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

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.

This textbook is a practical guide to real-time streamflow forecasting that provides a rigorous description of a coupled stochastic and physically based flow routing method and its practical applications. This method is used in current times of record-breaking floods to forecast flood levels by various hydrological forecasting services. By knowing

Hydrologic science, an important, interdisciplinary science dealing with the occurrence, distribution, and properties of water on Earth, is key to understanding and resolving many contemporary, large-scale environmental issues. The Water Science and Technology Board used the opportunity of its 1997 Abel Wolman Distinguished Lecture to assess the vitality of the hydrologic sciences by the hydrologic community. The format included focus by lecturer Thomas Dunne on the intellectual vitality of the hydrologic sciences, followed by a symposium featuring several invited papers and discussions. Hydrologic Sciences is a compilation of the Wolman Lecture and the papers, preceded by a summarizing overview. The volume stresses a number of needs for furtherance of hydrologic science, including development of a coherent body of transferable theory and an intellectual center for the science, communication across multiple geo- and environmental science disciplines, appropriate measurements and observations, and provision of central guidance for the field.

This book collected recent studies on the latest methodological and operational advances in hydrological forecasting. Specifically, the collection of papers covers a range of topics related to improving hydrological forecasting via new datasets and innovative approaches.

Short- to medium-range (forecast lead times from 0 to 14 days) streamflow forecasts are subject to uncertainties from various sources. A major source of uncertainty is due to the weather or meteorological forcing. In turn, the uncertainties from the meteorological forcing are propagated into the streamflow forecasts when using the meteorological forecasts (i.e., the outputs from a Numerical Weather Prediction (NWP) model) as forcing to hydrological models. Additionally, the hydrological models themselves are another important source of uncertainty, where uncertainty arises from model structure, parameters, initial and boundary conditions. To advance the science of hydrological modeling and forecasting, these uncertainties need to be quantified and modeled, using novel statistical techniques and robust verification strategies, with the goal of improving the skill and reliability of streamflow forecasts. This, ultimately, may allow generating in advance (i.e., with longer lead times) more informative forecasts, which could eventually translate into better emergency preparedness and response.The main research goal of this dissertation is to develop, implement and verify a new regional hydrological ensemble prediction system (RHEPS), comprised by a numerical weather prediction (NWP) model, different hydrological models and different statistical bias-correction techniques. To implement and verify the new RHEPS, the U.S. middle Atlantic region (MAR) is selected as the study area. This is a region of high socio-economic value with populated cities and, at the same time, vulnerable to floods and other natural disasters. To meet my research goal, the following objectives are carried out: Objective 1 (O1) - To choose a relevant NWP model or system by evaluating and verifying the outputs from different meteorological forecasting systems (i.e., the outputs or forecasts from their underlying NWP models); Objective 2 (O2) - To verify streamflow forecasts generated by forcing a distributed hydrological model with meteorological ensembles, and to develop and evaluate a statistical postprocessor to quantify the uncertainty and adjust biases in the streamflow forecasts; Objective 3 (O3) - To develop, implement and rigorously verify a multimodel approach for short- to medium-range streamflow forecasting. The overarching hypothesis of this dissertation is that the combination and configuration of the different system components in the streamflow forecasting system can have a significant influence on forecast uncertainty and that hydrological multimodeling is able to significantly enhance the quality of streamflow forecasts. The RHEPS is used to test this hypothesis.To meet O1, precipitation ensemble forecasts from two different NWP models are verified. The two NWP models are the National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2) and the 21-member Short Range Ensemble Forecast (SREF) system. The verification results for O1 reveal the quality of the meteorological forcing and serve to inform the decision of selecting a NWP model for O2. As part of O2, the meteorological outputs from the GEFSRv2 are used to force the NOAAs Hydrology Laboratory-Research Distributed Hydrological Model (HL-RDHM) and generate short- to medium-range (1-7 days) ensemble streamflow forecasts for different basins in the MAR. The streamflow forecasts are postprocessed (bias-corrected) using a time series model. The verification results from O2 show that the ensemble streamflow forecasts remain skillful for the entire forecast cycle of 7 days. Additionally, postprocessing increases forecast skills across lead times and spatial scales, particularly for the high flow conditions. Lastly, with O3, a multimodel hydrological framework is tested for medium-range ensemble streamflow forecasts. The results show that the multimodel consistently improves short- to medium-range streamflow forecasts across different basin sizes compared to the single model forecasts.

Climate Models offers a sampling of cutting edge research contributed by an international roster of scientists. The studies strive to improve our understanding of the physical environment for life on this planet. Each of the 14 essays presents a description of recent advances in methodologies for computer-based simulation of environmental variability. Subjects range from planetary-scale phenomena to regional ecology, from impacts of air pollution to the factors influencing floods and heat waves. The discerning reader will be rewarded with new insights concerning modern techniques for the investigation of the natural world.