Advances In Hydrologic Forecasts And Water Resources 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.

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 National Weather Service (NWS) is responsible for providing flood forecasts and warnings in the United States. The agency established the Advanced Hydrologic Prediction Services (AHPS) program in 1997 to advance technology for hydrologic services, specifically to provide accurate forecasts that support timely warnings for all users of hydrologic predictions. AHPS strives to provide information at the right time to facilitate adequate responses to mitigate damages to life, livelihoods, and property. AHPS is slated to be fully implemented nationwide in 2013. With seven years still remaining in its development and implementation timeline, a review of the program now is critical to providing NWS with information it needs to maximize the effectiveness of the AHPS program. This report assesses AHPS in respect to hydrologic science and technology research, river routing and mechanics, "systems" engineering aspects, and implementation. Overall, this report finds AHPS to be an ambitious program that promises to provide services and products that are timely and necessary. The report calls for AHPS to develop a detailed and comprehensive, multi-year implementation plan and for the program's goals and budget to be brought into closer alignment.

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.

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.

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.

Floods are by far the most devastating of all weather-related hazards in the United States. The National Weather Service (NWS) is charged by Congress to provide river and flood forecasts and warnings to the public to protect life and property and to promote the nation's economic and environmental well-being (such as through support for water resources management). As part of a modernization of its technologies and organizational structure, the NWS is undertaking a thorough updating of its hydrologic products and services and the activities that produce them. The National Weather Service Modernization Committee of the National Research Council undertook a comprehensive assessment of the NWS' plans and progress for the modernization of hydrologic and hydrometeorological operations and services. The committee's conclusions and recommendations and their related analysis and rationale are presented in this report.

Water managers rely on predicting changes in the hydrologic cycle on seasonal-to-interannual time frames to prepare for water resource needs. Seasonal to interannual predictability of the hydrologic cycle is related to local and remote influences involving land processes and ocean processes, such as the El Niño Southern Oscillation. Although advances in understanding land-surface processes show promise in improving climate prediction, incorporating this information into water management decision processes remains a challenge since current models provide only limited information for predictions on seasonal and longer time scales. To address these needs, the Global Energy and Water Cycle Experiment (GEWEX) Americas Prediction Project (GAPP) was established in 2001 to improve how changes in water resources are predicted on intraseasonal-to-interannual time scales for the continental United States. The GAPP program has developed a science and implementation plan to guide its science activities, which describes strategies for improving prediction and decision support in the hydrologic sciences. This report by the National Research Council provides a review of the GAPP Science and Implementation Plan, outlining suggestions to strengthen the plan and the GAPP program overall.