Increase in Bridge Scour Causing Increase in Foundation Costs

The frequency of extreme rainfall events in Southern Brazil is impacted by El Niño—Southern Oscillation (ENSO) episodes, especially in austral spring. There are two areas in which this impact is more significant: one is on the coast, where extreme events are more frequent during El Niño (EN) and the other one extends inland, where extreme events increase during EN and decrease during La Niña (LN). Atmospheric circulation patterns associated with severe rainfall in those areas are similar (opposite) to anomalous patterns characteristic of EN (LN) episodes, indicating why increase (decrease) of extreme events in EN (LN) episodes is favoured. The most recurrent precipitation patterns during extreme rainfall events in each of these areas are disclosed by Principal Component Analysis (PCA) and evidence the separation between extreme events in these areas: a severe precipitation event generally does not occur simultaneously in the coast and inland, although they may occur inland and in the coastal region in sequence. Although EN predominantly enhances extreme rainfall, there are EN years in which fewer severe events occur than the average of neutral years, and also the enhancement of extreme rainfall is not uniform for different EN episodes, because the interdecadal non‐ENSO variability also modulates significantly the frequency of extreme events in Southern Brazil. The inland region, which is more affected, shows increase (decrease) of extreme rainfall in association with the negative (positive) phase of the Atlantic Multidecadal Variability, with the negative (positive) phase of the Pacific Multidecadal Variability and with the positive (negative) phase of the Pacific Interdecadal Variability. Copyright © 2008 Royal Meteorological Society

As one of the most important extreme weather event types, extreme precipitation events have significant impacts on human and natural environment. This study assesses the projected long term trends in frequency of occurrence of extreme precipitation events represented by heavy precipitation days, very heavy precipitation days, very wet days and extreme wet days over Ontario, based on results of 21 CMIP3 GCM runs. To achieve this goal, first, all model data are linearly interpolated onto 682 grid points (0.45° × 0.45°) in Ontario; Next, biases in model daily precipitation amount are corrected with a local intensity scaling method to make the total wet days and total wet day precipitation from each of the GCMs are consistent with that from the climate forecast system reanalysis data, and then the four indices are estimated for each of the 21 GCM runs for 1968–2000, 2046–2065 and 2081–2100. After that, with the assumption that the rate parameter of the Poisson process for the occurrence of extreme precipitation events may vary with time as climate changes, the Poisson regression model which expresses the log rate as a linear function of time is used to detect the trend in frequency of extreme events in the GCMs simulations; Finally, the trends and their uncertainty are estimated. The result shows that in the twenty-first century annual heavy precipitation days, very heavy precipitation days and very wet days and extreme wet days are likely to significantly increase over major parts of Ontario and particularly heavy precipitation days, very wet days are very likely to significantly increase in some sub-regions in eastern Ontario. However, trends of seasonal indices are not significant.

The increasing frequency of extreme weather events poses significant challenges to the power and energy balance of new energy power systems, making it difficult to ensure the safe and stable operation of the power grid. Focusing on the impact of extreme weather on the source-grid-load elements of new energy power systems, this paper explores methods and strategies for maintaining power balance under extreme weather conditions, with frequency security as the main concern. It analyzes measures and strategies for power balance under extreme weather events across different time scales and proposes specific approaches from multiple perspectives—generation side, grid side, and load side—to ensure power balance. Additionally, the paper discusses an optimized dispatch framework for new energy power systems under extreme weather conditions and provides an outlook on future research directions for maintaining power and energy balance in such systems.

A new method of generating extreme building energy year and its application

As global temperature rises, the frequency of extreme climate events, e.g., severe droughts and floods, has increased significantly and caused severe damage over the past years. To this regard, precipitation efficiency, a crucial meteorological parameter, could provide valuable insights for a better understanding of the patterns and characteristics of these extreme events. In this study, taking Guangdong province as an exemplary region, we first obtained long-term and high-resolution historical records of precipitation efficiency by integrating the observations from a dense network of Global Navigation Satellite System (GNSS) stations with precipitation data, and then characterized the extreme drought and wetness through climate indices. We found a distinct seasonal trend in precipitation efficiency in Guangdong, with annual fluctuations ranging from 10 to 25%. Notably, precipitation efficiency is higher in proximity to the Pearl River Delta Plain and gradually decreases towards the east and west. The occurrence of anomalous peaks and valleys in precipitation efficiency generally corresponds to dry and wet conditions, respectively. A total of 9 extreme wet events and 6 dry events occurred from January 2007 to May 2022, with durations from 3 to 6 months. Our results also demonstrated that both wet and dry frequencies exhibit an increasing trend with the expansion of the time scale, and the frequency of extreme events near the Pearl River Delta Plain surpasses that of other regions. Furthermore, the propagation time from meteorological anomalies to agricultural and hydrological anomalies is about 3 months. The periodic characteristics of meteorological anomalies are identified as the primary driver for other anomalous periodic patterns. Our work unveils the long-term dynamic behavior of precipitation efficiency, as well as the characteristics of extreme drought and wetness events in the regions characterized by intricate land–atmosphere interactions.

