Time forecast of a break-off event from a hanging glacier

Abstract. A cold hanging glacier located on the south face of the Grandes Jorasses (Mont Blanc, Italy) broke off on the 23 and 29 September 2014 with a total estimated ice volume of 105 000 m3. Thanks to very accurate surface displacement measurements taken right up to the final break-off, this event could be successfully predicted 10 days in advance, enabling local authorities to take the necessary safety measures. The break-off event also confirmed that surface displacements experience a power law acceleration along with superimposed log-periodic oscillations prior to the final rupture. This paper describes the methods used to achieve a satisfactory time forecast in real time and demonstrates, using a retrospective analysis, their potential for the development of early-warning systems in real time.

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Time forecast of a break-off event from a hanging glacier

The Cryosphere ◽

10.5194/tc-10-1191-2016 ◽

2016 ◽

Vol 10 (3) ◽

pp. 1191-1200 ◽

Cited By ~ 6

Author(s):

Jérome Faillettaz ◽

Martin Funk ◽

Marco Vagliasindi

Keyword(s):

Real Time ◽

Power Law ◽

Surface Displacement ◽

Early Warning Systems ◽

Warning Systems ◽

Safety Measures ◽

Periodic Oscillations ◽

Ice Volume ◽

Surface Displacements ◽

Displacement Measurements

Abstract. A cold hanging glacier located on the south face of the Grandes Jorasses (Mont Blanc, Italy) broke off on the 23 and 29 September 2014 with a total estimated ice volume of 105 000 m3. Thanks to accurate surface displacement measurements taken up to the final break-off, this event was successfully predicted 10 days in advance, enabling local authorities to take the necessary safety measures. The break-off event also confirmed that surface displacements experienced a power law acceleration along with superimposed log-periodic oscillations prior to the final rupture. This paper describes the methods used to achieve a satisfactory time forecast in real time and demonstrates, using a retrospective analysis, their potential for the development of early-warning systems in real time.

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Real-time inversion of tsunamis generated by landslides

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-2511-2011 ◽

2011 ◽

Vol 11 (9) ◽

pp. 2511-2520 ◽

Cited By ~ 10

Author(s):

C. Cecioni ◽

A. Romano ◽

G. Bellotti ◽

M. Risio ◽

P. de Girolamo

Keyword(s):

Real Time ◽

Laboratory Experiments ◽

Early Warning Systems ◽

Time Inversion ◽

Warning Systems ◽

Optimal Position ◽

Tsunami Early Warning ◽

Tsunami Waves ◽

Inversion Procedure ◽

Tsunami Early Warning Systems

Abstract. In this paper, we test a method for forecasting in real-time the properties of offshore propagating tsunami waves generated by landslides, with the aim of supporting tsunami early warning systems. The method uses an inversion procedure, that takes input data measurements of water surface elevation at a point close to the tsunamigenic source. The measurements are used to correct the results of pre-computed numerical simulations, reproducing the wave field induced by different landslide scenarios. The accuracy of the method is evaluated using the results of laboratory experiments, aimed at studying tsunamis generated by landslides sliding along the flank of a circular shoreline island. The paper investigates what the optimal position is of where to measure the tsunamis, what the effects are, the accuracy of the results, and of uncertainties on the landslide scenarios. Finally, the method is successfully tested using partial input time series, simulating the behaviour of the system in real-time during the tsunami event when forecasts are updated, as the measurements become available.

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Landslide early warning based on failure forecast models: the example of Mt. de La Saxe rockslide, northern Italy

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-3-1511-2015 ◽

2015 ◽

Vol 3 (2) ◽

pp. 1511-1525 ◽

Cited By ~ 1

Author(s):

A. Manconi ◽

D. Giordan

Keyword(s):

Early Warning ◽

Early Warning Systems ◽

Northern Italy ◽

Warning Systems ◽

Surface Displacements ◽

Forecast Models ◽

Landslide Early Warning ◽

Critical Phases ◽

Temporal Windows

Abstract. We investigate the use of landslide failure forecast models by exploiting near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here we describe the main concepts of our method, and show an example of application to a real emergency scenario, the La Saxe rockslide, Aosta Valley region, northern Italy. Based on the herein presented case study, we identify operational thresholds based on the reliability of the forecast models, in order to support the management of early warning systems in the most critical phases of the landslide emergency.

