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Applying machine learning and optimization technologies to water management problems 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 book carefully considers hydrological models which are essential for predicting floods, droughts, soil moisture estimation, land use change detection, geomorphology and water structures. The book highlights recent advances in the area of hydrological modelling in the Ganga Basin and other internationally important river basins. The impact of climate change on water resources is a global concern. Water resources in many countries are already stressed, and climate change along with burgeoning population, rising standard of living and increasing demand are adding to the stress. Furthermore, river basins are becoming less resilient to climatic vagaries. Fundamental to addressing these issues is hydrological modelling which is covered in this book. Integrated water resources management is vital to ensure water and food security. Integral to the management is groundwater and solute transport, and this book encompasses tools that will be useful to mitigate the adverse consequences of natural disasters.

This book presents the fundamentals of data assimilation and reviews the application of satellite remote sensing in hydrological data assimilation. Although hydrological models are valuable tools to monitor and understand global and regional water cycles, they are subject to various sources of errors. Satellite remote sensing data provides a great opportunity to improve the performance of models through data assimilation.

This book offers a comprehensive overview of the challenges in hydrological modeling. Hydrology, on both a local and global scale, has undergone dramatic changes, largely due to variations in climate, population growth and the associated land-use and land-cover changes. Written by experts in the field, the book provides decision-makers with a better understanding of the science, impacts, and consequences of these climate and land-use changes on hydrology. Further, offering insights into how the changing behavior of hydrological processes, related uncertainties and their evolution affect the modeling process, it is of interest for all researchers and practitioners using hydrological modeling.

The availability of Earth observation and numerical weather prediction data for hydrological modelling and water management has increased significantly, creating a situation that today, for the same variable, estimates may be available from two or more sources of information. Yet, in hydrological modelling, usually, a particular set of catchment characteristics and input data is selected, possibly ignoring other relevant data sources. In this thesis, therefore, a framework is being proposed to enable effective use of multiple data sources in hydrological modelling. In this framework, each available data source is used to derive catchment parameter values or input time series. Each unique combination of catchment and input data sources thus leads to a different hydrological simulation result: a new ensemble member. Together, the members form an ensemble of hydrological simulations. By following this approach, all available data sources are used effectively and their information is preserved. The framework also accommodates for applying multiple data-model integration methods, e.g. data assimilation. Each alternative integration method leads to yet another unique simulation result. Case study results for a distributed hydrological model of Rijnland, the Netherlands, show that the framework can be applied effectively, improve discharge simulation, and partially account for parameter and data uncertainty.

This book provides essential background knowledge on a wide range of hydrological processes governing contaminant transport from soil to surface water across a range of scales, from hillslope to watershed. The mathematical description of these processes is based on both well-known and unique analytical solutions of different initial and boundary problems (primarily using methods from the kinematic wave theory and the reservoir/lumped-parameter concept), supported by numerical modelling studies. Some research topics, in particular several case studies, are illustrated by monitoring and experimental data analysis to show the importance of the research's applications in environmental practice and environmental education. Specific results concern the recognition of: (a) the effect of transient rainfall–runoff–infiltration partitioning on the chemical response of drainage areas to excess precipitation under certain field conditions related to the soil, hillslope characteristics, and contaminant properties; (b) soil erosion as a key factor that enhances the potential of adsorbed chemical transport in runoff; and (c) common tendencies in radionuclide behaviour in the near-surface environment contaminated by radioactive fallout from the Chernobyl (1986), Fukushima (2011) and the less known Kyshtym (1957) accidents, as well as from nuclear weapon tests in the atmosphere since 1952.The book's goal is to provide a conceptual foundation enabling readers to apply scientific knowledge to solve practical problems in environmental hydrology and radiology. More specifically, the book presents the state-of-the-art approaches thatscientists and natural resources experts need in order to significantly improve the prediction of changes in the soil–water system chemistry due to human activities.

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

Measurement, analysis and modeling of extreme precipitation events linked to floods is vital in understanding changing climate impacts and variability. This book provides methods for assessment of the trends in these events and their impacts. It also provides a basis to develop procedures and guidelines for climate-adaptive hydrologic engineering. Academic researchers in the fields of hydrology, climate change, meteorology, environmental policy and risk assessment, and professionals and policy-makers working in hazard mitigation, water resources engineering and climate adaptation will find this an invaluable resource. This volume is the first in a collection of four books on flood disaster management theory and practice within the context of anthropogenic climate change. The others are: Floods in a Changing Climate: Hydrological Modeling by P. P. Mujumdar and D. Nagesh Kumar, Floods in a Changing Climate: Inundation Modeling by Giuliano Di Baldassarre and Floods in a Changing Climate: Risk Management by Slodoban Simonović.

