Early warning of rainfall-induced shallow landslides and debris flows in the USA

Baum, Rex L.; Godt, Jonathan W.

doi:10.1007/s10346-009-0177-0

Cited by 468 publications

(275 citation statements)

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“…In the USA, the US Geological Survey has long been working on ReLEWSs in a number of states: California, Colorado, Oregon and Washington (Chleborad, 2000;Chleborad et al, 2008;Baum and Godt, 2010;NOAA-USGS, 2005;Cannon et al, 2011). The state of knowledge and resources available to issue alerts of precipitation-induced shallow, rapidly moving landslides and debris flows vary across the USA; for instance, in the city of Seattle, WA, the alert system includes four levels -Null, Outlook, Watch and Alarm -and warnings are based on the measured or expected exceedance of cumulated rainfall and intensity-duration thresholds combined with criteria using monitored soil moisture (Godt et al, 2006).…”

Section: Regional Systems For Rainfall-induced Landslidesmentioning

confidence: 99%

“…However, literature studies rarely back analyze the relationship between warnings, which would have been issued adopting those correlations, and landslides. Especially for LEWSs operating at regional scale (ReLEWSs), empirical evaluations are often carried out by simply analyzing the time frames during which significant high-consequence landslides occurred in the test area (Keefer et al, 1987;Baum and Godt, 2010;Capparelli and Tiranti, 2010;Aleotti, 2004). Alternatively, the performance evaluation is based on 2 by 2 contingency tables computed for the joint frequency distribution of landslides and alerts, both considered as dichotomous variables (Yu et al, 2003;Cheung et al, 2006;Godt et al, 2006;Restrepo et al, 2008;Tiranti and Rabuffetti, 2010;Kirschbaum et al, 2012;Martelloni et al, 2012;Peres and Cancelliere, 2012;Staley et al, 2013;Lagomarsino et al, 2013Lagomarsino et al, , 2015Greco et al, 2013;Segoni et al, 2014;Gariano et al, 2015;Stähli et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the performance of regional landslide early warning models: the EDuMaP method

Calvello

Piciullo

2016

Nat. Hazards Earth Syst. Sci.

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Abstract.A schematic of the components of regional early warning systems for rainfall-induced landslides is herein proposed, based on a clear distinction between warning models and warning systems. According to this framework an early warning system comprises a warning model as well as a monitoring and warning strategy, a communication strategy and an emergency plan. The paper proposes the evaluation of regional landslide warning models by means of an original approach, called the "event, duration matrix, performance" (EDuMaP) method, comprising three successive steps: identification and analysis of the events, i.e., landslide events and warning events derived from available landslides and warnings databases; definition and computation of a duration matrix, whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the early warning model performance by means of performance criteria and indicators applied to the duration matrix. During the first step the analyst identifies and classifies the landslide and warning events, according to their spatial and temporal characteristics, by means of a number of model parameters. In the second step, the analyst computes a time-based duration matrix with a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warning data from the municipal early warning system operating in Rio de Janeiro (Brazil).

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Section: Regional Systems For Rainfall-induced Landslidesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the performance of regional landslide early warning models: the EDuMaP method

Calvello

Piciullo

2016

Nat. Hazards Earth Syst. Sci.

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“…The latter is quite common in areas with scarce vegetation or that have been recently affected by forest fires, thus becoming more susceptible to erosion (e.g., Cannon et al, 2001a;Cannon and Gartner, 2005;Cannon et al, 2008;Santi et al, 2008;Baum and Godt, 2010;Cannon et al, 2011;Kean et al, 2013;Staley et al, 2013b;Staley et al, 2014). 25 The consumption of vegetation, ash deposition, changes in physical properties of soils and rocks and the presence of waterrepellent soils are typical consequences of fire (Cannon and Gartner, 2005;Cannon et al, 2010;Parise and Cannon, 2012).…”

Section: Introductionmentioning

confidence: 99%

Debris flow run-out simulation and analysis using a dynamic model

Melo¹,

Asch²,

Zêzere³

2017

Preprint

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Abstract.Only two months after a huge wildfire occurred in the upper part of a valley located in Central Portugal, several debris flows were triggered by intense rainfall. The event caused infrastructural and economical damage, although no life was lost. The present research aims to simulate the run-out of two debris flows occurred during the event as well as to calculate by back-analysis the rheological parameters and the excess rain involved. Thus, a dynamic model was used, which 10 integrates surface runoff, concentrated erosion along the channels, propagation and deposition of flow material. The rheological and entrainment parameters obtained for the most accurate simulation were then used to perform three scenarios of debris flows run-out at the basin scale. Due to the lack of quantitative information to validate these models, the results were compared with historical references of debris flow events in the study area. Six streams were identified, where debris flows occurred in the past and caused material damage and loss of lives. The worst-case scenario carried out at the basin 15 scale shows a potential inundation that may affect 345 buildings at the present day.

