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Extreme rainfall causes floods and landslides, and so damages humans and socioeconomics; for instance, floods and landslides have been triggered by repeated torrential precipitation and have caused severe damage in the Kyushu region, Japan. Therefore, evaluating extreme rainfall in Kyushu is necessary to provide basic information for measures of rainfall-induced disasters. In this study, we estimated the probability of daily rainfall in Kyushu. The annual maximum values for daily rainfall at 23 long-record stations were normalized using return values at each station, corresponding to 2 and 10 years, and were combined by the station-year method. Additionally, the return period (RP) was calculated by fitting them to the generalized extreme value distribution. Based on the relationship between the normalized values of annual maximum daily rainfall and the RP, we obtained a regression equation to accurately estimate the RP up to 300 years by using data at given stations, considering outliers. In addition, we verified this equation using data from short-record stations where extreme rainfall events triggering floods and landslides were observed, and thereby elucidated that our method was consistent with previous techniques. Thus, this study develops strategies of measures for floods and landslides.

Coastal regions are dynamic areas that often lie at the junction of different natural hazards. Extreme events such as storm surges and high precipitation are significant sources of concern for flood management. As climatic changes and sea-level rise put further pressure on these vulnerable systems, there is a need for a better understanding of the implications of compounding hazards. Recent computational advances in hydraulic modelling offer new opportunities to support decision-making and adaptation. Our research makes use of recently released features in the HEC-RAS version 5.0 software to develop an integrated 1D–2D hydrodynamic model. Using extreme value analysis with the Peaks-Over-Threshold method to define extreme scenarios, the model was applied to the eastern coast of the UK. The sensitivity of the protected wetland known as the Broads to a combination of fluvial, tidal and coastal sources of flooding was assessed, accounting for different rates of twenty-first century sea-level rise up to the year 2100. The 1D–2D approach led to a more detailed representation of inundation in coastal urban areas, while allowing for interactions with more fluvially dominated inland areas to be captured. While flooding was primarily driven by increased sea levels, combined events exacerbated flooded area by 5–40% and average depth by 10–32%, affecting different locations depending on the scenario. The results emphasise the importance of catchment-scale strategies that account for potentially interacting sources of flooding.

Reliable projections of extreme future precipitation are fundamental for risk management and adaptation strategies. Convection‐permitting models (CPMs) explicitly resolve large convective systems and represent sub‐daily extremes more realistically than coarser resolution models, but present short records due to the high computational costs. Here, we evaluate the potential of a non‐asymptotic approach, the Simplified Metastatistical Extreme Value (SMEV) to provide information on the future change of extreme sub‐daily return levels based on CPM simulations. We focus on a complex‐orography area in the North Eastern Italy and use three 10‐year time periods COSMO‐crCLIM simulations (2.2 km resolution) under RCP8.5 scenario. When compared to a block r‐maxima approach currently used in similar applications, the proposed approach shows reduced uncertainty in rare return level estimates (about 5%–10% smaller confidence interval) and can improve the quantification of future changes from CPM simulations. We evaluate these changes and their statistical significance in return levels for 1–24 hr durations. The changes show an interesting spatial organization associated with orography, with significant positive changes located at high elevations. These positive changes tend to increase with increasing return period and decreasing duration. Because SMEV can separate the roles of event intensity and occurrence, it allows for physical interpretations of these changes. We suggest that non‐asymptotic approaches permit the quantification of change in rare extremes within available CPM runs. Plain Language Summary Short duration heavy rainfall may lead to various natural hazards like floods and landslides. Expected change in extreme precipitation due to global warming is a major concern. However, we still cannot quantify these changes because typical climate models cannot reproduce extreme precipitation accurately. The few models that can are very computationally expensive so that we have too few simulations for properly quantifying changes in extremes using traditional statistical methods. Here, we show how to use a new statistical method to quantify extremes from short model simulations. This method is more accurate than currently used methods and may help provide additional insights on the reasons underlying the observed changes. This method could represent a new tool in the hands of the climate research community. Examining the simulations of one model over North‐Eastern Italy, we report an increase in extreme precipitation in mountainous areas and a non‐significant decrease in the low elevation areas. Key Points Future changes in extreme precipitation are estimated from a convection‐permitting climate model using a non‐asymptotic statistical approach The method allows to evaluate the significance of the future changes in return levels and to link them to the changing processes Significant increase in return levels is generally found in the mountains, higher at the short durations (1–3 hr) and for rarer return levels

