Exceedance Probability Research Articles

Modeling spatial correlation between earthquake insured losses in New Zealand: A mixed-effects analysis.

Earthquake insurance is a critical risk management strategy that contributes to improving recovery and thus greater resilience of individuals. Insurance companies construct premiums without taking into account spatial correlations between insured assets. This leads to potentially underestimating the risk, and therefore the exceedance probability curve. We here propose a mixed-effects model to estimate losses per ward that is able to account for heteroskedasticity and spatial correlation between insured losses. Given the significant impact of earthquakes in New Zealand due to its particular geographical and demographic characteristics, the government has established a public insurance company that collects information about the insured buildings and any claims lodged. We thus develop a two-level variance component model that is based on earthquake losses observed in New Zealand between 2000 and 2021. The proposed model aims at capturing the variability at both the ward and territorial authority levels and includes independent variables, such as seismic hazard indicators, the number of usual residents, and the average dwelling value in the ward. Our model is able to detect spatial correlation in the losses at the ward level thus increasing its predictive power and making it possible to assess the effect of spatially correlated claims that may be considerable on the tail of lossdistribution.

Portfolio default losses driven by idiosyncratic risks

We consider a portfolio of general defaultable assets with low individual default risk and study the probability of the portfolio default loss exceeding an arbitrary threshold. The latent variables driving defaults are modeled by a mixture structure that combines common shock, systematic risk, and idiosyncratic risk factors. While common shocks and systematic risk have been found by many studies to contribute significantly to portfolio losses, the role of idiosyncratic risks is often found to be negligible. Such conclusions are usually established under the assumption that the portfolio size tends to infinity and idiosyncratic risk factors are not dominant. We study under-investigated scenarios where the portfolio size is fixed and the idiosyncratic risk factors are heavy-tailed, exploring two distinct scenarios: an independence scenario and an asymptotic dependence scenario. The former is standard in the literature, while the latter is motivated by recent studies that have found the inadequacy of relying solely on common factors to capture default clustering. This consideration also reflects the possibility that idiosyncratic reasons can trigger contagion among firms with liabilities to each other. In the independence scenario, even with heavy-tailed idiosyncratic risk factors, the probability of a substantial portfolio loss remains low unless a single asset carries a disproportionately large weight. Conversely, in the asymptotic dependence scenario, the primary drivers of increased exceedance probability are the dependent idiosyncratic risk factors.

Evaluation of key flood risk drivers under climate change using a bottom-up approach

Our understanding of the key drivers of change in flood risk due to climate change remains incomplete. Here, to understand and quantify the key drivers of change in flood risk, we present a framework to undertake a ‘bottom-up’ (‘scenario-neutral’) climate change impact study on flood risk using an event-based flood model that considers non-stationarity in rainfall extremes and catchment wetness. A key advantage of this approach is that by using an event-based model, which explicitly represents key flood drivers, we can directly understand how changes in these drivers will influence changes in flood peaks. The utility of this modelling framework is demonstrated by applying it to one temperate and one tropical catchment in Australia, focusing on the sensitivity of frequent (1 in 5 annual exceedance probability, AEP) and rare (1 in 50 AEP) flood peaks to changes in thermodynamic and dynamic drivers of rainfall extremes, and changes in catchment processes, which are quantified by changes in rainfall losses.Response surfaces of the catchment sensitivity are first produced and demonstrate that increases in rainfall intensity drive increases in the flood peak. Smaller flood peaks in drier catchments are most sensitive to changes in rainfall losses, with the modulating impact of changes in losses decreasing as the runoff ratio increases. Climate model projections of these drivers are then superimposed on these surfaces to identify plausible shifts in flood risk under climate change. Several climate model ensemble members imply a decrease in future flood peaks, most notably 14 % of ensemble members suggest future decreases for the 1 in 5 AEP event for the temperate catchment. Despite large agreement on the direction of change, we found large variability in the expected magnitude of change for both frequent and rare events with flood peaks projected to increase between 0 % and 45 % for the tropical catchment and between −10 % to more than 50 % for the temperate catchment under RCP8.5 in 2085, highlighting the deep uncertainty associated with projecting flood risk under climate change. We believe this study acts as proof of concept for how a bottom-up climate change assessment can be undertaken within an event-based flood modelling framework, and provides insights to help us better understand and communicate the key drivers of changes to flood risk into the future.

