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Amidst changing climatic conditions, accurately predicting reservoir inflows in an extreme event is challenging and inevitable for reservoir management. This study proposed an innovative strategy under such circumstances through rigorous experimentation and investigations using 18 years of monthly data collected from the Huai Nam Sai reservoir in the southern region of Thailand. The study employed a two-step approach: (1) isolating extreme and normal events using quantile regression (QR) at the 75th, 80th, and 90th quantiles and (2) comparing the forecasting performance of individual machine learning models and their combinations, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Multiple Linear Regression (MLR). Forecasting accuracy was assessed at four lead times—3, 6, 9, and 12 months—using ten-fold cross-validation, resulting in 16 model configurations for each forecast period. The results show that combining quantile regression (QR) to distinguish between extreme and normal events with hybrid models significantly improves the accuracy of monthly reservoir inflow forecasting, except for the 9-month lead time, where the XG model continues to deliver the best performance. The top-performing models, based on normalized scores for 3-, 6-, 9-, and 12-month-ahead forecasts, are XG-MLR-75, RF-XG-80, XG-75, and XG-RF-75, respectively. Another crucial finding of this research is the uneven decline in prediction accuracy as lead time increases. Notably, the model performed best at t + 9, followed by t + 3, t + 12, and t + 6, respectively. This pattern is influenced by model characteristics, error propagation, temporal variability, data dynamics, and seasonal effects. Improving the accuracy and efficiency of hybrid model forecasting can greatly enhance hydrological operational planning and management. [ABSTRACT FROM AUTHOR]

Global temperatures were exceptionally high in 2023/24. Every month from June 2023 to June 2024 set a new record, and September shattered the previous record by 0.5 $0.5$°C. The 2023 annual average approached 1.5 $1.5$°C above pre‐industrial levels. This results from both long‐term warming and internal variability, with the occurrence of an El Niño episode. However the amplitude of the 2023/24 anomalies was remarkable and surprised the scientific community. Here we analyze the rarity of 2023/24 global temperatures from a climate perspective. We show that a 'normal' year 2023 would have roughly equaled the previous annual record, and that the most extreme events of 2023/24 rank among the most extreme since 1940. Our analysis suggests that the 2023/24 event can be reconciled with the long‐term trend and an intense, but not implausible, peak of internal variability. Plain Language Summary: 2023 was the warmest year on record at global scale, and early 2024 has continued to break records. This remarkable episode has received a great deal of attention from the general public and the scientific community. It is well established that it is linked to the long‐term global warming and the occurrence of an El Niño episode, but some temperature anomalies appeared so high, shattering previous records, that several scientists suggested that global warming may have been underestimated, which would have serious implications for future projections. Here we take a step back from the 2023/24 event, precisely quantify its rarity and compare it with other hot years. Using climate monitoring and extreme event attribution methods, we first show that at the current rate of warming, a 'normal' year 2023 would have equaled the 'old' record of 2016, even without any help of El Niño. We also find that the most extreme events of 2023/24 are among the most extreme of the entire record, but remain comparable with some past events. Our analysis thus suggests that the 2023/24 event is extreme but not incompatible with current estimates of global warming. Key Points: At the current rate of global warming, a normal year 2023 would have equaled the record of 2016, without any help of El NinoThe most extreme anomalies of 2023/24 rank among the most extreme of the entire record since 1940The 2023/24 heat can be reconciled with current estimates of global warming and an extreme but not implausible peak of internal variability [ABSTRACT FROM AUTHOR]

In dynamical systems, events that deviate significantly from usual or expected behavior are referred to as extreme events. This paper investigates the mechanism of extreme event generation in a 3D jerk system based on a generalized memristive device. In addition, regions of coexisting parallel bifurcation branches are explored as a way of investigating the multistability of the memristive system. The system is examined using bifurcation diagrams, Lyapunov exponents, time series, probability density functions of events, and inter-event intervals. It is found that extreme events occur via a period-doubling route and are due to an interior crisis that manifests itself as a sudden shift from low-amplitude to high-amplitude oscillations. Multistability is also identified when both control parameters and initial values are modified. Finally, an analog circuit based on the memristive jerk system is designed and experimentally realized. To our knowledge, this is the first time that extreme events have been reported in a memristive jerk system in particular and in jerk systems in general. [ABSTRACT FROM AUTHOR]

Estuarine phytoplankton communities are acclimated to environmental parameters that change seasonally. With climate change, they are having to respond to extreme weather events that create dramatic alterations to ecosystem function(s) on the scale of days. Herein, we examined the short term (<1 month) shifts in phytoplankton communities associated with four pulse disturbances (Tax Day Flood in 2016, Hurricane Harvey in 2017, Tropical Storm Imelda in 2019, and Winter Storm Uri in 2021) that occurred in Galveston Bay (TX, USA). Water samples collected daily were processed using an Imaging FlowCytobot (IFCB), along with concurrent measurements of temperature, salinity, and chlorophyll-a. Stronger storm events with localized heavy precipitation and flooding had greater impacts on community composition, increasing diversity (Shannon–Weiner and Simpson Indices) while a cold wave event lowered it. Diatoms and dinoflagellates accounted for the largest fraction of the community, cyanobacteria and chlorophytes varied mostly with salinity, while euglenoids, cryptophytes, and raphidophytes, albeit at lower densities, fluctuated greatly. The unconstrained variance of the redundancy analysis models pointed to additional environmental processes than those measured being responsible for the changes observed. These findings provide insights into the impact of pulse disturbances of different magnitudes, durations, and timings on phytoplankton communities. [ABSTRACT FROM AUTHOR]

AbstractExtreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change may affect the spatial extent of precipitation extremes, whose investigation often directly relies on simulations of precipitation from climate models. Here, we use a different strategy to investigate how future changes in spatial extents of precipitation extremes differ across climate zones and seasons in two river basins (Danube and Mississippi). We rely on observed precipitation extremes while exploiting a physics-based average-temperature covariate, enabling us to project future precipitation extents based on projected temperatures. We include the covariate into newly developed time-varying r-Pareto processes using suitably chosen spatial risk functionals r. This model captures temporal non-stationarity in the spatial dependence structure of precipitation extremes by linking it to the temperature covariate, derived from reanalysis data (ERA5-Land) for model calibration and from bias-corrected climate simulations (CMIP6) for projections. Our results show an increasing trend in the margins, with both significantly positive or negative trend coefficients depending on season and river (sub-)basin. During major rainy seasons, the significant trends indicate that future spatial extreme events will become relatively more intense and localized in several sub-basins. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

