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Elsevier

Books in Environmental data

  • Coastal Altimetry

    Selected Case Studies from Asian Shelf Seas
    • 1st Edition
    • Stefano Vignudelli + 1 more
    • English
    Coastal Altimetry: Selected Case Studies from Asian Shelf Seas provides information on developments over the past decade in the processing of remotely sensed altimetry in coastal areas, with an overview of expected errors and where they stem from, along with remaining gaps in processing. Challenges covered include the retracking of the altimetric signal to account for land contamination, tropospheric water corrections, and tidal model improvements, along with the pros and cons of widely available products. Additional chapters provide recent research in the regional seas of Asia and cover variability, dynamics, predictability and prediction, impacts of extreme events, effects to ecosystems, and more. This book offers readers a dataset that can illuminate our understanding of the propagation of planetary boundary waves that have a significant sea level signal in near coastal regions. As such, researchers and students who have a foundation in satellite altimetry and want to know the latest development of open ocean and coastal satellite altimetry, especially in Asian coastal regions, will benefit from this book.

  • Environmental Data Analysis with MatLab or Python

    Principles, Applications, and Prospects
    • 3rd Edition
    • William Menke
    • English
    Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment. Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases.

  • Data, Statistics, and Useful Numbers for Environmental Sustainability

    Bringing the Numbers to Life
    • 1st Edition
    • Benoit Cushman-Roisin + 1 more
    • English
    Data, Statistics, and Useful Numbers for Environmental Sustainability: Bringing the Numbers to Life is an accessible reference for researchers working in environmental and sustainability fields who need to communicate the latest data and statistics to reinforce their own research or message. The book compiles the most-needed numbers into one resource and covers a variety of relevant topics, including materials, energy, environment, city planning, electronics, and waste. This handbook is clearly indexed and full of comprehensive tables, making it easy to find answers. Researchers in environmental and sustainability-relat... fields will find it an invaluable resource.

  • Floods

    Volume 2- Risk Management
    • 1st Edition
    • Freddy Vinet
    • English
    The management of flood risk seems to be facing a daunting paradox. Despite increasingly effective risk knowledge tools and the efforts of international institutions to place risk reduction at the top of the agenda, the cost of disasters continues to increase. It is also increasingly difficult to avoid the urbanization or development of potential flood zones. The fundamental issue involves determining the conditions necessary for efficient prevention by focusing on adaptability to risk, which implies coping with the risk of flooding rather than directly fighting against it or simply ignoring it. This second volume of the Floods series of books explores existing policies and tools which mitigate the impact of flooding: the construction of protective structures, the reduction of vulnerability, land use planning, the improvement of crisis management, etc. The closing chapters focus on the question of adaptation through post-flood reconstruction, integrating disaster risk reduction measures, e.g. through resilient urbanism.

  • Environmental Data Analysis with MatLab

    • 2nd Edition
    • William Menke + 1 more
    • English
    Environmental Data Analysis with MatLab is a new edition that expands fundamentally on the original with an expanded tutorial approach, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. Since publication of the bestselling Environmental Data Analysis with MATLAB®, many advances have been made in environmental data analysis. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often noisy data drawn from a broad range of sources. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios. MATLAB®, a commercial data processing environment, is used in these scenarios. Significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. This new edition, though written in a self-contained way, is supplemented with data and MATLAB® scripts that can be used as a data analysis tutorial. New features include boxed crib sheets to help identify major results and important formulas and give brief advice on how and when they should be used. Numerical derivatives and integrals are derived and illustrated. Includes log-log plots with further examples of their use. Discusses new datasets on precipitation and stream flow. Topical enhancement applies the chi-squared test to the results of the generalized least squares method. New coverage of cluster analysis and approximation techniques that are widely applied in data analysis, including Taylor Series and low-order polynomial approximations; non-linear least-squares with Newton’s method; and pre-calculation and updating techniques applicable to real time data acquisition.