Download or read book Uncertainty and Sensitivity Analysis for Watershed Models with Calibrated Parameters written by Seunguk Lee and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Uncertainty and Sensitivity Analyses for Watershed Models written by Jennifer Benaman and published by . This book was released on 2003 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modeling Automated Parameter Calibration and Sensitivity Analysis of a Watershed Model of the Shaw Road Basin written by Alexandre Daniel Remnek and published by . This book was released on 2003 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Calibration of Watershed Models written by Qingyun Duan and published by John Wiley & Sons. This book was released on 2003-01-10 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Uncertainty Metrics for Coupled Watershed Models written by Geoff Parker and published by . This book was released on 2009 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Watershed Models written by Vijay P. Singh and published by CRC Press. This book was released on 2010-09-28 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Global Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.

Download or read book River Water Quality Model written by P. Reichert and published by IWA Publishing. This book was released on 2001-08-31 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Scientific and Technical Report (STR) presents the findings of the IWA Task Group on River Water Quality Modelling (RWQM). The task group was formed to create a scientific and technical base from which to formulate standardized, consistent river water quality models and guidelines for their implementation. This STR presents the first outcome in this effort: River Water Quality Model No. 1 (RWQM1). As background to the development of River Water Quality Model No.1, the Task Group completed a critical evaluation of the current state of the practice in water quality modelling. A major limitation in model formulation is the continued reliance on BOD as the primary state variable, despite the fact BOD does not include all biodegradable matter. A related difficulty is the poor representation of benthic flux terms. As a result of these limitations, it is impossible to close mass balances completely in most existing models. These various limitations in current river water quality models impair their predictive ability in situations of marked changes in a river's pollutant load, streamflow, morphometry, or other basic characteristics. RWQM 1 is intended to serve as a framework for river water quality models that overcome these deficiencies in traditional water quality models and most particularly the failure to close mass balances between the water column and sediment. To these ends, the model incorporates fundamental water quality components and processes to characterise carbon, oxygen, nitrogen, and phosphorus (C, O, N, and P) cycling instead of biochemical oxygen demand as used in traditional models. The model is presented in terms of process and components represented via a 'Petersen stoichiometry matrix', the same approach used for the IWA Activated Sludge Models. The full RWQM1 includes 24 components and 30 processes. The report provides detailed examples on reducing the numbers of components and processes to fit specific water quality problems. Thus, the model provides a framework for both complicated and simplified models. Detailed explanations of the model components, process equations, stoichiometric parameters, and kinetic parameters are provided, as are example parameter values and two case studies. The STR is intended to launch a participatory process of model development, application, and refinement. RWQM1 provides a framework for this process, but the goal of the Task Group is to involve water quality professionals worldwide in the continued work developing a new water quality modelling approach. This text will be an invaluable reference for researchers and graduate students specializing in water resources, hydrology, water quality, or environmental modelling in departments of environmental engineering, natural resources, civil engineering, chemical engineering, environmental sciences, and ecology. Water resources engineers, water quality engineers and technical specialists in environmental consultancy, government agencies or regulated industries will also value this critical assessment of the state of practice in water quality modelling. Key Features presents a unique new technical approach to river water quality modelling provides a detailed technical presentation of the RWQM1 water quality process model gives an informative critical evaluation of the state of the practice in water quality modelling, and problems with those practices provides a step by step procedure to develop a water quality model Scientific & Technical Report No. 12

Download or read book Integrated Sensitivity Analysis Calibration and Uncertainty Propagation Analysis Approaches for Supporting Hydrological Modeling written by Hongjing Wu and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.

Download or read book Sensitivity Analysis in Earth Observation Modelling written by George P. Petropoulos and published by Elsevier. This book was released on 2016-10-07 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling. Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement. Outlines challenges and future prospects of sensitivity analysis implementation in earth observation modeling Provides readers with a roadmap for directing future efforts Includes case studies with applications from different regions around the globe, helping readers to explore strengths and weaknesses of the different methods in earth observation modeling Presents a step-by-step guide, providing the principles of each method followed by the application of variants, making the reference easy to use and follow