Simulation of geological uncertainty based on improved three-dimensional coupled Markov chain model

The natural stratigraphic distribution is uncertain due to the geological tectonic movements and sedimentation. It is a challenge for traditional deterministic models to capture geological uncertainty. The objective of this study is to develop an improved three-dimensional coupled Markov chain (ICMC3D) model based on limited boreholes. The proposed method enhances the traditional three-dimensional coupled Markov chain model by modifying the transition probability equation for predicting unknown elements. The dip data is added to estimate the transition probability matrix with the borehole information. On this basis, a back analysis method is proposed for estimating the horizontal transition probability matrics. The method makes the predicted results more suitable for the possible stratigraphic distribution. The accuracy of the proposed method is verified through some applications. In this paper, an improved three-dimensional coupled Markov chain model and transition probability matrix estimation method are illustrated by the borehole data collected from a tunnel project in Qingdao. The results show that the presented method can reflect the asymmetry, continuity and anisotropy of the three-dimensional stratum. The exceedance probability is proposed to predict the fault zone development range in order to reduce the geological uncertainty.

Global application of a regional frequency analysis to extreme sea levels

Abstract. Coastal regions face increasing threats from rising sea levels and extreme weather events, highlighting the urgent need for accurate assessments of coastal flood risk. This study presents a novel approach to estimating global extreme sea level (ESL) exceedance probabilities using a regional frequency analysis (RFA) approach. The research combines observed and modelled hindcast data to produce a high-resolution (∼1 km) dataset of ESL exceedance probabilities, including wave setup, along the entire global coastline (excluding Antarctica). The methodology presented in this paper is an extension of the regional framework of Sweet et al. (2022), with innovations introduced to incorporate wave setup and apply the method globally. Water level records from tide gauges and a global reanalysis of tide and surge levels are integrated with a global ocean wave reanalysis. Subsequently, these data are regionalised, normalised, and aggregated and then fit with a generalised Pareto distribution. The regional distributions are downscaled to the local scale using the tidal range at every location along the global coastline obtained from a global tide model. The results show 8 cm of positive bias at the 1-in-10-year return level when compared to individual tide gauges. The RFA approach offers several advantages over traditional methods, particularly in regions with limited observational data. It overcomes the challenge of short and incomplete observational records by substituting long historical records with a collection of shorter but spatially distributed records. These spatially distributed data not only retain the volume of information but also address the issue of sparse tide gauge coverage in less populated areas and developing nations. The RFA process is illustrated using Cyclone Yasi (2011) as a case study, demonstrating how the approach can improve the characterisation of ESLs in regions prone to tropical cyclone activity. In conclusion, this study provides a valuable resource for quantifying the global coastal flood risk, offering an innovative global methodology that can contribute to preparing for – and mitigating against – coastal flooding.

Occurrence of pharmaceuticals in Latin America: case study on hazard assessment and prioritization in Costa Rica.

Pharmaceuticals are considered as contaminants of emerging concern, and their occurrence in diverse environmental matrices has been described during the last 25years. Nonetheless, pharmaceutical occurrence has not been evenly described worldwide, and reports from some geographical areas such as most parts of Latin America are scarce. This work aims to address the situation of water pollution due to pharmaceuticals in Latin America by means of two main goals: i. First, reviewing the monitoring studies performed in Latin America on this topic (period 2009-2024), which were conducted in Brazil, Mexico, Colombia, Ecuador, Peru and Argentina, to highlight the most frequently detected compounds from each therapeutic group in the region. ii. Second, analyzing the case of Costa Rica through the hazard assessment and prioritization of pharmaceuticals based on the monitoring performed in this country (years 2011; 2018-2019). The monitoring in Costa Rica comprised a total of 163 sampling points: wastewater treatment plants (WWTPs) (14 urban WWTPs plus two landfill WWTPs; total samples n = 44 influents and n = 34 effluents), nine hospital effluents (n = 32), wastewater from livestock farms (six swine farms and seven dairy farms; n = 23 influents and n = 37 effluents), 64 continental surface water sampling points (n = 137), and 61 coastal seawater sampling points (n = 61). Risk assessment of detected concentrations by the hazard quotient (HQ) approach (period 2018-2019) revealed a total of 25 mediumorhigh-hazard compounds (out of 37 detected compounds). The prioritization approach (which included the Frequency of Appearance (FoA), the Frequency of PNEC exceedance (FoE), and the Extent of predicted no-effect concentration (PNEC) exceedance (EoE)), showed a critical list of nine pharmaceuticals: caffeine, diphenhydramine, acetaminophen, lovastatin, gemfibrozil, ciprofloxacin, ibuprofen, doxycycline and norfloxacin. These compounds should be taken into account as a first concern during the implementation of environmental policies related to pharmaceutical products in the region.

