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| Author | : Annie Cung |
| Publisher | : |
| Total Pages | : 236 |
| Release | : 2007 |
| Genre | : Rainfall anomalies |
| ISBN | : |
"Furthermore, a regional analysis was performed and homogenous regions of weather stations within Quebec were identified. A method for the estimation of missing data at ungauged sites based on regional NCMs was found to yield good estimates." --
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 253 |
| Release | : 2013-02-14 |
| Genre | : Science |
| ISBN | : 0309278562 |
Climate change can reasonably be expected to increase the frequency and intensity of a variety of potentially disruptive environmental events-slowly at first, but then more quickly. It is prudent to expect to be surprised by the way in which these events may cascade, or have far-reaching effects. During the coming decade, certain climate-related events will produce consequences that exceed the capacity of the affected societies or global systems to manage; these may have global security implications. Although focused on events outside the United States, Climate and Social Stress: Implications for Security Analysis recommends a range of research and policy actions to create a whole-of-government approach to increasing understanding of complex and contingent connections between climate and security, and to inform choices about adapting to and reducing vulnerability to climate change.
| Author | : Taesam Lee |
| Publisher | : CRC Press |
| Total Pages | : 195 |
| Release | : 2018-09-03 |
| Genre | : Science |
| ISBN | : 0429861141 |
Global climate change is typically understood and modeled using global climate models (GCMs), but the outputs of these models in terms of hydrological variables are only available on coarse or large spatial and time scales, while finer spatial and temporal resolutions are needed to reliably assess the hydro-environmental impacts of climate change. To reliably obtain the required resolutions of hydrological variables, statistical downscaling is typically employed. Statistical Downscaling for Hydrological and Environmental Applications presents statistical downscaling techniques in a practical manner so that both students and practitioners can readily utilize them. Numerous methods are presented, and all are illustrated with practical examples. The book is written so that no prior background in statistics is needed, and it will be useful to graduate students, college faculty, and researchers in hydrology, hydroclimatology, agricultural and environmental sciences, and watershed management. It will also be of interest to environmental policymakers at the local, state, and national levels, as well as readers interested in climate change and its related hydrologic impacts. Features: Examines how to model hydrological events such as extreme rainfall, floods, and droughts at the local, watershed level. Explains how to properly correct for significant biases with the observational data normally found in current Global Climate Models (GCMs). Presents temporal downscaling from daily to hourly with a nonparametric approach. Discusses the myriad effects of climate change on hydrological processes.
| Author | : Assefa M. Melesse |
| Publisher | : Elsevier |
| Total Pages | : 580 |
| Release | : 2019-07-03 |
| Genre | : Science |
| ISBN | : 0128159995 |
Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation is a compilation of contributions by experts from around the world who discuss extreme hydrology topics, from monitoring, to modeling and management. With extreme climatic and hydrologic events becoming so frequent, this book is a critical source, adding knowledge to the science of extreme hydrology. Topics covered include hydrometeorology monitoring, climate variability and trends, hydrological variability and trends, landscape dynamics, droughts, flood processes, and extreme events management, adaptation and mitigation. Each of the book's chapters provide background and theoretical foundations followed by approaches used and results of the applied studies. This book will be highly used by water resource managers and extreme event researchers who are interested in understanding the processes and teleconnectivity of large-scale climate dynamics and extreme events, predictability, simulation and intervention measures. Presents datasets used and methods followed to support the findings included, allowing readers to follow these steps in their own research Provides variable methodological approaches, thus giving the reader multiple hydrological modeling information to use in their work Includes a variety of case studies, thus making the context of the book relatable to everyday working situations for those studying extreme hydrology Discusses extreme event management, including adaption and mitigation
| Author | : Sun Hee Lim |
| Publisher | : |
| Total Pages | : |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
"Millions of people are still exposed to unanticipated extreme rainfall events, and their devastating effects extend from communities to the surrounding environment. The impact extends across borders, to both developed and developing nations, causing massive casualties and financial loss. Accurate estimation of such events, however, requires an elaborated investigation covering different parameters, since precipitation patterns can be so diverse depending on the regional Topographical condition and even more so with progressive climate change. Prediction of extreme precipitations has been extensively studied and improved in recent years by various specialists from science and engineering. In particular, in current engineering practices for the estimation of extreme rainfall for design purposes, many probability models have been proposed for describing the distribution of this random variable. However, there is no general agreement as to which distribution should be used to provide the most accurate and most reliable design rainfall estimate. In view of the above-mentioned issues, the overall objective of the present research is therefore to propose a general procedure for assessing the descriptive and predictive abilities of ten probability distributions that have been used in extreme rainfall frequency analyses. The feasibility of the proposed procedure was tested using available 5-minute, 1-hour, and 24-hour annual maximum rainfall data from a network of 11 raingage stations located in the British Columbia region in Canada. Two commonly used methods, the maximum likelihood and L-moment methods, were used for estimating the parameters of the selected probability models. On the basis of the assessment of the descriptive and predictive abilities of each model, the GNO, PE3 and GEV models were found the best choice for the selected daily and sub-daily annual maximum rainfalls. Despite the popular use of GEV in Canada, the GNO distribution was found to have more robust and accurate descriptive and predictive ability from this study. However, no one distribution consistently outperformed the others among those distributions, and it is impossible to choose one distribution as the best to represent the versatile rainfall pattern of BC. The performance of the distribution models was not consistent with either the topographical or climatological condition of study stations. Yet it was evident that most distributions performed poorly with data sets with high skewness. However, it was difficult to define a pattern of skewness in data, as skewness can vary without relation to rainfall durations and climatological or Topographical condition. Using the proposed procedure for selecting the best distribution, the GNO, GEV and PE3 were found the best overall choice for its descriptive and predictive ability with annual maximum rainfall data in British Columbia." --
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 187 |
| Release | : 2016-07-28 |
| Genre | : Science |
| ISBN | : 0309380979 |
As climate has warmed over recent years, a new pattern of more frequent and more intense weather events has unfolded across the globe. Climate models simulate such changes in extreme events, and some of the reasons for the changes are well understood. Warming increases the likelihood of extremely hot days and nights, favors increased atmospheric moisture that may result in more frequent heavy rainfall and snowfall, and leads to evaporation that can exacerbate droughts. Even with evidence of these broad trends, scientists cautioned in the past that individual weather events couldn't be attributed to climate change. Now, with advances in understanding the climate science behind extreme events and the science of extreme event attribution, such blanket statements may not be accurate. The relatively young science of extreme event attribution seeks to tease out the influence of human-cause climate change from other factors, such as natural sources of variability like El Niño, as contributors to individual extreme events. Event attribution can answer questions about how much climate change influenced the probability or intensity of a specific type of weather event. As event attribution capabilities improve, they could help inform choices about assessing and managing risk, and in guiding climate adaptation strategies. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities.
