Shallow Landslides Risk Mitigation by Early Warning: The Sarno Case

Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

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Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-3-3487-2015 ◽

2015 ◽

Vol 3 (5) ◽

pp. 3487-3508

Author(s):

J. Huang ◽

N. P. Ju ◽

Y. J. Liao ◽

D. D. Liu

Keyword(s):

Early Warning ◽

Regional Scale ◽

Warning System ◽

Rainfall Threshold ◽

Empirical Method ◽

Shallow Landslides ◽

Data Sets ◽

Threshold Values ◽

Mountainous Regions ◽

Hourly Rainfall

Abstract. Rainfall-induced landslides not only cause property loss, but also kill and injure large numbers of people every year in mountainous areas in China. These losses and casualties may be avoided to some extent with rainfall threshold values used in an early warning system at a regional scale for the occurrence of landslides. However, the limited availability of data always causes difficulties. In this paper we present a method to calculate rainfall threshold values with limited data sets for the two rainfall parameters: maximum hourly rainfall intensity and accumulated precipitation. The method has been applied to the Huangshan region, in Anhui Province, China. Four early warning levels (Zero, Outlook, Attention, and Warning) have been adopted and the corresponding rainfall threshold values have been defined by probability lines. A validation procedure showed that this method can significantly enhance the effectiveness of a warning system, and finally reduce the risk from shallow landslides in mountainous regions.

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Quantifying the effectiveness of early warning systems for natural hazards

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-3-4479-2015 ◽

2015 ◽

Vol 3 (7) ◽

pp. 4479-4526 ◽

Cited By ~ 1

Author(s):

M. Sättele ◽

M. Bründl ◽

D. Straub

Keyword(s):

Natural Hazards ◽

Early Warning ◽

Risk Mitigation ◽

Early Warning Systems ◽

Mitigation Measures ◽

Management Approach ◽

Warning Systems ◽

Integrated Risk ◽

Framework Approach

Abstract. Early warning systems (EWS) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWS is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWS and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWS is provided, which focuses on the degree of automations encountered in varying EWS. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

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Review to "Adopting I3–R24 rainfall index and landslide susceptibility on the establishment of early warning model for rainfall-induced shallow landslides"

10.5194/nhess-2017-428-rc1 ◽

2018 ◽

Author(s):

Anonymous

Keyword(s):

Early Warning ◽

Landslide Susceptibility ◽

Shallow Landslides ◽

Early Warning Model ◽

Rainfall Index ◽

Warning Model

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Application of FLaIR model for early warning system in Chibo Pashyor, Kalimpong, India for rainfall-induced landslides

10.5194/nhess-2017-295 ◽

2017 ◽

Cited By ~ 2

Author(s):

Abhirup Dikshit ◽

Neelima Satyam

Keyword(s):

Early Warning ◽

Early Warning System ◽

Risk Mitigation ◽

Warning System ◽

Landslide Risk ◽

Rainfall Forecasting ◽

Landslide Occurrence ◽

Mobility Function ◽

Hourly Rainfall ◽

Rainfall Model

Abstract. The development of an early warning system for landslides due to rainfall has become an indispensable part for landslide risk mitigation. This paper explains the application of the hydrological FLaIR (Forecasting of Landslides Induced by Rainfall) model, correlating rainfall amount and landslide events. The FLaIR model comprises of two modules: RL (Rainfall-Landslide) which correlates rainfall and landslide occurrence and RF (Rainfall-Forecasting) which allows simulation of future rainfall events. The model can predetermine landslides based on identification of mobility function Y(.) which links actual rainfall and incidence of landslide occurrence. The critical value of mobility function was analyzed using 1st July 2015 event and applying it to 2016 monsoon to validate the results. These rainfall thresholds presented can be improved with intense hourly rainfall and landslide inventory data. This paper describes the details of the model and its performance for the study area.

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HELP - An Early warning dashboard System, built for the prevention, mitigation and assessment of disasters, with a flexible approach using open data and open source technologies

10.7287/peerj.preprints.2229v1 ◽

2016 ◽

Cited By ~ 1

Author(s):

Lorenzo Amato ◽

Dimitri Dello Buono ◽

Francesco Izzi ◽

Giuseppe La Scaleia ◽

Donato Maio

Keyword(s):

Early Warning ◽

Spatial Data ◽

Emergency Preparedness ◽

Risk Mitigation ◽

Open Data ◽

National Research Council ◽

Civil Defense ◽

Main Role ◽

Cooperation Agreement ◽

Data Infrastructure

H.E.L.P is an early warning dashboard system built for the prevention, mitigation and assessment of disasters, be they earthquakes, fires, or meteorological systems. It was built to be easily manageable, customizable and accessible to all users, to facilitate humanitarian and governmental response. In its essence it is an emergency preparedness web tool, which can be used for decision making for a better level of mitigation and response on any level.Risks or disasters are not events in our control, rather, they are situations to which we can better manage with a framework based on preparedness. The earlier and more precise the monitoring of hazards allow for faster response to manage and mitigate a disaster’s impact on a society, economy and environment.This is exactly what HELP offers, it plays a main role in the cycle of early warning and risk (Preparedness, Risk, Mitigation, and Resilience). It provides information in real time on events and hazards, allowing for the possibility to analyze the situation and find a solution whose outcome protects the most lives and has the least economic impact. As a tool it also provides the opportunity to respond to a hazard with resilience in mind, this means that not only does HELP prepare for and mitigate events, it can also be used to implement better organizational methods for future events, thus, minimizing overall risk. Providing people with the means to better be able to take care of themselves, lessening the effects of future hazards each and every time. HELP is a tool in a framework which was created to support governments in their efforts to protect their people, building their response efficiency and resilience. HELP (with the name of E.W.A.R.E. Early Warning and Awareness of Risks and Emergencies) was born as WFP (The World Food Program) and IMAA-CNR (Institute of Methodologies for Environmental Analysis of the National Research Council of Italy) entered into a Cooperation Agreement concerning the development of a Geo-Spatial Data Infrastructure System for the Palestinian Civil Defense with the aim of building an enhanced preparedness capacity in Palestine.HELP has a simple and flexible but very effective logic to perform the early warning: Watch to open data sources on risk themes (NASA satellite data, Weather Forecast, world wide seismic networks, etc); Apply (programmable) “intelligence” to detect critical situations, exceeding of thresholds, population potentially involved by events, etc; Highlight critical elements on the map; Send alerts to emergency managers.

