Review of Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting by B. Sivakumar and R. Berndtsson: World Scientific Publishing, Hackensack, NJ; 2010; Price: $122; ISBN 13-978-981-4307-97-0; 519 pp.

In the preface the authors write, “…there has been, in recent years, an exponential increase in the number of scientific approaches and their applications for hydrologic modeling and forecasting. Among these, the so-called ‘data-based’ or ‘data-driven’ approaches have become particularly popular…none of the existing books, it is fair to say, is adequate enough to learn about the overall progress and the state-of-the-art of data-based approaches in hydrologic modeling and forecasting…an attempt is made in this book to present a comprehensive account of the advances in data-based approaches for modeling and forecasting hydrologic systems and processes.” This book responds to the need expressed in the preface, providing a clear and balanced treatment of some of the major data-based approaches, encompassing 10 chapters written by hydrologists and water resources engineers who are well known for their contributions. Providing the background and organization of the book, Chapter 1, written by B. Sivakumar and R. Berndtsson, sets the stage for what is to come in the ensuing chapters.

Stochastic methods for modeling precipitation and streamflow, written by B. Rajagopalan, J. D. Salas, and U. Lall, constitute the subject matter of Chapter 2. In the stochastic simulation of precipitation, the writers discuss continuous precipitation models; models of cumulative precipitation over nonoverlapping time intervals, including Markov chain models, alternating renewal models, and models for precipitation amount; nonparametric models for simulating precipitation, including kernel density estimators and kernel-near-neighbor models; and precipitation disaggregation models. Stochastic streamflow simulation comprises continuous time to hourly simulation; weekly, monthly, and seasonal streamflow simulation at a single site; annual streamflow at single site; monthly streamflow simulation; temporal and spatial disaggregation models; nonparametric streamflow simulation models for both single sites and multiple sites; and extensions of the kernel-near-neighbor resampling approach. The treatment is lucid and comprehensive.

Written by K. K. Yilmaz, J. Vrugt, H. V. Gupta, and S. Sorooshian, Chapter 3 is devoted to model calibration in watershed hydrology. Beginning with a discussion of approaches to parameter estimation for watershed models, including an overview of the manual calibration approach and automated calibration approaches, the chapter goes on to discuss single criterion automated calibration methods; the shuffled complex evolution, the University of Arizona approach; multicriteria calibration methods, including simultaneous multicriteria calibration, stepwise multicriteria calibration, and the multicriteria constraining approach; automated calibration of spatially distributed watershed models; treatment of parameter uncertainty, including the random walk metropolis algorithm, differential evolution adaptive metropolis, and sequential data assimilation; and parallel computing. It is a well-written chapter providing an up-to-date account of model calibration.

The subject matter of Chapter 4 is scaling and fractals in hydrology, written by D. Veneziano and A. Langusis. Introducing the concepts of fractality and scale invariance, the chapter goes on to discuss fractal and scale-invariant sets, including fractal sets and fractal dimensions and scale-invariant sets and their dimensions; scale invariance for random processes comprising self-similar processes, scalar multifractal processes, and multifractal fields and vector-valued processes; generation of scale-invariant random processes entailing the relationship between scale-invariant and stationary processes, scale-invariant processes as renormalization limits, and scale invariant processes as weighted sums and products, scale-invariant processes from fractional integration of alpha-stable measures, and processes with limited scale invariance; properties of scale-invariant processes, including H-sssi processes, moment scaling of multifractal processes, existence, moments, and distributions of stationary multifractal measures, and extremes of stationary mulifractal measures and origin of multifractality; forecasting and downscaling of stationary multifractal measures involving forecasting and downscaling; methods of inference of scaling from data; selected applications in hydrology, with particular focus on rainfall, fluvial topography, floods, and flow through porous media. The treatment in the chapter is comprehensive, up-to-date, well presented, and balanced.

Remote sensing for precipitation and hydrologic applications are covered in Chapter 5, written by E. N. Anagnostou. Beginning with a discussion of prediction accuracy and surface modeling, the chapter covers precipitation nowcasting from sparse-based platforms; data uses in precipitation forecasting; and data uses in hydrology comprising soil moisture, flood forecasting, and water management; and concludes with an outlook on the future. This is a short but nicely written chapter.

Chapter 6, by R. J. Abrahart, M. See, C. W. Dawson, A. Y. Shamseldin, and R. L. Wilby, presents nearly two decades of neural network (NN) hydrologic modeling. Providing a short history of neural networking, the chapter discusses the spread of the field of NN modeling within hydrologic sciences; establishing the field; enlarging the field including traditional regression-type applications, merging of technologies, modular and ensemble modeling, and neuro-hybrid modeling; taking steps to deliver goods, comprising building collective intelligence, deciphering of internal components, and estimating confidence and uncertainty; evaluating the field by asking a series of questions: (1) can NN be made to reveal any physics? (2) Can an optimal training set be identified? (3) Can NN improve on time series analysis? (4) Can NN training be made more adaptive? (5) Are NN good extrapolators? The chapter concludes with final thoughts on searching for a killer App. The chapter is a well-developed joy to read.

Evolutionary computing in hydrology is presented in Chapter 7, written by V. Babovic and R. Rao. Beginning with a discussion of systems and processes, it goes on to discuss evolutionary computing comprising evolution principle, evolutionary computing in hydrology; genetic algorithm and its scope in hydrology, entailing modeling the observations, modeling the error, and modeling the model; and issues pertaining to genetic computing, such as selection of data sets, EC setting, model structure, and defect of noise. The chapter is short but to the point.

Chapter 8, written by D. Labat, dwells on wavelet analyses in hydrology from Fourier to wavelet; the continuum wavelet transform; discrete-time wavelet transform and multiresolution analysis consisting of discrete wavelet transform, and orthogonal wavelet bases and orthogonal wavelet expansions; signal energy repartition in the wavelet frame; statistical entropy in the wavelet frame; wavelet analysis of the time-scale relationship between two signals; wavelet cross-spectrum and coherence; applications of wavelet transforms in hydrology and earth sciences; and concludes with a discussion on challenges and future directions. It is a well-written chapter providing a comprehensive account.

Nonlinear dynamics and chaos in hydrology is the subject matter of Chapter 9, written by B. Svakumar and R. Berndtsson. First discussing the nonlinear nature of hydrologic systems and processes, the chapter goes on to discuss chaos identification and prediction, including autocorrelation function and power spectrum, phase space reconstruction, correlation dimension method, nonlinear prediction method, and inverse approach; chaos theory in hydrology, consisting of rainfall, river flow, rainfall runoff, lake volume, sediment transport, groundwater, and other hydrologic problems; current status and challenges in chaos theory in hydrology; and is concluded with closing remarks on chaos theory as a bridge between deterministic and stochastic theories. This chapter is lucid in style, comprehensive in coverage, and up-to-date in information.

The concluding Chapter 10 provides a summary and reflects on the future. It contains a summary of findings, data versus physics, integration of different approaches, communication, and conclusion.

The book is written in a clear and easy-to-understand manner, containing a wealth of information reflecting the contributors’ knowledge and experience. Each chapter includes a rich list of references. The book would have been stronger had it contained discussions (or chapters) on entropy-based modeling, data mining, database management, and geographical information systems. Nevertheless, I believe the book will be useful to teachers, professionals, managers, and researchers in water and hydrologic engineering, climatology and hydrometeorology, geological sciences, ecology, and environmental science and engineering. It will make an excellent addition to one’s bookshelf.