| Author | : Truong Huy Nguyen |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
"Information on the variability of extreme rainfalls in time and in space is of critical importance for many types of hydrologic studies. In addition, in recent years, climate change has been recognized as having a profound impact on the hydrologic cycle at different temporal and spatial scales. The present study is therefore was carried out to develop appropriate methods for improving the accuracy of design rainfall estimation at gauged and ungauged locations in the current climate as well as in the context of climate change. This study can be divided into five primary parts.The first part presents a general procedure for assessing systematically the performance of different commonly used probability distributions in extreme rainfall frequency analyses based on their descriptive as well as predictive abilities. To test the feasibility of the proposed procedure, an illustrative application was carried out using annual maximum rainfall data from a network of 21 raingages located in the Ontario region in Canada. Results have indicated that the Generalized Extreme Values (GEV), Generalized Normal (GNO), and Pearson Type 3 (PE3) models were the best models for describing the distribution of daily and sub-daily annual maximum rainfalls in this region.The second part introduces a new probability-weighted-moment-based scaling Generalized Extreme Value (GEV/PWM) distribution model for modeling rainfall extremes across a wide range of time scales. A comparative study was then carried out to asses the performance of the proposed model using the available extreme rainfall data from a network of 74 raingages located across Canada. Results of this comparative study have indicated the superior performance of the proposed GEV/PWM model as compared to the existing models based on an extensive set of graphical and numerical comparison criteria.The third part proposes an innovative spatio-temporal statistical downscaling approach for establishing the linkage between daily extreme rainfalls at regional scales and daily and sub-daily extreme rainfalls at a given local site. The performance of the proposed method was assessed for a case study in Ontario using observed extreme rainfall data from seven raingages and climate simulation outputs from 21 different Global Climate Models that have been downscaled to a regional 25-km scale. Results based on various graphical and numerical comparison criteria have indicated the feasibility and accuracy of the proposed downscaling approach. The fourth part introduces new scale-invariancce models for modeling rainfall extremes across a wide range of time scales. The present study presented some general mathematical frameworks for three commonly-used probability distributions in hydrologic frequency analyses such as the Generalized Logistic (GLO), GNO, and PE3 using both non-central moment (NCM) and PWM estimation methods. Results of an illustrative application using the observed IDF data from a network of 74 raingages located across Canada have indicated the feasibility and accuracy of these new scale-invariance models. Finally, the fifth part consists of developing a convenient decision-support tool for the construction of robust rainfall IDF relations in consideration of model uncertainty and potential climate change impacts for the design of urban water systems at a given location of interest. More specifically, this tool can readily be used to identify in an objective and systematic manner the most suitable probability models for accurate and robust estimation of design rainfalls. In addition, in the context of a changing climate, the proposed tool was able to establish the linkage between large-scale climate predictors given by GCMs and the daily and sub-daily extreme rainfalls at a given site"--
| Author | : Patrick Willems |
| Publisher | : IWA Publishing |
| Total Pages | : 239 |
| Release | : 2012-09-14 |
| Genre | : Science |
| ISBN | : 1780401256 |
Impacts of Climate Change on Rainfall Extremes and Urban Drainage Systems provides a state-of-the-art overview of existing methodologies and relevant results related to the assessment of the climate change impacts on urban rainfall extremes as well as on urban hydrology and hydraulics. This overview focuses mainly on several difficulties and limitations regarding the current methods and discusses various issues and challenges facing the research community in dealing with the climate change impact assessment and adaptation for urban drainage infrastructure design and management. Authors: Patrick Willems, University of Leuven, Hydraulics division; Jonas Olsson, Swedish Meteorological and Hydrological Institute; Karsten Arnbjerg-Nielsen, Technical University of Denmark, Department of Environmental Engineering; Simon Beecham, University of South Australia, School of Natural and Built Environments; Assela Pathirana, UNESCO-IHE Institute for Water Education; Ida Bulow Gregersen, Technical University of Denmark, Department of Environmental Engineering; Henrik Madsen, DHI Water & Environment, Water Resources Department; Van-Thanh-Van Nguyen, McGill University, Department of Civil Engineering and Applied Mechanics