IM-based seismic reliability assessment of the pre-code masonry building stock in metropolitan area of Lisbon

Earthquakes have a long history of causing catastrophic damage to communities, resulting in structural collapses, loss of life, and economic turmoil. To enable informed decision-making and reduce the impact of these events in earthquake-prone regions, seismic risk studies provide relevant information to support stakeholders in the implementation of effective risk-based policies. The present work addresses the seismic reliability of the pre-code masonry building stock in the Metropolitan Area of Lisbon, which is the region of Portugal that faces the highest seismic risk due to the coexistence of moderate to high seismic hazard and highest demographic-economic exposure. The adopted general framework combines several hazard studies developed for the region under investigation and a synthetic database of masonry buildings representative of the pre-code building stock in Lisbon. Through analytical–numerical probabilistic approaches, new second-order hazard solutions with structural dependency are derived for the mean annual frequency of limit-state exceedance, which can be integrated into national application documents for Eurocode 8. In light of these results, the reliability assessment of the building stock is conducted in several Local Administrative Units by means of an improved SAC/FEMA formulation. The study represents the first comprehensive investigation of its kind in this region, providing essential information to define appropriate target safety level for code calibration and support future risk studies.

Unravelling factors influencing demand for modern contraception and evaluating coverage progress since 2015 in Ethiopia, Kenya, and Nigeria: insights from multilevel and geostatistical modelling

IntroductionThe United Nations established the Sustainable Development Goals (SDGs) in 2015 to enhance global development. In this study, we examine an SDG indicator: the percentage of women aged 15–49 whose family planning needs are met by modern contraception (mDFPS). We evaluate both the factors influencing its coverage and its progress since 2015.MethodsWe used nationally representative surveys data (Demographic and Health Surveys (DHS) and Performance Monitoring for Action (PMA)) from Ethiopia, Kenya, and Nigeria. We assessed predictors of mDFPS. We also computed mDFPS coverage across countries and subnational areas, assessing coverage changes from the SDGs onset to the most recent period, using a Bayesian model-based geostatistical approach. We assessed whether the subnational areas exceeded the minimum recommended WHO mDFPS coverage of 75%.ResultsVaried individual and community-level determinants emerged, highlighting the countries’ uniqueness. Factors such as being part of a female-headed household, and low household wealth, lowered the odds of mDFPS, while rural-residence had low odds only in Ethiopia and Nigeria. The results indicate mDFPS stagnation in most administrative areas across the three countries. Geographic disparities persisted over time, favouring affluent regions. The predicted posterior proportion of mDFPS and exceedance probability (EP) for WHO target for Ethiopia was 39.85% (95% CI: [4.51, 83.01], EP = 0.08) in 2016 and 46.28% (95% CI: [7.15, 85.99], EP = 0.13) in 2019. In Kenya, the adjusted predicted proportion for 2014 was 30.19% (95% CI: [2.59, 80.24], EP = 0.06) and 44.16% (95%CI: [9.35, 80.24], EP = 0.13) in 2022. In Nigeria, the predicted posterior proportion of mDFPS was 17.91% (95% CI: [1.24, 61.29], EP = 0.00) in 2013, and it was 23.08% (95% CI: [1.80, 56.24], EP = 0.00) in 2018. None of the sub-national areas in Ethiopia and Nigeria exceeded the WHO target. While 9 out of 47 counties in Kenya in 2022 exceeded the WHO mDFPS target.ConclusionThe study unveils demographic, geographic, and socioeconomic mDFPS disparities, signalling progress and stagnation across administrative areas. The findings offer policymakers and governments insights into targeting interventions for enhanced mDFPS coverage. Context-specific strategies can address local needs, aiding SDG attainment.

