Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides

Abstract. To manage natural risks, an increasing effort is being put in the development of early warning systems (EWS), namely, approaches facing catastrophic phenomena by timely forecasting and alarm spreading throughout exposed population. Research efforts aimed at the development and implementation of effective EWS should especially concern the definition and calibration of the interpretative model. This paper analyses the main features characterizing predictive models working in EWS by discussing their aims and their features in terms of model accuracy, evolutionary stage of the phenomenon at which the prediction is carried out and model architecture. Original classification criteria based on these features are developed throughout the paper and shown in their practical implementation through examples of flowlike landslides and earth flows, both of which are characterized by rapid evolution and quite representative of many applications of EWS.

Section: Introductionmentioning

confidence: 99%

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

Greco

Pagano

2017

“…They generally pretend to be based on an optimal set of input parameter settings. Only recently have empirical approaches, such as the commonly used rainfall thresholds in landslide forecasting and early warning applications, started to incorporate estimates of uncertainty by defining rainfall thresholds at different exceedance probabilities (e.g., Melillo et al, 2016;Piciullo et al, 2017). Yet, they rely on very good landslide event catalogues and knowledge of the triggering conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Bogaard and Greco (2018) critically analyze the role of rainfall thresholds for shallow landslides and debris flows from a hydrometeorological point of view. It is beyond the scope of this paper to provide a review of past and present landslide early warning systems, which can be found elsewhere (e.g., Alfieri et al, 2012a;Thiebes, 2012;Thiebes and Glade, 2016;Piciullo et al, 2018). Guidelines on how landslide early warning systems can be implemented have been put forward by Intrieri et al (2013), for example.…”

Section: Probabilistic Forecasting In Hydrology and Ties To Landslidementioning

confidence: 99%

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Canli

Mergili

Thiebes

et al. 2018

Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslidetriggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

“…Case B uses a landslide dataset composed of rainfall-induced landslides with a higher accuracy of information than Case A. As stated in Piciullo et al (2016b), the result of a performance evaluation is strictly connected to the availability of a landslide catalogue and to the accuracy of the information included in it.…”

Section: Duration Matrices and Performance Indicatorsmentioning

confidence: 99%

Adapting the EDuMaP method to test the performance of the Norwegian early warning system for weather-induced landslides

Piciullo

Dahl

Devoli

et al. 2017

Self Cite

Abstract. The Norwegian national landslide early warning system (LEWS), operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate and was designed for monitoring and forecasting the hydrometeorological conditions potentially triggering slope failures. Decision-making in the LEWS is based upon rainfall thresholds, hydrometeorological and real-time landslide observations as well as on landslide inventory and susceptibility maps. Daily alerts are issued throughout the country considering variable size warning zones. Warnings are issued once per day for the following 3 days and can be updated according to weather forecasts and information gathered by the monitoring network. The performance of the LEWS operational in Norway has been evaluated applying the EDuMaP method, which is based on the computation of a duration matrix relating number of landslides and warning levels issued in a warning zone. In the past, this method has been exclusively employed to analyse the performance of regional early warning models considering fixed warning zones. Herein, an original approach is proposed for the computation of the elements of the duration matrix in the case of early warning models issuing alerts on variable size areas. The approach has been used to evaluate the warnings issued in Western Norway, in the period 2013-2014, considering two datasets of landslides. The results indicate that the landslide datasets do not significantly influence the performance evaluation, although a slightly better performance is registered for the smallest dataset. Different performance results are observed as a function of the values adopted for one of the most important input parameters of EDuMaP, the landslide density criterion (i.e. setting the thresholds to differentiate among classes of landslide events). To investigate this issue, a parametric analysis has been conducted; the results of the analysis show significant differences among computed performances when absolute or relative landslide density criteria are considered.