Skip to main content
SpringerLink
Log in

Models in Geosciences

  • Chapter
Book cover Springer Handbook of Model-Based Science

Part of the book series: Springer Handbooks ((SHB))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 269.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 349.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ABM:

agent-based model

ANN:

artificial neural network

CAESAR:

cellular automaton evolutionary slope and river

CHILD:

channel-hillslope integrated landscape development

CMIP:

coupled model intercomparison project

ESM:

Earth system model

GCM:

general circulation model

GLUE:

generalized likelihood uncertainty estimation

GOLEM:

geomorphic-orogenic landscape evolution model

GTF:

geomorphic transport function

LEM:

landscape evolution model

RCM:

regional climate model

References

  1. N. Oreskes: How earth science has become a social science. In: Special Issue: Climate and Beyond: The Production of Knowledge about the Earth as a Signpost of Social Change, ed. by A. Westermann, C. Rohr, Historical Soc. Res. 40 (2015) 246--270

    Google Scholar 

  2. M. Kleinhans, C. Buskes, H. de Regt: Terra incognita: Explanation and reduction in earth science, Int. Stud. Phil. Sci. 19(3), 289–317 (2005)

    Article  Google Scholar 

  3. G.K. Gilbert: Report on the Geology of the Henry Mountains (Government Printing Office, Washington 1877)

    Book  Google Scholar 

  4. G.E. Grant, J.E. O’Connor, M.G. Wolman: A river runs through it: Conceptual models in fluvial geomorphology. In: Treatise on Geomorphology, Vol. 9, ed. by J.F. Shroder (Academic, San Diego 2013) pp. 6–21

    Chapter  Google Scholar 

  5. W.M. Davis: The systematic description of land forms, Geogr. J. 34, 300–318 (1909)

    Article  Google Scholar 

  6. W.M. Davis: The geographical cycle, Geogr. J. 14, 481–504 (1899)

    Article  Google Scholar 

  7. I. Kant: Universal natural history and theory of the heavens or essay on the constitution and the mechanical origin of the whole universe according to Newtonian principles. In: Kant: Natural Science, ed. by E. Watkins (Cambridge Univ. Press, Cambridge 2012), transl. by O. Reinhardt, originally published in 1755

    Chapter  Google Scholar 

  8. P.-S. Laplace: Exposition du Système du Monde (Cambridge Univ. Press, Cambridge 2009), originally published in 1796

    Book  MATH  Google Scholar 

  9. N. Oreskes: From scaling to simulation: Changing meanings and ambitions of models in the earth sciences. In: Science without Laws: Model Systems, Cases, and Exemplary Narratives, ed. by A. Creager, E. Lunbeck, M.N. Wise (Duke Univ. Press, Durham 2007) pp. 93–124

    Chapter  Google Scholar 

  10. A. Daubrée: Études Synthétiques de Géologie Expérimentale (Dunod, Paris 1879), in French

    Google Scholar 

  11. A. Bokulich: How the tiger bush got its stripes: How possibly versus how actually model explanations, Monist 97(3), 321–338 (2014)

    Article  Google Scholar 

  12. M.K. Hubbert: Strength of the earth, Bull. Am. Assoc. Petroleum Geol. 29(11), 1630–1653 (1945)

    Google Scholar 

  13. R. Bagnold: The Physics of Blown Sand and Desert Dunes (Dover, Mineola 2005), originally published in 1941

    Google Scholar 

  14. D. Green: Modelling geomorphic systems: Scaled physical models. In: Geomorphological Techniques (Online Edition), ed. by S.J. Cook, L.E. Clarke, J.M. Nield (British Society for Geomorphology, London 2014), Chap. 5, Sect. 3

    Google Scholar 

  15. M. Weisberg: Simulation and Similarity (Oxford Univ. Press, Oxford 2013)

    Book  Google Scholar 

  16. E. Winsberg: Computer simulations in science. In: The Stanford Encyclopedia of Philosophy, ed. by E. Zalta http://plato.stanford.edu/archives/sum2015/entries/simulations-science (Summer 2015 Edition)