Expectiles define a least squares analogue of quantiles. They have been the focus of a substantial quantity of research in the context of actuarial and financial risk assessment over the last decade. The behaviour and estimation of unconditional extreme expectiles using independent and identically distributed heavy-tailed observations have been investigated in a recent series of papers. We build here a general theory for the estimation of extreme conditional expectiles in heteroscedastic regression models with heavy-tailed noise; our approach is supported by general results of independent interest on residual-based extreme value estimators in heavy-tailed regression models, and is intended to cope with covariates having a large but fixed dimension. We demonstrate how our results can be applied to a wide class of important examples, among which are linear models, single-index models as well as ARMA and GARCH time series models. Our estimators are showcased on a numerical simulation study and on real sets of actuarial and financial data.

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new “probabilistic” gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj).

This work is motivated by the challenge organized for the 10th International Conference on Extreme-Value Analysis (EVA2017) to predict daily precipitation quantiles at the 99.8%\\(99.8\\%\\) level for each month at observed and unobserved locations. Our approach is based on a Bayesian generalized additive modeling framework that is designed to estimate complex trends in marginal extremes over space and time. First, we estimate a high non-stationary threshold using a gamma distribution for precipitation intensities that incorporates spatial and temporal random effects. Then, we use the Bernoulli and generalized Pareto (GP) distributions to model the rate and size of threshold exceedances, respectively, which we also assume to vary in space and time. The latent random effects are modeled additively using Gaussian process priors, which provide high flexibility and interpretability. We develop a penalized complexity (PC) prior specification for the tail index that shrinks the GP model towards the exponential distribution, thus preventing unrealistically heavy tails. Fast and accurate estimation of the posterior distributions is performed thanks to the integrated nested Laplace approximation (INLA). We illustrate this methodology by modeling the daily precipitation data provided by the EVA2017 challenge, which consist of observations from 40 stations in the Netherlands recorded during the period 1972–2016. Capitalizing on INLA’s fast computational capacity and powerful distributed computing resources, we conduct an extensive cross-validation study to select the model parameters that govern the smoothness of trends. Our results clearly outperform simple benchmarks and are comparable to the best-scoring approaches of the other teams.

Hwang, S.M.; Kim, Y.S.; Kim, J.Y.; Lee, H.Y.; Seo, K.H., and Kang, T.S., 2023. A study on the development of coastal disaster risk assessment. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 156-160. Charlotte (North Carolina), ISSN 0749-0208. With the increasing likelihood of coastal disasters due to the impact of climate change, a system is needed to assessment the risks caused by coastal disasters in advance and support adaptation and response measures. The Intergovernmental Panel on Climate Change (IPCC) presented the need for a transition from vulnerability-based adaptation to risk management-based measures in the 5th Assessment Report (AR5) published in 2014. In addition, in the 6th report published in 2022, the concept of risk was expanded and the process of linking climate, human and ecosystem systems was presented. Accordingly, this study developed a coastal disaster risk assessment framework consisting of Hazard, Exposure and Vulnerability. Assessment considering the possibility of likelihood of hazards, which are external forces of natural phenomena that cause coastal disasters. For the assessment, the indicators suitable for the South Korea coast were reviewed, and weights obtained by expert survey were applied to each factor. In addition, by applying various risk assessment methods, the results of a pilot assessment of Jeju Island, which has relatively high damage from coastal disasters, are presented. Finally, the most reliable assessment method is identified by comparing the assessment results with the actual damaged area. The results of this study are expected to be used to establish reduction measures by calculating the risk level of coastal disasters to reflect the characteristics of coastal areas.

