Articles | Volume 18, issue 12
https://doi.org/10.5194/nhess-18-3179-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-3179-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Nov 2018
Research article |
| 30 Nov 2018
| 30 Nov 2018
Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception
Department of Earth Sciences, University of Firenze, Florence, Italy
Luca Piciullo
Norwegian Geotechnical Institute, Oslo, Norway
Stefano Luigi Gariano
CNR IRPI (Research Institute for Geo-Hydrological Protection), Perugia, Italy
Related authors
Samuele Segoni, Ascanio Rosi, Daniela Lagomarsino, Riccardo Fanti, and Nicola Casagli
Nat. Hazards Earth Syst. Sci., 18, 807–812, https://doi.org/10.5194/nhess-18-807-2018, https://doi.org/10.5194/nhess-18-807-2018, 2018
Short summary
Short summary
We improve the warning system (WS) used to forecast landslides in Emilia Romagna (Italy) by using averaged soil moisture estimates. We tested two approaches. The first (based on a soil moisture threshold under which the original WS is not used) is very simple, reduces false alarms and can be easily applied elsewhere. The second (integrating rainfall and soil moisture thresholds in the WS) is more complicated but reduces both false alarms and missed alarms.
D. Lagomarsino, S. Segoni, A. Rosi, G. Rossi, A. Battistini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 2413–2423, https://doi.org/10.5194/nhess-15-2413-2015, https://doi.org/10.5194/nhess-15-2413-2015, 2015
S. Segoni, A. Battistini, G. Rossi, A. Rosi, D. Lagomarsino, F. Catani, S. Moretti, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 853–861, https://doi.org/10.5194/nhess-15-853-2015, https://doi.org/10.5194/nhess-15-853-2015, 2015
Short summary
Short summary
We monitor and forecast (with lead times up to 48h) regional-scale landslide hazard with an early warning system (EWS) implemented on a user-friendly WebGIS interface.
The EWS detects the most critical rainfall conditions using a mosaic of 25 site-specific thresholds. Moreover, when the rainfall paths recorded by the instruments are compared with the thresholds, the thresholds are shifted in the time axis and adjusted to all possible starting times until the most hazardous scenario is found.
S. Segoni, A. Rosi, G. Rossi, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 14, 2637–2648, https://doi.org/10.5194/nhess-14-2637-2014, https://doi.org/10.5194/nhess-14-2637-2014, 2014
F. Catani, D. Lagomarsino, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, https://doi.org/10.5194/nhess-13-2815-2013, 2013
P. Mercogliano, S. Segoni, G. Rossi, B. Sikorsky, V. Tofani, P. Schiano, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 771–777, https://doi.org/10.5194/nhess-13-771-2013, https://doi.org/10.5194/nhess-13-771-2013, 2013
G. Martelloni, S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani
Hydrol. Earth Syst. Sci., 17, 1229–1240, https://doi.org/10.5194/hess-17-1229-2013, https://doi.org/10.5194/hess-17-1229-2013, 2013
V. Tofani, S. Segoni, A. Agostini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 299–309, https://doi.org/10.5194/nhess-13-299-2013, https://doi.org/10.5194/nhess-13-299-2013, 2013
G. Rossi, F. Catani, L. Leoni, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 151–166, https://doi.org/10.5194/nhess-13-151-2013, https://doi.org/10.5194/nhess-13-151-2013, 2013
Silvia Peruccacci, Stefano Luigi Gariano, Massimo Melillo, Monica Solimano, Fausto Guzzetti, and Maria Teresa Brunetti
Earth Syst. Sci. Data, 15, 2863–2877, https://doi.org/10.5194/essd-15-2863-2023, https://doi.org/10.5194/essd-15-2863-2023, 2023
Short summary
Short summary
ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue) is the largest catalogue of rainfall-induced landslides accurately located in space and time available in Italy. ITALICA currently lists 6312 landslides that occurred between January 1996 and December 2021. The information was collected using strict objective and homogeneous criteria. The high spatial and temporal accuracy makes the catalogue suitable for reliably defining the rainfall conditions capable of triggering future landslides.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
Short summary
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Maria Teresa Brunetti, Massimo Melillo, Stefano Luigi Gariano, Luca Ciabatta, Luca Brocca, Giriraj Amarnath, and Silvia Peruccacci
Hydrol. Earth Syst. Sci., 25, 3267–3279, https://doi.org/10.5194/hess-25-3267-2021, https://doi.org/10.5194/hess-25-3267-2021, 2021
Short summary
Short summary
Satellite and rain gauge data are tested to predict landslides in India, where the annual toll of human lives and loss of property urgently demands the implementation of strategies to prevent geo-hydrological instability. For this purpose, we calculated empirical rainfall thresholds for landslide initiation. The validation of thresholds showed that satellite-based rainfall data perform better than ground-based data, and the best performance is obtained with an hourly temporal resolution.