  17. M. Kirkby, P. Naden, T. Burt, D. Butcher: Computer Simulation in Physical Geography (Wiley, New York 1987)

    Google Scholar 

  18. G. Tucker: Models. In: Encyclopedia of Geomorphology, Vol. 2, ed. by A. Goudie (Routledge, London 2004) pp. 687–691

    Google Scholar 

  19. G. Tucker, S. Lancaster, N. Gasparini, R. Bras: The channel-hillslope integrated landscape development model (CHILD). In: Landscape Erosion and Evolution Modeling, ed. by H. Doe (Kluwer Acadmic/Plenum, New York 2001)

    Google Scholar 

  20. T. Coulthard, M. Macklin, M. Kirkby: A cellular model of holocene upland river basin and alluvial fan evolution, Earth Surf. Process. Landf. 27(3), 268–288 (2002)

    Article  Google Scholar 

  21. A. Rowan: Modeling geomorphic systems: Glacial. In: Geomorphological Techniques, ed. by L.E. Clark, J.M. Nield (British Society for Geomorphology, London 2011), Sect. 5, Chap. 5.6.5 (Online Version)

    Google Scholar 

  22. CMIP5: World Climate Research Programme’s Coupled Model Intercomparison Project, Phase 5 Multi-Model Dataset, http://cmip-pcmdi.llnl.gov/cmip5/ (2011)

  23. J. Katzav, W. Parker: The future of climate modeling, Clim. Change 132, 475–487 (2015)

    Article  Google Scholar 

  24. N. Oreskes: The role of quantitative models in science. In: Models in Ecosystem Science, ed. by C. Canham, J. Cole, W. Lauenroth (Princeton UP, Princeton 2003)

    Google Scholar 

  25. A. Nicholas, T. Quine: Crossing the divide: Representation of channels and processes in reduced-complexity river models at reach and landscape scales, Geomorphology 90, 318–339 (2007)

    Article  Google Scholar 

  26. A.B. Murray, C. Paola: A cellular model of braided rivers, Nature 371, 54–57 (1994)

    Article  Google Scholar 

  27. T. Coulthard, D. Hicks, M. Van De Wiel: Cellular modeling of river catchments and reaches: Advantages, limitations, and prospects, Geomorphology 90, 192–207 (2007)

    Article  Google Scholar 

  28. A.B. Murray: Contrasting the goals, strategies, and predictions associated with simplified numerical models and detailed simulations. In: Prediction in Geomorphology, ed. by P. Wilcock, R. Iverson (American Geophysical Union, Washington 2003) pp. 151–165

    Google Scholar 

  29. B.T. Werner: Complexity in natural landform patterns, Science 284, 102–104 (1999)

    Article  Google Scholar 

  30. A. Bokulich: Explanatory models versus predictive models: Reduced complexity modeling in geomorphology, Proc. Eur. Philos. Sci. Assoc.: EPSA11 Perspect. Found. Probl. Philos. Sci., ed. by V. Karakostas, D. Dieks (Springer, Cham 2013)

    Google Scholar 

  31. A.B. Murray: Reducing model complexity for explanation and prediction, Geomorphology 90, 178–191 (2007)

    Article  Google Scholar 

  32. S. Hall: At fault?, Nature 477, 264–269 (2011)

    Article  Google Scholar 

  33. J. Wainwright, M. Mulligan: Mind, the gap in landscape evolution modelling, Earth Surf. Process. Landf. 35, 842–855 (2010)

    Article  Google Scholar 

  34. T. Kuhn: The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago 2012), [1962]

    Book  Google Scholar 

  35. P. Suppes: Models of data, Proc. Int. Congr. Logic, Methodol. Philos. Sci., ed. by E. Nagel, P. Suppes, A. Tarski (Stanford Univ. Press, Stanford 1962) pp. 251–261

    Google Scholar 

  36. I. Lakatos: Falsification and the methodology of scientific research programmes, Proc. Int. Colloquium Phil. Sci.: Crit. Growth Knowl., Vol. 4, ed. by I. Lakatos, A. Musgrave (Cambridge Univ. Press, Cambridge 1970), London, 1965

