Early Warning for Geological Disasters

Wenzel, Friedemann; Zschau, Jochen

doi:10.1007/978-3-642-12233-0

Cited by 35 publications

(4 citation statements)

References 135 publications

(191 reference statements)

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“…The term EEW refers to the prompt detection of an earthquake within few seconds after its actual onset and may provide a viable solution for real-time risk mitigation (Wenzel & Zschau, 2014;Wu et al, 2016). A socalled regional EEW approach is based on the early detection of the seismic waves generated by the earthquake's rupture process by means of an extended network of seismic sensors located in proximity of the epicenter.…”

Section: Earthquakesmentioning

confidence: 99%

Impact Forecasting to Support Emergency Management of Natural Hazards

Merz

Kuhlicke

Kunz

et al. 2020

Reviews of Geophysics

139

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Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact-based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain Language Summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services, or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organizing evacuations. We review and compare the state of the art of impact forecasting across a wide range of natural hazards and outline opportunities and key challenges for research and development of impact forecasting.

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Section: Earthquakesmentioning

confidence: 99%

Impact Forecasting to Support Emergency Management of Natural Hazards

Merz

Kuhlicke

Kunz

et al. 2020

Reviews of Geophysics

139

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“…Earthquake Early Warning (EEW) systems can reduce these risks by providing users a short time window for taking a basic protective action before the strong shaking arrives. EEWs have proven to be capable of providing timely alerts during earthquakes in Mexico (Aranda et al, 1995), Japan (Wenzel and Zschau, 2014), and Taiwan (Chen et al, 2015). Multiple other EEW systems are either in development or undergoing testing (Allen and Melgar, 2019), such as on the US West coast (Kohler et al, 2018), in Italy (Satriano et al, 2011), and in China (Jin et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Grillo sensor, a low-cost accelerometer for IoT-based Real-time seismology

Kuna¹,

Melgar²,

Meira³

2021

Preprint

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“…The traditional approach to tsunami early warning is based on seismic source inversion using data from onshore seismometer networks. The inverted earthquake hypocenter and magnitude are input into a database of precomputed tsunami scenarios to predict tsunami arrival time and maximum wave height (Hoshiba & Ozaki, 2014). There have been suggestions to update these forecasts using finite-fault slip inversions from high-rate Global Navigation Satellite System data (Blewitt et al, 2009;Melgar et al, 2016;Melgar & Bock, 2013;Tsushima et al, 2014;Xu & Song, 2013).…”

Section: Introductionmentioning

confidence: 99%

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

Yang

Dunham

Barnier

et al. 2019

Geophysical Research Letters

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Offshore sensor networks like DONET and S‐NET, providing real‐time estimates of wave height through measurements of pressure changes along the seafloor, are revolutionizing local tsunami early warning. Data assimilation techniques, in particular, optimal interpolation (OI), provide real‐time wavefield reconstructions and forecasts. Here we explore an alternative assimilation method, the ensemble Kalman filter (EnKF), and compare it to OI. The methods are tested on a scenario tsunami in the Cascadia subduction zone, obtained from a 2‐D coupled dynamic earthquake and tsunami simulation. Data assimilation uses a 1‐D linear long‐wave model. We find that EnKF achieves more accurate and stable forecasts than OI, both at the coast and across the entire domain, especially for large station spacing. Although EnKF is more computationally expensive than OI, with development in high‐performance computing, it is a promising candidate for real‐time local tsunami early warning.

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Early Warning for Geological Disasters

Cited by 35 publications

References 135 publications

Impact Forecasting to Support Emergency Management of Natural Hazards

Impact Forecasting to Support Emergency Management of Natural Hazards

Evaluation of the Grillo sensor, a low-cost accelerometer for IoT-based Real-time seismology

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

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