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of a spatially-resolved Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST. This allows us to extend the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Amanzi–ATS, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5000 annual synthetic storm events in a ∼2000km2 Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

Numerical analysis of underground displacements stirred by compaction grouting during tunnel construction

Compaction grouting is widely accepted to mitigate the surface settlements during tunnel excavation. Currently, compaction grouting relies heavily upon field observations due to the limit understanding of the amounts of soil displacements induced by a given volume of grout. In this study, parametric studies and probabilistic investigations are performed to examine the underground displacements caused by compaction grouting during tunneling. Under high injection pressure, soils flow around the center of a circle, which is approximately 2 m ∼ 2.8 m above the elliptical grouting bulbs and 3.3 m away from the far-end elliptical grouting bulb. An influenced zone, outside which effect of the burial depth of grouting bulbs can be neglected, is determined by connecting the intersections of vertical displacements curves. Influence of burial depth of elliptical grouting bulbs on vertical displacements is greater than that of the total length of elliptical grouting bulbs. When the total volume of elliptical grouting bulbs is identical, better grouting efficiency is achieved by increasing the volume of the elliptical grouting bulbs adjacent to the tunnel center, especially for deeper monitoring points. Locations of the maximum lateral displacements in soils are within a band underground. Moreover, probability of exceedance of vertical displacements on the tunnel center line and formulas describing the distributions of heaves in soils are presented for the benefit of assessing the project risk and liability and predicting the heaves in soils.

Characterizing localized nitrogen sensitivity of tree species and the associated influences of mediating factors

AbstractCritical loads (CLs) are frequently used to quantify terrestrial ecosystem impacts from nitrogen (N) deposition using ecological responses such as the growth and mortality of tree species. Typically, CLs are reported as a single value, with uncertainty, for an indicator across a species' entire range. Mediating factors such as climate and soil conditions can influence species' sensitivity to N, but the magnitudes of these effects are rarely calculated explicitly. Here, we quantify the spatial variability and estimation error in N CLs for the growth and survival of 10 different tree species while accounting for key environmental factors that mediate species sensitivity to N (e.g., soil characteristics). We used a bootstrapped machine learning approach to determine the level of N deposition at which a 1% decrease occurs in growth rate or survival probability at forest plot locations across the United States. We found minimal differences (<5 kg N ha−1 year−1) when comparing a single species' CLs across climatic regimes but found considerable variability in species' local N CLs (>8.5 kg N ha−1 year−1) within these regimes. We also evaluated the most important factors for predicting tree growth rates and mortality and found that climate, competition, and air pollution generally have the greatest influence on growth rates and survival probability. Lastly, we developed a new probability of exceedance metric for each species and found high likelihoods of exceedance across large portions (46%) of some species' ranges. Our analysis demonstrates that machine learning approaches provide a unique capability to: (1) quantify mediating factor influences on N sensitivity of trees, (2) estimate the error in local N CL estimates, and (3) generate localized N CLs with probabilities of exceedance for tree species.

Probabilistic Seismic Hazard Assessment of Sitamarhi near the Central Himalayan Region

ABSTRACT Sitamarhi, situated in the North-Bihar region of the Himalayan foothills is one of the seismically vulnerable regions of India. This region faced several devastating earthquakes, such as the 1934 and 1988 Bihar Nepal earthquake and the 2015 Nepal earthquake due to the movement of tectonic plates. The purpose of the present study is to exhibit the uniform hazard spectrum (UHS) and peak ground acceleration (PGA) values based on the probabilistic seismic hazard method using different seismotectonic parameters. The Gutenberg Richter (G-R) seismicity parameters ‘a’ and ‘b’ were estimated on the basis of regional earthquake magnitude data for the studied region. Regional earthquakes data were taken from the United States Geological Survey (USGS), International Seismological Centre (ISC), Indian Meteorological Department (IMD), and Seismotectonic Atlas of India. The region of interest for the probabilistic seismic hazard analysis (PSHA) is within a 500 km radius considering past seismicity and 62 seismotectonic sources which were identified in the study area. Large-scale geological features were utilised to describe the regional seismic source zones, which were used to derive the relationship between return period and ground motion parameters. Estimated PGA values are 0.92g for 2% and 0.68g for 10% probability of exceedance in 50 years time period, respectively. This research infer that the West Patna fault (WPF) and Sitamarhi fault (SMF) are the two maximum contributors in PGA for the Sitamarhi region.