This study investigates the behavior of the Izhikevich discrete neuron model across various parameter configurations. Bifurcation diagrams and Lyapunov exponents are utilized to examine the impact of these parameters on the behavior of the system. The study specifically identifies important parameter ranges in which the attractor undergoes a sudden expansion, displaying characteristics of extreme events. Within the system, two distinct categories of extreme events can be identified: rare occurrences of small probability events located in the tail of the probability distribution and Dragon-King (DK) events, which possess a high probability amplitude. DK events are verified through the use of the DK test. The research concludes by examining the practical ramifications of these findings. The significance of forecasting and controlling extreme events in intricate systems is underscored, along with the cruciality of identifying their happening. [ABSTRACT FROM AUTHOR]

Effective cash management is key in banking operations and has implications for cost control, customer service, and risk management. As transactions become more diverse, manual forecasting methods have become inadequate for accurate vault cash forecasting, which involves extensive data analysis. To address this challenge, the banking industry has adopted FinTech tools based on big data and deep learning for various client services. These methods are generally accurate but perform poorly in cases with extreme events, for which data are scarce. In this study, we propose a time series prediction model with long short-term memory and an attention mechanism that effectively predicts the presence of extreme values. We applied extreme value theory to define the extreme value loss for extreme situations and use a sliding window to process time series data. The enhanced extreme value loss function in our model yields improved prediction accuracy for time series data. We evaluated the proposed model against previous methods in evaluation experiments on data from three branches of a commercial bank in Taiwan, where the vault cash data of each exhibited extreme values. The proposed model was highly accurate: it had a lower mean absolute percentage error and higher trend accuracy than competing methods on a majority of time series, and it was also more accurate in predicting extreme values in time series data. [ABSTRACT FROM AUTHOR]

The protection against a storm event provided by nourishment to Costa da Caparica beaches near Lisbon, Portugal, is investigated numerically with a two-dimensional-horizontal morphodynamic model able to generate and propagate the longer infragravity waves. The beach has a groyne field and a multi-typology backshore. The nourishment of 106 m3 of sand was placed at the beach face and backshore. Pre- and post-nourishment topo-bathymetric surveys of the beach, which suffers from chronic erosion, were performed under a monitoring program. The morphodynamics of the pre- and post-nourished beach when exposed to a simulated historically damaging storm event and the post-storm morphologies were compared to evaluate the efficacy of the nourishment. Results indicate that the lower surface level of the beach face and backshore of the pre-nourished beach induces a larger erosion volume. The nourishment prevented the extreme retreat of the shoreline that occurred during the storm in the pre-nourished beach and reduced the storm-induced erosion volume by 20%, thus protecting the beach effectively against the storm. The beach backshore typology (seawall vs. dune) exerts differential influences on the sandy bottom. As a result, multi-typology backshores induce alongshore variability in cross-shore dynamics. The backshore seawalls exposed to direct wave action cause higher erosion volumes and a larger cross-shore extension of the active zone. The most vulnerable alongshore sectors of the beach were identified and related to the mechanisms responsible for the erosion phenomenon. These findings strengthen the importance of sand nourishment for the protection and sustainability of beaches, particularly those with a seawall at the backshore, where storm events cause higher erosion. [ABSTRACT FROM AUTHOR]

The aim of this research is to introduce and investigate a novel entropy-based approach to market efficiency assessment that utilizes symbolic encoding procedure including volatility estimates. The proposed new method is compared with the existing symbolic time series procedures with two thresholds. Statistical analyses of symbol-sequence histograms are conducted. The main research question is whether extreme events influence stock market returns and informational efficiency measured by entropy. The findings of empirical experiments for real-data from selected financial markets confirm that the new approach is especially useful in assessing stock market informational efficiency as it allows to capture extreme changes in market returns, specifically during turbulent periods. [ABSTRACT FROM AUTHOR]

The storm Daniel and subsequent floods hit the Region of Thessaly (Greece) in early September 2023, causing extensive damage to the built environment (buildings, networks, and infrastructure), the natural environment (water bodies and soil), and the population (fatalities, injured, homeless, and displaced people). Additionally, the conditions and factors favorable for indirect public health impact (infectious diseases) emerged in the flood-affected communities. The factors had to do with infectious diseases from rodents and vectors, injuries, respiratory infections, water contamination, flood waste and their disposal sites as well as structural damage to buildings and the failures of infrastructure. The conditions that evolved necessitated the mobilization of the Civil Protection and Public Health agencies not only to cope with the storm and subsequent floods but also to avoid and manage indirect public health impact. The instructions provided to affected residents, health experts, and Civil Protection staff were consistent with the best practices and lessons learned from previous disasters. The emphasis should be on training actions for competent agencies, as well as education and increasing the awareness of the general population. Non-structural and structural measures should be implemented for increasing the climate resilience of infrastructures including the health care systems within a One Health approach. [ABSTRACT FROM AUTHOR]

The studies on progressive collapse have primarily focused on threat-independent methods, wherein a sudden column removal is suggested in codes. However, a real collapse scenario is necessarily threat-dependent. Focusing on blast- and impact-induced progressive collapses, the current study considers cases in which damage is concentrated in a single member, without resulting in complete column loss. It is demonstrated that the progressive collapse performance under specific threats can be better or worse compared to that of sudden column removal. Thus, dynamic column removal does not necessarily guarantee the most critical scenario, as the response in a damaged system can sometimes exceed expectations. A simple analytical model is proposed to describe in detail the observed phenomena and emphasizes the development of catenary forces in the column under lateral extreme loading scenarios. The results provide a deeper insight into the progressive collapse performance of frame systems and the involved member-level resisting mechanisms.

Abstract This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.

Abstract A Mediterranean cyclone is a weather phenomenon capable of producing extremely severe conditions, including heavy rainfall and strong winds. Between March 24 and 26, 2023, a cyclone passed along the western Egyptian Mediterranean coast, spanning three days. This paper aims to investigate the cyclone's impact on wave characteristics, focusing particularly on simulating changes in the energy transported from wind to waves during its passage, which constitutes the core objective of this study. The research methodology involved collecting meteorological and hydrodynamic data over five days from March 23 to 27, 2023, utilizing databases of the Bologna Limited Area Model (BOLAM) and the General Bathymetric Chart of the Oceans (GEBCO). This data, combined with field data for model calibration and validation, was analyzed using the Simulating the WAves Nearshore (SWAN) model packaged within the Delft 3D hydrodynamical model, integrated with other data manipulation tools. (SWAN) demonstrated the ability to simulate energy transport during extreme weather events along the coastal area with high resolution, up to 500 m. The results indicate a significant increase in significant wave height, reaching up to 2.5 m, and disturbances in wind direction, with velocities exceeding 10 m per second. These conditions pose risks to the infrastructure in some cities along the study area and have severe impacts on coastal communities. A notable finding from the simulations is the excess energy transport, which reached up to 12,000 watts per meter over the sea surface during the cyclone. Furthermore, calibration and validation results affirm the (SWAN) model's capability to accurately study wave characteristics.

