Rainfall-Runoff Modelling

The Primer

Author: K. J. Beven

Publisher: John Wiley & Sons

ISBN: 047071459X

Category: Science

Page: 457

View: 8617

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

The Primer

Author: Keith J. Beven

Publisher: John Wiley & Sons

ISBN: 9780470866719

Category: Nature

Page: 372

View: 5917

"Rainfall-Runoff Modelling The Primer" is the first comprehensive introduction and survey of rainfall-runoff modelling since 1975. Dramatic increases in computer power and spatial databases since then have made unprecedented resources available to the modeller today. However, the early modellers would not have expected that the representations of hydrological processes by computer models would have proven such a difficult scientific problem. This book provides both a primer for the novice and a detailed and practical description of techniques and difficulties demanded by more advanced users and developers. The complete range of rainfall-runoff models is reviewed including models for real time flood forecasting and for predicting the impacts of land use and climate changes with example applications. This is the first text to include methods for estimating the uncertainty in predictions as an essential tool for the novice in making hydrological predictions. This book will appeal to the novice, final year undergraduates and graduate students, hydrological researchers and consultants, and environmental agencies.

Rainfall-runoff Modelling in Gauged and Ungauged Catchments

Author: Thorsten Wagener,Howard Wheater,Hoshin Vijai Gupta

Publisher: World Scientific

ISBN: 1860944663

Category: Science

Page: 306

View: 8139

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.

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


Author: Durga Lal Shrestha

Publisher: CRC Press

ISBN: 9781138424098


Page: N.A

View: 7026

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: 8154

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: 9671

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: 8691

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.