Impact of complex interplay of the flash floods and seawater levels on seismic resilience of underground structures: numerical simulations using PLAXIS software

Decadal trends and climatic influences on flash droughts and flash floods in Indian cities

Simultaneous and overlapping tornadoes and flash floods are a meteorological hazard with complex societal implications as, when issued at the same time, tornado and flash flood warnings provide conflicting public safety advice. This work assessed potential tornado and flash flood (TORFF) events in a portion of the Southeast from an interdisciplinary perspective with a focus on the climatology, vulnerability, and public perceptions surrounding these hazards. Our results suggest that, in addition to the conflicting warning advice, TORFFs present a challenge to the public because they can occur at night or in cool seasons when they are least expected, though they are most common in spring. Also, the storms causing TORFFs are often not clearly organized, causing a forecast and communication challenge. The public responding to the tornado and flash flood warnings in our study area is more vulnerable to TORFFs than those in other areas and may lack vehicles and structures to respond safely to one or both hazard threats. Administered survey results suggest that many believe they know what protective actions to take in a TORFF, though they may not believe they are likely in their area. Those that believe they are likely are also more likely to feel prepared to respond. Many climatology and vulnerability characteristics vary between, and at times within, NWS county warning areas, highlighting the different communication and preparation needs across the region. Approximately a quarter of flash flood and tornado warnings overlap in the region for an average of 31 min. The frequency of TORFFs and their associated public safety challenges warrant continued investigation. Significance Statement The purpose of this work is to increase our understanding of overlapping tornado and flash flood events by studying them from a multidisciplinary perspective. We found that residents in the southeastern United States are especially vulnerable to overlapping tornado and flash floods. This vulnerability is heightened by the climatology of overlapping tornado and flash floods because they can occur when they are unexpected, for example, at night or in the winter, and the public perceptions of overlapping tornado and flash floods, which is that they may not be likely in their area. These findings are important because much of the Southeast includes a population vulnerable to overlapping tornado and flash floods who may be underestimating their risk, and therefore may be unprepared for an event that requires critical decision-making.

Seismic performance and fragility of two-story and three-span underground structures using a random forest model and a new damage description method

The concurrent occurrence of extreme events can significantly impact societies and infrastructure systems, especially when the instructions provided to the exposed communities demand conflicting responses. In this study, a probabilistic assessment of concurrent tornado and storm‐related flash flood (TORFF) events is performed across southern Canada. We quantify the interdependencies between tornadoes and flash floods (extreme precipitation as proxy) using ground‐based and reanalysis datasets. Windspeed values corresponding to tornado events, categorized based on the recorded Fujita rating, are derived through a resampling approach. The TORFF events are clustered and the corresponding bivariate probability distributions of the resampled windspeed and associated precipitation are characterized based on Copula framework. The individual and joint return periods of concurrent tornadoes and flash floods are then assessed under the AND (when both variables exceed predefined thresholds), OR (when either one of the two variables exceeds predefined thresholds), and conditional hazard scenarios across Canada. Results show positive strong dependencies between resampled windspeed and associated precipitation in Saskatchewan, and weaker dependencies followed by Alberta, Manitoba, Ontario, and Quebec. The Saskatchewan region shows the highest risk underestimation considering the independence scenario compared to the other regions. Higher precipitation is also expected during extreme windspeed, as observed in the conditional assessment of precipitation given windspeed. This study provides insights for more robust recurrence interval estimation for tornadoes and flash floods to aid in emergency planning of evacuation decision‐making process.

Quantification of seismic performance index limits and evaluation of seismic fragility for a new rectangular prefabricated subway station structure

The measured vertical peak ground acceleration was larger than the horizontal peak ground acceleration. It is essential to consider the vertical seismic effect in seismic fragility evaluation of large‐space underground structures. In this research, an approach is presented to construct fragility curves of large‐space underground structures considering the vertical seismic effect. In seismic capacity, the soil‐underground structure pushover analysis method which considers the vertical seismic loading is used to obtain the capacity curve of central columns. The thresholds of performance levels are quantified through a load‐drift backbone curve model. In seismic demand, it is evaluated through incremental dynamic analysis (IDA) method under the excitation of horizontal and vertical acceleration, and the soil‐structure‐interaction and ground motion characteristics are also considered. The IDA results are compared in terms of peak ground acceleration and peak ground velocity. To construct the fragility curves, the evolutions of performance index versus the increasing earthquake intensity are performed, considering related uncertainties. The result indicates that if we ignore the vertical seismic effect to the fragility assessment of large‐space underground structures, the exceedance probabilities of damage of large‐space underground structures will be underestimated, which will result in an unfavorable assessment result.

