Hazard Assessment Model Research Articles

Basement Neo-Tectonics of Western Himalayas from Seismic and Gravity Data Perspective

An earthquake of magnitude (Mw) 4.3 occurring on April 6, 2024 near Sargodha (Mianwali NW Punjab, Pakistan) has been analyzed through waveform inversion to understand the subsurface geological structure. This shallow-depth (19 km) event represents a strike-slip faulting with a dextral sense of movement. Gravity data of the epicentral area depicts distinct anomalies representing two separate blocks showing an off-set in the same direction as determined by the seismic inversion validating modeling results. In our opinion, these structures represent second-order tectonics, potentially emerging as a response to the hindrance caused by the Sargodha High to southward movement of the Himalayan deformation front. Alternatively, R-shears associated with the western boundary of the Indian plate could provide another explanation for such strike-slip mechanism events. Crustal shortening along the deformation front is being accommodated through aseismic slip along a viscous décollement in the Salt Range and seismic slip within the brittle basement rocks of the Sargodha region as represented by the analyzed seismic event. This dual process plays a key role in shaping the tectonic features in study area. Detailed studies of small to moderate seismic events can help in delineating the subsurface seismogenic structures for development of better seismo-tectonic model for realistic seismic hazard assessment in the region.

Knowledge graph-driven mountain railway alignment optimization integrating karst hazard assessment

Karst hazard is a considerable threat that should be considered in railway alignment design for mountainous regions with dense water systems. Nevertheless, alignment design principles in karst regions have not been systematically studied. Moreover, a quantitative karst hazard assessment model is currently lacking for automated alignment optimization. To solve the above problems, based on the analyses of karst inducing factors and hazard representation, the railway alignment design principles in karst regions are summarized through an event tree. A highly-coupled knowledge graph (called KaRAD-KG) modeling method is proposed. Then, a bi-objective alignment optimization model considering railway construction cost and karst hazard (mainly including hazard components of synclinal karst, anticlinal karst and karst depression) is constructed. To solve the optimization model, a knowledge-driven distance transform algorithm incorporating a karst hazard assessment method and a multicriteria tournament decision method is customized. Finally, the application in a real-world case indicates that the proposed method can generate an alignment which reduces construction cost by 3.39 % and karst hazard by 18.73 % compared to the best manually-designed alternative, which verifies the effectiveness of this method for assisting actual railway alignment design in a karst-dense mountainous region.

Spatial analysis and soft computational modeling for hazard assessment of potential toxic elements in potable groundwater

Swiftly increasing population and industrial developments of urban areas has accelerated the worsening of the water quality in recent years. Groundwater samples from different locations of the Doon valley, Garhwal Himalaya were analyzed to measure concentrations of six potential toxic elements (PTEs) viz. chromium (Cr), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), and lead (Pb) using Inductively Coupled Plasma Mass Spectrometer (ICP-MS) with the aim to study the spatial distribution and associated hazards. In addition, machine learning algorithms have been used for prediction of water quality and identification of influencing PTEs. The results inferred that the mean values (in the units of µg L−1) of analyzed PTEs were observed in the order of Mo (1.066) > Ni (0.744) > Pb (0.337) > As (0.186) > Cr (0.180) > Cd (0.026). The levels and computed risks of PTEs were found below the safe limits. The radial basis function neural network (RBF-NN) algorithms showed high level of accuracy in the predictions of heavy metal pollution index (HPI), heavy metal evaluation index (HEI), non-carcinogenic (N-CR) and carcinogenic (CR) parameters with determination coefficient values ranged from 0.912 to 0.976. However, the modified heavy metal pollution index (m-HPI) and contamination index (CI) predictions showed comparatively lower coefficient values as 0.753 and 0.657, respectively. The multilayer perceptron neural network (MLP-NN) demonstrated fluctuation in precision with determination coefficient between 0.167 and 0.954 for the prediction of computed indices (HPI, HEI, CI, m-HPI). In contrast, the proficiency in forecasting of non-carcinogenic and carcinogenic hazards for both sub-groups showcased coefficient values ranged from 0.887 to 0.995. As compared to each other, the radial basis function (RBF) model indicated closer alignments between predicted and actual values for pollution indices, while multilayer perceptron (MLP) model portrayed greater precision in prediction of health risk indices.