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges. [ABSTRACT FROM AUTHOR]

The present study investigates different combinations and methods for estimating the extreme Total Water Level (TWL) and its implications for predicting flood extension caused by coastal storms. This study analyses various TWL components and approaches and assesses how different methodologies alter flood predictions, with implications for warning systems and emergency responses. Using different combinations of individual TWL components, flood extension simulations were conducted using a hydrodynamic model in the Volano Beach area (Emilia-Romagna, Italy). A real coastal storm event was used as a reference for comparison. The findings indicate that the selection of individual TWL components and calculation methods significantly impacts flood extension predictions. The approaches, which involve calculating extreme values from a combined time series or the water level time series plus the extreme value of wave setup, yield the most realistic results, excluding the runup component. In comparison, the other combinations overestimate the flood. Incorporating hydromorphological models like XBeach could enhance the accuracy of runup estimations and improve the overall method reliability. Despite limitations such as runup estimation and the use of generic regional parameters, this study underscores the importance of the TWL combination selection in accurately predicting flood extents, emphasising the need for context-specific adaptations in environmental contexts. [ABSTRACT FROM AUTHOR]

The study of extreme events (EEs) in photonics has expanded significantly due to straightforward implementation conditions. EEs have not been discussed systematically, to the best of our knowledge, in the chaotic dynamics of a Fabry–Perot laser with optical feedback, so we address this in the current contribution. Herein, we not only find EEs in all modes but also divide the EEs in total output into two categories for further discussion. The two types of EEs have similar statistical features to conventional rogue waves. The occurrence probability of EEs undergoes a saturation effect as the feedback strength increases. Additionally, we analyze the influence of feedback strength, feedback delay, and pump current on the probability of EEs defined by two criteria of EEs and find similar trends. We hope that this work contributes to a deep understanding and serves as inspiration for further research into various multimode semiconductor laser systems. [ABSTRACT FROM AUTHOR]

Abstract Global temperatures were exceptionally high in 2023/24. Every month from June 2023 to June 2024 set a new record, and September shattered the previous record by 0.5°C. The 2023 annual average approached 1.5°C above pre‐industrial levels. This results from both long‐term warming and internal variability, with the occurrence of an El Niño episode. However the amplitude of the 2023/24 anomalies was remarkable and surprised the scientific community. Here we analyze the rarity of 2023/24 global temperatures from a climate perspective. We show that a ‘normal’ year 2023 would have roughly equaled the previous annual record, and that the most extreme events of 2023/24 rank among the most extreme since 1940. Our analysis suggests that the 2023/24 event can be reconciled with the long‐term trend and an intense, but not implausible, peak of internal variability.

The Yangtze River basin experienced record-breaking high temperatures in July–August 2022, leading the China Meteorological Administration to issue its first ever “red heat warning”. We use simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) of the Coupled Model Intercomparison Project 6 (CMIP6) to investigate the role of anthropogenic drivers in this extreme event. We have demonstrated that the strong Western Pacific Subtropical High (WPSH), attributed to internal variability, serves as the clear proximate driver for such extreme event, whether in the factual world or in the counterfactual world. When considering similar circulation patterns in 2022, the results show that anthropogenic forcing has contributed to the 2022-like heatwave by a factor about 7 compared to natural forcing under the present climate of the past 30 years. Specifically, the anthropogenic greenhouse gases made the event about 10 times more likely, while anthropogenic aerosols had negative effect. The results were similar but differed in exact contribution values when specific circulation regimes of 2022 were not considered. In general, global warming caused by anthropogenic activities has made extreme summer heatwaves far more frequent, especially in recent decades.

This study unravels the characteristics, mechanisms, and predictability of four consecutive record‐breaking heatwaves hitting North China in June and July 2023. The first three heatwaves primarily influenced the northern part of North China and were accompanied by consistent anticyclonic anomalies in the upper troposphere. The anomalous anticyclone was caused by the British–Baikal corridor teleconnection along the polar front jet, particularly during the second heatwave. In contrast, the fourth heatwave was induced by a distinct low‐pressure system, attributed to the Silk Road pattern along the subtropical jet. The presence of this low‐pressure system and its interaction with atmospheric rivers and local topography led to the foehn wind, further contributing to the rise in surface temperatures. Sub‐seasonal to seasonal models can effectively predict the occurrence of all heatwaves 2–5 days in advance despite underestimating the intensity. However, models exhibit limitations in providing reliable predictions when the lead time exceeds 2 weeks. Plain Language Summary: In the summer of 2023, North China experienced four consecutive extreme high‐temperature events, which are called heatwaves. This study investigates the main factors that cause the four events and shows how well the operational numerical models can predict the heatwaves. The first three heatwaves shared similar circulation, with high‐pressure systems controlling North China. This local circulation anomaly was related to an upstream quasi‐stationary wave train along the polar front jet. The fourth heatwave was associated with a low‐pressure system over North China and an upstream quasi‐stationary wave train along the subtropical jet. Moreover, predictions from operational models demonstrate their capability to forecast the occurrence of high temperatures 2–5 days ahead, with an underestimation in the intensity. However, models are not reliable when it comes to predicting heatwaves 2 weeks in advance. Key Points: North China witnessed record‐breaking heatwaves in June and July 2023, consisting of four sequential synoptic‐scale eventsThe first three and the last heatwaves are controlled by distinct local circulations induced by atmospheric wave trains across EurasiaS2S models capture the heatwave occurrences 2–5 days in advance, but predictive skill notably diminishes beyond 2 weeks [ABSTRACT FROM AUTHOR]