Peak ground acceleration (PGA) is one of the most important parameters in structural seismic design. Since many variables such as soil characteristics and attenuation relations are probabilistic parameters, the PGA cannot be determined using deterministic approaches. In this paper, by using the concept of reliability, effects of soil characteristics on PGA have been investigated for Kermanshah (Iran) as the case study. Several deterministic analyses were performed using the PLAXIS software, and the amplification factor of soil was obtained. From the obtained results an artificial neural network (ANN) was trained according to the input–output pairs and these emulators were used as an alternative for PLAXIS software. As the next step for effective parameters on PGA, a great number of input data according to the probability density function, average, and standard deviation were generated. According to these data, PGA was determined on the ground by using ANN, and reliability was calculated according to Monte Carlo simulation. In fact, reliability was calculated by comparing the PGA in deterministic and probabilistic approach. Finally, sensitivity analyses for the effect of soil characteristics on PGA were performed.

Current models aiming to simulate contemporary sediment yield (SY) implicitly assume that tectonic effects are either irrelevant or are reflected by catchment topography. In this study we analyse the relation between SY and seismic activity, a component of tectonic processes. Results show a spatial correlation between SY and seismic activity expressed as the estimated peak ground acceleration (PGA) with a 10% exceedance probability in 50 years. PGA has a significant impact on the spatial variation of SY, even after correcting for cross-correlations with topography, lithology or other factors that may influence SY. Based on three distinct data sets, we demonstrate that this effect is significant both for small catchments in Europe (0.3–3940 km2) and for large river systems worldwide (1580–6.15×106 km2) and that seismic activity may be even more important for explaining regional variation in SY than land use or many other commonly considered factors (e.g. catchment area, climate). We show that explicitly considering seismic activity may lead to SY-estimates that easily deviate a factor 2 or more compared to estimates that do not consider seismic activity. This is not only the case for highly seismically active regions: also in regions with a weak to moderate seismic regime seismic activity helps explaining regional patterns in SY. We argue that these findings have important implications for a better understanding of SY and its sensitivity to human impacts, as well as for our comprehension of sediment fluxes at longer timescales.

FF-BERT: A BERT-based ensemble for automated classification of web-based text on flash flood events

<p>Flash floods, as a result of frequent torrential rainfalls caused by tropical storms, thunderstorms,<br>and hurricanes, are a prevalent natural disaster in the southeast U.S. (SEUS), which frequently<br>threaten human lives and properties in the region. According to the U.S. National Weather<br>Service (NWS), flash floods generally initiate within less than six hours of an intense rainfall<br>onset. Therefore, there is a limited chance for effective and timely decision-making. Due to the<br>rapid onset of flash floods, they are costly events, such that only during 1996 to 2017 flash<br>floods imposed 7.5 billion dollars property damage to the SEUS. Therefore, estimating the<br>potential economic damages as a result of flash floods are crucial for flood risk management and<br>financial appraisals for decision makers. A multitude of studies have focused on flood damage<br>modeling, few of which investigated the issue on a large domain. Here, we propose a systematic<br>framework that considers a variety of factors that explain different risk components (i.e., hazard,<br>vulnerability, and exposure) and leverages Machine Learning (ML) for flood damage prediction.<br>Over 14,000 flash flood events during 1996 to 2017 were assessed to analyze their characteristics<br>including frequency, duration, and intensity. Also, different data sources were utilized to derive<br>information related to each event. The most influential features are then selected using a multi<br>criteria variable selection approach. Then, the ML model is implemented for not only binary<br>classification of damage (i.e., whether a flash flood event caused any damage or not), but also for<br>developing a model to predict the financial consequences associated with flash flood events. The<br>results indicate a high accuracy for the classifier, significant correlation and relatively low bias<br>between the predicted and observed property damages showing the effectiveness of proposed<br>methodology for flash flood damage modeling applicable to variety of flood prone regions.</p>

Fragility functions for pipeline in liquefiable sand : a case study on the Groningen gas-network

More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow have highlighted the need for improved representations of the complex flow processes that are inherent in flash floods. This study aimed to identify an optimal modeling approach for characterizing leaf litter losses during flash floods. At a gauged watershed in the Hidegvíz Valley in Hungary, a physical-based model was calibrated using two distinct rainfall–runoff events. Two modeling methodologies were implemented, integrating canopy interception and leaf litter storage, to understand their contributions during flash flood events. The results from the model’s calibration demonstrated this approach’s effectiveness in determining the impact of leaf litter on steep-sloped watersheds. Soil parameters can estimate the behavior of leaf litter during flash flood events. In this study, hydraulic conductivity and initial water content emerged as critical factors for effective parametrization. The findings underscore the potential of a hydrodynamic model to explore the relationship between leaf litter and flash flood events, providing a framework for future studies in watershed management and risk-mitigation strategies.