Size-frequency distribution of submarine mass movements on the Palomares continental slope (W Mediterranean)

In this work, over 3620 km2 from the Palomares continental slope, which is located in the W. Mediterranean Sea, was analysed to quantify the impact of recent mass movements on this margin. A total of 936 landslides were identified, mapped and characterised by defining several morphometric variables that outline the accumulated impact of landslides equivalent to 918 km2 and 10.34 km3 of eroded sediment on the continental slope. The smallest event area was 0.0014 km2, whereas the largest event area was 32.48 km2. Smaller scars with a higher headwall gradient tend to dominate when the environment is steeper, and major mass movements are located on open slopes and structural highs. However, the slight or null correlations between variables indicate that a wide range of sizes may occur on any slope gradient and at any depth.The Palomares continental slope is intensively affected by mass movements. Compared with other passive margins (e.g., the U.S. Atlantic continental margin), landslides mobilised a limited amount of sediment, although it is comparable to other Mediterranean areas where small- to moderate-sized events are characteristic.The cumulative size distribution can be defined by a power-law function that describes events larger than 0.7 km2 with an exponent of α = 1.269. These results are consistent with those of other published inventories, including onshore cases. This result allows us to assume that the scale-invariant properties of the events are mapped. Scale-invariant properties can be explained by different models; self-organised criticality (SOC) is probably the most assumed by the scientific community, although alternative models may be nominated. Each model has important implications in terms of the landslide distribution and long-term landslide history of any slope. Alternative scenarios, such as submarine slopes, with more precise landslide inventories may contribute to new hazard assessment models that consider scaling exponents derived from size–frequency distributions.

Recent and active faulting along the exposed front of the Northern Apennines (Italy): New insights from field and geochronological constraints

Establishing genetic links between active shallow faulting and deep seismogenic sources is challenging, especially in areas where seismogenic faults lack clear and readily interpretable geological evidence at the surface. The architecture of the Pedeapenninic margin of the Northern Apennines (Italy) reflects a regional-scale and complex NE-verging blind thrust system, which is dissected by transpressive/transtensive faults resulting from active NE-SW orogenic compression. The local geological framework is defined by allochthonous ocean-derived units resting atop Pliocene-to-present successions exposed along the Northern Apennines margin and to the NE of it, while the innermost chain sector mostly contains Adria-related units. We present results from field structural-geological investigations to identify and characterize potential active faults along the margin. There, the Pliocene-to-present units are faulted and folded, indicating that tectonic activity is still on-going, thus contributing to the local seismic hazard. Top-to-the NE and SW normal faults are common in the area and deform the Pliocene-to-present succession together with NE-SW strike-slip and transpressional/transtensional faults. Based on field evidence, we define four potentially active thrust segments affecting Middle Pleistocene to Holocene deposits exposed along the margin. Calcite U-Th dating on samples from faults extend the most recent datable tectonic activity back to the Middle Pleistocene. Paleostress analysis from inversion of fault-slip data from the most recent identified striated fault planes constrains a NE-SW shortening direction parallel to the Apennines regional migration direction. A distinct but coaxial extensional stress regime, recorded by structures measured within Plio-Pleistocene formations, was also identified. Our results offer a sound starting point for future investigations aimed at improving our understanding of active and seismogenic faulting in the area, so as to create robust Probabilistic Seismic and Fault Displacement Hazard Assessment (PSHA and PFDHA) models that can implement refined seismic hazard maps benefitting from structural-geological deterministic inputs in addition to the classic seismological constraints.

Real-time hazard assessment of maize based on the chilling injury process —— Using a standard curve to establish a daily cumulative assessment method

Cold damage caused by low temperatures is known as chilling injury (CI), and it has consistently been one of the primary meteorological disasters affecting maize. With ongoing global climate change, the issue of chilling injury is becoming more prominent, exhibiting new characteristics and presenting new challenges. Consequently, understanding the disaster process and conducting a more refined real-time chilling injury identification have become significant challenges. In this study, we divided maize planting areas into seven maturity types based on the accumulated temperature, constructed a standard curve of the daily accumulated temperature from 1991 to 2020, proposed real-time identification indicators based on the CI process, and developed a real-time CI hazard assessment model. The results indicated that the model can capture independent CI events and rapidly determine the location, intensity, duration and scope of CIs, thereby providing a basis for accurately understanding the impact of chilling injury and taking timely countermeasures. The combination of accumulated temperature standard curves for seven maturity types of maize and the CI curve was used to construct the CI daily scale identification indicator, ΔEAT. Judgment thresholds for the CI identification indicators at various maturity levels were obtained by correlating them with historical disaster data. The frequency and intensity of maize CI gradually increased from the extremely late-maturing zone to the extremely early-maturing zone, with the seeding and emergence periods being the peak periods for CI. The spatiotemporal evolution characteristics of the three different degrees of CI events in 1992, 2004, and 2017 were consistent with the historical disaster records. Northeastern Inner Mongolia and most of Heilongjiang were found to be high-hazard areas for maize CIs. The constructed daily CI identification indicators can accurately and rapidly identify maize CIs, providing practical and targeted guidance for combating these injuries.

