Springer Science and Business Media LLC

In this work, we consider historical earthquakes registered in Chile (from 1900 up to 2010) with epicenters located between 19 and 40°S latitude, in order to evaluate the probabilities of the occurrence of strong earthquakes along Chile in the near future. Applying Gumbel’s technique of first asymptotic distribution, Wemelsfelder’s theory and Gutenberg–Richter relationship, we estimate that during the next decade strong earthquakes with Richter magnitudes larger than 8.7–8.9 could occur along Chile. According to our analysis, probabilities for the occurrence of such a strong earthquake range between 64 and 46% respectively. Particularly in the very well known “seismic gap” of Arica, a convergence motion between Nazca and South American plates of 77–78 mm/year represents more than 10 m of displacement accumulated since the last big interplate subduction earthquake in this area over 134 years ago. Therefore, this area already has the potential for an earthquake of magnitude >8.

This study investigates disaster resilience during major hurricane events by analyzing Twitter data. The study focuses on resilience-relevant tweets during major hurricanes from 2013 to 2020 in the USA that caused over 1 billion dollars in losses. The study explores variations in resilience perceptions across different racial, gender, and political groups, as well as the sentiment expressed in tweets during different phases of the disaster. Additionally, the study examines the alignment of Twitter discussions with the actual phases of the disasters from a spatiotemporal perspective. The findings highlight disparities in resilience perception among racial and gender groups, emphasizing the need for targeted approaches to promote inclusivity and equity in disaster resilience efforts. The importance of early awareness and preparedness is underscored, suggesting the significance of investing in forecasting and early alert systems to reduce the impact of disasters.

Quantitative risk assessments (QRAs) for landslide hazards are increasingly being executed to determine an unmitigated level of risk and compare it with risk tolerance criteria set by the local or federal jurisdiction. This approach allows urban planning with a scientific underpinning and provides the tools for emergency preparedness. Debris-flow QRAs require estimates of the hazard probability, spatial and temporal probability of impact (hazard assessment) and vulnerability of the elements at risk. The vulnerability term is perhaps the most difficult to estimate confidently because (a) human death in debris flows is most commonly associated with building damage or collapse and is thus an indirect consequence and (b) the type and scale of building damage is very difficult to predict. To determine building damage, an intensity index (I DF) was created as the product of maximum expected flow depth d and the square of the maximum flow velocity v (I DF = dv 2). The I DF surrogates impact force and thus correlates with building damage. Four classes of building damage were considered ranging from nuisance flood/sedimentation damage to complete destruction. Sixty-six well-documented case studies in which damage, flow depth and flow velocity were recorded or could be estimated were selected through a search of the global literature, and I DF was plotted on a log scale against the associated damage. As expected, the individual damage classes overlap but are distinctly different in their respective distributions and group centroids. To apply this vulnerability model, flow velocity and flow depth need to be estimated for a given building location and I DF calculated. Using the existing database, a damage probability (P DF) can then be computed. P DF can be applied directly to estimate the likely insurance loss or associated loss of life. The model presented here should be updated with more case studies and is therefore made openly available to international researchers who can access it at http://chis.nrcan.gc.ca/QRA-EQR/index-eng.php .

Debris flow moves in the form of surge waves and consists of dozens or even hundreds of surges that are separated in time and space and have a variety of appearances, as exemplified in Jiangjia Gully, China. Observations there indicate that the deposit is made up by superposition of successive surges and deposit of a single surge is in effect a “frozen” surge. Then the study of debris flow is reduced to the study of surge sequence, which leads to a probabilistic picture of debris flow. This study attempts to find the probability distribution of velocity of surge using a huge data set of Jiangjia Gully. Statistics of the data shows that the velocity satisfies the Weibull distribution, which is believed to be universally valid because the distribution parameters vary little between events, with the shape parameter being well related to the average of velocity. It follows that the same distribution applies also to other quantities of debris flow, such as the flow depth and the discharge. Therefore, the distribution can be used to assess the magnitude and overflow range of a potential debris flow, as well as to the parameter calculation for engineering design.

We present regional maps of earthquake-induced liquefaction hazard in Mexico considering the lateral or horizontal spreading displacement, as index, D LL . The methodology to prepare the liquefaction hazard maps consists of five steps: (1) identifying zones with soil deposits that are more susceptible to displaying liquefaction based on geologic available information at a 1:1,000,000 scale; (2) characterizing the seismic hazard as a set of stochastic events that collectively describe the hazard, compatible with the distribution of location, depth, frequencies, magnitudes and attenuation of the seismic strong ground motion; (3) employing a parametric method, based on empirical data, to estimate the demand of permanent ground deformation expected due to liquefaction (in this study, the lateral spreading displacement of the ground, D LL ) by event and for the site required; (4) characterizing the earthquake-induced liquefaction hazard as a set of stochastic events that describe the spatial distribution demand of liquefaction for each event; and (5) performing a probabilistic analysis of liquefaction hazard. The results of liquefaction hazard associated with return periods of 150 and 500 years are shown on maps of Mexico. Those maps are compared qualitatively with historical information collected from sites where, based on descriptions, the phenomenon of earthquake-induced liquefaction is seen to have occurred from the year 1593 to 2010. The results obtained in this study provide a first approximation to the liquefaction hazard zones in the country, in accordance with sites where historical evidence of liquefaction has been reported. In addition, the application could be important in land-use planning and urban development, particularly in regions with a historical certainty of earthquake-induced liquefaction, but with little or no geotechnical and/or geophysical studies. These maps can be used to locate zones where more in-depth studies are required to estimate, with less uncertainty, the potential for earthquake-induced liquefaction.

Frequent floodings in Semarang City have generated increasing damages and losses in property and life quality. The cause of flooding is related to the coupled impacts of land subsidence, hydraulics hazards along with poor drainage and water retention systems. This paper studies the most recent flooding hazards caused by hydrological origins (i.e., river discharge, tidal) and land subsidence. In the study, riverine origin of flooding is simulated with the help of HEC-RAS 2D, while the tidal origin is simulated to high highest water level. However, due to the absence of the most recent topographic data, the role of land subsidence is measured by estimating the vertical changes of digital elevation model taken from Sentinel 1A. Flooding extent, in terms of depth and coverage, is verified based on satellite imagery Sentinel-2 which is cloud-processed using Google Earth Engine (GEE) and field survey. Fluvial flood is simulated with several boundary condition scenarios using combinations of 5-, 25-, or 50-year return periods of flood which is integrated with mean sea level (MSL) or high highest water level (HHWL) tides. Those boundary conditions are then incorporated into different terrains, namely LiDAR, DEMNAS, and TerraSAR DEM, to see how different digital elevation models (DEMs) can impact model sensitivity. By overlaying model outputs and land cover map, it can be concluded that settlements and water bodies are among the most potentially affected areas, covering up to 17 km2. This study is expected to help policymakers make a primary assessment of combined tidal and fluvial flood hazard through mitigation and adaptation measures.