Flash floods frequently co-occur with landslides, during which landslides can deliver large amounts of hillslope material into the river system. Their interaction can lead to exacerbated and destructive impacts. While such geo-hydrological hazards are typically triggered by intense rainfall over only a few hours, daily to monthly variations in rainfall drive soil moisture changes and alter their likelihood of occurrence, alone or in combination. The influence of this preconditioning rainfall on compounding landslides and flash floods, however, remains overlooked. Acquired through the combined use of optical and radar satellite imagery, we present a unique multi-temporal inventory of a hundred new landslide and flash flood events located in a large region in the African tropics that is characterized by active rifting and strong human influences on the landscape. From this inventory we show that preconditioning rainfall plays a central role in the occurrence of landslide and flash flood events, along with land use/land cover and landscape geological history. Wetter-than-average conditions in human-dominated cultivated areas on rejuvenated hillslopes associated with the rift formation more frequently lead to compounding flash floods and landslides. On the other hand, drier-than-average conditions in forested regions outside these rejuvenated landscapes more often lead to compounding, densely spaced and larger landslides without flash floods. This research shows that preconditioning rainfall can exacerbate the severity of co-occurring and interacting landslide and flash flood events, stressing the need to understand these geo-hydrological hazard in a compounding manner.

Using advanced geospatial analysis technologies, flash flood risk is assessed for the island of Kyushu, Japan. In this study, the flash flood risk is redefined in terms of the flash flood potential index (FFPI) and the flash flood residential hazard (FFRH). The island experiences rainy weather, especially in the summer (June–August), when catastrophic flash flood events have historically occurred. Studies of the surface hydrological properties of the island are very rare and localized; hence, geospatial techniques are most appropriate for the assessment process. The Soil Conservation Service rainfall-runoff model was used to estimate hydrological responses on the island. Four factors were included in the flash flood assessment. A multi-criteria analysis was carried out to map the FFPI and FFRH from the evaluation factors. The results show that the highest flash flood risk occurs in the northern parts of the island, where the soil displays relatively low infiltration rates and relatively high curve numbers, despite the comparatively low precipitation rates that occur there. The results indicate that soil hydrological properties are the main driving forces of flash floods, especially in regions with low precipitation rates. The results of this research are consistent with previous in situ measurements of runoff made at several sites on the island. The results also show a strong geographic correlation with historical flash flood events on the island. This research validates the use of geospatial analysis for large geographic regions where in situ measurements cannot be taken due to time or cost constraints. The results of this study provide decision makers with the information needed to select a management strategy to address possible future flash flood events that considers safety and water harvesting.

Abstract. On 18 May 2015, a severe rainfall event triggered a flash flood in the municipality of Salgar, located in the northwestern Colombian Andes. This work aims to reconstruct the main hydrological features of the flash flood to better understand the processes modulating the occurrence of the event. Radar quantitative precipitation estimates (QPEs), satellite information, and post-event field visits are used to reconstruct the Salgar flash flood, in an ungauged basin, addressing the relationship among rainfall spatiotemporal structure, soil moisture, and runoff generation during successive rainfall events by using a conceptual modeling framework including landslide and hydraulic submodels. The hydrological model includes virtual tracers to explore the role of runoff and subsurface flow and the relative importance of convective and stratiform precipitation in flash flood generation. Despite potential shortcomings due to the lack of data, the modeling results allow an assessment of the impact of the interactions between runoff, subsurface flow, and convective–stratiform rainfall on the short-term hydrological mechanisms leading to the flash flood event. The overall methodology reproduces the magnitude and timing of the La Liboriana flash flood peak discharge considerably well, as well as the areas of landslide occurrence and flood spots, with limitations due to the spatial resolution of the available digital elevation model. Simulation results indicate that the flash flood and regional landslide features were strongly influenced by the antecedent rainfall, which was associated with a northeasterly stratiform event. The latter recharged the gravitational and capillary storages within the model, moistening the entire basin before the occurrence of the flash flood event and impacting the subsurface–runoff partitioning during the flash flood event. Evidence suggests that the spatial structure of the rainfall is at least as important as the geomorphological features of the basin in regulating the occurrence of flash flood events.