Geographical landslide early warning systems

Guzzetti, Fausto; Gariano, Stefano Luigi; Peruccacci, Silvia; Brunetti, Maria Teresa; Marchesini, Ivan; Rossi, Mauro; Melillo, Massimo

doi:10.1016/j.earscirev.2019.102973

Cited by 292 publications

(190 citation statements)

References 156 publications

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“…However, evidence shows that landslide processes are not stationary in our study area, and arguably in other similar areas in Central Italy and in other similar physiographical and climatic settings. The finding poses questions on the reliability of landslide forecast and prediction models based on past landslide and rainfall records (Rossi et al, 2010b;Witt et al, 2010;Segoni et al, 2018;Guzzetti et al, 2019).…”

Section: Hazard Considerationsmentioning

confidence: 99%

“…In the literature, approaches to predict the temporal occurrence of landslides are equally if not more diversified. Depending on the scope, the temporal coverage, the return time of the predictions, and the extent of the study area, methods include (i) the use of empirical rainfall thresholds for the possible occurrence of landslides (Glade et al, 2000;Dai and Lee, 2001;Crosta and Frattini, 2000;Aleotti, 2004;Guzzetti et al, 2007Guzzetti et al, , 2008Saito et al, 2010;Ko and Lo, 2018;Segoni et al, 2018;Guzzetti et al, 2019), (ii) physically-based, coupled, distributed rainfall and infiltration slope stability models (Montgomery and Dietrich, 1994;Van Asch et al, 1996;Baum et al, 2008;Lanni et al, 2013;Formetta et al, 2014;Reid et al, 2015;Formetta et al, 2016;Alvioli and Baum, 2016;Bout et al, 2018), and (iii) the analysis of time series of historical landslides and landslide events (Crovelli and Coe, 2009;Rossi et al, 2010b;Witt et al, 2010). Only physically-based models (inherently) consider the spatio-temporal interactions that condition landslide occurrence, which are not considered by the threshold models or by the analysis of the historical records.…”

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confidence: 99%

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Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

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Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a novel Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km 2 area into 889 slope units (SUs). In each SU, we computed the percentage of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model, and 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to a "traditional" landslide susceptibility model. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In partic...

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Section: Hazard Considerationsmentioning

confidence: 99%

mentioning

confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Where no local or regional LEWS is available, global models can be applied (Guzzetti et al 2007). Many operational LEWS exist around the world, reviews of which are given for local (Michoud et al 2013) and regional LEWS (Bell et al 2010;Piciullo et al 2018;Guzzetti et al 2020), for systems applied to a range of processes and scales (Stähli et al 2015), and more specifically for LEWS in Europe (Guzzetti et al 2007) and the USA (Baum and Godt 2010).…”

Section: Introductionmentioning

confidence: 99%

Assessing the potential of soil moisture measurements for regional landslide early warning

et al. 2020

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In mountainous terrain, rainfall-induced landslides pose a serious risk to people and infrastructure. Regional landslide early warning systems (LEWS) have proven to be a cost-efficient tool to inform the public about the imminent landslide danger. While most operational LEWS are based on rainfall exceedance thresholds only, recent studies have demonstrated an improvement of the forecast quality after the inclusion of soil hydrological information. In this study, the potential of in situ soil moisture measurements for regional landslide early warning is assessed. For the first time, a comprehensive soil moisture measurement database was compiled for Switzerland and compared with a national landslide database (Swiss flood and landslide damage database, WSL). The time series were homogenized and normalized to represent saturation values. From ensembles of sensors, the mean and standard deviation saturation were calculated and infiltration events were delimited, characterized, and classified as landslidetriggering or non-triggering based on the occurrence of landslides within a specified forecast distance. A logistic regression function was applied to model the landslide activity based on the infiltration event characteristics and several models were analysed and compared with receiver operating characteristics (ROC). A strong distance dependence becomes apparent showing a forecast goodness decrease with increasing distance between water content measurement site and landslide, and a better forecast goodness for long-lasting as opposed to short-duration precipitation events. While most variability can be explained by the two event properties antecedent saturation and change of saturation during an infiltration event, event properties that describe antecedent conditions are more important for long-lasting as opposed to shortduration precipitation events that can be better explained by properties describing event dynamics. Overall, the analysis demonstrated that in situ soil moisture data effectively contains specific information useful for landslide early warning.

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“…For better evaluation of landslide hazards induced by rainfall-triggered LSLs, it is essential to comprehend the circumstances that induce failure and the mass movement following collapse [11,12]. Accurate landslide information on occurrence time, size, and location are beneficial for comprehending when, where, and how slopes may collapse following heavy rainfall [13]. Rainfall is well known as one of the significant factors in landslide occurrence, so in-depth knowledge of the effects of rainfall conditions is required.…”

Section: Introductionmentioning

confidence: 99%

Using a Tank Model to Determine Hydro-Meteorological Thresholds for Large-Scale Landslides in Taiwan

Lin

Kuo

Chen

et al. 2020

Water

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Rainfall thresholds for slope failures are essential information for establishing early-warning systems and for disaster risk reduction. Studies on the thresholds for rainfall-induced landslides of different scales have been undertaken in recent decades. This study attempts to establish a warning threshold for large-scale landslides (LSLs), which are defined as landslides with a disturbed area more massive than 0.1 km2. The numerous landslides and extensive rainfall records make Taiwan an appropriate area to investigate the rainfall conditions that can result in LSLs. We used landslide information from multiple sources and rainfall data captured by 594 rain gauges to create a database of 83 rainfall events associated with LSLs in Taiwan between 2001 and 2016. The corresponding rainfall duration, cumulative event rainfall, and rainfall intensity for triggering LSLs were determined. This study adopted the tank model to estimate conceptual water depths (S1, S2, S3) in three-layer tanks and calculated the soil water index (SWI) by summing up the water depths in the three tanks. The empirical SWI and duration (SWI–D) threshold for triggering LSLs occurring during 2001–2013 in Taiwan is determined as SWI = 155.20 − 1.56D and D ≥ 24 h. The SWI–D threshold for LSLs is higher than that for small-scale landslides (SSLs), those with a disturbed area smaller than 0.1 km2. The LSLs that occurred during 2015–2016 support this finding. It is notable that when the SWI and S3 reached high values, the potential of LSLs increased significantly. The rainfall conditions for triggering LSLs gradually descend with increases in antecedent SWI. Unlike the rainfall conditions for triggering SSLs, those for triggering LSLs are related to the long duration–high intensity type of rainfall event.

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Geographical landslide early warning systems

Cited by 292 publications

References 156 publications

Space-Time Landslide Predictive Modelling

Space-Time Landslide Predictive Modelling

Assessing the potential of soil moisture measurements for regional landslide early warning

Using a Tank Model to Determine Hydro-Meteorological Thresholds for Large-Scale Landslides in Taiwan

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