Climate-driven sea level rise (SLR) will intensify extreme sea level (ESL) events along China’s coast. This study reconstructs continuous hourly total sea level (TSL) by incorporating SLA, tide, storm surge, and wave components, addressing the sparse coverage of tide gauges and their often overlooked extreme events (waves). Using a peak-over-threshold method (thresholds optimised between the 97.00-99.99th percentiles) and a 3-day declustering interval, extreme samples were fitted with generalised Pareto distributions via maximum likelihood estimation. Present-day 1-year return levels exceed 1.6 m at most stations, while centennial return levels surpass 4.1 m at Lusi and Kanmen. Spatial variability is evident, with the East China Sea exhibiting higher extremes (~ 3.9–4.7 m for 100-year events) compared to the Yellow Sea and Bohai Sea (~ 2.7–3.5 m) and the South China Sea (~ 1.7–3.7 m). Under the SSP5-8.5 scenario, centennial return levels rise by 0.83 m by 2100, shifting 100-year events to below 50-year return periods by mid-century and less than 10 years by 2100. This study highlights the urgent need for regional adaptation strategies due to the rising intensity and frequency of extreme events, offering an approach that can be applied to other coastal systems to assess current and future ESL hazards under climate change.

In May 2024, a series of coronal mass ejections resulted in the first “severe” (G4‐level) geomagnetic storm watch in nearly 20 years. This event evolved into a significant space weather event, including an “extreme” (G5) geomagnetic storm, moderate (S2) solar radiation storm, and strong (R3) radio blackout. The widespread visibility of auroras at unusually low latitudes attracted global media attention. Using extreme value theory (EVT), this study estimates the return periods for the May 2024 storm based on several geomagnetic indices. The results indicate that while the storm's magnitude was a 1‐in‐12.5‐year event, its duration was a 1‐in‐41‐year event. This discrepancy highlights the storm's unusual longevity compared to its intensity. Updated EVT analyses incorporating recent data refine these return period estimates, providing critical insights into the frequency of such extreme space weather events.

Relativistic electrons in the Earth's outer radiation belt are a significant space weather hazard. Satellites in GPS‐type orbits pass through the heart of the outer radiation belt where they may be exposed to large fluxes of relativistic electrons. In this study we conduct an extreme value analysis of the daily average relativistic electron flux in Global Positioning System orbit as a function of energy and L using data from the US NS41 satellite from 10 December 2000 to 25 July 2020. The 1 in 10 year flux at L = 4.5, in the heart of the outer radiation belt, decreases with increasing energy ranging from 8.2 × 106 cm−2s−1sr−1 MeV−1 at E = 0.6 MeV to 33 cm−2s−1sr−1 MeV−1 at E = 8.0 MeV. The 1 in 100 year is a factor of 1.1–1.7 larger than the corresponding 1 in 10 year event. The 1 in 10 year flux at L = 6.5, on field lines which map to the vicinity of geostationary orbit, decrease with increasing energy ranging from 6.2 × 105 cm−2s−1sr−1 MeV−1 at E = 0.6 MeV to 0.48 cm−2s−1sr−1 MeV−1 at E = 8.0 MeV. Here, the 1 in 100 year event is a factor of 1.1–13 times larger than the corresponding 1 in 10 year event, with the value of the factor increasing with increasing energy. Our analysis suggests that the fluxes of relativistic electrons with energies in the range 0.6 ≤ E ≤ 2.0 MeV in the region 4.25 ≤ L ≤ 4.75 have an upper bound. In contrast, further out and at higher energies the fluxes of relativistic electrons are largely unbounded.