APPLICATION OF EXTREME VALUE ANALYSIS MODEL TO ANNUAL MAXIMUM SEISMIC MAGNITUDE DATA USING HISTORICAL INFORMATION AND ITS ACCURACY EVALUATION

Author(s):  
Mutsuki Ota ◽  
Takashi IZUMIYA
Author(s):  
Yoshihiro UTSUNOMIYA ◽  
Hirokazu NONAKA ◽  
Masataka YAMAGUCHI ◽  
Kunimitsu INOUCHI ◽  
Yoshio HATADA ◽  
...  

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1273
Author(s):  
Tosiyuki Nakaegawa ◽  
Takuro Kobashi ◽  
Hirotaka Kamahori

Extreme precipitation is no longer stationary under a changing climate due to the increase in greenhouse gas emissions. Nonstationarity must be considered when realistically estimating the amount of extreme precipitation for future prevention and mitigation. Extreme precipitation with a certain return level is usually estimated using extreme value analysis under a stationary climate assumption without evidence. In this study, the characteristics of extreme value statistics of annual maximum monthly precipitation in East Asia were evaluated using a nonstationary historical climate simulation with an Earth system model of intermediate complexity, capable of long-term integration over 12,000 years (i.e., the Holocene). The climatological means of the annual maximum monthly precipitation for each 100-year interval had nonstationary time series, and the ratios of the largest annual maximum monthly precipitation to the climatological mean had nonstationary time series with large spike variations. The extreme value analysis revealed that the annual maximum monthly precipitation with a return level of 100 years estimated for each 100-year interval also presented a nonstationary time series which was normally distributed and not autocorrelated, even with the preceding and following 100-year interval (lag 1). Wavelet analysis of this time series showed that significant periodicity was only detected in confined areas of the time–frequency space.


2020 ◽  
Vol 148 (4) ◽  
pp. 1431-1447
Author(s):  
Xiang Ni ◽  
Andreas Muehlbauer ◽  
John T. Allen ◽  
Qinghong Zhang ◽  
Jiwen Fan

Abstract Hail size records are analyzed at 2254 stations in China and a hail size climatology is developed based on gridded hail observations for the period 1960–2015. It is found that the annual percentiles of hail size records changed sharply and national-wide after 1980, therefore two periods, 1960–79 and 1980–2015, are studied. There are some similarities between the two periods in terms of the characteristics of hail size such as the spatial distribution patterns of mean annual maximum hail size and occurrence week of annual maximum hail size. The 1980–2015 period had higher observation density than the 1960–79 period, but showed smaller mean annual maximum hail size, especially in northern China. In the majority of grid boxes, the annual maximum hail size experienced a decreasing trend during the 1980–2015 period. A Gumbel extreme value model is fitted to each grid box to estimate the return periods of maximum hail size. The scale and location parameter of the fitted Gumbel distributions are higher in eastern China than in western China, thereby reflecting a greater likelihood of large hail in eastern China. In southern China, the maximum hail size exceeds 127 mm for a 10-yr return period, whereas in northern China maximum hail size exceeds this threshold for a 50-yr return period. The Gumbel model is found to potentially underestimate the maximum hail size for certain return periods, but provides a more informed picture of the spatial distribution of extreme hail size and the regional differences.


2022 ◽  
Author(s):  
Leigh R. MacPherson ◽  
Arne Arns ◽  
Svenja Fischer ◽  
Fernando J. Méndez ◽  
Jürgen Jensen

Abstract. Extreme value analysis seeks to assign probabilities to events which deviate significantly from the mean and is thus widely employed in disciplines dealing with natural hazards. In terms of extreme sea levels (ESLs), these probabilities help to define coastal flood risk which guides the design of coastal protection measures. While tide gauge and other systematic records are typically used to estimate ESLs, combining systematic data with historical information has been shown to reduce uncertainties and better represent statistical outliers. This paper introduces a new method for the incorporation of historical information in extreme value analysis which outperforms other commonly used approaches. Monte-Carlo Simulations are used to evaluate a posterior distribution of historical and systematic ESLs based on the prior distribution of systematic data. This approach is applied at the German town of Travemünde, providing larger ESL estimates compared to those determined using systematic data only. We highlight a potential to underestimate ESLs at Travemünde when historical information is disregarded, due to a period of relatively low ESL activity for the duration of the systematic record.


2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes

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