    Chapter  Google Scholar 

  37. E. Rykiel: Testing ecological models: The meaning of validation, Ecol. Model. 90, 229–244 (1996)

    Article  Google Scholar 

  38. P. Edwards: Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming (MIT Press, Cambrindge 2010)

    Google Scholar 

  39. E. Lloyd: The role of complex empiricism in the debates about satellite data and climate models, Stud. Hist. Philos. Sci. 43, 390–401 (2012)

    Article  Google Scholar 

  40. R. Benson, P. Mannion: Multi-variate models are essential for understanding vertebrate diversification in deep time, Biol. Lett. 8(1), 127–130 (2012)

    Article  Google Scholar 

  41. A. Mc Gowan, A. Smith (Eds.): Comparing the Geological and Fossil Records: Implications for Biodiversity Studies (Geological Society, London 2011), No. 358. The Geological Society Special Publication

    Google Scholar 

  42. R. Giere: Using models to represent reality. In: Model-Based Reasoning in Scientific Discovery, ed. by L. Magnani, N. Nersessian, P. Hagard (Springer, New York 1999)

    Google Scholar 

  43. S. Norton, F. Suppe: Why atmospheric modeling is good science. In: Changing the Atmosphere: Expert Knowledge and Environmental Governance, ed. by C. Miller, P. Edwards (MIT Press, Cambridge 2001) pp. 67–106

    Google Scholar 

  44. N. Oreskes: Models all the way down (review of Edwards A Vast Machine), Metascience 21, 99–104 (2012)

    Article  Google Scholar 

  45. K. Beven: Environmental Modelling: An Uncertain Future? An Introduction to Techniques for Uncertainty Estimation in Environmental Prediction (Routledge, New York 2009)

    Google Scholar 

  46. H. Chang: Inventing Temperature: Measurement and Scientific Progress (Oxford Univ. Press, Oxford 2004)

    Book  Google Scholar 

  47. N.K.S. Oreskes: Frechette, K. Belitz: Verification, validation, and confirmation of numerical models in the earth sciences, Science 263, 641–646 (1994)

    Article  Google Scholar 

  48. N. Oreskes, K. Belitz: Philosophical issues in model assessment. In: Model Validation: Perspectives in Hydrological Science, ed. by M. Anderson, P. Bates (Wiley, West Sussex 2001) pp. 23–42

    Google Scholar 

  49. N. Oreskes: Evaluation (not validation) of quantitative models, Environ. Health Perspect. 106(supp. 6), 1453–1460 (1998)

    Article  Google Scholar 

  50. G. Lauder: On the inference of function from structure. In: Functional Morphology in Vertebrate Paleontology, ed. by J. Thomason (Cambridge Univ. Press, Cambridge 1995) pp. 1–18

    Google Scholar 

  51. J. Hutchinson, M. Garcia: Tyrannosaurus was not a fast runner, Nature 415, 1018–1021 (2002)

    Article  Google Scholar 

  52. M. Weisberg: Robustness analysis, Phil. Sci. 73, 730–742 (2006)

    Article  MathSciNet  Google Scholar 

  53. B. Calcott: Wimsatt and the robustness family: Review of Wimsatt’s re-engineering philosophy for limited beings, Biol. Phil. 26, 281–293 (2011)

    Article  Google Scholar 

  54. A. Saltelli, K. Chan, M. Scott: Sensitivity Analysis (Wiley, West Sussex 2009)

    MATH  Google Scholar 

  55. J. Hutchinson: On the inference of structure using biomechanical modelling and simulation of extinct organisms, Biol. Lett. 8(1), 115–118 (2012)

    Article  Google Scholar 

  56. D. Hamby: A review of techniques for parameter sensitivity analysis of environmental models, Environ. Monit. Assess. 32, 135–154 (1994)

    Article  Google Scholar 

  57. A. Saltelli, M. Ratto, T. Andres, F. Campologno, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola: Global Sensitivity Analysis: The Primer (Wiley, West Sussex 2008)

    MATH  Google Scholar 

  58. R. Snieder, J. Trampert: Inverse problems in geophysics. In: Wavefield Inversion, ed. by A. Wirgin (Springer, New York 1999) pp. 119–190