The importance of extreme events in controlling erosion and sediment transport in semi‐arid areas has long been appreciated but in practice being by definition rare and episodic they are difficult to study. When they are observed this is frequently in catchments for which little data are available. Another difficulty is that even when catchments are being monitored the instruments that record discharge, sediment load and hillslope sediment fluxes perform inaccurately or unpredictably during extreme weather conditions. This paper describes slope and channel processes that were actually observed by the authors during a (at least) 1 : 30 year 30 minute event with a rainfall intensity of 240 mm h−1 in a second‐order tributary of the Rio Camacho near Tarija in southern Bolivia. During the event, it could be observed how different tributary streams and slope sections contributed sediments and flow to the main channel. Evidence for these contributions did not survive the event, which has implications for both modelling and monitoring. Before the onset of the event open erosion plots were functioning on the slopes where rainfall and runoff were being measured. Rainfall experiments were used to obtain infiltration rates.The storm began with a moderate intensity of about 5 cm per hour but increased after 5 minutes to 30 cm per hour and continued for 30 minutes. At this time, the rainfall intensity greatly exceeded the infiltration capacity and water started draining the steep slopes. The ephemeral channel rapidly filled up with runoff. Erosion by hailstones was considerable.Provisionally, the discharge during peak runoff was estimated at 43·7 m3 s−1 (Manning equation). On the basis of sediment loads carried by previous storms, (average concentration of 21 g l−1) the total suspended load discharge during the storm would have been 15 ton ha−1. Within the ephemeral channel, 10 to 50 cm thick layers of coarse sediments were deposited. The collectors of the open erosion plots could not handle the large amounts of runoff and sediment and were completely filled to overflowing.Comparing these data with soil losses during less intense storms it can be concluded that extreme events largely contribute to erosion and sediment transport and that the majority of the rainstorms play only a minor role. The results also show how limited the values of rainfall experiments are in understanding geomorphic events. This makes modelling of erosion and soil losses a difficult and hazardous task. Copyright © 2002 John Wiley & Sons, Ltd.

In recent years, Japan has experienced a notable increase in extreme rainfall events, which have frequently triggered severe flooding, resulting in significant loss of life and property damage. Consequently, the overall cost of flood-related disasters has continued to rise. Climate change projections indicate that the frequency and intensity of such extreme events are likely to increase in the future.With advancements in observational technologies, high-resolution radar-based rainfall data are now available in near real-time across Japan, providing valuable input for flood risk assessment. In this study, we rapidly developed a flood risk mapping framework using a combination of radar rainfall data, hydrological data, historical rainfall records, and various physiographic factors. The risk assessment was conducted using a Geographic Information System (GIS)-based Analytic Hierarchy Process (AHP) approach, focusing on past extreme events.Our analysis produced several risk maps, including real-time hydrological hotspot maps and overall flood susceptibility maps for specific extreme rainfall events. To validate our results, we compared them with observed flood damage data and insurance claim records. The comparison showed promising agreement, suggesting the reliability of our approach. However, further refinement is necessary to enhance the robustness and predictive capability of the method. We believe the proposed methodology offers a valuable tool for flood disaster risk management and can support decision-making for stakeholders preparing for future extreme flood events.