Bridging the gap: Innovative human-based in vitro approaches for nanomaterials hazard assessment and their role in safe and sustainable by design, risk assessment, and life cycle assessment

The application of nanomaterials in industry and consumer products is growing exponentially, which has pressed the development and use of predictive human in vitro models in pre-clinical analysis to closely extrapolate potential toxic effects in vivo. The conventional cytotoxicity investigation of nanomaterials using cell lines from cancer origin and culturing them two-dimensionally in a monolayer without mimicking the proper pathophysiological microenvironment may affect a precise prediction of in vitro effects at in vivo level. In recent years, complex in vitro models (also belonging to the new approach methodologies, NAMs) have been established in unicellular to multicellular cultures either by using cell lines, primary cells or induced pluripotent stem cells (iPSCs), and reconstituted into relevant biological dimensions mimicking in vivo conditions. These advanced in vitro models retain physiologically reliant exposure scenarios particularly appropriate for oral, dermal, respiratory, and intravenous administration of nanomaterials, which have the potential to improve the in vivo predictability and lead to reliable outcomes. In this perspective, we discuss recent developments and breakthroughs in using advanced human in vitro models for hazard assessment of nanomaterials. We identified fit-for-purpose requirements and remaining challenges for the successful implementation of in vitro data into nanomaterials Safe and Sustainable by Design (SSbD), Risk Assessment (RA), and Life Cycle Assessment (LCA). By addressing the gap between in vitro data generation and the utility of in vitro data for nanomaterial safety assessments, a prerequisite for SSbD approaches, we outlined potential key areas for future development.

Analysis of Magnitude–Frequency Distribution of Earthquakes in the Sichuan Basin, Southwest China

Abstract The Sichuan basin is characterized by underdeveloped active geological structures and low seismic activities. In recent years, an increase in seismicity has been observed in the southern Sichuan basin, which includes small earthquakes induced by water injection during shale gas exploitation and M ≥ 5.0 earthquakes, causing unexpected damage and casualties. This necessitates a comprehensive analysis of the magnitude–frequency distribution of the current seismicity. In this study, we examined the current seismicity using instrumental earthquake records at different periods and estimated the temporal and spatial variations in b-value. We found that the b-values widely vary in the Sichuan basin and analyzed the spatial differences in b-value in seven different earthquake clusters. The results show that the intense fault movement on the western boundary of the Sichuan basin might be the main controlling factor of the increased seismicity in the southern basin instead of being induced by water injection. Furthermore, earthquakes in the Sichuan basin follow the same size distribution as the events on the boundary faults and the Sichuan–Yunnan rhombus active block. Thus, it is appropriate to consider the magnitude–frequency distribution of earthquakes for the Sichuan basin and the Sichuan–Yunnan rhombus block together rather than separately. These results may help improve the recurrence models in probabilistic seismic hazard assessment of the region.

Hazard assessment of rice cold damage based on energy balance in paddy field

Cold stress seriously affects rice yield in Northeast China and, as a result of climate change, there are new trends in the characterization of cold damage. Accurate simulation of water temperature in paddy fields and assessment of cold damage hazard can contribute to improving the accuracy of agrometeorological disaster risk assessment in the context of climate change. Hence, in this study, first, we simulated the water temperature of paddy fields based on the paddy field energy-balance model and evaluated the simulation results. Subsequently, a composite cooling-degree-day (CCDD) indicator for rice fields was constructed based on water and air temperatures to characterize cold stress and identify cold damage events. Finally, a cold damage hazard-assessment model was constructed for rice based on the frequency and intensity of cold damage to assess cold damage hazard in Northeast China from 1979 to 2018. The results showed that the root-mean-square error between simulated and observed water temperatures of the rice field energy-balance model was 0.45, which means that the model accurately simulated the water temperature of the rice field on a large spatial scale. In 2004, cold damage to late-maturity rice accounted for 30% of the total study area, which was one of the years with most severe cold damage in late-maturity rice-growing areas thus far in the 21st century. Water temperature affected 76.4% of the area in 2004, and was related to radiation and saturated vapor pressure. The low-hazard areas accounted for more than 20% in 2009–2018, and the high-hazard areas were still higher than 10%, indicating that the threat of cold damage in the northeastern region of China remained severe as the climate has become increasingly warm.