    Google Scholar 

  59. G. Backus, J. Gilbert: Numerical applications of a formalism for geophysical inverse problems, Geophys. J. R. Astron. Soc. 13, 247–276 (1967)

    Article  Google Scholar 

  60. M. Sen, P. Stoffa: Inverse theory, global optimization. In: Encyclopedia of Solid Earth Geophysics, Vol. 1, ed. by H. Gupta (Springer, Dordrecht 2011)

    Google Scholar 

  61. W. Sandham, D. Hamilton: Inverse theory, artificial neural networks. In: Encyclopedia of Solid Earth Geophysics, ed. by H. Gupta (Springer, Dordrecht 2011) pp. 618–625

    Chapter  Google Scholar 

  62. G. Belot: Down to earth underdetermination, Phil. Phenomenol. Res. XCI 2, 456–464 (2015)

    Article  Google Scholar 

  63. T. Miyake: Uncertainty and modeling in seismology. In: Reasoning in Measurement, ed. by N. Mössner, A. Nordmann (Taylor Francis, London 2017)

    Google Scholar 

  64. E. Tal: The Epistemology of Measurement: A Model-Based Account, Ph.D. Thesis (Univ. Toronto, London 2012)

    Google Scholar 

  65. S. Schumm: To Interpret the Earth: Ten Ways to be Wrong (Cambridge UP, Cambridge 1998)

    Google Scholar 

  66. S. Lane: Numerical modelling: Understanding explanation and prediction in physical geography. In: Key Methods in Geography, 2nd edn., ed. by N. Clifford, S. French, G. Valentine (Sage, Los Angeles 2010) pp. 274–298, 2003

    Google Scholar 

  67. J. O’Reilly, K. Brysse, M. Oppenheimer, N. Oreskes: Characterizing uncertainty in expert assessments: Ozone depletion and the west antarctic ice sheet, WIREs Clim. Change 2(5), 728–743 (2011)

    Article  Google Scholar 

  68. W. Parker: Predicting weather and climate: Uncertainty, ensembles, and climate, Stud. Hist. Phil. Mod. Phys. 41, 263–272 (2010)

    Article  Google Scholar 

  69. R. Frigg, S. Bradley, H. Du, L. Smith: Laplace’s demon and the adventures of his apprentices, Phil. Sci. 81, 31–59 (2014)

    Article  MathSciNet  Google Scholar 

  70. E.L. Thompson: Modelling North Atlantic Storms in a Changing Climate, Ph.D. Thesis (Imperial College, London 2013)

    Google Scholar 

  71. N. Odoni, S. Lane: The significance of models in geomorphology: From concepts to experiments. In: The SAGE Handbook of Geomorphology, ed. by K. Gregory, A. Goudie (SAGE, London 2011)

    Google Scholar 

  72. G. Sella, S. Stein, T. Dixon, M. Craymer, T. James, S. Mazzotti, R. Dokka: Observation of glacial isostatic adjustment in stable North America with GPS, Geophys. Res. Lett. 34(2), 1–6 (2007), L02306

    Article  Google Scholar 

  73. T. Chamberlin: The method of multiple working hypotheses, Science 15(366), 92–96 (1890)

    Google Scholar 

  74. R. Laudan: The method of multiple working hypotheses and the development of plate tectonic theory. In: Scientific Discovery: Case Studies, Boston Studies in the Philosophy of Science, Vol. 60, ed. by T. Nickles (Springer, Dordrecht 1980) pp. 331–343

    Chapter  Google Scholar 

  75. M. Richards: The cretaceous-tertiary mass extinction: What really killed the dinosaurs?, http://hmnh.harvard.edu/file/366291 (2015) Lecture given on February 3rd, 2015 at the Harvard Museum of Natural History

  76. C. Cleland: Prediction and explanation in historical natural science, Br. J. Phil. Sci. 62, 551–582 (2011)

    Article  Google Scholar 

  77. A.B. Murray: Cause and effect in geomorphic systems: Complex systems perspectives, Geomorphology 214, 1–9 (2014)

    Article  Google Scholar 

  78. C. Paola, K. Straub, D. Mohrig, L. Reinhardt: The unreasonable effectiveness of stratigraphic and geomorphic experiments, Earth Sci. Rev. 97(1–4), 1–43 (2009)