Massimo Melillo, Stefano Luigi Gariano, Silvia Peruccacci, Roberto Sarro, Rosa Marìa Mateos, and Maria Teresa Brunetti
Nat. Hazards Earth Syst. Sci., 20, 2307–2317, https://doi.org/10.5194/nhess-20-2307-2020, https://doi.org/10.5194/nhess-20-2307-2020, 2020
Short summary
Short summary
In the Canary Islands, a link between rainfall and rockfall occurrence is found for most of the year, except for the warm season. Empirical rainfall thresholds for rockfalls are first proposed for Gran Canaria and Tenerife, and the dependence of the thresholds on the mean annual rainfall is discussed. The use of thresholds in early-warning systems might contribute to the mitigation of the rockfall hazard in the archipelago and reduce the associated risk.
Samuele Segoni, Ascanio Rosi, Daniela Lagomarsino, Riccardo Fanti, and Nicola Casagli
Nat. Hazards Earth Syst. Sci., 18, 807–812, https://doi.org/10.5194/nhess-18-807-2018, https://doi.org/10.5194/nhess-18-807-2018, 2018
Short summary
Short summary
We improve the warning system (WS) used to forecast landslides in Emilia Romagna (Italy) by using averaged soil moisture estimates. We tested two approaches. The first (based on a soil moisture threshold under which the original WS is not used) is very simple, reduces false alarms and can be easily applied elsewhere. The second (integrating rainfall and soil moisture thresholds in the WS) is more complicated but reduces both false alarms and missed alarms.
Luca Piciullo, Mads-Peter Dahl, Graziella Devoli, Hervé Colleuille, and Michele Calvello
Nat. Hazards Earth Syst. Sci., 17, 817–831, https://doi.org/10.5194/nhess-17-817-2017, https://doi.org/10.5194/nhess-17-817-2017, 2017
Short summary
Short summary
The landslide early warning system operational in Norway is managed, since 2013, by the NVE and has been designed for monitoring and forecasting the hydrometeorological conditions potentially triggering slope failures. Daily alerts are issued throughout the country considering variable size warning zones. The performance of the warnings issued is evaluated through the adapted EDuMaP method. Different performance results are observed as a function of the landslide density criterion use.
M. Calvello and L. Piciullo
Nat. Hazards Earth Syst. Sci., 16, 103–122, https://doi.org/10.5194/nhess-16-103-2016, https://doi.org/10.5194/nhess-16-103-2016, 2016
Short summary
Short summary
An original approach, called EDuMaP method, is proposed to assess the performance of landslide early warning models operating at regional scale. EDuMaP comprises three successive steps: definition and temporal analysis of warning and landslide events (E); computation of a duration matrix (DuMa); evaluation of the warning model performance (P). The EDuMaP method may be easily adapted to evaluate the performance of regional early warning models addressing other hazardous phenomena.
