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The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.
The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.
Advanced Hydroinformatics Advanced Hydroinformatics Machine Learning and Optimization for Water Resources The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts. Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management. Volume Highlights Include: Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics Advances in modeling hydrological systems Different data analysis methods and models for forecasting water resources New areas of knowledge discovery and optimization based on using machine learning techniques Case studies from North America, South America, the Caribbean, Europe, and Asia The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Highlighting recent trends that employ innovative management and conservation approaches, this volume provides an informative overview of the issues and challenges in water resources affected by climate change, such as drought, flooding, glacier changes, and overbuilt-up urban areas. Focusing on surface and groundwater related issues, the book presents solutions that include such methods as morphometric assessment, parameter estimation, long-term trend analysis, sustainability indexes, storm water management models, entropy-based measurement of long-term precipitation, and more. The volume focuses on providing a better understanding of climatic uncertainty through hydrometeorological data sets and their application in hydrological modeling. These analyses help to serve as the basis for the design of flood-control and water-usage management policies.
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.
In the past two decades several activities in the field of water resources management have been enhanced and intensified. This . rise had at least two independent reasons. The first and main one was the constantly increasing water demand for agriculture and industry on one side and the concern about the deteriorating environment on the other. While this last concern was lately overshadowed by deterioration of national economies, the quantity of available water resources has certainly not increased with the growing scarcity of funds for its development and protection. Furthermore, the standard of living, which raised across the world, even in India and China, countries which concentrate more than a third of the world population, has made people and their governments more aware of natural disasters caused by weather. Since a large percentage of losses in human life and material damage from weather-related disasters are caused by water, either by its excess or scarcity, the concern about water has been increasingly associated with these disasters. The second reason for intensified water resources management is man's spectacular technological advance in electronics, computers and use of satellites. The Koran says that two things cannot be predicted: the sex of the child in its mother's womb and the quantity of water that falls from the sky and flows in rivers. Technological progress has disproved both of these caveats.
This volume provides in-depth coverage of such topics as multi-reservoir system operation theory and practice, management of aquifer systems connected to streams using semi-analytical models, one-dimensional model of water quality and aquatic ecosystem-ecotoxicology in river systems, environmental and health impacts of hydraulic fracturing and shale gas, bioaugmentation for water resources protection, wastewater renovation by flotation for water pollution control, determination of receiving water’s reaeration coefficient in the presence of salinity for water quality management, sensitivity analysis for stream water quality management, river ice process, and computer-aided mathematical modeling of water properties. This critical volume will serve as a valuable reference work for advanced undergraduate and graduate students, designers of water resources systems, and scientists and researchers. The goals of the Handbook of Environmental Engineering series are: (1) to cover entire environmental fields, including air and noise pollution control, solid waste processing and resource recovery, physicochemical treatment processes, biological treatment processes, biotechnology, biosolids management, flotation technology, membrane technology, desalination technology, water resources, natural control processes, radioactive waste disposal, hazardous waste management, and thermal pollution control; and (2) to employ a multimedia approach to environmental conservation and protection since air, water, soil and energy are all interrelated.
This second edition explores some of the latest techniques used to provide forecasts for a wide range of water-related applications in areas such as floods, droughts, water resources and environmental impacts. The practical uses can range from decisions on whether to issue a flood warning through to providing longer-term advice such as on when to plant and harvest crops or how to operate reservoirs for water supply and hydropower schemes. It provides an introduction to the topic for practitioners and researchers and useful background for courses in areas such as civil engineering, water resources, meteorology and hydrology. As in the first edition, the first section considers topics such as monitoring and forecasting techniques, demand forecasting and how forecasts are interpreted when issuing warnings or advice. Separate chapters are now included for meteorological and catchment monitoring techniques allowing a more in-depth discussion of topics such as weather radar and water quality observations. The chapters on meteorological and hydrological forecasting now include a greater emphasis on rainfall forecasting and ensemble and probabilistic techniques. Regarding the interpretation of forecasts, an updated chapter discusses topics such as approaches to issuing warnings and the use of decision support systems and risk-based techniques. Given the rapid pace of development in flash flood fore casting techniques, flash floods and slower responding riverine floods are now considered in separate chapters. This includes more detail on forecasting floods in large river basins and on methods for providing early warnings of debris flows, surface water flooding and ice jam and dam break floods. Later chapters now include more information on developing areas such as environmental modelling and seasonal flow forecasting. As before examples of operational systems are provided throughout and the extensive sets of references which were a feature of the first edition have been revised and updated. Key themes • floods • droughts • meteorological observations • catchment monitoring • meteorological forecasts • hydrological forecasts • demand forecasts • reservoirs • water resources • water quality • decision support • data assimilation • probabilistic forecasts Kevin Sene is a civil engineer and researcher with wide experience in flood risk management, water resources and hydrometeorology. He has previously published books on flood warning, forecasting and emergency response and flash floods (Springer 2008, 2013).
Hydrology and water resources management in a changing world reflects important challenges for both researchers and practitioners in the public and private sectors. This book features contributions from all sectors on the following themes: water in urban areas; groundwater; floods; climate services; hydrological processes and models; hydropower; water consumption; environmental impact and water quality. In Focus – a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.