Long-term trends of ozone in the Yangtze River Delta, China: Spatiotemporal impacts of meteorological factors, local, and non-local emissions

The Yangtze River Delta (YRD) region has witnessed a consistent decrease in NO2, CO, and PM2.5 from 2016 to 2023. However, ozone has exhibited fluctuating patterns. Quantifying ozone contributions from emissions, both within and outside the YRD, is essential for understanding city-cluster-scale ozone pollution (CCSOP). To address these concerns, a comprehensive approach combining Kolmogorov-Zurbenko filtering, Empirical Orthogonal Function, Absolute Principal Component Score, and Multiple Linear Regression methods (KZ-EOF-APCs-MLR) was employed to quantify the impacts of meteorological factors, local and non-local emission contributions of ozone (LECO and NECO). Emission changes were identified as the predominant factor shaping annual fluctuations in ambient ozone. Notably, during the previous and middle stages of the COVID-19 pandemic (from 2017 to 2021), emissions reductions led to a marked decrease in YRD ozone levels (-7.01 µg/m3), with a pronounced rebound post-pandemic (2022 to 2023) (+8.04 µg/m3). Seasonally, the emission-induced ozone exhibited fluctuating upward trend during autumn and winter, suggesting a transition of ozone pollution towards colder seasons. Spatially, high LECO concentrated in the eastern YRD (EYRD) across spring, autumn, and winter, becoming prominent in the central YRD (CYRD) during summer. During CCSOP, the CYRD exhibited the highest LECO and exceedance frequency (20.82 µg/m3 and 45.27 %). LECO explained a large portion of ozone variability during CCSOP, particularly in the EYRD, while NECO showed less explanatory power but consistently high contributions (148.05 ± 15.52 µg/m3). These findings offer valuable insights for a deeper understanding of the evolving patterns of ozone pollution and the issue of CCSOP in the YRD.

Impacts of synoptic weather patterns on Hefei's ozone in warm season and analysis of transport pathways during extreme pollution events

Extreme ozone pollution events (EOPEs) are associated with synoptic weather patterns (SWPs) and pose severe health and ecological risks. However, a systematic investigation of the meteorological causes, transport pathways, and source contributions to historical EOPEs is still lacking. In this paper, the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta (YRD) region from 2016 to 2022. It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs. Using the WRF-FLEXPART model, the transport pathways (TPPs) and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated. The results reveal that Hefei experienced the highest ozone concentration (134.77 ± 42.82 µg/m³), exceedance frequency (46 days (23.23 %)), and proportion of EOPEs (21 instances, 47.7 %) under the control of peripheral subsidence of typhoon (Type 5). Regional southeast winds correlated with the ozone pollution in Hefei. During EOPEs, a high boundary layer height, solar radiation, and temperature; low humidity and cloud cover; and pronounced subsidence airflow occurred over Hefei and the broader YRD region. The East-South (E_S) patterns exhibited the highest frequency (28 instances, 65.11 %). Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs. The YRD was the main source for land-originating air masses under E_S patterns (50.28 %), with Hefei, southern Anhui, southern Jiangsu, and northern Zhejiang being key contributors. These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.