D. Lagomarsino, S. Segoni, A. Rosi, G. Rossi, A. Battistini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 2413–2423, https://doi.org/10.5194/nhess-15-2413-2015, https://doi.org/10.5194/nhess-15-2413-2015, 2015
S. L. Gariano, O. Petrucci, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 15, 2313–2330, https://doi.org/10.5194/nhess-15-2313-2015, https://doi.org/10.5194/nhess-15-2313-2015, 2015
Short summary
Short summary
We study temporal and geographical variations in the occurrence of 1466 rainfall-induced landslides in Calabria, southern Italy, in the period 1921–2010. To evaluate the impact on the population, we compare the number of rainfall-induced landslides with the size of population in the 409 municipalities in Calabria. We find variations in yearly and geographical distribution of rainfall-induced landslides, variations in rainfall triggering conditions, and changes in the impact on the population.
O. G. Terranova, S. L. Gariano, P. Iaquinta, and G. G. R. Iovine
Geosci. Model Dev., 8, 1955–1978, https://doi.org/10.5194/gmd-8-1955-2015, https://doi.org/10.5194/gmd-8-1955-2015, 2015
Short summary
Short summary
A model for predicting the timing of activation of rainfall-induced landslides is presented. Calibration against real events is based on genetic algorithms, and provides a family of optimal solutions (kernels) that maximize a fitness function. Accordingly, a set of mobility functions is obtained through convolution with rainfall. Once properly validated, the model allows one to estimate future landslide activations in the same study area, by employing either recorded or forecasted rainfall.
S. Segoni, A. Battistini, G. Rossi, A. Rosi, D. Lagomarsino, F. Catani, S. Moretti, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 853–861, https://doi.org/10.5194/nhess-15-853-2015, https://doi.org/10.5194/nhess-15-853-2015, 2015
Short summary
Short summary
We monitor and forecast (with lead times up to 48h) regional-scale landslide hazard with an early warning system (EWS) implemented on a user-friendly WebGIS interface.
The EWS detects the most critical rainfall conditions using a mosaic of 25 site-specific thresholds. Moreover, when the rainfall paths recorded by the instruments are compared with the thresholds, the thresholds are shifted in the time axis and adjusted to all possible starting times until the most hazardous scenario is found.
S. Segoni, A. Rosi, G. Rossi, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 14, 2637–2648, https://doi.org/10.5194/nhess-14-2637-2014, https://doi.org/10.5194/nhess-14-2637-2014, 2014
O. G. Terranova and S. L. Gariano
Nat. Hazards Earth Syst. Sci., 14, 2423–2434, https://doi.org/10.5194/nhess-14-2423-2014, https://doi.org/10.5194/nhess-14-2423-2014, 2014
C. Vennari, S. L. Gariano, L. Antronico, M. T. Brunetti, G. Iovine, S. Peruccacci, O. Terranova, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 317–330, https://doi.org/10.5194/nhess-14-317-2014, https://doi.org/10.5194/nhess-14-317-2014, 2014
F. Catani, D. Lagomarsino, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, https://doi.org/10.5194/nhess-13-2815-2013, 2013
P. Mercogliano, S. Segoni, G. Rossi, B. Sikorsky, V. Tofani, P. Schiano, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 771–777, https://doi.org/10.5194/nhess-13-771-2013, https://doi.org/10.5194/nhess-13-771-2013, 2013
G. Martelloni, S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani
Hydrol. Earth Syst. Sci., 17, 1229–1240, https://doi.org/10.5194/hess-17-1229-2013, https://doi.org/10.5194/hess-17-1229-2013, 2013
V. Tofani, S. Segoni, A. Agostini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 299–309, https://doi.org/10.5194/nhess-13-299-2013, https://doi.org/10.5194/nhess-13-299-2013, 2013
G. Rossi, F. Catani, L. Leoni, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 151–166, https://doi.org/10.5194/nhess-13-151-2013, https://doi.org/10.5194/nhess-13-151-2013, 2013
Cited articles
Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on
precipitation intensity-duration thresholds for landslide initiation:
proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18,
31–39, https://doi.org/10.5194/nhess-18-31-2018, 2018.
