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This book deals with the concept of moments, and how they find application in subsurface hydrologic problems-particularly those dealing with solute transport. Both temporal and spatial moments are dealt with in some detail for a wide variety of problems. Several examples using experimental data, both from laboratory columns and field experiments, are provided to give the readers a clear idea about the scope of this method.
A valuable reference work for research workers and professionals in hydrology, environmental issues, petroleum and geological engineering, and applied mathematics.
This volume describes new stochastic subsurface hydrology techniques and results and examines the basic stochastic methods used to treat flow and contaminant transport in naturally heterogeneous permeable earth materials.
This authoritative book presents a comprehensive account of the essential roles of nonlinear dynamic and chaos theories in understanding, modeling, and forecasting hydrologic systems. This is done through a systematic presentation of: (1) information on the salient characteristics of hydrologic systems and on the existing theories for their modeling; (2) the fundamentals of nonlinear dynamic and chaos theories, methods for chaos identification and prediction, and associated issues; (3) a review of the applications of chaos theory in hydrology; and (4) the scope and potential directions for the future. This book bridges the divide between the deterministic and the stochastic schools in hydrology, and is well suited as a textbook for hydrology courses.
Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 171. Groundwater is a critical resource and the PrinciPal source of drinking water for over 1.5 billion people. In 2001, the National Research Council cited as a "grand challenge" our need to understand the processes that control water movement in the subsurface. This volume faces that challenge in terms of data integration between complex, multi-scale hydrologie processes, and their links to other physical, chemical, and biological processes at multiple scales. Subsurface Hydrology: Data Integration for Properties and Processes presents the current state of the science in four aspects: Approaches to hydrologie data integration Data integration for characterization of hydrologie properties Data integration for understanding hydrologie processes Meta-analysis of current interpretations Scientists and researchers in the field, the laboratory, and the classroom will find this work an important resource in advancing our understanding of subsurface water movement.
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.
With an emphasis on methodology, this reference provides a comprehensive examination of water movement as well as the movement of various pollutants in the earth's subsurface. The multidisciplinary approach integrates earth science, fluid mechanics, mathematics, statistics, and chemistry. Ideal for both professionals and students, this is a practical guide to the practices, procedures, and rules for dealing with groundwater.
Mankind has manipulated the quantity and quality of soil water for millennia. Food production was massively increased through fertilization, irrigation and drainage. But malpractice also caused degradation of immense areas of once fertile land, rendering it totally unproductive for many generations. In populated areas, the pollutant load ever more often exceeds the soil’s capacity for buffering and retention, and large volumes of potable groundwater have been polluted or are threatened to be polluted in the foreseeable future. In the past decades, the role of soil water in climate patterns has been recognized but not yet fully understood. The soil-science community responded to this diversity of issues by developing numerical models to simulate the behavior of water and solutes in soils. These models helped improve our understanding of unsaturated-zone processes and develop sustainable land-management practices. Aimed at professional soil scientists, soil-water modelers, irrigation engineers etc., this book discusses our progress in soil-water modeling. Top scientists present case studies, overviews and analyses of strengths, weaknesses, opportunities and threats related to soil-water modeling. The contributions cover a wide range of spatial scales, and discuss fundamental aspects of unsaturated-zone modeling as well as issues related to the application of models to real-world problems.
Mathematical models are being used more and more widely to study complex dynamic systems (global weather, ecological systems, hydrological systems, nuclear reactors etc. including the specific subject of this book, crop-soil systems). The models are important aids in understanding, predicting and managing these systems. Such models are complex and imperfect. One fundamental research direction is to seek a better understanding of how these systems function, and to propose mathematical expressions embodying that understanding. However, this is not sufficient. It is also essential to have tools (often mathematical and statistical methods) to aid in developing, improving and using the models built from those equations. The book is specifically concerned with the application of methods to crop models, but much of the material is also applicable to dynamic system models in other fields. The goal of this book is to fill that gap.* State-of-the-art methods explained simply and illustrated specifically for crop models* Parameter estimation – applying statistical methods to the complex case of crop models, including Bayesian methods * Includes model evaluation, understanding and estimating prediction error* Offers a unique data assimilation by using the Kalman filter and beyond