Dual-polarization radar often detects columnar regions of enhanced differential reflectivity (ZDR) extending vertically above the environmental 0 °C level. Indicative of supercooled liquid drops and wet ice particles lofted by strong updrafts, these ZDR columns are increasingly understood to be of use in predicting extreme rainfall. With the aim of achieving practical application of ZDR column measurements, this paper focuses on the relationship between the height of ZDR columns and rainfall intensity near the ground. All the data on ZDR columns analyzed in this study was collected from weather radar stations in Japan. The height of each column and rainfall rates at low levels were analyzed using an automated algorithm. A regression analysis result reveals peak column height to be positively correlated with maximum rainfall rate near ground level, and that rainfall intensity on the ground is likely to exceed 50 mm h-1 when radar identifies a ZDR column. Furthermore, extreme rainfall with an intensity of 180 mm h-1 or more is likely associated with a column over 3 km tall from the 0 °C level. These findings suggest that surveillance of ZDR columns can contribute to the reliability of very short-range forecasts or nowcasts as well as assist with the issue of early warnings of extreme rainfall and flash floods. [ABSTRACT FROM AUTHOR]

Shallow landslides, frequently triggered by extreme events such as heavy rainfall, snowmelt, or earthquakes, affect vast areas with remarkable density. In the immediate aftermath of such events, it becomes crucial to rapidly assess landslides distribution and pinpoint the most severely affected areas to prioritize damage assessments and guide field survey operations effectively. Once the emergency phase subsides, the attention can shift to enhancing the accuracy of landslide inventory. In this work, we introduce the two-phase methodology “PANDA”, the unsuPervised shAllow laNdslide rapiD mApping, for the low-cost mapping of the potential landslides, firstly in the emergency phase and then, with an improved version, in the post-emergency one. This approach utilizes variations in NDVI derived from Sentinel-2 satellite imagery and geomorphological filters. We applied PANDA to rainfall events in the northeastern Apennine range, Italy, occurred in May 2023, causing dramatic social and economic consequences for this mountain territory. Within just five days of obtaining Sentinel-2 post-event imagery, we produced a reliable, ready-to-use map covering a vast area (∼4000 km2). The map tested during emergency field mapping shows positive feedback. In the post-emergency phase, accuracy was enhanced using completely cloud-free imagery, a filter to identify false positives associated with land use changes, a higher resolution digital terrain model (DTM), and an iterative approach to optimize NDVI and slope thresholds. Potential landslide density related with rainfall, indicating that the most severely affected region attained a density of approximately 50 landslides/km2. Validation against an independent manual inventory based on high-resolution imagery demonstrated encouraging accuracy results from both inventories, with a noticeable increase in the F1 score for the post-emergency version.

Distribution systems are facing challenges in serving lifeline loads after extreme events. Network reconfiguration is a traditional and practical method for power supply restoration, which has strong but inflexible power transfer capabilities influenced by network topology. Multiple failures of utility power under extreme events will further limit the efficiency of network reconfiguration. Electric buses (EBs) can be utilized to achieve power supply considering their discharging capabilities as mobile storage devices. However, the mobility of EBs and the influences of transport systems must be carefully considered to enhance the resilience of distribution systems. Reconfiguration and EBs are complementary in terms of recovery capabilities and location flexibility, and more important loads can be recovered by the coordination between EBs and network reconfiguration. This paper proposes a coordinated restoration method for EBs and reconfigurations considering the influences of transport systems. The post-disaster restoration problem is formulated as a bi-level model, in which the network topology is optimized in the upper-level aiming at maximizing restoration loads through the main grid and EBs, while the traffic paths of all EBs are optimized with the goal of maximizing the restoration loads by the EBs in the lower-level considering time consumption and energy consumption during movement. The PSO and a genetic algorithm are used to solve the proposed bi-level optimization problem. Simulation studies are performed to verify the superiority of the proposed method.

Climate change is altering the severity and intensity of extreme weather events. Occupying microhabitats that buffer extreme weather may help species avoid harsh environmental conditions. We describe the thermal microclimate of Atlantic Puffin (Fratercula arctica) and Leach’s Storm-petrel (Hydrobates leucorhous) burrows and quantify whether burrows are thermal refuges during extreme cold weather events. We further test for the effect of weather conditions and burrow characteristics on nest microclimate and buffering capacity during extreme cold weather. We find that both species actively breed in burrow microclimates that are below their lower critical temperatures, which may impose significant thermoregulatory costs. However, burrows do act as thermal refuges because nests are kept 7.4–8.0 °C warmer than ambient temperatures during extreme cold weather events. Overall, external temperature and wind speed were strong drivers of burrow temperature, but burrow and habitat characteristics did not explain the variability in burrow buffering capacity during extreme cold weather. Our results suggest that burrows may provide a direct line of defence for seabird chicks against cold events. Given the complex responses of burrow microclimates to extreme events, quantifying how changes in environmental conditions will impact burrow-nesting seabirds in the future is key.

Abstract Background Forest ecosystems undergo significant transformations due to harvesting and climate fluctuations, emphasizing the critical role of seeding in natural regeneration and long-term structural preservation. Climate change further amplifies these dynamics, affecting phenology across species and regions. In Tierra del Fuego (Argentina), Nothofagus pumilio (lenga) forests represent the most important timber resource, and it is managed through different silvicultural strategies. This species demonstrates notable post-disturbance regeneration, yet seed fall exhibits significant variability, leading to variations in seed quality (e.g., viability). This study aims to assess fluctuations in N. pumilio seed quality, determine how it varies concerning forest management strategies, annual productivity, and the co-occurrence of climatic phenomena including El Niño-Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). Results Viable seeds represented 18.4% of the total, notably higher in unharvested than in managed areas. Conversely, empty seeds were more prevalent in harvested areas (> 75%). Seed quality exhibited significant differences across silvicultural treatments, except for insect-predated seeds, which had similar proportions across all areas, though dispersed retention showed higher predation. When considering years with varying production levels, high-production years favoured full and viable seeds, particularly in unharvested forests and aggregated retention, while low-production years saw reduced viability across all treatments. Quadratic models revealed that viability increased with seed production, where unharvested forests achieved the highest values. Climate variability influenced seed proportions, where ENSO+/SAM+ promoting more full and viable seeds, while ENSO–/SAM+ favoured non-predated seeds, especially in unharvested stands. Conclusions Seed quality varies among treatments and years with different levels of seeding. Variations in seed quality, linked to climatic events, influence seed viability. Seed quality plays a critical role in forest regeneration, ensuring a seedling bank for harvested stands to face climate variability. These findings are relevant for forest management and ecosystem services, considering the increasing climate variability and extreme events. Understanding these influences is crucial for Nothofagus pumilio forests' sustainability and global forest adaptation strategies.