Brunetti, M. T., Melillo, M., Peruccacci, S., Ciabatta, L., and Brocca, L.:
How far are we from the use of satellite rainfall products in landslide
forecasting?, Remote Sens. Environ., 210, 65–75,
https://doi.org/10.1016/j.rse.2018.03.016, 2018.
Calvello, M.: Early warning strategies to cope with landslide risk, Rivista
Italiana di Geotecnica, 2/2017, 63–91, https://doi.org/10.19199/2017.2.0557-1405.063,
2017.
Canli, E., Mergili, M., Thiebes, B., and Glade, T.: Probabilistic landslide
ensemble prediction systems: lessons to be learned from hydrology, Nat.
Hazards Earth Syst. Sci., 18, 2183–2202, https://doi.org/10.5194/nhess-18-2183-2018,
2018.
Capra, L., Coviello, V., Borselli, L., Márquez-Ramírez, V.-H., and
Arámbula-Mendoza, R.: Hydrological control of large hurricane-induced
lahars: evidence from rainfall-runoff modeling, seismic and video monitoring,
Nat. Hazards Earth Syst. Sci., 18, 781–794, https://doi.org/10.5194/nhess-18-781-2018,
2018.
Catani, F. and Guzzetti, F. (Eds.): Landslide Prediction & Forecasting, Nat.
Nat. Hazards Earth Syst. Sci.,
https://www.nat-hazards-earth-syst-sci.net/special_issue206.html, 2014.
Catani, F., Casagli, N., Segoni, S., Tofani, V., Jaboyedoff, M., and
Günther, A. (Eds.): New developments and applications in early warning,
monitoring and remote sensing of landslides, Nat. Hazards Earth Syst. Sci.,
https://www.nat-hazards-earth-syst-sci.net/special_issue168.html, 2012.
Chaturvedi, P., Arora, A., and Dutt, V.: Learning in an interactive
simulation tool against landslide risks: the role of strength and
availability of experiential feedback, Nat. Hazards Earth Syst. Sci., 18,
1599–1616, https://doi.org/10.5194/nhess-18-1599-2018, 2018.
Destro, E., Marra, F., Nikolopoulos, E. I., Zoccatelli, D., Creutin, J. D.,
and Borga, M.: Spatial estimation of debris flows-triggering rainfall and its
dependence on rainfall return period, Geomorphology, 278, 269–279,
https://doi.org/10.1016/j.geomorph.2016.11.019, 2017.
Devoli, G., Tiranti, D., Cremonini, R., Sund, M., and Boje, S.: Comparison of
landslide forecasting services in Piedmont (Italy) and Norway, illustrated by
events in late spring 2013, Nat. Hazards Earth Syst. Sci., 18, 1351–1372,
https://doi.org/10.5194/nhess-18-1351-2018, 2018.
Frodella, W., Salvatici, T., Pazzi, V., Morelli, S., and Fanti, R.: GB-InSAR
monitoring of slope deformations in a mountainous area affected by debris
flow events, Nat. Hazards Earth Syst. Sci., 17, 1779–1793,
https://doi.org/10.5194/nhess-17-1779-2017, 2017.
Gariano, S. L., Brunetti, M. T., Iovine, G., Melillo, M., Peruccacci, S.,
Terranova, O., Vennari, C., and Guzzetti, F.: Calibration and validation of
rainfall thresholds for shallow landslide forecasting in Sicily, southern
Italy, Geomorphology, 228, 653–665, https://doi.org/10.1016/j.geomorph.2014.10.019,
2015.
Glade, T. and Nadim, F.: Early warning systems for natural hazards and risks,
Nat. Hazards, 70, 1669–1671, https://doi.org/10.1007/s11069-013-1000-8, 2014.
Greco, R. and Pagano, L.: Basic features of the predictive tools of early
warning systems for water-related natural hazards: examples for shallow
landslides, Nat. Hazards Earth Syst. Sci., 17, 2213–2227,
https://doi.org/10.5194/nhess-17-2213-2017, 2017.