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HELP - An Early warning dashboard System, built for the prevention, mitigation and assessment of disasters, with a flexible approach using open data and open source technologies

10.7287/peerj.preprints.2229v2 ◽

2016 ◽

Cited By ~ 1

Author(s):

Lorenzo Amato ◽

Dimitri Dello Buono ◽

Francesco Izzi ◽

Giuseppe La Scaleia ◽

Donato Maio

Keyword(s):

Early Warning ◽

Spatial Data ◽

Emergency Preparedness ◽

Risk Mitigation ◽

Open Data ◽

National Research Council ◽

Civil Defense ◽

Main Role ◽

Cooperation Agreement ◽

Data Infrastructure

H.E.L.P is an early warning dashboard system built for the prevention, mitigation and assessment of disasters, be they earthquakes, fires, or meteorological systems. It was built to be easily manageable, customizable and accessible to all users, to facilitate humanitarian and governmental response. In its essence it is an emergency preparedness web tool, which can be used for decision making for a better level of mitigation and response on any level.Risks or disasters are not events in our control, rather, they are situations to which we can better manage with a framework based on preparedness. The earlier and more precise the monitoring of hazards allow for faster response to manage and mitigate a disaster’s impact on a society, economy and environment.This is exactly what HELP offers, it plays a main role in the cycle of early warning and risk (Preparedness, Risk, Mitigation, and Resilience). It provides information in real time on events and hazards, allowing for the possibility to analyze the situation and find a solution whose outcome protects the most lives and has the least economic impact. As a tool it also provides the opportunity to respond to a hazard with resilience in mind, this means that not only does HELP prepare for and mitigate events, it can also be used to implement better organizational methods for future events, thus, minimizing overall risk. Providing people with the means to better be able to take care of themselves, lessening the effects of future hazards each and every time. HELP is a tool in a framework which was created to support governments in their efforts to protect their people, building their response efficiency and resilience. HELP (with the name of E.W.A.R.E. Early Warning and Awareness of Risks and Emergencies) was born as WFP (The World Food Program) and IMAA-CNR (Institute of Methodologies for Environmental Analysis of the National Research Council of Italy) entered into a Cooperation Agreement concerning the development of a Geo-Spatial Data Infrastructure System for the Palestinian Civil Defense with the aim of building an enhanced preparedness capacity in Palestine.HELP has a simple and flexible but very effective logic to perform the early warning: Watch to open data sources on risk themes (NASA satellite data, Weather Forecast, world wide seismic networks, etc); Apply (programmable) “intelligence” to detect critical situations, exceeding of thresholds, population potentially involved by events, etc; Highlight critical elements on the map; Send alerts to emergency managers.

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An early warning system for rainfall-triggered shallow slides and debris flows. Application in Catalonia, Spain and Canton of Bern, Switzerland

10.5194/egusphere-egu2020-479 ◽

2020 ◽

Author(s):

Rosa M Palau ◽

Marc Berenguer ◽

Marcel Hürlimann ◽

Daniel Sempere-Torres ◽

Catherine Berger ◽

...

Keyword(s):

Early Warning ◽

Debris Flows ◽

Risk Mitigation ◽

Regional Scale ◽

Slope Angle ◽

Early Warning Systems ◽

Warning Systems ◽

Radar Rainfall ◽

Susceptibility Maps ◽

Landslide Event

Risk mitigation for rainfall-triggered shallow slides and debris flows at regional scale is challenging. Early warning systems are a helpful tool to depict the location and time of future landslide events so that emergency managers can act in advance. Recently, some of the regions that are recurrently affected by rainfall triggered landslides have developed operational landslide early warning systems (LEWS). However, there are still many territories where this phenomenon also represents an important hazard and lack this kind of risk mitigation strategy.The main objective of this work is to study the feasibility to apply a regional scale LEWS, which was originally designed to work over Catalonia (Spain), to run in other regions. To do so we have set up the LEWS to Canton of Bern (Switzerland).The LEWS combines susceptibility maps to determine landslide prone areas and in real time high-resolution radar rainfall observations and forecasts. The output is a qualitative warning level map with a resolution of 30 m.Susceptibility maps have been derived using a simple fuzzy logic methodology that combines the terrain slope angle, and land use and land cover (LULC) information. The required input parameters have been obtained from regional, pan-European and global datasets.Rainfall inputs have been retrieved from both regional weather radar networks, and the OPERA pan-European radar composite. A set of global rainfall intensity-duration data has been used to asses if a rainfall event has the potential of triggering a landslide event.The LEWS has been run in the region of Catalonia and Canton of Bern for specific rainfall events that triggered important landslides. Finally, the LEWS performance in Catalonia has been assessed.Results in Catalonia show that the LEWS performance strongly depends on the quality of both the susceptibility maps and rainfall data. However, in both regions the LEWS is generally able to issue warnings for most of the analysed landslide events.

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