The protection against a storm event provided by nourishment to Costa da Caparica beaches near Lisbon, Portugal, is investigated numerically with a two-dimensional-horizontal morphodynamic model able to generate and propagate the longer infragravity waves. The beach has a groyne field and a multi-typology backshore. The nourishment of 106 m3 of sand was placed at the beach face and backshore. Pre- and post-nourishment topo-bathymetric surveys of the beach, which suffers from chronic erosion, were performed under a monitoring program. The morphodynamics of the pre- and post-nourished beach when exposed to a simulated historically damaging storm event and the post-storm morphologies were compared to evaluate the efficacy of the nourishment. Results indicate that the lower surface level of the beach face and backshore of the pre-nourished beach induces a larger erosion volume. The nourishment prevented the extreme retreat of the shoreline that occurred during the storm in the pre-nourished beach and reduced the storm-induced erosion volume by 20%, thus protecting the beach effectively against the storm. The beach backshore typology (seawall vs. dune) exerts differential influences on the sandy bottom. As a result, multi-typology backshores induce alongshore variability in cross-shore dynamics. The backshore seawalls exposed to direct wave action cause higher erosion volumes and a larger cross-shore extension of the active zone. The most vulnerable alongshore sectors of the beach were identified and related to the mechanisms responsible for the erosion phenomenon. These findings strengthen the importance of sand nourishment for the protection and sustainability of beaches, particularly those with a seawall at the backshore, where storm events cause higher erosion.

Identifying the behavior of precipitation is one of the most important planning principles related to water resources. In this research, an attempt was made to analyze the trend of time changes in extreme rainfall profiles of the country by using the daily rainfall data of 3423 synoptic, climatology, and rain gauge stations for the period from 1970 to 2016 and by performing interpolation using the kriging method. Then, using percentile profiles (percentile less than 10, less than 25, 25 to 75, 75 to 90, and above 90) and regression analysis, changes in the frequency of member days of each of the percentile methods over time were calculated and mapped. The results showed that during the studied period, 86.6% of cells associated with days with the tenth percentile or less in the country had an increasing trend. On the other hand, the pixels associated with days with the 90th percentile and more have shown an increasing trend. Considering that the pixels with the 25th, 25th-75th percentiles (normal), and 75th percentile have shown a decreasing trend in terms of the number of days in their group, it can be concluded that the country's rainfall conditions and the days with rainfall are towards the limit values has moved and the possibility of drought or destructive floods has increased in the country.

Flood, drought, and frost may be disruptive events for agriculture. The subsidised crop insurance schemes are coping strategies that increase farms resilience to weather shocks and in fact the occurrence of extreme weather events and the level of subsidised crop insurance are correlated. Stronger evidence is found in Southern geographical areas, where drought (a major risking risk) is more frequent, and for spring-summer crops, that are less resilient to weather shocks. The article points at the need to reform extant policies to move toward a holistic approach for risk management.

Abstract This study unravels the characteristics, mechanisms, and predictability of four consecutive record‐breaking heatwaves hitting North China in June and July 2023. The first three heatwaves primarily influenced the northern part of North China and were accompanied by consistent anticyclonic anomalies in the upper troposphere. The anomalous anticyclone was caused by the British–Baikal corridor teleconnection along the polar front jet, particularly during the second heatwave. In contrast, the fourth heatwave was induced by a distinct low‐pressure system, attributed to the Silk Road pattern along the subtropical jet. The presence of this low‐pressure system and its interaction with atmospheric rivers and local topography led to the foehn wind, further contributing to the rise in surface temperatures. Sub‐seasonal to seasonal models can effectively predict the occurrence of all heatwaves 2–5 days in advance despite underestimating the intensity. However, models exhibit limitations in providing reliable predictions when the lead time exceeds 2 weeks.

In September 2020, Western North America was impacted by a highly anomalous meteorological event. Over the Pacific Northwest, strong and dry easterly winds exceeded historically observed values for the time of year and contributed to the rapid spread of several large wildfires. Nine lives were lost and over 5000 homes and businesses were destroyed in Oregon. The smoke from the fires enveloped the region for nearly two weeks after the event. Concurrently, the same weather system brought record-breaking cold, dramatic 24-h temperature falls, and early-season snowfall to parts of the Rocky Mountains. Here we use synoptic analysis and air parcel backward trajectories to build a process-based understanding of this extreme event and to put it in a climatological context. The primary atmospheric driver was the rapid development of a highly amplified 500 hPa tropospheric wave pattern that persisted for several days. A record-breaking ridge of high pressure characterized the western side of the wave pattern with a record-breaking trough of low pressure to the east. A notable anticyclonic Rossby wave breaking event occurred as the wave train amplified. Air parcel backward trajectories show that dry air over the Pacific Northwest, which exacerbated the fire danger, originated in the mid-troposphere and descended through subsidence to the surface. At the same time, dramatic temperature falls were recorded along the east side of the Rocky Mountains, driven by strong transport of high-latitude air near the surface.

In June 2020, a record-breaking Saharan dust storm, known as the "Godzilla" extreme event, caused significant dust transport from the Sahara Desert across the Atlantic Ocean to the United States. Based on satellite observations, the magnitude of aerosol optical depth (AOD) has consistently remained highest over the Atlantic Ocean for the past 18 years. This study uses satellite observations (including MODIS and CALIOP) and MERRA-2 reanalysis products to investigate the relationships between dust and marine clouds. During this extreme event, the concentration of AOD exhibits a synchronous anomaly with the cloud fraction (CF). Principal components analysis (PCA) results show that the enhanced temperature and specific humidity near the surface contribute the most to cloud development over the tropical Atlantic Ocean. Despite the reduced sensitivity of CF to aerosols, the semi-direct effect of dust can still play a crucial role during this extreme dust storm. We found that the presence of absorbing aerosols above the cloud layers warms the air, accompanied by an enhancement of surface moisture, thereby benefiting low-level cloud coverage. [ABSTRACT FROM AUTHOR]

