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A primer on modeling concepts and applications that is specifically geared toward the environmental field. Sections on modeling terminology, the uses of models, the model-building process, and the interpretation of output provide the foundation for detailed applications. After an introduction to the basics of dynamic modeling, the book leads students through an analysis of several environmental problems, including surface-water pollution, matter-cycling disruptions, and global warming. The scientific and technical context is provided for each problem, and the methods for analyzing and designing appropriate modeling approaches is provided. While the mathematical content does not exceed the level of a first-semester calculus course, the book gives students all of the background, examples, and practice exercises needed both to use and understand environmental modeling. It is suitable for upper-level undergraduate and beginning-graduate level environmental professionals seeking an introduction to modeling in their field.
The effects of disturbed ecosystems, from devastating algal blooms to the loss of whale populations, have demonstrated the vulnerability of the oceans'biodiversity. This book provides methods for learning how ocean systems function, how natural and human actions put them in peril, and how we can influence the marine world in order to maintain biodiversity. The difficulties of research in the oceans make computer modeling particularly helpful for marine conservation. The authors demonstrate dynamic modeling through the use of the STELLA modeling program and case studies from marine conservation.
The book uses STELLA software to develop simulation models, thus allowing readers to convert their understanding of a phenomenon to a computer model, and then run it to yield the inevitable dynamic consequences built into the structure. Part I provides an introduction to modeling dynamic systems, while Part II offers general modeling methods. Parts III through VIII then apply these methods to model real-world phenomena from chemistry, genetics, ecology, economics, and engineering. A clear, approachable introduction to the modeling process, of interest in any field where real problems can be illuminated by computer simulation.
This book/software package brings the tools and excitement of modeling to pre-college teachers, to researchers involved in curriculum development, and to software developers interested in the pre-college market.
In the process of building and using models to comprehend the dynamics of the atmosphere, ocean and climate, the reader will learn how the different components of climate systems function, interact with each other, and vary over time. Topics include the stability of climate, Earths energy balance, parcel dynamics in the atmosphere, the mechanisms of heat transport in the climate system, and mechanisms of climate variability. Special attention is given to the effects of climate change.
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Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history.
Models help us understand the dynamics of real-world processes by using the computer to mimic the actual forces that are known or assumed to result in a system's behavior. This book does not require a substantial background in mathematics or computer science.
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