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.

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.

Hydroinformatics is an emerging subject that is expected to gather speed, momentum and critical mass throughout the forthcoming decades of the 21st century. This book provides a broad account of numerous advances in that field - a rapidly developing discipline covering the application of information and communication technologies, modelling and computational intelligence in aquatic environments. A systematic survey, classified according to the methods used (neural networks, fuzzy logic and evolutionary optimization, in particular) is offered, together with illustrated practical applications for solving various water-related issues. ...

It is the task of the engineer, as of any other professional person, to do everything that is reasonably possible to analyse the difficulties with which his or her client is confronted, and on this basis to design solutions and implement these in practice. The distributed hydrological model is, correspondingly, the means for doing everything that is reasonably possible - of mobilising as much data and testing it with as much knowledge as is economically feasible - for the purpose of analysing problems and of designing and implementing remedial measures in the case of difficulties arising within the hydrological cycle. Thus the aim of distributed hydrologic modelling is to make the fullest use of cartographic data, of geological data, of satellite data, of stream discharge measurements, of borehole data, of observations of crops and other vegetation, of historical records of floods and droughts, and indeed of everything else that has ever been recorded or remembered, and then to apply to this everything that is known about meteorology, plant physiology, soil physics, hydrogeology, sediment transport and everything else that is relevant within this context. Of course, no matter how much data we have and no matter how much we know, it will never be enough to treat some problems and some situations, but still we can aim in this way to do the best that we possibly can.

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.

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.

Hydrometeorological prediction involves the forecasting of the state and variation of hydrometeorological elements -- including precipitation, temperature, humidity, soil moisture, river discharge, groundwater, etc.-- at different space and time scales. Such forecasts form an important scientific basis for informing public of natural hazards such as cyclones, heat waves, frosts, droughts and floods. Traditionally, and at most currently operational centers, hydrometeorological forecasts are deterministic, “single-valued” outlooks: i.e., the weather and hydrological models provide a single best guess of the magnitude and timing of the impending events. These forecasts suffer the obvious drawback of lacking uncertainty information that would help decision-makers assess the risks of forecast use. Recently, hydrometeorological ensemble forecast approaches have begun to be developed and used by operational collection of hydrometeorological services. In contrast to deterministic forecasts, ensemble forecasts are a multiple forecasts of the same events. The ensemble forecasts are generated by perturbing uncertain factors such as model forcings, initial conditions, and/or model physics. Ensemble techniques are attractive because they not only offer an estimate of the most probable future state of the hydrometeorological system, but also quantify the predictive uncertainty of a catastrophic hydrometeorological event occurring. The Hydrological Ensemble Prediction Experiment (HEPEX), initiated in 2004, has signaled a new era of collaboration toward the development of hydrometeorological ensemble forecasts. By bringing meteorologists, hydrologists and hydrometeorological forecast users together, HEPEX aims to improve operational hydrometeorological forecast approaches to a standard that can be used with confidence by emergencies and water resources managers. HEPEX advocates a hydrometeorological ensemble prediction system (HEPS) framework that consists of several basic building blocks. These components include:(a) an approach (typically statistical) for addressing uncertainty in meteorological inputs and generating statistically consistent space/time meteorological inputs for hydrological applications; (b) a land data assimilation approach for leveraging observation to reduce uncertainties in the initial and boundary conditions of the hydrological system; (c) approaches that address uncertainty in model parameters (also called ‘calibration’); (d) a hydrologic model or other approach for converting meteorological inputs into hydrological outputs; and finally (e) approaches for characterizing hydrological model output uncertainty. Also integral to HEPS is a verification system that can be used to evaluate the performance of all of its components. HEPS frameworks are being increasingly adopted by operational hydrometeorological agencies around the world to support risk management related to flash flooding, river and coastal flooding, drought, and water management. Real benefits of ensemble forecasts have been demonstrated in water emergence management decision making, optimization of reservoir operation, and other applications.