Fundamentals of Glacier Dynamics

2nd Edition

C.J. van der Veen

CRC Press
Published March 29, 2017
Reference - 403 Pages - 164 B/W Illustrations
ISBN 9781138077218 - CAT# K34284

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Measuring, monitoring, and modeling technologies and methods changed the field of glaciology significantly in the 14 years since the publication of the first edition of Fundamentals of Glacier Dynamics. Designed to help readers achieve the basic level of understanding required to describe and model the flow and dynamics of glaciers, this second edition provides a theoretical framework for quantitatively interpreting glacier changes and for developing models of glacier flow.

See What’s New in the Second Edition:

  • Streamlined organization focusing on theory, model development, and data interpretation
  • Introductory chapter reviews the most important mathematical tools used throughout the remainder of the book
  • New chapter on fracture mechanics and iceberg calving
  • Consolidated chapter covers applications of the force-budget technique using measurements of surface velocity to locate mechanical controls on glacier flow
  • The latest developments in theory and modeling, including the addition of a discussion of exact time-dependent similarity solutions that can be used for verification of numerical models

The book emphasizes developing procedures and presents derivations leading to frequently used equations step by step to allow readers to grasp the mathematical details as well as physical approximations involved without having to consult the original works. As a result, readers will have gained the understanding needed to apply similar techniques to somewhat different applications.

Extensively updated with new material and focusing more on presenting the theoretical foundations of glacier flow, the book provides the tools for model validation in the form of analytical steady-state and time-evolving solutions. It provides the necessary background and theoretical foundation for developing more realistic ice-sheet models, which is essential for better integration of data and observations as well as for better model development.


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