Iadanza, C., Trigila, A., and Napolitano, F.: Identification and
characterization of rainfall events responsible for triggering of debris
flows and shallow landslides, J. Hydrol., 541, 230–245,
https://doi.org/10.1016/j.jhydrol.2016.01.018, 2016.
Intrieri, E., Gigli, G., Casagli, N., and Nadim, F.: Brief communication
“Landslide Early Warning System: toolbox and general concepts”, Nat.
Hazards Earth Syst. Sci., 13, 85–90, https://doi.org/10.5194/nhess-13-85-2013, 2013.
Intrieri, E., Bardi, F., Fanti, R., Gigli, G., Fidolini, F., Casagli, N.,
Costanzo, S., Raffo, A., Di Massa, G., Capparelli, G., and Versace, P.: Big
data managing in a landslide early warning system: experience from a
ground-based interferometric radar application, Nat. Hazards Earth Syst.
Sci., 17, 1713–1723, https://doi.org/10.5194/nhess-17-1713-2017, 2017.
Iovine, G. G. R., Huebl, J., Pastor, M., and Sheridan, M. F.: Outcomes of the
Special Issue on Approaches to hazard evaluation, mapping, and mitigation,
Nat. Hazards Earth Syst. Sci., 11, 2433–2436,
https://doi.org/10.5194/nhess-11-2433-2011, 2011.
Kirschbaum, D. B., Stanley, T., and Simmons, J.: A dynamic landslide hazard
assessment system for Central America and Hispaniola, Nat. Hazards Earth
Syst. Sci., 15, 2257–2272,https://doi.org/10.5194/nhess-15-2257-2015, 2015.
Krøgli, I. K., Devoli, G., Colleuille, H., Boje, S., Sund, M., and Engen,
I. K.: The Norwegian forecasting and warning service for rainfall- and
snowmelt-induced landslides, Nat. Hazards Earth Syst. Sci., 18, 1427–1450,
https://doi.org/10.5194/nhess-18-1427-2018, 2018.
Lacasse, S. and Nadim, F.: Learning to live with geohazards: from research to
practice, in: GeoRisk 2011: Geotechnical Risk Assessment and Management,
26–28 June 2011, Atlanta, Georgia, 64–116, https://doi.org/10.1061/41183(418)4, 2011.
Lagomarsino, D., Segoni, S., Rosi, A., Rossi, G., Battistini, A., Catani, F.,
and Casagli, N.: Quantitative comparison between two different methodologies
to define rainfall thresholds for landslide forecasting, Nat. Hazards Earth
Syst. Sci., 15, 2413–2423, https://doi.org/10.5194/nhess-15-2413-2015, 2015.
Liu, Y., Qin, Z., Hu, B., and Feng, S.: State fusion entropy for continuous
and site-specific analysis of landslide stability changing regularities, Nat.
Hazards Earth Syst. Sci., 18, 1187–1199, https://doi.org/10.5194/nhess-18-1187-2018,
2018.
Marra, F.: Rainfall thresholds for landslide occurrence: systematic
underestimation using coarse temporal resolution data, Nat. Hazards, 1–8,
https://doi.org/10.1007/s11069-018-3508-4, online first, 2018.
Marra, F., Destro, E., Nikolopoulos, E. I., Zoccatelli, D., Creutin, J. D.,
Guzzetti, F., and Borga, M.: Impact of rainfall spatial aggregation on the
identification of debris flow occurrence thresholds, Hydrol. Earth Syst.
Sci., 21, 4525–4532, https://doi.org/10.5194/hess-21-4525-2017, 2017.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., Roccati, A.,
and Guzzetti, F.: A tool for the automatic calculation of rainfall thresholds
for landslide occurrence, Environ. Modell. Softw., 105, 230–243,
https://doi.org/10.1016/j.envsoft.2018.03.024, 2018.
Melo, R., van Asch, T., and Zêzere, J. L.: Debris flow run-out simulation
and analysis using a dynamic model, Nat. Hazards Earth Syst. Sci., 18,
555–570, https://doi.org/10.5194/nhess-18-555-2018, 2018.
