Models in Geosciences

  • Alisa Bokulich
  • Naomi Oreskes
Part of the Springer Handbooks book series (SHB)


The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structural model uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete.


Couple Model Intercomparison Project Generalize Likelihood Uncertainty Estimation Reduce Complexity Model Structural Model Uncertainty Structural Model Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

agent-based model


artificial neural network


cellular automaton evolutionary slope and river


channel-hillslope integrated landscape development


coupled model intercomparison project


Earth system model


general circulation model


generalized likelihood uncertainty estimation


geomorphic-orogenic landscape evolution model


geomorphic transport function


landscape evolution model


regional climate model


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Center for Philosophy and History of ScienceBoston UniversityBostonUSA
  2. 2.Department of the History of ScienceHardvard UniversityCambridgeUSA

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