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

Rosi, Ascanio; Segoni, Samuele; Canavesi, Vanessa; Monni, Antonio; Gallucci, Angela; Casagli, Nicola

doi:10.1007/s10346-020-01523-2

Cited by 46 publications

(40 citation statements)

References 52 publications

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“…The main relieves are the Alps, which span from East to West of the northern border of the country, and the Apennines, that cross the country from North to South (Figure 1B). In the alpine area, which is formed mainly of metamorphic rocks (Vai and Martini 2001;Salvatici et al 2018), the most frequent phenomena are rock fall and debris avalanches (Agliardi and Crosta 2003;Panizza et al 2011), while in the Apennines, which are formed mainly of arenaceous flysch (Vai and Martini 2001;Agostini et al 2014;Rosi et al 2018Rosi et al , 2021, the most common landslides are represented by rotational and translational landslides, both surficial and deep-seated.…”

Section: Study Areamentioning

confidence: 99%

Exploring a landslide inventory created by automated web data mining: the case of Italy

et al. 2022

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Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowdsourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of “landslide events”. This source of information allows continuous feedback from real world, and news concerning landslide events can be rapidly collected. In this work, the newspaper articles about landslides in Italy are automatically collected by an existing data mining algorithm, based on a semantic engine. The news has been analysed to assess their distribution over the territory and to verify the possibility of using them for hazard mapping purpose. In 10 years, from 2010 to 2019, the algorithm identified and geolocated 184322 articles referring to 32525 generical events (“news”). At first, the collected data underwent to a manual verification, followed by a classification based on news relevance, localization accuracy and time of publication. Then, these data have been used to identify the areas and the periods most affected by landslide phenomena. The analyses show that almost 42% of Italian municipalities have been affected by landslide. According to the results, the use of data mining is helpful for the creation of landslide databases where the day and the approximative location (municipality) of the possible landslide triggers are known. This database, in turn, can be used for scientific purposes, as the definition of the meteorological condition associated with landslide initiation, the validation of risk maps. It can also be used for a proper land use or risk mitigation planning, since the most landslide-prone municipalities can be defined.

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Section: Study Areamentioning

confidence: 99%

Exploring a landslide inventory created by automated web data mining: the case of Italy

et al. 2022

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“…Antecedent rainfall is crucial to the landslide occurrence mechanism due to the increase of pore water pressure and the deterioration of slope stability (Crozier 1999;Iverson 2000;Glade et al 2000;Segoni et al 2018b;Valenzuela et al 2018). Wicki et al (2020), Rosi et al (2020), Kim et al (2020), Yang et al (2020), and Ponziani et al (2012) highlighted the necessity of using antecedent rainfall or other proxies to account for more complex hydrological conditions.…”

Section: Introductionmentioning

confidence: 99%

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

2021

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The purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

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“…These methods include cumulative rainfall amount versus rainfall duration, or average rainfall intensity versus rainfall duration. However, these precipitation thresholds include numerous uncertainties that limit their application in early warning systems (Rosi et al 2020;Melillo et al 2018;Guzzetti et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Rockfall and Rainfall Correlation in the Anaga Nature Reserve in Tenerife (Canary Islands, Spain)

et al. 2022

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Rockfalls are frequent and damaging phenomena that occur on steep or vertical slopes, in coastal areas, mountains and along coastal cliff. Water, in different forms, is the most common triggered factor of rockfalls. Consequently, we can consider that precipitation is the most influential factor for slope instabilities and it influences almost all other water parameters. Besides, the specific geology of the Anaga nature reserve in the volcanic island of Tenerife, together with its steep landscape, contributes to the instability of the slopes and frequent rockfalls. Recently, due to climate change and global warming, the annual precipitation/rainfall has declined but the number of heavy storms, associated with intense rainfall and strong winds, events that exceed precipitation thresholds in a brief period has increased which triggers slope movements. This paper describes the analysis of information on rainfall-induced rockfalls in Anaga, Tenerife (Canary Islands), to forecast rock failures of social significance and to improve the capability to respond and emergency decision making. To define reliable thresholds for a certain area, we analized information during the period 2010–2016, reconstructed the rockfall events, and statistically analyzed the historical rainfall conditions that led to landslides. The summary graph correlating precipitation to the probability of occurrence of an event was plotted. Statistical and probability graphs were made with the direct relationship between the number of rockfall events and total rainfall in that period by examining the maximum daily precipitation, not only on the day of the event but up to 3 days before. Hence, the results of this study would serve as a guide for the possible forecasting of rainfall-induced rockfalls, especially for road maintenance services, so that they can be on alert or mobilize the necessary resources in advance depending on the intensity of the expected rainfall.

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

Cited by 46 publications

References 52 publications

Exploring a landslide inventory created by automated web data mining: the case of Italy

Exploring a landslide inventory created by automated web data mining: the case of Italy

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

Rockfall and Rainfall Correlation in the Anaga Nature Reserve in Tenerife (Canary Islands, Spain)

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