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Author: Qingyun Duan Publisher: John Wiley & Sons ISBN: 087590355X Category : Science Languages : en Pages : 356
Book Description
Published by the American Geophysical Union as part of the Water Science and Application Series, Volume 6. During the past four decades, computer-based mathematical models of watershed hydrology have been widely used for a variety of applications including hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the watershed processes that transform natural forcing (e.g., rainfall over the landscape) into response (e.g., runoff in the rivers). The user of a watershed hydrology model must specify the model parameters before the model is able to properly simulate the watershed behavior.
Author: Qingyun Duan Publisher: John Wiley & Sons ISBN: 087590355X Category : Science Languages : en Pages : 356
Book Description
Published by the American Geophysical Union as part of the Water Science and Application Series, Volume 6. During the past four decades, computer-based mathematical models of watershed hydrology have been widely used for a variety of applications including hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the watershed processes that transform natural forcing (e.g., rainfall over the landscape) into response (e.g., runoff in the rivers). The user of a watershed hydrology model must specify the model parameters before the model is able to properly simulate the watershed behavior.
Author: Bellie Sivakumar Publisher: World Scientific ISBN: 9814464759 Category : Science Languages : en Pages : 542
Book Description
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Author: Vijay P. Singh Publisher: CRC Press ISBN: 1420037439 Category : Science Languages : en Pages : 678
Book Description
Watershed modeling is at the heart of modern hydrology, supplying rich information that is vital to addressing resource planning, environmental, and social problems. Even in light of this important role, many books relegate the subject to a single chapter while books devoted to modeling focus only on a specific area of application. Recognizing the
Author: Seunguk Lee Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This thesis provides a critique and evaluation of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, and provides an appraisal of sensitivity analysis methods for watershed models with calibrated parameters. The first part of this thesis explores the strengths and weaknesses of the GLUE methodology with commonly adopted subjective likelihood measures using a simple linear watershed model. Recent research documents that the widely accepted GLUE procedure for describing forecasting precision and the impact of parameter uncertainty in rainfall-runoff watershed models fails to achieve the intended purpose when used with an informal likelihood measure (Christensen, 2004; Montanari, 2005; Mantovan and Todini, 2006; Stedinger et al., 2008). In particular, GLUE generally fails to produce intervals that capture the precision of estimated parameters, and the distribution of differences between predictions and future observations. This thesis illustrates these problems with GLUE using a simple linear rainfall-runoff model so that model calibration is a linear regression problem for which exact expressions for prediction precision and parameter uncertainty are well known and understood. The results show that the choice of a likelihood function is critical. A likelihood function needs to provide a reasonable distribution for the model errors for the statistical inference and resulting uncertainty and prediction intervals to be valid. The second part of this thesis discusses simple uncertainty and sensitivity analysis for watershed models when parameter estimates result form a joint calibration to observed data. Traditional measures of sensitivity in watershed modeling are based upon a framework wherein parameters are specified externally to a model, so one can independently investigate the impact of uncertainty in each parameter on model output. However, when parameter estimates result from a joint calibration to observed data, the resulting parameter estimators are interdependent and different sensitivity analysis procedures should be employed. For example, over some range, evaporation rates may be adjusted to correct for changes in a runoff coefficient, and vice versa. As a result, descriptions of the precision of such parameters may be very large individually, even though their joint response is well defined by the calibration data. These issues are illustrated with the simple abc watershed model. When fitting the abc watershed model to data, in some cases our analysis explicitly accounts for rainfall measurement errors so as to adequately represent the likelihood function for the data given the major source of errors causing lack of fit. The calibration results show that the daily precipitation from one gauge employed provides an imperfect description of basin precipitation, and precipitation errors results in correlation among flow errors and degraded the goodness of fit.
Author: Michael B. Abbott Publisher: Springer Science & Business Media ISBN: 9400902573 Category : Science Languages : en Pages : 323
Book Description
It is the task of the engineer, as of any other professional person, to do everything that is reasonably possible to analyse the difficulties with which his or her client is confronted, and on this basis to design solutions and implement these in practice. The distributed hydrological model is, correspondingly, the means for doing everything that is reasonably possible - of mobilising as much data and testing it with as much knowledge as is economically feasible - for the purpose of analysing problems and of designing and implementing remedial measures in the case of difficulties arising within the hydrological cycle. Thus the aim of distributed hydrologic modelling is to make the fullest use of cartographic data, of geological data, of satellite data, of stream discharge measurements, of borehole data, of observations of crops and other vegetation, of historical records of floods and droughts, and indeed of everything else that has ever been recorded or remembered, and then to apply to this everything that is known about meteorology, plant physiology, soil physics, hydrogeology, sediment transport and everything else that is relevant within this context. Of course, no matter how much data we have and no matter how much we know, it will never be enough to treat some problems and some situations, but still we can aim in this way to do the best that we possibly can.