Agriculture is strongly affected by changes in soil hydrology as well as by changes in land use and management practices and the complex interactions between them. This book aims to expand our knowledge and understanding of these interactions on a watershed scale, using soil hydrology models, and to address the consequences of land use and management changes on agriculture from a research perspective. Case studies illustrate the impact of land use and management practices on various soil hydrological parameters under different climates and ecosystems.

'This book provides relevant theoretical frameworks and empirical research findings in the area hydroinformatics to assist professionals to improve their understanding of the development and use of decision support tools to support decision making and integrated water management at different organizational levels and domains'--Provided by publisher.

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 volume is a collection of a selected number of articles based on presentations at the 2005 L'Aquila (Italy) Summer School on the topic of “Hydrologic Modeling and Water Cycle: Coupling of the Atmosphere and Hydrological Models”. The p- mary focus of this volume is on hydrologic modeling and their data requirements, especially precipitation. As the eld of hydrologic modeling is experiencing rapid development and transition to application of distributed models, many challenges including overcoming the requirements of compatible observations of inputs and outputs must be addressed. A number of papers address the recent advances in the State-of-the-art distributed precipitation estimation from satellites. A number of articles address the issues related to the data merging and use of geo-statistical techniques for addressing data limitations at spatial resolutions to capture the h- erogeneity of physical processes. The participants at the School came from diverse backgrounds and the level of - terest and active involvement in the discussions clearly demonstrated the importance the scienti c community places on challenges related to the coupling of atmospheric and hydrologic models. Along with my colleagues Dr. Erika Coppola and Dr. Kuolin Hsu, co-directors of the School, we greatly appreciate the invited lectures and all the participants. The members of the local organizing committee, Drs Barbara Tomassetti; Marco Verdecchia and Guido Visconti were instrumental in the success of the school and their contributions, both scienti cally and organizationally are much appreciated.

Hydrologists, climatologists, soil scientists and environmental engineers are frequently asked to analyse complex environmental problems. It is becoming increasingly apparent that these problems usually involve feedbacks between atmospheric, ecological, and hydrological systems, as well as human society. It is often the feedbacks between systems that are of greatest interest because they may produce unanticipated responses. That is why coupling of different compartments of the Earth system has emerged as a general challenge to the modelling community. This book considers an array of state-of-the-art coupling and modelling concepts. First the relevant Earth system cycles are presented, followed by a discussion on scale issues and multiple equilibria. Inter- and intra-compartmental coupling is addressed, along with a debate on non-linearities and questions of parameterisation. Several applications are presented, where a focus is on cases where the hydrological cycle plays a central role.

1. 5 REFERENCES 127 7 DIGITAL TERRAIN 129 1. 1 INTRODUCTION 129 1. 2 DRAINAGE NETWORK 130 1. 3 DEFINITION OF CHANNEL NETWORKS 135 1. 4 RESOLUTION DEPENDENT EFFECTS 138 1. 5 CONSTRAINING DRAINAGE DIRECTION 141 1. 6 SUMMARY 145 1. 7 REFERENCES 146 8 PRECIPITATION MEASUREMENT 149 1. 1 INTRODUCTION 149 1. 2 RAIN GAUGE ESTIMATION OF RAINFALL 151 ADAR STIMATION OF RECIPITATION 1. 3 R E P 155 1. 4 WSR-88D RADAR CHARACTERISTICS 167 1. 5 INPUT FOR HYDROLOGIC MODELING 172 1. 6 SUMMARY 174 1. 7 REFERENCES 175 9 FINITE ELEMENT MODELING 177 1. 1 INTRODUCTION 177 1. 2 MATHEMATICAL FORMULATION 182 1. 3 SUMMARY 194 1. 4 REFERENCES 195 10 DISTRIBUTED MODEL CALIBRATION 197 1. 1 INTRODUCTION 197 1. 2 CALIBRATION APPROACH 199 1. 3 DISTRIBUTED MODEL CALIBRATION 201 1. 4 AUTOMATIC CALIBRATION 208 1. 5 SUMMARY 214 1. 6 REFERENCES 214 11 DISTRIBUTED HYDROLOGIC MODELING 217 1. 1 INTRODUCTION 218 1. 2 CASE STUDIES 218 1. 3 SUMMARY 236 1. 4 REFERENCES 237 12 HYDROLOGIC ANALYSIS AND PREDICTION 239 1. 1 INTRODUCTION 239 x Distributed Hydrologic Modeling Using GIS 1. 2 VFLO™ EDITIONS 241 1. 3 VFLO™ FEATURES AND MODULES 242 1. 4 MODEL FEATURE SUMMARY 245 1. 5 VFLO™ REAL-TIME 256 1. 6 DATA REQUIREMENTS 258 1. 7 RELATIONSHIP TO OTHER MODELS 259 1. 8 SUMMARY 260 1.

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