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Titlebook: Extremes in a Changing Climate; Detection, Analysis Amir AghaKouchak,David Easterling,Soroosh Sorooshi Book 2013 Springer Science+Business

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發(fā)表于 2025-3-21 19:12:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Extremes in a Changing Climate
副標(biāo)題Detection, Analysis
編輯Amir AghaKouchak,David Easterling,Soroosh Sorooshi
視頻videohttp://file.papertrans.cn/321/320079/320079.mp4
概述A comprehensive reference book of methodologies and approaches in extreme value analysis.Relevant to many graduate courses in Civil & Environmental Engineering, Earth System Science, Meteorology and A
叢書(shū)名稱Water Science and Technology Library
圖書(shū)封面Titlebook: Extremes in a Changing Climate; Detection, Analysis  Amir AghaKouchak,David Easterling,Soroosh Sorooshi Book 2013 Springer Science+Business
描述.This book provides a collection of the state-of-the-art methodologies and approaches suggested for detecting extremes, trend analysis, accounting for nonstationarities, and uncertainties associated with extreme value analysis in a changing climate. This volume is designed so that it can be used as the primary reference on the available methodologies for analysis of climate extremes. Furthermore, the book addresses current hydrometeorologic global data sets and their applications for global scale analysis of extremes. While the main objective is to deliver recent theoretical concepts, several case studies on extreme climate conditions are provided.? .Audience.The book is suitable for teaching in graduate courses in the disciplines of Civil and Environmental Engineering, Earth System Science, Meteorology and Atmospheric Sciences..
出版日期Book 2013
關(guān)鍵詞Analysis of Extremes; Climate Change; Climate Extremes; Water Resources; Weather Extremes
版次1
doihttps://doi.org/10.1007/978-94-007-4479-0
isbn_softcover978-94-017-8337-8
isbn_ebook978-94-007-4479-0Series ISSN 0921-092X Series E-ISSN 1872-4663
issn_series 0921-092X
copyrightSpringer Science+Business Media Dordrecht 2013
The information of publication is updating

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Gian Paolo Clemente,Nino Savelliruction of regional non-stationary models. Such models are based on the concept of “regional parameters”, i.e. parameters being assumed identical for all sites within a homogeneous region. The inference of regional models poses an additional difficulty compared to the at-site approach: the existence
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Consequences for Insurance Workers,ltivariate Return Periods, quantiles, and design events, which represent quantities of utmost interest in applications, is rather tricky. In this Chapter we show how the use of Copulas may help in dealing with (and, possibly, solving) these problems.
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https://doi.org/10.1007/978-3-030-38185-1n the predominant time of observation are provided. Sampling uncertainties are greater for precipitation than temperature extremes, because precipitation extremes are smaller in scale. A Monte Carlo approach to quantify sampling uncertainty is described, based on station distribution in the U.S. coo
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Jacoby Adeshei Carter,Darryl Scrivennd the use of covariates. A changing climate may prove impetus to change some of the existing paradigms and explore new avenues. The need to reduce uncertainty, or alternatively derive more reliable uncertainty estimates, is exacerbated in a changing climate. One of the key strategies should be a mo
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Bayesian Methods for Non-stationary Extreme Value Analysis,ruction of regional non-stationary models. Such models are based on the concept of “regional parameters”, i.e. parameters being assumed identical for all sites within a homogeneous region. The inference of regional models poses an additional difficulty compared to the at-site approach: the existence
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