Rainfall-Runoff Modelling

The Primer

Author: K. J. Beven

Publisher: John Wiley & Sons

ISBN: 047071459X

Category: Science

Page: 457

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Rainfall-Runoff Modelling: The Primer Second Edition focuses on predicting hydrographs using models based on data and on representations of hydrological process. Dealing with the history of the development of rainfall-runoff models, uncertainty in mode predictions, good and bad practice and ending with a look at how to predict future catchment hydrological responses this book provides an essential underpinning of rainfall-runoff modelling topics."--pub. desc.

Rainfall-Runoff Modelling in Gauged and Ungauged Catchments

Author: Thorsten Wagener,Howard S Wheater,Hoshin V Gupta

Publisher: World Scientific

ISBN: 1783260661

Category: Technology & Engineering

Page: 332

View: 1609

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This important monograph is based on the results of a study on the identification of conceptual lumped rainfall-runoff models for gauged and ungauged catchments. The task of model identification remains difficult despite decades of research. A detailed problem analysis and an extensive review form the basis for the development of a Matlab® modelling toolkit consisting of two components: a Rainfall-Runoff Modelling Toolbox (RRMT) and a Monte Carlo Analysis Toolbox (MCAT). These are subsequently applied to study the tasks of model identification and evaluation. A novel dynamic identifiability approach has been developed for the gauged catchment case. The theory underlying the application of rainfall-runoff models for predictions in ungauged catchments is studied, problems are highlighted and promising ways to move forward are investigated. Modelling frameworks for both gauged and ungauged cases are developed. This book presents the first extensive treatment of rainfall-runoff model identification in gauged and ungauged catchments. Contents:Rainfall-Runoff Modelling — A ReviewA Toolkit for Rainfall-Runoff ModellingModelling Gauged Catchments — Local ProceduresModelling Ungauged Catchments — Regional ProceduresDiscussion, Conclusions and Recommendations for Future Research Readership: Graduate students, academics, researchers, practitioners and consultants in hydrology, civil engineering and environmental engineering. Key Features:The only monograph to describe in detail the application of rainfall-runoff models to gauged and ungauged catchmentsThe only text to focus on the most popular approach to rainfall-runoff modellingAll the Matlab® tools developed and used for the presented research can be downloaded free of charge for non-commercial applications (teaching and research)Keywords:Hydrology;Rainfall-Runoff Modelling;Parameter Estimation;Predictions in Ungauged Basins;Regionalisation;Uncertainty Analysis;Information Content;Multi-Criteria Analysis;Monte Carlo

Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

UNESCO-IHE PhD Thesis

Author: Durga Lal Shrestha

Publisher: CRC Press

ISBN: 9781138424098

Category:

Page: N.A

View: 1428

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This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models, focusing mainly on the development and testing of two different models. The first focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling uncertainty by building an ensemble of specialized machine learning models on the basis of past hydrological model�s performance. The book then demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.

Soil Conservation Service Curve Number (SCS-CN) Methodology

Author: S.K. Mishra,Vijay Singh

Publisher: Springer Science & Business Media

ISBN: 9781402011320

Category: Science

Page: 516

View: 3997

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The Soil Conservation Service (SCS) curve number (CN) method is one of the most popular methods for computing the runoff volume from a rainstorm. It is popular because it is simple, easy to understand and apply, and stable, and accounts for most of the runoff producing watershed characteristics, such as soil type, land use, hydrologic condition, and antecedent moisture condition. The SCS-CN method was originally developed for its use on small agricultural watersheds and has since been extended and applied to rural, forest and urban watersheds. Since the inception of the method, it has been applied to a wide range of environments. In recent years, the method has received much attention in the hydrologic literature. The SCS-CN method was first published in 1956 in Section-4 of the National Engineering Handbook of Soil Conservation Service (now called the Natural Resources Conservation Service), U. S. Department of Agriculture. The publication has since been revised several times. However, the contents of the methodology have been nonetheless more or less the same. Being an agency methodology, the method has not passed through the process of a peer review and is, in general, accepted in the form it exists. Despite several limitations of the method and even questionable credibility at times, it has been in continuous use for the simple reason that it works fairly well at the field level.

Hydrological Data Driven Modelling

A Case Study Approach

Author: Renji Remesan,Jimson Mathew

Publisher: Springer

ISBN: 3319092359

Category: Science

Page: 250

View: 4614

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This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

Hydrologic Frequency Modeling

Proceedings of the International Symposium on Flood Frequency and Risk Analyses, 14–17 May 1986, Louisiana State University, Baton Rouge, U.S.A.

Author: Vijay Singh

Publisher: Springer Science & Business Media

ISBN: 9789027725721

Category: Science

Page: 645

View: 2008

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Floods constitute a persistent and serious problem throughout the United States and many other parts of the world. They are respon sible for losses amounting to billions of dollars and scores of deaths annually. Virtually all parts of the nation--coastal, mountainous and rural--are affected by them. Two aspects of the problem of flooding that have long been topics of scientific inquiry are flood frequency and risk analyses. Many new, even improved, techniques have recently been developed for performing these analyses. Nevertheless, actual experience points out that the frequency of say a 100-year flood, in lieu of being encountered on the average once in one hundred years, may be as little as once in 25 years. It is therefore appropriate to pause and ask where we are, where we are going and where we ought to be going with regard to the technology of flood frequency and risk analyses. One way to address these questions is to provide a forum where people from all quarters of the world can assemble, discuss and share their experience and expertise pertaining to flood frequency and risk analyses. This is what constituted the motivation for organizing the International Symposium on Flood Frequency and Risk Analyses held May 14-17, 1986, at Louisiana State University, Bat-on Rouge, Louisiana.

Neural Networks for Hydrological Modeling

Author: Robert Abrahart,P.E. Kneale,Linda M. See

Publisher: CRC Press

ISBN: 0203024117

Category: Science

Page: 316

View: 516

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A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil engineering, hydrology and physical geography. Each chapter has been written by one or more eminent experts working in various fields of hydrological modelling. The book covers an introduction to the concepts and technology involved, numerous case-studies with practical applications and methods, and finishes with suggestions for future research directions. Wide in scope, this book offers both significant new theoretical challenges and an examination of real-world problem-solving in all areas of hydrological modelling interest.