Author: Vijay P. Singh Publisher: Water Resources Publications, LLC ISBN: 9781887201742 Category : Hydrology Languages : en Pages : 0
Book Description
This book stemmed from a desire to provide a comprehensive account of some of the world's popular computer models of watershed hydrology. To achieve this objective, a variety of models that together spanned a range of characteristics were included. Some of those models represent a large class of models, some are comprehensive, some are applicable to not only civil works but also to agricultural, range and forest, and nonpoint source pollution fields; some are equipped with the GIS and remote sensing capability, and some represent a large cross-section of models from around the world. The subject matter of this book is divided into 29 chapters. Beginning with introductory remarks on watershed modeling in Chapter 1, model calibration and reliability estimation are presented in Chapters 2 and 3, respectively. The next ten chapters (4 to 13) present some of the popular models from around the world. These models are in the realm of civil engineering applications of watershed hydrology models. Some of the models are more comprehensive than others and some have the management capabilities. The next two models, presented in Chapters 14 and 15, are large-scale models and embody GIS and remote sensing technology. The models presented in Chapters 16 to 23 are more physically-based and distributed in nature, quite suited to nonpoint source pollution modeling, and to assess environmental impact of land use changes. The remaining 5 models presented in Chapters 24 to 29 are within the realm of agricultural and forestry applications. Nonpoint source pollution, erosion and impact on soil productivity, drainage design, etc., can be modeled by applying these models. Computer Models of Watershed Hydrology will be of interest to practicing hydrologists, especially to hydrologic modelers and the model users, as well as specialists in the fields of civil engineering, agricultural engineering, environmental science, forest and range science, earth science, climatology, and watershed sciences. Graduate students, teachers engaged in graduate instruction, and researchers will also find this book useful. Due to the popularity of this book and with innovations in printing, this was reprinted in 2012 with the original information. It is now part of WRP’s Classic Resource Edition.
Author: Linfield C. Brown Publisher: CRC Press ISBN: 9781420056631 Category : Mathematics Languages : en Pages : 584
Book Description
Two critical questions arise when one is confronted with a new problem that involves the collection and analysis of data. How will the use of statistics help solve this problem? Which techniques should be used? Statistics for Environmental Engineers, Second Edition helps environmental science and engineering students answer these questions when the goal is to understand and design systems for environmental protection. The second edition of this bestseller is a solutions-oriented text that encourages students to view statistics as a problem-solving tool. Written in an easy-to-understand style, Statistics for Environmental Engineers, Second Edition consists of 54 short, "stand-alone" chapters. All chapters address a particular environmental problem or statistical technique and are written in a manner that permits each chapter to be studied independently and in any order. Chapters are organized around specific case studies, beginning with brief discussions of the appropriate methodologies, followed by analysis of the case study examples, and ending with comments on the strengths and weaknesses of the approaches. New to this edition: Thirteen new chapters dealing with topics such as experimental design, sizing experiments, tolerance and prediction intervals, time-series modeling and forecasting, transfer function models, weighted least squares, laboratory quality assurance, and specialized control charts Exercises for classroom use or self-study in each chapter Improved graphics Revisions to all chapters Whether the topic is displaying data, t-tests, mechanistic model building, nonlinear least squares, confidence intervals, regression, or experimental design, the context is always familiar to environmental scientists and engineers. Case studies are drawn from censored data, detection limits, regulatory standards, treatment plant performance, sampling and measurement errors, hazardous waste, and much more. This revision of a classic text serves as an ideal textbook for students and a valuable reference for any environmental professional working with numbers.
Author: Qingyun Duan
Author: Qingyun Duan
Author: Bellie Sivakumar
Author: Laura A. McGrath
Author: Vijay P. Singh
Author: Seunguk Lee
Author: Michael B. Abbott
Author: Vijay P. Singh
Author: Linfield C. Brown
Author: Joshua Duke Woodbury
Author: Alexandre Daniel Remnek