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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 intensity-duration... more
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 intensity-duration thresholds and the other based on cumulative rainfall thresholds. The first model identifies rainfall intensity-duration thresholds by means of a software called MaCumBA (MAssive CUMulative Brisk Analyzer) (Segoni et al., 2014a) that analyzes rain-gauge records, extracts the intensities (I) and durations (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram, and identifies 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 amount 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 rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the SD (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 SDs in the proposed methodology. To perform a quantitative and objective comparison, these two methodologies were applied in two different areas, each time performing a site-specific calibration against available rainfall and landslide data. After each application, a validation procedure was carried out on an independent dataset and a confusion matrix was build. 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 assessing the most effective model in each case of 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 on the other, since each model performed better in a test site and worse in the other one, depending on the physical characteristics of the area. This conclusion can be generalized and it can be assumed that the effectiveness of a threshold model depends on the test site characteristics (including the quality and quantity of the input data) and that a validation and a comparison with alternative models should be performed before the implementation in operational early warning systems.
Rainfall is widely recognized as one of the major causes for landsliding. When studying the conditions of triggering of mass movements at regional scale, a process-based approach is seldom possible because of the complexity in the spatial... more
Rainfall is widely recognized as one of the major causes for landsliding. When studying the conditions of triggering of mass movements at regional scale, a process-based approach is seldom possible because of the complexity in the spatial organization of the involved independent variables (e.g. soil properties). Therefore, empirical methods based on the definition of triggering thresholds are usually employed for
We set up an early warning system for rainfallinduced landslides in Tuscany (23 000 km2). 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... more
We set up an early warning system for rainfallinduced
landslides in Tuscany (23 000 km2). 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.
Research Interests:
In this paper, the updating of rainfall thresholds for landslide early warning systems (EWSs) is presented. Rainfall thresholds are widely used in regional-scale landslide EWSs, but the efficiency of those systems can decrease during the... more
In this paper, the updating of rainfall thresholds for landslide early warning
systems (EWSs) is presented. Rainfall thresholds are widely used in regional-scale landslide
EWSs, but the efficiency of those systems can decrease during the time, so a periodically
updating should be required to keep their functionality. The updating of 12 of the
25 thresholds used in the EWS of Tuscany region (central Italy) is presented, and a
comparison between performances of new and previous thresholds has been made to
highlight the need of their periodical update. The updating has been carried out by collecting
ca. 1200 new landslide reports (from 2010 to March 2013) and their respective
rainfall data, collected by 332 rain gauges. The comparison has been made by the use of
several statistical indexes and showed a marked increasing in the performances of the new
thresholds with respect to previous ones.
Research Interests:
Rainfall is widely recognized as one of the major causes for landsliding. When studying the conditions of triggering of mass movements at regional scale, a process-based approach is seldom possible because of the complexity in the spatial... more
Rainfall is widely recognized as one of the major causes for landsliding. When studying the conditions of triggering of mass movements at regional scale, a process-based approach is seldom possible because of the complexity in the spatial organization of the involved independent variables (e.g. soil properties). Therefore, empirical methods based on the definition of triggering thresholds are usually employed for the definition of warning systems or for landslide hazard assessments. Such thresholds are defined by observing the characteristics of past rainfall events that have resulted in landslides and selecting the lower bound envelope curve in intensity-duration plots depicting such events. These curves, generally represented by power-law type functions linking, e.g., intensity and duration of the critical rainfall, define the lowest level above which landslides should be expected. In the present work, concerning the territory of Tuscany (ab. 23,000 km2), a similar approach is adopted and described which presents some improvements with respect to traditional methods. First of all, the strong variability of environmental, meteorological and geological factors within the study area, together with evidences from available data on triggering conditions, imply that a single general threshold would be affected by a too large degree of overestimation of hazard and suggest the adoption of locally defined thresholds. The studied area was then partitioned in 25 Alert Zones and each of them has been analyzed separately to provide distinct rainfall thresholds. Secondly, to handle the amount of available data (the analysis regards the period 2000-2007 and involves 408 rainfall events, which were registered by a network of 332 rain-gauges and that caused 2132 landslides), a software has been developed for automatically analyzing rainfall patterns and defining such thresholds. In particular, the automated analysis performs the following tasks: i) Defining, for every rain-gauge registration, the critical Intensity (I) and Duration (D) of the triggering event, its return time and the amount of antecedent rain; ii) Designating the most proper rain-gauge to represent every single landslide (the choice is performed combining geographic position and return times of the recorded rainfall); iii) Plotting the corresponding ID values on a log-log graph. iv) Automatic drawing of the rainfall threshold using a geometric criterion (lower-bound line of the plotted points) or a statistical predictor. By means of this automated procedure, multiple thresholds (differentiated on the basis of the severity of the event or the amount of antecedent rain) can be defined and different alarm levels set up. Varying the criteria the automated analysis is based upon, different thresholds can be obtained, all of them calibrated only with the past rainfalls that did trigger landslides. To select the most effective one, a calibration based upon rainfalls not connected with landslides was also performed: the 8-years rain-gauges measurements were compared to the thresholds and the one which minimizes the false positives (rainfall events beyond the threshold without associated landslides) was chosen to represent the rainfall conditions that should trigger landslides in that alert zone. This procedure allows to balance the thresholds between false positives (occurring when a threshold is too low) and missed alarms (related to excessively high thresholds). The results have been validated using rainfall registrations and landslides occurred during the period 2008 - 2009. Results were quite satisfactory and therefore the thresholds will soon be combined into a standard open procedure with a high degree of automation for the use of civil protection agencies in Tuscany.