of the scientific community.<br>This procedure was tested in a test site located in Northern Tuscany (Italy) and the work showed the possibility of obtaining results which are balanced between the scientific soundness and the needs of end-users like mayors, local administrators and civil protection personnel.</p><p> </p>

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