Mendes, R. M., de Andrade, M. R. M., Tomasella, J., de Moraes, M. A. E., and
Scofield, G. B.: Understanding shallow landslides in Campos do Jordão
municipality – Brazil: disentangling the anthropic effects from natural
causes in the disaster of 2000, Nat. Hazards Earth Syst. Sci., 18, 15–30,
https://doi.org/10.5194/nhess-18-15-2018, 2018.
Nikolopoulos, E. I., Borga, M., Creutin, J. D., and Marra, F.: Estimation of
debris flow triggering rainfall: influence of rain gauge density and
interpolation methods, Geomorphology, 243, 40–50,
https://doi.org/10.1016/j.geomorph.2015.04.028, 2015.
Pan, H.-L., Jiang, Y.-J., Wang, J., and Ou, G.-Q.: Rainfall threshold
calculation for debris flow early warning in areas with scarcity of data,
Nat. Hazards Earth Syst. Sci., 18, 1395–1409,
https://doi.org/10.5194/nhess-18-1395-2018, 2018.
Pecoraro, G., Calvello, M., and Piciullo, L.: Monitoring strategies for local
landslide early warning systems, Landslides, 1–19,
https://doi.org/10.1007/s10346-018-1068-z, online first, 2018.
Peres, D. J., Cancelliere, A., Greco, R., and Bogaard, T. A.: Influence of
uncertain identification of triggering rainfall on the assessment of
landslide early warning thresholds, Nat. Hazards Earth Syst. Sci., 18,
633–646, https://doi.org/10.5194/nhess-18-633-2018, 2018.
Piciullo, L., Gariano, S. L., Melillo, M., Brunetti, M .T., Peruccacci, S.,
Guzzetti, F., and Calvello, M.: Definition and performance of a
threshold-based regional early warning model for rainfall-induced landslides,
Landslides, 14, 995–1008, https://doi.org/10.1007/s10346-016-0750-2, 2017.
Piciullo, L., Calvello, M., and Cepeda, J. M.: Territorial early warning
systems for rainfall-induced landslides, Earth-Sci. Rev., 179, 228–247,
https://doi.org/10.1016/j.earscirev.2018.02.013, 2018.
Posner, A. J. and Georgakakos, K. P.: Soil moisture and precipitation
thresholds for realtime landslide prediction in El Salvador, Landslides, 12,
1179–1196, https://doi.org/10.1007/s10346-015-0618-x, 2015.
Reder, A., Rianna, G., and Pagano, L.: Physically based approaches
incorporating evaporation for early warning predictions of rainfall-induced
landslides, Nat. Hazards Earth Syst. Sci., 18, 613–631,
https://doi.org/10.5194/nhess-18-613-2018, 2018.
Reichenbach, P., Günther, A., and Glade, T.: Preface “Landslide hazard
and risk assessment at different scales”, Nat. Hazards Earth Syst. Sci., 13,
2169–2171, https://doi.org/10.5194/nhess-13-2169-2013, 2013.
Robbins, J. C.: A probabilistic approach for assessing landslide-triggering
event rainfall in Papua New Guinea, using TRMM satellite precipitation
estimates, J. Hydrol., 541, 296–309, https://doi.org/10.1016/j.jhydrol.2016.06.052,
2016.
Rosi, A., Lagomarsino, D., Rossi, G., Segoni, S., Battistini, A., and
Casagli., N.: Updating EWS rainfall thresholds for the triggering of
landslides, Nat. Hazards, 78, 297–308, https://doi.org/10.1007/s11069-015-1717-7,
2015.
Rossi, M., Luciani, S., Valigi, D., Kirschbaum, D., Brunetti, M. T.,
Peruccacci, S., and Guzzetti, F.: Statistical approaches for the definition
of landslide rainfall thresholds and their uncertainty using rain gauge and
satellite data, Geomorphology, 285, 16–27,
https://doi.org/10.1016/j.geomorph.2017.02.001, 2017.