Abstract. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used to study the effect of extreme weather events on ozone in the US for historical (2001–2010) and future (2046–2055) periods under the RCP8.5 scenario. During extreme weather events, including heat waves, atmospheric stagnation, and their compound events, ozone concentration is much higher compared to the non-extreme events period. A striking enhancement of effect during compound events is revealed when heat wave and stagnation occur simultaneously as both high temperature and low wind speed promote the production of high ozone concentrations. In regions with high emissions, compound extreme events can shift the high-end tails of the probability density functions (PDFs) of ozone to even higher values to generate extreme ozone episodes. In regions with low emissions, extreme events can still increase high-ozone frequency but the high-end tails of the PDFs are constrained by the low emissions. Despite the large anthropogenic emission reduction projected for the future, compound events increase ozone more than the single events by 10 to 13 %, comparable to the present, and high-ozone episodes with a maximum daily 8 h average (MDA8) ozone concentration over 70 ppbv are not eliminated. Using the CMIP5 multi-model ensemble, the frequency of compound events is found to increase more dominantly compared to the increased frequency of single events in the future over the US, Europe, and China. High-ozone episodes will likely continue in the future due to increases in both frequency and intensity of extreme events, despite reductions in anthropogenic emissions of its precursors. However, the latter could reduce or eliminate extreme ozone episodes; thus improving projections of compound events and their impacts on extreme ozone may better constrain future projections of extreme ozone episodes that have detrimental effects on human health.

In this work, we apply a downscaling strategy to analyze extreme weather events that may impact wind farm operation. The coupling applies mesoscale momentum budget components (tendencies) from the WRF model as forcing terms to the governing microscale equations. Our study focuses on flow over complex terrain during specific days to reproduce extreme weather events that produced wind turbine damage. The interaction of the meso- and micro-scale features are relevant in the simulation of extreme conditions. The simulation results are compared with observations from nacelle anemometers of the wind turbines in two different wind farms by analyzing time series and wind profiles.The microscale code Alya, developed at the Barcelona Supercomputing Center (BSC), is closed with URANS and LES closures to solve the momentum and energy equations. Both closures use the same mesoscalar to microscalar coupling methodology and are used in this work to simulate the wind flow. We present the implementation of the mesoscalar coupling to the microscale solver when using URANS and LES closures.We show that the coupling via tendencies has excellent potential for understanding transient events under extreme weather conditions in very complex terrain. The wind industry can use such simulations to enhance forensic analysis in cases of wind turbine accidents or any other event that may impact turbine operation, such as high turbulence phenomena. We test the ability of the meso- to microscale coupling model to reproduce extreme events with regard to quantities of interest in wind energy.Simulation results using URANS and LES closures agree reasonably well with observations. In some scenarios, the LES provides results that are closer to measurements. LES models have the advantage of providing wind gusts. We compare the accuracy and performance (CPU-time) of the URANS vs. LES approaches.

ABSTRACTExtreme weather and climate-related events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, dust storms, flooding rains, coastal flooding, storm surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden. More information is needed about the impacts of climate change on public health and economies to effectively plan for and adapt to climate change. This paper describes some of the ways extreme events are changing and provides examples of the potential impacts on human health and infrastructure. It also identifies key research gaps to be addressed to improve the resilience of public health to extreme events in the future.Implications: Extreme weather and climate events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, flooding rains, coastal flooding, surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden.

Abstract. A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

Global warming in the 21st century will alter the frequency of extreme climatic events, such as high-temperature anomalies and “heat waves”. Observations of extreme high temperatures during recent decades have detected upward trends in their frequency of occurrence, and recent state-of-the-art Global Climate Models (GCMs), e.g., Climate Model Intercomparison Projects (CMIPs), notably CMIP5 and CMIP6, have predicted acceleration of temperature trends and high-temperature events by 2100 under projected greenhouse-gas emission scenarios. Hence, the 21st century is expected to experience substantial shifts in the occurrence of extreme events, where present-day, extreme-but-rare high-temperature events will become common during the summer months. The increasing frequency of extreme heat may affect the health and resiliency of social, biological, and infrastructure systems in many regions worldwide, underscoring the need for accurate and reliable long-term assessments of climatic change across global and regional scales. So far, many investigations of high-temperature extremes have been carried out under end-point scenarios, e.g., by comparing GCM-projected changes in the frequency of high-temperature extremes expected in the late 21st century to the late 20th century. In this study, we use extreme value theory and decades of observations of high-temperature extremes at thousands of meteorological stations across North America to investigate continuous shifts in the frequency of extreme high-temperature events due to projected local warming trends. We find that the odds of exceedance of 50-year extreme high-temperature events increases exponentially with increases in mean local temperature. At a majority of the stations studied here, a local mean temperature increase of 0.5–1 ^{circ }C can double the odds of exceedance of 50-year extreme high-temperature events. Based on time-dependent temperature projections, the odds of exceedance of 50-year extreme high-temperature events doubles approximately every 20 years (or sooner) for sim 96% of the stations. Moreover, we find that, for sim 80% of the stations in North America, investigated here, the 50-year extreme high-temperature events will be exceeded annually before 2100.