Flooding Resiliency of Surigao del Sur, Caraga Region, Philippines Residences Through Rainwater Catchment and Storage System

Urbanised areas in Northeastern Mindanao have a problem of addressing flooding occurrences. This study primarily aimed to provide insights into how the rainwater catchment system of uptown communities and their cooperation could increase flood resiliency of downtown communities in the Surigao del Sur, Caraga Region, Philippines. This research employed quantitative analysis of the eleven (11) year (2010-2020) data from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration - Hinatuan, Surigao del Sur station. The recommendable optimum rainwater storage capacities for a given number of household occupants, roof areas at run-off efficiency of 90%, and three (3) day rainfall characteristics at 36.9% averaged probability of exceedance were initially determined. Through scenario analysis, uptown communities emptying their rainwater storages before heavy downpour occurs could provide sufficient flood volume reduction and buffer time for downtown communities to prepare. The output of this research is vital in the environmental planning, management, and policies of cities and regions.

Change in high-temperature intensity-duration-frequency under different warming scenarios over India

This study examined the hottest temperature events in each year for a period of 1 to 10 consecutive days across India under 1.5 °C, 2 °C, and 3 °C global warming levels (GWLs) and two periods (T1: 2021–2050, T2: 2071–2100) using the Coupled Model Intercomparison Phase 6 (CMIP6) framework. Bivariate copula analysis was applied to determine the joint probability distribution of the intensity and duration of high-temperature extremes across various GWLs. We also evaluated the change in return values for 10, 20, 50, and 100-year return periods over 2, 5, and 10-day durations. The intensity of the high-temperature extremes indicates a gradual rising trend across the country for durations ranging from 1 to 10 days for different GWLs. For the 2-day duration, the intensity of average temperature increases by 0.7–2.6 °C under all GWLs. The Western Himalaya region (2–4.3 °C) shows the highest increase in intensity of 2-day duration followed by the Northwest (0.93–2.51 °C) and Eastern Coastal (0.86–2.5 °C) regions under different GWLs. Whereas, the Interior Peninsula (0.56–2.15 °C) and Western coastal (0.56–1.9 °C) regions show a lower increase. Similar patterns were observed for 5- and 10-day duration. The 20-year return value of the intensity for a 2-day duration increases by 7–24% under all GWLs across India. The highest increase was observed in the Western Himalayas (23–48%) followed by the Northeast and Eastern Coastal (9–25%) regions under all GWLs. We have derived Temperature-Intensity-Duration-Frequency (TIDF) curves for eight selected urban agglomerations cities. Among them, Srinagar exhibits the highest increase in intensity (ranging from 19 to 32%) followed by Guwahati (9–16%) and Mumbai (5.5–12.8%). Whereas, Hyderabad exhibits the smallest increase in intensity (0.02%–8%). The duration of high-temperature extreme events increases for a given intensity and return period. It is also observed that for a given intensity and duration, the exceedance probability of high-temperature extremes increases under different GWLs. These insights are crucial for climate mitigation and adaptation strategies and can inform decisions by urban planners, policymakers, and communities in addressing the challenges posed by high-temperatures extreme.

Modeling Riverbed Elevation and Bedload Tracer Transport Resting Times Using Fractional Laplace Motion

AbstractRiverbed elevations play a crucial role in sediment transport and flow resistance, making it essential to understand and quantify their effects. This knowledge is vital for various fields, including river engineering and stream ecology. Previous observations have revealed that fluctuations in the bed surface can exhibit both multifractal and monofractal behaviors. Specifically, the probability distribution function (PDF) of elevation increments may transition from Laplace (two‐sided exponential) to Gaussian with increasing scales or consistently remain Gaussian, respectively. These differences at the finest timescale lead to distinct patterns of bedload particle exchange with the bed surface, thereby influencing particle resting times and streamwise transport. In this paper, we utilize the fractional Laplace motion (FLM) model to analyze riverbed elevation series, demonstrating its capability to capture both mono‐ and multi‐fractal behaviors. Our focus is on studying the resting time distribution of bedload particles during downstream transport, with the FLM model primarily parameterized based on the Laplace distribution of increments PDF at the finest timescale. Resting times are extracted from the bed elevation series by identifying pairs of adjacent deposition and entrainment events at the same elevation. We demonstrate that in cases of insufficient data series length, the FLM model robustly estimates the tail exponent of the resting time distribution. Notably, the tail of the exceedance probability distribution of resting times is much heavier for experimental measurements displaying Laplace increments PDF at the finest scale, compared to previous studies observing Gaussian PDF for bed elevation.

