Flood Forecasting

This book exposes practitioners and students to the theory and application of river and lake ice processes to gain a better understanding of these processes for modelling and forecasting. It focuses on the following processes of the surface water ice: freeze-up, ice cover thickening, ice cover breakup and ice jamming. The reader will receive a fundamental understanding of the physical processes of each component and how they are applied in monitoring and modelling ice covers during the winter season and forecasting ice floods. Exercises accompany each component to reinforce the theoretical principles learned. These exercises will also expose the reader to different tools to process data, such a space-borne remote sensing imagery for ice cover classification. A thread supporting numerical modelling of river ice and lake ice processes runs through the book.

Flood Forecasting: A Global Perspective describes flood forecast systems and operations as they currently exist at national and regional centers around the globe, focusing on the technical aspects of flood forecast systems. This book includes the details of data flow, what data is used, quality control, the hydrologic and hydraulic models used, and the unique problems of each country or system, such as glacial dam failures, ice jams, sparse data, and ephemeral streams and rivers. Each chapter describes the system, including details about its strengths and weaknesses, and covers lessons learned. This helpful resource facilitates sharing knowledge that will lead to improvements of existing systems and provides a valuable reference to those wishing to develop new forecast systems by drawing on best practices. Covers global systems allowing readers to see a worldwide perspective with different approaches used by existing flood forecast systems Provides historical coverage allowing readers to understand why forecast systems have developed as they have and to see how specific systems have dealt with common problems encountered Presents a vision of what appears to be the future of hydrologic forecasting and difficulties facing hydrologic forecasting Provides a helpful resource to facilitate improvements to existing systems based on a best practices approach

Like all natural hazards, flooding is a complex and inherently uncertain phenomenon. Despite advances in developing flood forecasting models and techniques, the uncertainty in forecasts remains unavoidable. This uncertainty needs to be acknowledged, and uncertainty estimation in flood forecasting provides a rational basis for risk-based criteria. This book presents the development and applications of various methods based on probablity and fuzzy set theories for modelling uncertainty in flood forecasting systems. In particular, it presents a methodology for uncertainty assessment using disaggregation of time series inputs in the framework of both the Monte Carlo method and the Fuzzy Extention Principle. It reports an improvement in the First Order Second Moment method, using second degree reconstruction, and derives qualitative scales for the interpretation of qualitative uncertainty. Application is to flood forecasting models for the Klodzko catchment in POland and the Loire River in France. Prospects for the hybrid techniques of uncertainty modelling and probability-possibility transformations are also explored and reported.

This book comprises the proceedings of the 26th International Conference on Hydraulics, Water Resources and Coastal Engineering (HYDRO 2021) focusing on broad spectrum of emerging opportunities and challenges in the field of flood forecasting and hydraulic structures. It covers a range of topics, including, but not limited to, early warning system, urban flood modelling and management, dam hazard classification, river training and protection works, structural and non-structural measures for flood mitigation, assessment and development of flood vulnerability, hazard and risk maps rehabilitation of old dams, streamflow turbines, canal operation and related structure, operation and management of dams including their instrumentation etc. Presenting recent advances in the form of illustrations, tables, and text, it offers readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the field of flood forecasting and hydraulic structures, making it a valuable resource for both beginners and researchers wanting to further their understanding of hydraulics, water resources and coastal engineering.

Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.

The nation's network of more than 130 Next Generation Radars (NEXRADs) is used to detect wind and precipitation to help National Weather Service forecasters monitor and predict flash floods and other storms. This book assesses the performance of the Sulphur Mountain NEXRAD in Southern California, which has been scrutinized for its ability to detect precipitation in the atmosphere below 6000 feet. The book finds that the Sulphur Mountain NEXRAD provides crucial coverage of the lower atmosphere and is appropriately situated to assist the Los Angeles-Oxnard National Weather Service Forecast Office in successfully forecasting and warning of flash floods. The book concludes that, in general, NEXRAD technology is effective in mountainous terrain but can be improved.

The aim of this book is to contribute to understanding risk knowledge and to forecasting components of early flood warning, particularly in the environment of tropical high mountains in developing cities. This research covers a challenge, taking into account the persistent lack of data, limited resources and often complex climatic, hydrologic and hydraulic conditions. In this research, a regional method is proposed for assessing flash flood susceptibility and for identifying debris flow predisposition at the watershed scale. An indication of hazard is obtained from the flash flood susceptibility analysis and continually, the vulnerability and an indication of flood risk at watershed scale was obtained. Based on risk analyses, the research follows the modelling steps for flood forecasting development. Input precipitation is addressed in the environment of complex topography commonly found in mountainous tropical areas. A distributed model, a semi-distributed model and a lumped model were all used to simulate the discharges of a tropical high mountain basin with a páramo upper basin. Performance analysis and diagnostics were carried out in order to identify the most appropriate model for the study area for flood early warning. Finally, the Weather Research and Forecasting (WRF) model was used to explore the added value of numerical weather models for flood early warning in a páramo area.