An overview of basic research on climate change in recent years in China is presented. In the past 100 years in China, average annual mean surface air temperature (SAT) has increased at a rate ranging from 0.03°C (10 yr)−1 to 0.12°C (10 yr)−1. This warming is more evident in northern China and is more significant in winter and spring. In the past 50 years in China, at least 27% of the average annual warming has been caused by urbanization. Overall, no significant trends have been detected in annual and/or summer precipitation in China on a whole for the past 100 years or 50 years. Both increases and decreases in frequencies of major extreme climate events have been observed for the past 50 years. The frequencies of extreme temperature events have generally displayed a consistent pattern of change across the country, while the frequencies of extreme precipitation events have shown only regionally and seasonally significant trends. The frequency of tropical cyclone landfall decreased slightly, but the frequency of sand/dust storms decreased significantly. Proxy records indicate that the annual mean SAT in the past a few decades is the highest in the past 400–500 years in China, but it may not have exceeded the highest level of the Medieval Warm Period (1000–1300 AD). Proxy records also indicate that droughts and floods in eastern China have been characterized by continuously abnormal rainfall periods, with the frequencies of extreme droughts and floods in the 20th century most likely being near the average levels of the past 2000 years. The attribution studies suggest that increasing greenhouse gas (GHG) concentrations in the atmosphere are likely to be a main factor for the observed surface warming nationwide. The Yangtze River and Huaihe River basins underwent a cooling trend in summer over the past 50 years, which might have been caused by increased aerosol concentrations and cloud cover. However, natural climate variability might have been a main driver for the mean and extreme precipitation variations observed over the past century. Climate models generally perform well in simulating the variations of annual mean SAT in China. They have also been used to project future changes in SAT under varied GHG emission scenarios. Large uncertainties have remained in these model-based projections, however, especially for the projected trends of regional precipitation and extreme climate events.

Today, the contemporary world is witnessing a growing number of individuals facing displacement due to the escalating frequency and intensity of extreme climate events. Devastating disasters such as floods, tornadoes, typhoons, storms, tsunamis, heatwaves, and droughts are causing the displacement of thousands, if not millions, of people globally. As temperatures continue to rise, these extreme events are expected to worsen, leading to rising sea levels, increased droughts, and further displacement of vulnerable populations especially in developing countries. The Internal Displacement Monitoring Center (IDMC) highlights a concerning trend, revealing a steady increase in the number of climate refugees forced to leave their homes since 2008. The estimated figure of 318 million people worldwide displaced due to climate change between 2008 and 2021 underscores the urgency of addressing this issue. Regrettably, efforts to address this significant and persistent challenge have been largely limited in both national and international spheres. Climate migrants have often been inadequately protected and their plight insufficiently acknowledged. Additionally, a disconcerting absence of a clear legal classification exists for climate migrants, with the 1951 Refugee Convention failing to encompass their specific circumstances. This paper presents a comprehensive exploration of the climate-induced refugee crisis, with a particular focus on Bangladesh as a case study. By shedding light on the effects of climate change on individuals and society at large, the aim is to draw global attention to the increasingly unacknowledged challenge of climate refugees. Employing a blend of community risk assessment tools, this study adopts an engaged scholarship research paradigm to investigate awareness, vulnerability, adaptation, and resilience-building concerning climate change impacts. The findings of this research underscore the persistence and exacerbation of the climate refugee crisis. Consequently, the paper asserts that it is incumbent upon governments and policymakers worldwide to urgently develop effective policy mechanisms that ensure the protection of global citizens affected by climate change and the recognition of climate refugee status within the UN system. Such mechanisms should facilitate the accommodation and provision of necessary care for climate refugees both within their home countries and abroad. Addressing this pressing issue requires immediate attention and concerted international efforts to safeguard the rights and well-being of those affected by climate-induced displacement.

Projected frequency of low to high-intensity rainfall events over India using bias-corrected CORDEX models