FaultQuake: An open-source Python tool for estimating Seismic Activity Rates in faults

In regions experiencing ongoing aseismic deformation, fault’s Activity Rate (AR) calculations often lead to an overestimation of hazard potential. This study proposes a novel methodology that integrates the Seismic Coupling Coefficient (SCC) into the fault Seismic Activity Rate (SAR) calculation process to discriminate seismic moment rates. We introduce FaultQuake, an open-source Python tool equipped with a Graphical User Interface (GUI), designed to implement this methodology and accurately estimate SAR for faults. These activity rates can be included in Probabilistic Seismic Hazard Assessment (PSHA) frameworks and assist in differentiating the seismic and aseismic deformation. FaultQuake also presents an innovative embedded workflow, the Optimal Value Computation Workflow (OVCW), based on Conflation of Probabilities (CoP), for calculating the Maximum Magnitude (Mmax) from the empirical relationships and the observed magnitudes (Mobs) assigned to a single fault. This enhancement improves the estimation of seismic moment rates and the SAR calculation process. FaultQuake outputs are provided in the format of OpenQuake engine input files to facilitate the PSHA process. We present a sample case study focusing on the PSHA of a region in southern Iran characterized by a substantial aseismic deformation to illustrate the practical application of FaultQuake in seismic hazard analysis. Peak Ground Acceleration (PGA) maps for 10% and 2% Probabilities of Exceedance (PoE) are plotted to compare PGAs with and without applying the FaultQuake algorithm. The results provide an enhanced view of the area’s hazard with mitigation of the overestimation, resulting in more representative hazard maps. The source codes of FaultQuake are available at the FaultQuake GitHub repository, contributing to the computer and geoscience community.

Non-linear seismic behavior of historic Adana Great Clock Tower

In this study, the non-linear seismic behavior of the historic Adana Great Clock Tower was investigated. The dynamic characteristics (natural frequencies and mode shapes) of the Clock Tower were determined by operational vibration tests. The finite element model of the Clock Tower was manually updated in the light of the experimentally obtained dynamic characteristics. For the updated finite element model, pushover analyses and non-linear time history analyses were performed. Incremental pushover analyses were performed in the x and y directions proportional to the first lateral displacement modes of the Clock Tower in the x and y directions. The time history analyses took into account the Adana-Ceyhan (1998) earthquake acceleration records. Each of the two horizontal components of the earthquake acceleration records was separately scaled according to the design spectrum defined in TBEC-2018 for earthquake levels with a 10 % and 50 % probability of exceedance in 50 years. These scaled earthquake acceleration records were applied to the structure. The two scaled horizontal components for the same earthquake level were rotated 90° and reapplied to the structure. As the response quantities, displacement, base shear force and plastic strains were evaluated.

Dynamic response and seismic vulnerability assessment of the near-fault steep slope

Existing field data demonstrates that structures situated near faults, such as slopes, dams, and bridges, are particularly vulnerable to damage. Near-fault ground motions exhibit marked disparities from their far-field counterparts, necessitating thorough investigation. This study conducts a comprehensive analysis of ground motion characteristics utilizing response spectra, Hilbert-Huang Transform (HHT), and marginal spectra to discern near-fault pulse-like ground motions (PLGMs), near-fault non-pulse-like ground motions (NPGMs), and far-fault ground motions (FFGMs). PLGMs distinguished by their heightened energy content, typically induce more pronounced amplification effects compared to NPGMs and FFGMs. Employing an efficacy criterion, optimal intensity measures (IMs) are identified and linked to dynamic response, referred to as engineering demand parameters (EDPs). Velocity-based IMs demonstrate substantial correlations across diverse ground motion types for both plastic volume ratio (PVR) and crest displacement (Dc). Lastly, probabilistic seismic demand models are leveraged to establish seismic vulnerability curves for steep slopes exposed to PLGMs and NPGMs. Notably, with Dc as the EDP, the exceedance probabilities associated with the three IMs under PLGMs generally surpass those under NPGMs. This underscores the propensity of PLGMs to trigger larger displacements in slopes, rendering them more prone to failure.