Download or read book SENSITIVITY ANALYSIS AND CALIBRATION OF THE SWAT MODEL FOR IMPROVED PEAK FLOW SIMULATION written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : Climate change and anthropogenic activities create uncertainty with respect to future hydrological conditions, and thus pose challenges in predicting streamflow, particularly the magnitude of extreme events. Several studies have focused on understanding future flood risk under climate and land use/land cover (LULC) changes using hydrological models. In addition to biases from climate data, biases from hydrological models, especially on peak flow simulations were reported to be large (usually underestimations). This could limit the dependability of flood risk projections and their applicability for future decision making. This research study investigates techniques and approaches for improved simulation of streamflows with focus on peak flows using the Soil and Water Assessment Tool (SWAT) for three case study watersheds. In particular, evaluations include choice of criteria for sensitivity analysis and parameter identification, choice of objective function for calibration, and impact assessment when calibrated models are applied for periods with alternate climate and physical characteristics. For ease of calibration, sensitivity analysis is crucial to identify relevant parameters; however, it can provide different parameter sets based upon the implemented sensitivity criteria. Herein, four sensitivity criteria, namely the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R2), modified R2 (bR2), and percent bias (PBIAS) were compared in watersheds of contrasting climate, hydrology, and land cover. For rainfall-runoff dominated agricultural watersheds, NSE, bR2, and R2 produced relatively similar parameter sets, and thus these criteria can be used individually or together for the purposes of sensitivity analysis, especially if peak flows are the target. For a snowmelt dominated forested watershed, R2 was found to be the best sensitivity criterion to identify parameters affecting peak flows. Moreover, for this watershed, sensitivity analysis and light calibration of snowmelt related parameters separately followed by calibration of the hydrological parameters resulted in improved flow simulations compared to the default approach where all parameters were analyzed together. The ability of models calibrated to a given set of climate and LULC data to simulate flood risk under altered conditions was assessed in each watershed by applying parameters calibrated for 2002-2005 to 1970-1999. Simulated annual maximum daily flows for the latter period were used to estimate the instantaneous annual maximum flow (AMF) series, and the impact of altered parameter values on the resulting flood distribution was assessed via a one-at-a-time sensitivity analysis. As anticipated, AMFs in the agricultural rainfall-runoff dominated watersheds were sensitive to changes in runoff related parameters, whereas AMFs in the forested snowmelt and dominated watershed were sensitive to changes in snowmelt related parameters. Alteration of the bank storage recession constant was found to significantly affect AMFs in all three watersheds. It was observed that simulation of the flood risk distribution under altered climate can be improved by modifying snow related parameters based upon the observed change in temperature from the calibration period. In flood risk studies with projected urbanization and expansion of agricultural areas, the curve number parameter should be adjusted by the proportion of change relative to the baseline (or calibration) period.

Download or read book Watershed Hydrology Management and Modeling written by Abrar Yousuf and published by CRC Press. This book was released on 2019-10-31 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a comprehensive insight into watersheds and modeling of the hydrological processes in the watersheds. It covers the concepts of watershed hydrology and watershed management in depth. The basic types, of soil erosion and its measurement and estimation of runoff and soil loss from the small and large watersheds are discussed. Recent advances in the watershed management like the application of remote sensing and GIS and hydrological models are a part of the book. The book serve as a guide for professional and competitive examinations for undergraduate students of Agriculture and Agricultural Engineering and graduate students of Soil Science, Soil and Water Engineering, Agricultural Physics, Hydrology and Watershed Management.

Download or read book Sensitivity Analysis in Practice written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2004-07-16 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.

Download or read book Stochastic Modeling and Uncertainty Assessment for Watershed Water Quality Management written by Yi Zheng and published by . This book was released on 2007 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex watershed water quality models have been increasingly used to support Total Maximum Daily Load (TMDL) development. However, systematic approaches for addressing the significant simulation uncertainty are lacking. For TMDLs supported by complex watershed models, defining the margin of safety (MOS) component through a rigorous uncertainty analysis remains a significant challenge. This study aimed to develop (1) a systematic approach of uncertainty analysis for complex watershed water quality models in the watershed management context; and (2) a framework for defining the MOS with an explicit consideration of uncertainty and degree of protection. A global sensitivity analysis technique was first applied to select critical model parameters. A framework for sources of uncertainty and their interactions was built. Based on this framework, Generalized Likelihood Uncertainty Estimation (GLUE) was initially evaluated as a potential approach for conducting stochastic simulation and uncertainty analysis for complex watershed models. The limitations of GLUE became evident, which led to the development of a new Bayesian approach, Management Objectives Constrained Analysis of Uncertainty (MOCAU). The concept Compliance of Confidence (CC) was then introduced to bridge the gap between modeling uncertainty and MOS. An optimization model was also developed for cost-minimized TMDLs. This study used WARMF as an example of a complex watershed model and constructed a synthetic watershed for developing and testing methodologies. The methodologies were also implemented to study the diazinon TMDL in the Newport Bay watershed (southern California). This research contributes to the theory of stochastic watershed water quality modeling, as well as to the practices of managing watershed water quality.