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“…Various similar landslide prediction systems have been developed worldwide (allasia et al, 2013;Baum et al, 2010;osanai et al, 2010;merCogliano et al, 2010;TiranTe et al, 2014;THieBes, 2012). In general, they vary by their observed parameters, technology used, and technological readiness level.…”

Section: Introductionmentioning

confidence: 99%

Landslide prediction system for rainfall induced landslides in Slovenia (Masprem)

Auflič¹,

Šinigoj²,

Krivic³

et al. 2016

Geologija

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In this paper we introduce a landslide prediction system for modelling the probabilities of landslides through time in Slovenia (Masprem). The system to forecast rainfall induced landslides is based on the landslide susceptibility map, landslide triggering rainfall threshold values and the precipitation forecasting model. Through the integrated parameters a detailed framework of the system, from conceptual to operational phases, is shown. Using fuzzy logic the landslide prediction is calculated. Potential landslide areas are forecasted on a national scale (1: 250,000) and on a local scale (1: 25,000) for five selected municipalities where the exposure of inhabitants, buildings and different type of infrastructure is displayed, twice daily. Due to different rainfall patterns that govern landslide occurrences, the system for landslide prediction considers two different rainfall scenarios (M1 and M2). The landslides predicted by the two models are compared with a landslide inventory to validate the outputs. In this study we highlight the rainfall event that lasted from the 9th to the 14th of September 2014 when abundant precipitation triggered over 800 slope failures around Slovenia and caused large material damage. Results show that antecedent rainfall plays an important role, according to the comparisons of the model (M1) where antecedent rainfall is not considered. Although in general the landslides areas are over-predicted and largely do not correspond to the landslide inventory, the overall performance indicates that the system is able to capture the crucial factors in determining the landslide location. Additional calibration of input parameters and the landslide inventory as well as improved spatially distributed rainfall forecast data can further enhance the model's prediction. IzvlečekV članku predstavljamo sistem za napovedovanje verjetnosti nastanka plazov v času v Sloveniji (Masprem). Sistem napovedovanja plazov, ki se bodo sprožili zaradi padavin, je osnovan na karti verjetnosti pojavljanja plazov, sprožilnih/mejnih količin padavin za posamezne geološke enote ter modelskih napovedi padavin. Preko vključenih parametrov je prikazan potek dela, od idejne do operativne stopnje. Pri izračunu napovedovanja plazov je bila uporabljena mehka logika. Območja nastanka možnih plazov se računajo dvakrat dnevno, in sicer na državni ravni (v merilu 1:250.000) ter na lokalni ravni (merilo 1:25.000), kjer se za pet izbranih občin računa izpostavljenost prebivalcev, objektov in infrastrukture. Zaradi različnega vpliva padavin na pojav plazov, sistem napovedovanja upošteva dva različna scenarija za padavine (M1 in M2). Plazovi, ki jih napovedujeta ta dva modela, so primerjani z plazovi v bazi plazov, z namenom preverjanja ujemanja in validacije. Posebej so obravnavane obsežne padavine med 9. in 14. septembrom 2014, ki so botrovale sprožiti preko 800 plazov po celotni Sloveniji ter povzročile veliko gmotno škodo. Rezultati modelov kažejo, da so predhodne padavine pomembne pri napovedovanju. Kar je razvidno iz rezu...

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Early warning of rainfall-induced shallow landslides and debris flows in the USA

Cited by 468 publications

References 39 publications

Assessing the performance of regional landslide early warning models: the EDuMaP method

Assessing the performance of regional landslide early warning models: the EDuMaP method

Debris flow run-out simulation and analysis using a dynamic model

Landslide prediction system for rainfall induced landslides in Slovenia (Masprem)

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