Abstract
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).
Highlights
In generic terms, early warning constitutes a process whereby information generated from tailored observations of natural phenomena is provided to communities at risk, or to institutions which are involved in emergency response operations, so that certain tasks may be executed before a catastrophic event impacts such communities (Villagrán de León et al, 2013)
A regional landslide early warning system may be schematized as a system with the following components: regional warning model, monitoring and warning strategy, communication strategy and emergency plan
The focus of this article was on the performance evaluation of regional warning models for rainfall-induced landslides, that is to say on the effectiveness of the functional relationship between rainfall events and landslide events used within the decision-making procedures adopted to issue the warnings
Summary
Early warning constitutes a process whereby information generated from tailored observations of natural phenomena is provided to communities at risk, or to institutions which are involved in emergency response operations, so that certain tasks may be executed before a catastrophic event impacts such communities (Villagrán de León et al, 2013). The four elements of these tables – i.e., correct alerts (CAs) or true positives; missed alerts, false negatives or type II errors; false alerts, false positives or type I errors; true negatives (TNs) – are used to assess the weight of the correct predictions in relation to the model errors by means of a series of statistical indicators of the model performance In all these cases, model performance is assessed, neglecting some important aspects that are peculiar to ReLEWSs, such as the possible occurrence of multiple landslides in the warning zone, the duration of the warnings in relation to the time of occurrence of the landslides, the level of the issued warning in relation to the landslide spatial density in the warning zone and the relative importance system managers attribute to different types of errors. The applicability of the EDuMaP method is tested and discussed using both synthetic data and real landslides and warnings data from the municipal early warning system operating in Rio de Janeiro (Brazil)
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