The heat wave in late June of 2021 (PNW21) set new temperature records in the Pacific Northwest (PNW). In Lytton the highest temperature ever recorded in Canada was measured. Several studies have already explored this extreme event in detail, however, here we compare the atmospheric air mass transport and heating processes associated with this heat wave with the 34 other most extreme heat events in the same region during the period 1960–2021, using a long backtracking time of 25 days. We found significant differences in the heat waves. During PNW21 most of the air was coming from the Philippine Sea, with more than 40% of the air located south of 15°N, and anomalous advection of sensible and latent heat from the Tropics was the dominant cause of PNW21. The latent heat was efficiently converted into sensible heat by precipitation, which was unique, as most other extremes experienced net diabatic cooling. Plain Language Summary: At the end of June in 2021 a heat wave occurred over the Pacific Northwest (PNW) and led to catastrophic damage in the region, notably the destruction of the town of Lytton by a wild fire 1 day after a new temperature record for Canada was set there. Here we look at where the air during this event was coming from and compare the atmospheric mass transport to 34 other extreme heat events in this region. We found that during the heat wave in June 2021 most of the air was coming from the Philippine Sea where it took up large amounts of heat and moisture. This source region is deeper in the Tropics than for all other extreme events. At the same time, the Philippine Sea was anomalously warm. Thus, the air was warmer and moister than for all the other extreme events already 3 weeks before reaching the PNW. The energy associated with the high tropical moisture content was efficiently converted into heat by precipitation along the Meiyu‐Baiu front—a unique process not found for any other extreme heat event. Key Points: The air causing the heat wave in late June 2021 (PNW21) came from deep in the Tropics, more south and west compared to other eventsThree weeks prior to the event, the air was warmer and moister, indicating that advection of heat from the Tropics was an important driverCondensation in a warm conveyor belt caused heating for PNW21, while most other extreme events showed overall diabatic cooling [ABSTRACT FROM AUTHOR]

One fifth of the world’s population and critical infrastructures are close to the coast and regions of high-risk sea level rise elevation. The last decades have been characterized by increasing extreme events, including storm surges, flooding, coastal erosion, enhanced coastal vulnerability with associated livelihood, and economic losses. Nature-based engineering solutions are being adopted as sustainable solutions for helping existing technologies live their design life and providing climate change adaptation and resilience for coastal and riverine communities. This paper involves the investigation of nature-based eco-hydraulic soft coastal engineering to cultivate seaweed for coastal protection. In this context, the present study involves an advanced risk evaluation performed by conducting an extreme bore interaction with seaweed as a soft engineering coastal protection measure. The load reduction on the inland structure during extreme flooding conditions, incorporating seaweed, is addressed. The present study indicates that the load on inland structures can be reduced by as much as 14% in extreme flooding conditions in the presence of seaweed with two rows of seaweed, indicating the usage of seaweed as a part of coastal protection over existing site protection infrastructure for improved coastal mitigation.

The experiences from recent urban flooding events suggest that the conventional centralized detention systems based on detention size expansion with minimum costs need to accommodate the extreme flooding events exceeding their design capacity. This study investigated the resilience effects of decentralized detention systems to address extreme flooding events in urban areas. An integrated flood resilience measure that integrates the regional performance of sub-basins with a decentralized flood control system was proposed and applied to an urban watershed including five sub-basins. Hydrologic and hydraulic analysis to quantify the hydrologic responses and resilience of the watershed to flooding scenarios was performed using the Hydrological Modeling System and Geospatial River Analysis System of Hydrological Engineering Centre models. The results identified the best locations of distributed detention ponds and characterized the effects of the decentralization levels of the detention system on enhancing flood resilience to extreme events. Additionally, an increase in the resilience effects of the decentralized detention system was found in the cases of more extreme flooding events from the combined impacts of high-intensity rainfall and land cover change. The findings suggest insights into incorporating decentralization strategies into decision-making on detention systems to resiliently cope with extreme flooding events in urban watersheds. HIGHLIGHTS The best locations of single or distributed detention ponds were in upstream or midstream sub-basins rather than downstream sub-basins.; The increase in decentralization level resulted in reduced peak flow and flooding duration during high-intensity rainfall events.; Decentralized detention systems were resilient to extreme flooding events due to synergistic storage effects on the overall watershed.; The resilience effects of the decentralized system increased as the decentralization levels of detention systems, and extremity of flooding events increased.; The proposed resilience measure was useful in evaluating the resilience effects of decentralized detention systems over a wide range of flooding scenarios.;

The present study investigates different combinations and methods for estimating the extreme Total Water Level (TWL) and its implications for predicting flood extension caused by coastal storms. This study analyses various TWL components and approaches and assesses how different methodologies alter flood predictions, with implications for warning systems and emergency responses. Using different combinations of individual TWL components, flood extension simulations were conducted using a hydrodynamic model in the Volano Beach area (Emilia-Romagna, Italy). A real coastal storm event was used as a reference for comparison. The findings indicate that the selection of individual TWL components and calculation methods significantly impacts flood extension predictions. The approaches, which involve calculating extreme values from a combined time series or the water level time series plus the extreme value of wave setup, yield the most realistic results, excluding the runup component. In comparison, the other combinations overestimate the flood. Incorporating hydromorphological models like XBeach could enhance the accuracy of runup estimations and improve the overall method reliability. Despite limitations such as runup estimation and the use of generic regional parameters, this study underscores the importance of the TWL combination selection in accurately predicting flood extents, emphasising the need for context-specific adaptations in environmental contexts.

The study of extreme events (EEs) in photonics has expanded significantly due to straightforward implementation conditions. EEs have not been discussed systematically, to the best of our knowledge, in the chaotic dynamics of a Fabry–Perot laser with optical feedback, so we address this in the current contribution. Herein, we not only find EEs in all modes but also divide the EEs in total output into two categories for further discussion. The two types of EEs have similar statistical features to conventional rogue waves. The occurrence probability of EEs undergoes a saturation effect as the feedback strength increases. Additionally, we analyze the influence of feedback strength, feedback delay, and pump current on the probability of EEs defined by two criteria of EEs and find similar trends. We hope that this work contributes to a deep understanding and serves as inspiration for further research into various multimode semiconductor laser systems.

Abstract The heat wave in late June of 2021 (PNW21) set new temperature records in the Pacific Northwest (PNW). In Lytton the highest temperature ever recorded in Canada was measured. Several studies have already explored this extreme event in detail, however, here we compare the atmospheric air mass transport and heating processes associated with this heat wave with the 34 other most extreme heat events in the same region during the period 1960–2021, using a long backtracking time of 25 days. We found significant differences in the heat waves. During PNW21 most of the air was coming from the Philippine Sea, with more than 40% of the air located south of 15°N, and anomalous advection of sensible and latent heat from the Tropics was the dominant cause of PNW21. The latent heat was efficiently converted into sensible heat by precipitation, which was unique, as most other extremes experienced net diabatic cooling.

