 |
| Titel |
Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting |
| VerfasserIn |
D. Lagomarsino, S. Segoni, A. Rosi, G. Rossi, A. Battistini, F. Catani, N. Casagli |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1561-8633
|
| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 15, no. 10 ; Nr. 15, no. 10 (2015-10-23), S.2413-2423 |
| Datensatznummer |
250119726
|
| Publikation (Nr.) |
 copernicus.org/nhess-15-2413-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
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 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|