Application of different earthquake-induced landslide hazard assessment models on the 2022 Ms 6.8 luding earthquake

Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model’s prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment.

Near Pan-Svalbard permafrost cryospheric hazards inventory (SvalCryo)

Cryospheric hazards - in this case, thaw slumps (TS) and thermo-erosion gullies (TEG) - are phenomena typical of permafrost-dominated landscapes. Open datasets informing about their spatial, temporal and size distributions in the Arctic are still uncommon, as opposed to the systematic availability of this information for geomorphic processes in mid- to low- latitudes. To date, only the most populated region of Svalbard was covered with TS and TEG inventories. Here, we extend the respective information to most of the Archipelago, totalling 8491 polygons, out of which 3679 are TSs and 4812 are TEGs. These have been manually mapped from aerial photographs throughout Svalbard across the 14 largest ice-free regions. The SvalCryo inventory is highly relevant as the Arctic environment undergoes alarming changes in response to global warming. The idea behind the two inventories is to support the geoscientific community in the quest to evaluate the environmental response to climate change, by creating a baseline for change monitoring, and ultimately to serve as basis for susceptibility, hazard and risk assessment models.

Research on landslide hazard assessment based on improved analytic hierarchy process optimizing multiple rainfall indicators

Rainfall is the key factor that leads to landslide, so this study introduces multiple rainfall indexes to optimize the rainfall model in view of the single evaluation index of the rainfall model for landslide hazard assessment. In this study, Xiangxiang City of Hunan Province was selected as the study area, and eight types of susceptibility assessment factors including slope, aspect, elevation, normalized vegetation index (NDVI), road, fault, lithology and land use were extracted. By analyzing the characteristics of local rainfall, six types of rainfall induced assessment factors were selected for hazard assessment of the study area. The two types of evaluation factors were substituted into the improved AHP and RF combined weighting models respectively to obtain the susceptibility zoning map and rainfall induced model of the study area, and finally superimposed to obtain the hazard zoning map of the study area.Using ROC curve and hazard zoning in the studied area test results, the results show that:The AUC value of the multi-rainfall index is 17.7% higher than that of the single rainfall index, and the AUC value of the improved AHP is 6% higher than that of the traditional AHP method. It is verified by the disaster points on the day of extreme rainfall in the study area, and the actual occurrence of the disaster points is basically consistent with the hazard evaluation and zoning of the multi-rainfall index. Therefore, the rainfall model of landslides is optimized by using multiple rainfall indexes, which significantly improves the rationality of landslide hazard assessment.The study of multiple rainfall induced indicators can fill the knowledge gap in the current field, provide new insights and understanding for the field, and provide assistance for predicting and preventing landslides in related areas.

An integrated Petri net-pseudo bond graph model for nuclear hazard assessment

A pseudo-bond graph is presented to model the heat transferred from the fuel rods to the coolant via its cladding in a generic nuclear reactor case study. Simulations performed using this model are used to explore the temperatures of the core components under ordinary and emergency scenarios, considering various conditions of coolant supply and reactor power output. The model is combined with a timed stochastic Petri net to produce a hybrid model, in which the reactor operation and fault status is determined by the Petri net and fed into the bond graph to examine the resulting impact on core temperatures, which in turn are fed back into the Petri net process. The results predict the distribution of the reactor operational durations before a disruption occurs. The model provides the temperature profiles attained by the cladding and fuel components, indicating a low probability of dangerous temperatures.

Dynamic evolution characteristics and hazard assessment of compound drought/waterlogging and low temperature events for maize

Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001–2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011–2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security.

Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region

The Red Sea region, situated between the Arabian and African Plates, experiences significant seismic activity due to its tectonic dynamics, with earthquakes ranging from minor to potentially destructive events. This study aims to develop smoothed seismicity models for the region by using an enhanced seismic catalog specific to the Red Sea. This facilitates a detailed spatial and temporal analysis of seismic events, focusing on seismic source characterization essential for probabilistic seismic hazard assessments. A rigorous declustering method excludes foreshocks and aftershocks, focusing on independent seismic events. The analysis uses a spatial grid (0.1∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} cells in latitude and longitude) to determine seismic event rates, which are then refined using various smoothing techniques. Special attention is given to seismic activity within 0–35 kms of depth, leading to distinct rate models that inform urban development and seismic hazard mitigation strategies in the Red Sea area. These models are crucial for improving resilience, safety, and informed decision-making for urban planning and disaster preparedness, addressing the challenges posed by the region’s tectonic and seismic complexities.

Predictive Geogenic Radon Potential (P-GRP): A novel approach for comprehensive hazard assessment and risk modeling in subsurface environment

Geogenic radon potential (GRP) is traditionally used for mapping radon-prone areas. However, this has challenges in the accurate assessment of radon risk because of limitations such as oversimplified soil measurements and lack of geological profiles. This study presents predictive geogenic radon potential (P-GRP), integrating geological characterization and advanced modeling for the emanation and transport of radon in the subsurface environment. Seoul, South Korea, was selected as the research area for the evaluation of hazards using P-GRP, while subway station A was selected for the assessment of indoor health risks. The geology was characterized by the layers of bedrock and soil using uranium contents and porosity. The emanation of radon was modeled considering the radioactive decay chain of uranium and the pore structures. The vertical transport of radon was modeled considering the porosity variation within geological media, which was used for the calculation of P-GRP. Without loss of continuity, the P-GRP map was constructed by calculating P-GRP at a specific depth over the Seoul area. The calculation of P-GRP in the case of subway station A demonstrates that the radon concentration in the bedrock at the platform depth was expected to be 382 million Bqm−3. The indoor radon risk was calculated using the P-GRP by coupling the vapor intrusion process. This presented a high cancer risk for the employees as well as commuters. The P-GRP map of Seoul demonstrated higher hazards in granite zones compared to banded gneiss zones. These results have demonstrated that the P-GRP could be a novel and promising approach for assessing hazard and risk by geogenic radon during subsurface development.

Landslide hazard assessment of the fault zone considering the fault effect: a case study of the Lixian–Luojiabu fault zone in Gansu Province (China)

The earthquake landslide hazard assessment method is mainly based on the traditional Newmark model. However, when the landslide hazard assessment is carried out along the fault zone, the calculated results are often different from the actual situation because the influence of fault effect is not fully considered. Therefore, how to construct a landslide hazard assessment model suitable for the fault zone is a technical problem to be solved by researchers. Taking the Lixian–Luojiabu fault zone in Gansu Province in China as the study area, this paper put forward the concept of fault effect correction coefficient exploringly, systematically studied the relative distance relationship between the landslide and fault zone, and the relative position relationship between landslide and upper and lower sides of the fault zone. The value table of the fault effect correction coefficient along the Lixian–Luojiabu fault zone was established, and the corresponding distribution map of the fault effect correction coefficient was drawn. Based on this, an improved Newmark model for the landslide hazard assessment along the fault zone was constructed. On the basis of systematic analysis of the slope and engineering geological rock group in the study area, the traditional Newmark model and improved Newmark model considering fault effects were used, respectively, to carry out the earthquake landslide hazard assessment under the condition of 10% exceeding probability in 50 years, and the ROC curve and Kappa coefficient methods were used to compare and analyze the evaluation results. The results showed that the AUC value and Kappa coefficient of the danger area obtained by the improved model with the Newmark model were 0.841 and 0.822, respectively, which were significantly higher than the calculated values of the traditional Newmark model, indicating that the model had a good improvement effect. The Newmark improved model, considering the fault effect, fully considered the influence of distance from the fault zone and fault upper and lower side effects, and the research results can provide a new reference for the landslide hazard assessment along the fault zone.

