Advances in computer technology, in the technology of communication and in mathematical modelling of processes in the hydrological cycle have recently improved our potential to protect ourselves against damage through floods and droughts and to control quantities and qualities in our water systems. This development was demonstrated in a 1983 post-experience course at Wageningen University where an international group of experts reviewed successful modelling techniques and described the design and operation of a number of forecasting and control systems in drainage basins and river reaches of various sizes and under various geographical and climat ological conditions. A special effort was made to bridge the gap between theory and practice; case studies showed that each forecasting system was designed to meet a set of specific requirements and they illustrated that the forecasting system can only be expected to operate reliably if, on the one hand, it is based on sound theoretical concepts and methods and if, on the other hand, it is robust so that, also under adverse conditions, it will continue to collect and process the necessary input data and produce correct and timely signals. We were pleased to meet with encouragement for preserving the course material and making it available to a wider public. This was effected by the team of authorf who elaborated, updated and harmonized the materia in two stages; first into an issue of our university department and finally into the manuscript of this book.

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.

Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties. This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting. This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest. This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions. Contributions from renowned researchers/experts of the subject from all over the world to provide the most authoritative outlook on streamflow forecasting Provides an excellent overview and advances made in streamflow forecasting over the past more than five decades and covers both traditional and modern data-driven approaches in streamflow forecasting Includes case studies along with detailed flowcharts demonstrating a systematic application of different data-driven models in streamflow forecasting, which helps understand the step-by-step procedures