ABSTRACTGeomorphological field surveys and mapping have been carried out in a catchment of the Dolomites (eastern Italian Alps) as part of a research project aiming at the assessment of sediment availability for mass wasting in mountain environments. The study area is the Liera catchment which was severely affected by a high-intensity, regional-scale storm named ‘Vaia’ (27–30 October 2018), which induced relevant morphological changes, caused a major flood in the main stream and triggered debris flows in many tributaries. This contribution gives a snapshot of the present-day state of the study area to improve the understanding of such a landscape by characterizing the processes that control its geomorphological spatio-temporal dynamics and evolution.

The increasing prevalence of warmer trends and climate extremes exacerbate the population's exposure to urban settlements. This work investigated population exposure changes to mean and extreme climate events in different Agro‐Ecological Zones (AEZs) of Pakistan and associated mechanisms (1979−2020). Spatiotemporal trends in mean and extreme temperatures revealed significant warming mainly over northern, northeastern, and southern AEZs. In contrast, mean‐to‐extreme precipitation changes showed non‐uniform patterns with a significant increase in the northeast AEZs. Population exposure to mean (extreme) temperature and precipitation events increased two‐fold during 2000–2020. The AEZs in urban settlements (i.e., Indus Delta, Northern Irrigated Plain, and Barani/Rainfall) show a maximum exposure to extreme temperatures of about 70–100 × 106 (person‐days) in the reference period (1979−1999), which increases to 140–200 × 106 person‐days in the recent period (2000−2020). In addition, the highest exposure to extreme precipitation days also increases to 40–200 × 106 person‐days during 2000–2020 than 1979−1999 (20–100 × 106) person‐days. Relative changes in exposure are large (60%–90%) for the AEZs across northeast Pakistan, justifying the spatial population patterns over these zones. Overall, the observed changes in exposure are primarily attributed to the climate effect (50%) over most AEZs except Northern Irrigated Plain for R10 and R20 events, where the interaction effect takes the lead. The population exposure rapidly increased over major AEZs of Pakistan, which could be more vulnerable to extreme events due to rapid urbanization and population growth in the near future. Plain Language Summary: This study investigates the impact of climate change on population in different AEZs of Pakistan from 1979 to 2020. The findings depict widespread warming trends in northern, northeastern, and southern AEZs of Pakistan, whereas changes in precipitation patterns are non‐uniform, with a notable increase in the northeast zone. The study reveals that population exposure to temperature and precipitation events doubled between 2000 and 2020. Urban AEZs such as the Indus Delta, Northern Irrigated Plain, and Barani/Rainfall exhibit the largest exposure to extreme temperatures and precipitation, with exposure increasing over time. The northeast zones of Pakistan experience the highest relative changes in exposure, highlighting the vulnerability of these AEZs to climate events. Climate effect account for the majority of observed exposure changes in AEZs, except Northern Irrigated Plain, where interaction effect plays a key role. The findings suggest that as urbanization and population growth continue, major AEZs in Pakistan are becoming increasingly susceptible to extreme climate events in the future. Key Points: Spatiotemporal trends in mean to extreme temperature (precipitation) events reveal widespread warming across Agro‐Ecological ZonesPopulation exposure to mean (extreme) temperature and precipitation events increases two‐fold in recent climate periodObserved changes in population exposure are primarily attributed to the climate effect (50%) over major Agro‐Ecological Zones [ABSTRACT FROM AUTHOR]

In the manuscript, we report the dynamics of the Takagi–Sugeno (T–S) fuzzy memristor-based hidden system via sampled-data control. For an open-loop formulation, the system dynamics are studied. We found extreme events, hidden attractors, and trivial period doubling scenarios and confirmed them through numerical, analytical, statistical and experimental analyses. Furthermore, to enable stability analysis and control combination, the (T–S) fuzzy algorithm is employed to control the dynamics of a nonlinear system. First, we designed the sampled data fuzzy controller (SDFC) for the proposed system. Second, the Lyapunov–Krasovskii functional (LKF) strategy, novel integral inequality mechanisms, and certain sufficient conditions are determined by deriving the linear matrix inequalities (LMIs), which ensure the asymptotic stability of the system. Moreover, the sampled data control gains are computed for the large sampling interval, and numerically obtained results confirm the theoretical results. Additionally, a simple real-time analog electronic circuit is constructed, and experimental data is obtained, and finally, numerically simulated results were verified through MATLAB. [ABSTRACT FROM AUTHOR]

One fifth of the world's population and critical infrastructures are close to the coast and regions of high-risk sea level rise elevation. The last decades have been characterized by increasing extreme events, including storm surges, flooding, coastal erosion, enhanced coastal vulnerability with associated livelihood, and economic losses. Nature-based engineering solutions are being adopted as sustainable solutions for helping existing technologies live their design life and providing climate change adaptation and resilience for coastal and riverine communities. This paper involves the investigation of nature-based eco-hydraulic soft coastal engineering to cultivate seaweed for coastal protection. In this context, the present study involves an advanced risk evaluation performed by conducting an extreme bore interaction with seaweed as a soft engineering coastal protection measure. The load reduction on the inland structure during extreme flooding conditions, incorporating seaweed, is addressed. The present study indicates that the load on inland structures can be reduced by as much as 14% in extreme flooding conditions in the presence of seaweed with two rows of seaweed, indicating the usage of seaweed as a part of coastal protection over existing site protection infrastructure for improved coastal mitigation. [ABSTRACT FROM AUTHOR]

The input of trace elements from a small urban river (Las River, Toulon, France) located on the northern Mediterranean coast was studied during both base flow and flood events. A 2-year monitoring period of water flow and suspended particulate matter (SPM) showed a typical Mediterranean hydrological regime: a strong increase in water flow and SPM during short flood periods. During the flood event, an up to 2-fold increase in dissolved trace element (DTM) concentrations and particulate trace element content in SPM (PTM) was observed compared to the baseline discharge. The enrichment factor of elements in the SPM ranges from low or moderate for Co, Ni and Cr (1.0–4.7) to extremely high for Cd (157). However, the enrichment factors decrease from base flow to flood, indicating a dilution effect with a large yield of weathering particles with higher particle size. The most significant total trace element loading occurred during flood, ranging from 78% for As and Ni to 91% for Pb, while PTM loading during flood ranged from 35% for As to 77% for Pb. The specific dissolved fluxes during the flood are significantly higher for Pb, Cu and Zn than in the surrounding rivers, indicating specificity in the catchment (lithology). This study shows the importance of monitoring the transport of pollutants through small urban rivers and their potential impact on the coastal region, especially when they enter small and closed bays, as a receiving pool. [ABSTRACT FROM AUTHOR]