Index construction and real-time hazard assessment of rice sterile-type chilling injury process in Northeast China

Rice is a warm-temperature crop, and sterile-type chilling injuries may be induced by acute periods of low-temperature weather during the reproductive growth period. This study considered the daily mean and minimum temperatures in Northeast China from 1979 to 2018 as influencing factors to construct a process-based rice accumulated intensity index (PARII) and hazard assessment model based on known crop disaster periods, analyzed the spatiotemporal distribution characteristics of chilling injury during the rice growth period (booting and flowering stages), used solar-induced chlorophyll fluorescence (SIF) and yield fluctuation data to calculate the adaptability of the hazard assessment model, and selected two typical events of disaster processes to verify the proposed real-time hazard assessment model. The change in the cumulative intensity index of rice sterile-type chilling injury indicated an increasing trend in chilling injury in the rice booting stage . Based on the probability density static hazard and cumulative probability dynamics, the results of real-time hazard analysis showed that the chilling injury hazard area was significantly expanded compared with the sterile-type chilling injury during the booting and flowering periods. The hazard value was greater than 0.2 in central and southeastern Heilongjiang, and the static hazard index was significantly negatively correlated with SIF and fluctuating yield, which indicated the chilling injury impacted rice yield and physiology. Regional-scale hazard levels were assessed by daily real-time hazard evaluation to highlight spatial heterogeneity in crop stress and damage. The results of this study provide a scientific basis for real-time risk assessment and management of chilling injury in rice crops and may support future adaptation strategies for addressing risks associated with severe low-temperature events.

Constraining Between-Event Variability of Kinematic Rupture Scenarios by Empirical Ground-Motion Model: A Case Study in Central Italy

ABSTRACT The region of central Italy is well known for its moderate-to-large earthquakes. Events such as 2016 Mw 6.2 Amatrice, generated in the shallow extensional tectonic regime, motivate numerical simulations to gain insights into source-related ground-motion complexities. We utilize a hybrid integral–composite kinematic rupture model by Gallovič and Brokešová (2007) to predict ground motions for other hypothetical Amatrice fault rupture scenarios (scenario events). The synthetic seismograms are computed in 1D crustal velocity models, including region-specific 1D profiles for selected stations up to 10 Hz. We create more than ten thousand rupture scenarios by varying source parameters. The resulting distributions of synthetic spectral accelerations at periods 0.2–2 s agree with the empirical nonergodic ground-motion model of Sgobba et al. (2021) for central Italy in terms of the mean and total variability. However, statistical mixed-effect analysis of the residuals indicates that the between-event variability of the scenarios exceeds the empirical one significantly. We quantify the role of source model parameters in the modeling and demonstrate the pivotal role of the so-called stress parameter that controls high-frequency radiation. We propose restricting the scenario variability to keep the between-event variability within the empirical value. The presented validation of the scenario variability can be generally utilized in scenario modeling for more realistic physics-based seismic hazard assessment.

CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment): a new model for geo-hydrological hazard assessment at the basin scale

Abstract. This work presents the new model called CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment), a tool for geo-hydrological hazard evaluation. CRHyME is a physically based and spatially distributed model written in the Python language that represents an extension of the classic hydrological models working at the basin scale. CRHyME's main focus consists of simulating rainfall-induced geo-hydrological instabilities such as shallow landslides, debris flows, catchment erosion and sediment transport into a river. These phenomena are conventionally decoupled from a hydrological routine, while in CRHyME they are simultaneously and quantitatively evaluated within the same code through a multi-hazard approach. CRHyME is applied within some case studies across northern Italy. Among these, the Caldone catchment, a well-monitored basin of 27 km2 located near the city of Lecco (Lombardy), was considered for the calibration of solid-transport routine testing, as well as the spatial-scale dependence related to digital terrain resolution. CRHyME was applied across larger basins of the Valtellina (Alps) and Emilia (Apennines) areas (∼2600 km2) which have experienced severe geo-hydrological episodes triggered by heavy precipitation in the recent past. CRHyME's validation has been assessed through NSE (Nash–Sutcliffe efficiency) and RMSE (root mean square error) hydrological-error metrics, while for landslides the ROC (receiver operating characteristic) methodology was applied. CRHyME has been able to reconstruct the river discharge at the reference hydrometric stations located at the outlets of the basins to estimate the sediment yield at some hydropower reservoirs chosen as a reference and to individuate the location and the triggering conditions of shallow landslides and debris flows. The good performance of CRHyME was reached, assuring the stability of the code and a rather fast computation and maintaining the numerical conservativity of water and sediment balances. CRHyME has shown itself to be a suitable tool for the quantification of the geo-hydrological process and thus useful for civil-protection multi-hazard assessment.