Download or read book Parameter Sensitivity and Uncertainty Analysis in Simplified Conceptual Urban Drainage Models written by Cintia Brum Siqueira Dotto and published by . This book was released on 2013 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stormwater models are powerful tools to aid the planning, design and performance of different stormwater management strategies. Although these models provide a great platform for decision making, they all have an intrinsic level of uncertainty. Little is understood about the sources and magnitude of this uncertainty, which could be due to the errors in measured data (input and calibration data) and/or due to the model itself. To better understand these sources and their impacts on the model predictions, robust model calibration and sensitivity analysis should be performed. The methodologies used for such an exercise should not only be able to provide an assessment of the uncertainties in the model's parameter values and an evaluation of the confidence level of the model's predictions, but also be able to identify and propagate the different sources of uncertainties. The main aim of this research project is to assess uncertainties in conceptual urban stormwater flow and pollution generation models, with different levels of complexity, by evaluating the impact of different sources of uncertainties on the model predictions and parameter sensitivity. The research focuses on three main steps: (i) identifying suitable global sensitivity analysis method(s) to perform parameter calibration, model sensitivity and uncertainty analysis in stormwater models; (ii) exploring parameter calibration, model sensitivity and the resulting predictive uncertainties in models with different level of complexities; and, (iii) investigating the impact of measured input and calibration data uncertainty on the performance, sensitivity and predictive uncertainty of stormwater models. Four methods were applied for calibration, sensitivity and uncertainty analysis of a simple stormwater (quantity and quality) model: one is a formal Bayesian approach, and three are methods based on Monte Carlo simulations coupled with different sampling and acceptance criteria. While the application of the four methods generated similar posterior parameter distributions and predictive uncertainty, results indicated that the selection of the most appropriate method is a trade-off between the need for a strong theory-based description of uncertainty (but limited by the requirements on prior knowledge), simplicity (but limited by the subjectivity) and computational efficiency (also affected by subjectivity). The results also suggested that modellers should select the method which is most suitable for the system they are modelling, their skill/knowledge level, the available information, and the purpose of their study. Further analysis of the application of the Bayesian approach verified the potential of the method to assess urban drainage models (with different level of complexities) in urban catchments of different sizes and land-use types. The tested Bayesian approach was selected to be used in the remaining activities of this research.The likelihood function in the applied Bayesian approach assumes that the model errors (residuals) are normally distributed. This study demonstrated that this assumption is often not met in stormwater modelling (i.e. model residuals are not normally distributed), and therefore, the data was transformed (Box-Cox) to ensure the normality of the model residuals. The main finding was that the parameter sensitivity varied significantly between the scenarios in which the normality assumption of the residuals was verified or not. The main reason for this being the fact that the data transformation method to meet the assumption altered the intrinsic content of the measured data, which then influenced the emphasis on various parts of the hydrograph. The Bayesian approach was used to assess two conceptual catchment rainfall runoff models (MUSIC, which simulates runoff from both impervious and pervious areas as a series of reservoirs; and, KAREN that simulates runoff from impervious surfaces using the time-area method) and few simple stormwater quality models (empirical regressions and build-up/wash-off based models). Results from parameter calibration and sensitivity analysis of the rainfall runoff models demonstrated that the effective impervious fraction is the main parameter governing the prediction of runoff in urbanised catchments. Other key parameters are those related to the time of concentration. Indeed, the analysis indicated that the pervious area parameters play a secondary role when modelling highly urbanised catchments, which implies that the tested models could be simplified. The uncertainty analysis showed that the total predictive uncertainty bands (i.e. the total uncertainty derived from the specific modelling application) was considerably larger than the uncertainty bands contributed from parameter uncertainty alone, indicating that there are other prominent sources of uncertainty for these models. The water quality models were shown to be 'ill-posed' and unable to reproduce the pollutant processes in the catchment. The impact of both input and calibration data errors on the parameter sensitivity and predictive uncertainty was evaluated by means of propagating these errors through the selected urban stormwater model (rainfall runoff model KAREN coupled with a build-up/wash-off water quality model). It was found that random errors in measured data had minor impact on the model performance and sensitivity. Systematic errors in input and calibration data impacted the parameter distributions (e.g. changed their shapes and location of peaks). In most of the systematic error scenarios (especially those where uncertainty in input and calibration data was represented using 'best-case' assumptions), the errors in measured data were fully compensated by the parameters. For example, when rainfall was systematically under or overestimated, the effective impervious area parameter varied systematically to compensate for the changes in the input data. Parameters were unable to compensate in some of the scenarios where the systematic uncertainty in the input and calibration data were represented using extreme worst-case scenarios. As such, in these few worst case scenarios, the model's performance was reduced considerably. Systematic errors in the calibration data error did not significantly impact the parameter probability distributions of the water quality model, mainly because the model cannot even reproduce TSS concentrations when the 'true' data is used. This finding suggested that the current main limitation in water quality modelling is related to poor model structure, and not to errors in measured data.This research provides a comprehensive study of the propagation of different sources of uncertainties through stormwater models. It identifies how the different uncertainty sources impact on parameter sensitivity and the predictive uncertainty. In addition, the analysis of model parameters and their interactions provides practical recommendations for refining and further developing stormwater rainfall runoff and pollution generation models.

Download or read book Water Resource Systems Planning and Management written by Daniel P. Loucks and published by Springer. This book was released on 2017-03-02 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY-NC 4.0 license. This revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment. Previously published in 2005 by UNESCO and Deltares (Delft Hydraulics at the time), this new edition, written again with contributions from Jery R. Stedinger, Jozef P. M. Dijkman, and Monique T. Villars, is aimed equally at students and professionals. It introduces readers to the concept of viewing issues involving water resources as a system of multiple interacting components and scales. It offers guidelines for initiating and carrying out water resource system planning and management projects. It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.

Download or read book Advances In Data based Approaches For Hydrologic Modeling And Forecasting written by Bellie Sivakumar and published by World Scientific. This book was released on 2010-08-10 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.