Salvatici, T., Tofani, V., Rossi, G., D'Ambrosio, M., Tacconi Stefanelli, C.,
Masi, E. B., Rosi, A., Pazzi, V., Vannocci, P., Petrolo, M., Catani, F.,
Ratto, S., Stevenin, H., and Casagli, N.: Application of a physically based
model to forecast shallow landslides at a regional scale, Nat. Hazards Earth
Syst. Sci., 18, 1919–1935, https://doi.org/10.5194/nhess-18-1919-2018, 2018.
Sättele, M., Bründl, M., and Straub, D.: Quantifying the
effectiveness of early warning systems for natural hazards, Nat. Hazards
Earth Syst. Sci., 16, 149–166, https://doi.org/10.5194/nhess-16-149-2016, 2016.
Segoni, S., Rossi, G., Rosi, A., and Catani, F.: Landslides triggered by
rainfall: a semiautomated procedure to define consistent intensity-duration
thresholds, Comput. Geosci., 3063, 123–131,
https://doi.org/10.1016/j.cageo.2013.10.009, 2014.
Segoni, S., Rosi, A., Lagomarsino, D., Fanti, R., and Casagli, N.: Brief
communication: Using averaged soil moisture estimates to improve the
performances of a regional-scale landslide early warning system, Nat. Hazards
Earth Syst. Sci., 18, 807–812, https://doi.org/10.5194/nhess-18-807-2018, 2018a.
Segoni, S., Piciullo, L., and Gariano, S. L.: A review of the recent
literature on rainfall thresholds for landslide occurrence, Landslides, 15,
1483–1501, https://doi.org/10.1007/s10346-018-0966-4, 2018b.
Shi, Z., Wei, F., and Chandrasekar, V.: Radar-based quantitative
precipitation estimation for the identification of debris flow occurrence
over earthquake-affected regions in Sichuan, China, Nat. Hazards Earth Syst.
Sci., 18, 765–780, https://doi.org/10.5194/nhess-18-765-2018, 2018.
Staley, D. M., Kean, J. W., Cannon, S. H., Schmidt, K. M., and Laber, J. L.:
Objective definition of rainfall intensity-duration thresholds for the
initiation of post-fire debris flows in southern California, Landslides, 10,
547–562, https://doi.org/10.1007/s10346-012-0341-9, 2013.
United Nations International Strategy for Disaster Reduction (UNISDR):
Developing Early Warning Systems: A Checklist, available at:
http://www.unisdr.org/2006/ppew/info-resources/ewc3/checklist/English.pdf
(last access: 16 November 2018), 2006.
Uzielli, M., Rianna, G., Ciervo, F., Mercogliano, P., and Eidsvig, U. K.:
Temporal evolution of flow-like landslide hazard for a road infrastructure in
the municipality of Nocera Inferiore (southern Italy) under the effect of
climate change, Nat. Hazards Earth Syst. Sci., 18, 3019–3035,
https://doi.org/10.5194/nhess-18-3019-2018, 2018.
Vaz, T., Zêzere, J. L., Pereira, S., Oliveira, S. C., Garcia, R. A. C.,
and Quaresma, I.: Regional rainfall thresholds for landslide occurrence using
a centenary database, Nat. Hazards Earth Syst. Sci., 18, 1037–1054,
https://doi.org/10.5194/nhess-18-1037-2018, 2018.
Vessia, G., Pisano, L., Vennari, C., Rossi, M., and Parise, M.: Mimic expert
judgement through automated procedure for selecting rainfall events
responsible for shallow landslide: a statistical approach to validation,
Comput. Geosci., 86, 146–153, https://doi.org/10.1016/j.cageo.2015.10.015, 2016.
Wei, L.-W., Huang, C.-M., Chen, H., Lee, C.-T., Chi, C.-C., and Chiu, C.-L.:
Adopting the I3–R24 rainfall index and landslide susceptibility for
the establishment of an early warning model for rainfall-induced shallow
landslides, Nat. Hazards Earth Syst. Sci., 18, 1717–1733,
https://doi.org/10.5194/nhess-18-1717-2018, 2018.
Altmetrics