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P-wave picking for earthquake early warning: Refinement of a T pd method

Geophysical Journal International ◽

10.1093/gji/ggab349 ◽

2021 ◽

Author(s):

Masumi Yamada ◽

Jim Mori

Keyword(s):

Real Time ◽

Early Warning ◽

Arrival Time ◽

Early Warning Systems ◽

Earthquake Early Warning ◽

P Wave ◽

Warning Systems ◽

Wave Detection ◽

Earthquake Early Warning Systems ◽

Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

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A global database of historic and real-time flood events based on social media

Scientific Data ◽

10.1038/s41597-019-0326-9 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 7

Author(s):

Jens A. de Bruijn ◽

Hans de Moel ◽

Brenden Jongman ◽

Marleen C. de Ruiter ◽

Jurjen Wagemaker ◽

...

Keyword(s):

Social Media ◽

Real Time ◽

Early Warning Systems ◽

Radar Data ◽

Global Scale ◽

Early Event ◽

Warning Systems ◽

Flood Events ◽

Global Database ◽

Use Of Social Media

AbstractEarly event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10,000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE were detected. All results are publicly available on www.globalfloodmonitor.org.

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Towards an operational database for real-time environmental monitoring and early warning systems

Procedia Computer Science ◽

10.1016/j.procs.2017.05.193 ◽

2017 ◽

Vol 108 ◽

pp. 2250-2259 ◽

Cited By ~ 5

Author(s):

Bartosz Balis ◽

Marian Bubak ◽

Daniel Harezlak ◽

Piotr Nowakowski ◽

Maciej Pawlik ◽

...

Keyword(s):

Environmental Monitoring ◽

Real Time ◽

Early Warning ◽

Early Warning Systems ◽

Warning Systems ◽

Monitoring And Early Warning

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African Rainfall Climatology Version 2 for Famine Early Warning Systems

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0238.1 ◽

2013 ◽

Vol 52 (3) ◽

pp. 588-606 ◽

Cited By ~ 195

Author(s):

Nicholas S. Novella ◽

Wassila M. Thiaw

Keyword(s):

Real Time ◽

Early Warning ◽

Correlation Coefficients ◽

Global Precipitation Climatology Project ◽

Early Warning Systems ◽

Warning Systems ◽

Telecommunication System ◽

African Rainfall ◽

Global Telecommunication System ◽

Rainfall Climatology

AbstractThis paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.

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Landslide early warning based on failure forecast models: the example of the Mt. de La Saxe rockslide, northern Italy

Natural Hazards and Earth System Science ◽

10.5194/nhess-15-1639-2015 ◽

2015 ◽

Vol 15 (7) ◽

pp. 1639-1644 ◽

Cited By ~ 24

Author(s):

A. Manconi ◽

D. Giordan

Keyword(s):

Real Time ◽

Early Warning ◽

Early Warning Systems ◽

Northern Italy ◽

Surface Displacements ◽

Forecast Models ◽

Landslide Early Warning ◽

Unstable Slope ◽

Critical Phases

Abstract. We apply failure forecast models by exploiting near-real-time monitoring data for the La Saxe rockslide, a large unstable slope threatening Aosta Valley in northern Italy. Starting from the inverse velocity theory, we analyze landslide surface displacements automatically and in near real time on different temporal windows and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here, we present the result obtained for the La Saxe rockslide, a large unstable slope located in Aosta Valley, northern Italy. Based on this case study, we identify operational thresholds that are established on the reliability of the forecast models. Our approach is aimed at supporting the management of early warning systems in the most critical phases of the landslide emergency.

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