Sandspits are important natural defences against the effects of storm events in estuarine regions, and their temporal and spatial dynamics are related to river flow, wave energy, and wind action. Understanding the impact of extreme wave events on the morphodynamics of these structures for current conditions and future projections is of paramount importance to promote coastal and navigation safety. In this work, a numerical analysis of the impact of a storm on the sandspit of the Douro estuary (NW Portugal) was carried out considering several mean sea level conditions induced by climate change. The selected numerical models were SWAN, for hydrodynamics, and XBeach, for hydrodynamic and morphodynamic assessments. The extreme event selected for this study was based on the meteo-oceanic conditions recorded during Hurricane Christina (January 2014), which caused significant damage on the western Portuguese coast. The analysis focused on the short-term (two days) impact of the storm on the morphodynamics of the sandspit in terms of its erosion and accretion patterns. The obtained results demonstrate that the mean sea level rise will induce some increase in the erosion/accretion volumes on the seaward side of the sandspit. Overtopping of the detached breakwater and the possibility of wave overtopping of the sandspit crest were observed for the highest simulated mean sea levels. [ABSTRACT FROM AUTHOR]

Extreme events occur frequently in China against the background of global warming. Understanding the spatiotemporal variation of extreme events and predicting their future trends can provide a theoretical basis for formulating regional strategies that adapt to climate change. Using the CN05.1 grid meteorological data and eleven global climate models based on Coupled Model Intercomparison Project Phase 6 (CMIP6), we analyzed the evolution characteristics of extreme precipitation and high temperature events in China from 1975 to 2014, predicted the evolution of extreme events from 2015 to 2054, and proposes policy suggestions for dealing with these events. The results indicate that, from 1975 to 2014, the heavy precipitation exhibited an increasing-decreasing-increasing pattern from the northwest to southeast region of China, and the risk and catastrophability of extreme precipitation in regions located to the east of the Hu Line were great. Under SSP1-2.6 and SSP5-8.5 climate change scenarios, extreme precipitation in China will generally increase and become stronger by 2054, with a significant increase in North and Northeast China and a further increase in Northwest China. From 1975 to 2014, the number of warm nights and warm days in China increased significantly, and the increase in warm nights was higher than that of warm days. Under the SSP1-2.6 and SSP5-8.5 climate change scenarios, extreme heat events in China will increase significantly by 2054, with the greatest increase in Northwest, Southwest, and South China. To mitigate the impact of climate change and cope with the risk of extreme events in the future, China should further improve its response and emergency management capacities for dealing with flood and extreme heat risks, strengthen international cooperation, and formulate strategies adapted to local conditions.

Abstract The increasing prevalence of warmer trends and climate extremes exacerbate the population's exposure to urban settlements. This work investigated population exposure changes to mean and extreme climate events in different Agro‐Ecological Zones (AEZs) of Pakistan and associated mechanisms (1979−2020). Spatiotemporal trends in mean and extreme temperatures revealed significant warming mainly over northern, northeastern, and southern AEZs. In contrast, mean‐to‐extreme precipitation changes showed non‐uniform patterns with a significant increase in the northeast AEZs. Population exposure to mean (extreme) temperature and precipitation events increased two‐fold during 2000–2020. The AEZs in urban settlements (i.e., Indus Delta, Northern Irrigated Plain, and Barani/Rainfall) show a maximum exposure to extreme temperatures of about 70–100 × 106 (person‐days) in the reference period (1979−1999), which increases to 140–200 × 106 person‐days in the recent period (2000−2020). In addition, the highest exposure to extreme precipitation days also increases to 40–200 × 106 person‐days during 2000–2020 than 1979−1999 (20–100 × 106) person‐days. Relative changes in exposure are large (60%–90%) for the AEZs across northeast Pakistan, justifying the spatial population patterns over these zones. Overall, the observed changes in exposure are primarily attributed to the climate effect (50%) over most AEZs except Northern Irrigated Plain for R10 and R20 events, where the interaction effect takes the lead. The population exposure rapidly increased over major AEZs of Pakistan, which could be more vulnerable to extreme events due to rapid urbanization and population growth in the near future.

Identifying the behavior of precipitation is one of the most important planning principles related to water resources. In this research, an attempt was made to analyze the trend of time changes in extreme rainfall profiles of the country by using the daily rainfall data of 3423 synoptic, climatology, and rain gauge stations for the period from 1970 to 2016 and by performing interpolation using the kriging method. Then, using percentile profiles (percentile less than 10, less than 25, 25 to 75, 75 to 90, and above 90) and regression analysis, changes in the frequency of member days of each of the percentile methods over time were calculated and mapped. The results showed that during the studied period, 86.6% of cells associated with days with the tenth percentile or less in the country had an increasing trend. On the other hand, the pixels associated with days with the 90th percentile and more have shown an increasing trend. Considering that the pixels with the 25th, 25th-75th percentiles (normal), and 75th percentile have shown a decreasing trend in terms of the number of days in their group, it can be concluded that the country's rainfall conditions and the days with rainfall are towards the limit values has moved and the possibility of drought or destructive floods has increased in the country. [ABSTRACT FROM AUTHOR]

Symbolic encoding of information is the foundation of Shannon's mathematical theory of communication. The concept of the informational efficiency of capital markets is closely related to the issue of information processing by equity market participants. Therefore, the aim of this comprehensive research is to examine and compare a battery of methods based on symbolic coding with thresholds and the modified Shannon entropy in the context of stock market efficiency. As these methods are especially useful in assessing the market efficiency in terms of sequential regularity in financial time series during extreme events, two turbulent periods are analyzed: (1) the COVID-19 pandemic outbreak and (2) the period of war in Ukraine. Selected European equity markets are investigated. The findings of empirical experiments document that the encoding method with two 5% and 95% quantile thresholds seems to be the most effective and precise procedure in recognizing the dynamic patterns in time series of stock market indices. Moreover, the Shannon entropy results obtained with the use of this symbolic encoding method are homogenous for all investigated markets and unambiguously confirm that the market informational efficiency measured by the entropy of index returns decreases during extreme event periods. Therefore, we can recommend the use of this STSA method for financial time series analyses. [ABSTRACT FROM AUTHOR]