Abstract.We set up an early warning system for rainfallinduced landslides in Tuscany (23 000 km 2 ). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges.The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds.To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently.An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface.The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

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Abstract. This work proposes a methodology to compare the forecasting effectiveness of different rainfall threshold models for landslide forecasting. We tested our methodology with two state-of-the-art models, one using intensityduration thresholds and the other based on cumulative rainfall thresholds.The first model identifies rainfall intensity-duration thresholds by means of a software program called MaCumBA (MAssive CUMulative Brisk Analyzer) (Segoni et al., 2014a) that analyzes rain gauge records, extracts intensity (I ) and duration (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram and identifies the thresholds that define the lower bounds of the I -D values. A back analysis using data from past events is used to identify the threshold conditions associated with the least number of false alarms.The second model (SIGMA) (Sistema Integrato Gestione Monitoraggio Allerta) (Martelloni et al., 2012) is based on the hypothesis that anomalous or extreme values of accumulated rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the standard deviation (σ ) are used as thresholds to discriminate between ordinary and extraordinary rainfall events. The name of the model, SIGMA, reflects the central role of the standard deviations.To perform a quantitative and objective comparison, these two models were applied in two different areas, each time performing a site-specific calibration against available rainfall and landslide data. For each application, a validation procedure was carried out on an independent data set and a confusion matrix was built. The results of the confusion matrixes were combined to define a series of indexes commonly used to evaluate model performances in natural hazard assessment. The comparison of these indexes allowed to identify the most effective model in each case study and, consequently, which threshold should be used in the local early warning system in order to obtain the best possible risk management.In our application, none of the two models prevailed absolutely over the other, since each model performed better in a test site and worse in the other one, depending on the characteristics of the area.We conclude that, even if state-of-the-art threshold models can be exported from a test site to another, their employment in local early warning systems should be carefully evaluated: the effectiveness of a threshold model depends on the test site characteristics (including the quality and quantity of the input data), and a validation procedure and a comparison with alternative models should be performed before its implementation in operational early warning systems.

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