    Article  Google Scholar 

  79. P. Duhem: The Aim and Structure of Physical Theory (Princeton Univ. Press, Princeton 1954), trans. P. Weiner, 1906

    MATH  Google Scholar 

  80. K. Stanford: Underdetermination of scientific theory. In: Stanford Encyclopedia of Philosophy, ed. by N. Edward, E. Zalta http://plato.stanford.edu/archives/win2013/entries/scientific-underdetermination (Winter 2013 Edition)

  81. K. Beven: Prophecy, reality and uncertainty in distributed hydrological modelling, Adv. Water Resour. 16(1), 41–51 (1993)

    Article  Google Scholar 

  82. K. Beven, J. Freer: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol. 249(1–4), 11–29 (2001)

    Article  Google Scholar 

  83. K. Beven: Equifinality and uncertainty in geomorphological modeling, Proc. 27th Binghampton symp. geomorphol.: Sci. Nat. Geomorphol., ed. by B. Rhoads, C. Thorn (Wiley, Hoboken 1996) pp. 289–313

    Google Scholar 

  84. K. Beven, A. Binley: GLUE: 20 years on, Hydrol. Process. 28(24), 5897–5918 (2014)

    Article  Google Scholar 

  85. J.P.C. Kleijnen: Experimental design for sensitivity analysis, optimization, and validation of simulation models. In: Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. by J. Banks (Wiley, New York 1998) pp. 173–223

    Chapter  Google Scholar 

  86. N. Odoni: Exploring Equifinality in a Landscape Evolution Model, Ph.D. Thesis (Univ. Southhampton, School of Geography, Southhampton 2007)

    Google Scholar 

  87. R.L. Slingerland, G. Tucker: Erosional dynamics, flexural isostasy, and long-lived escarpments, J. Geophys. Res. 99, 229–243 (1994)

    Google Scholar 

  88. R. Knutti, J. Sedlácek: Robustness and uncertainties in the new CMIP5 climate model projections, Nat. Clim. Change 3, 369–373 (2013)

    Article  Google Scholar 

  89. E. Lloyd: Confirmation and robustness of climate models, Phil. Sci. 77, 971–984 (2010)

    Article  Google Scholar 

  90. W. Parker: When climate models agree: The significance of robust model predictions, Phil. Sci. 78, 579–600 (2011)

    Article  MathSciNet  Google Scholar 

  91. W. Parker: Ensemble modeling, uncertainty, and robust predictions, WIREs Clim. Change 4, 213–223 (2013)

    Article  Google Scholar 

  92. J. Lenhard, E. Winsberg: Holism, entrenchment, and the future of climate model pluralism, Stud. Hist. Phil. Mod. Phys. 41, 253–262 (2010)

    Article  Google Scholar 

  93. D. Masson, R. Knutti: Climate model genealogy, Geophys. Res. Lett. 38, L08703 (2011)

    Article  Google Scholar 

  94. R. Knutti, R. Furrer, C. Tebaldi, J. Cermak, G. Meehl: Challenges in combining projections from multiple climate models, J. Clim. 23(10), 2739–2758 (2010)

    Article  Google Scholar 

  95. A. Bokulich: How scientific models can explain, Synthese 180(1), 33–45 (2011)

    Article  Google Scholar 

  96. A. Bokulich: Models and explanation. In: Handbook of Model-Based Science, ed. by L. Magnani, T. Bertolotti (Springer, Dordrecht 2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Philosophy and History of Science, Boston University, 745 Commonwealth Ave., MA 02215, Boston, USA

    Alisa Bokulich

  2. Department of the History of Science, Hardvard University, 1 Oxford Street, MA 02138, Cambridge, USA

    Naomi Oreskes

Authors
  1. Alisa Bokulich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naomi Oreskes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alisa Bokulich .

Editor information

Editors and Affiliations

  1. Department of Humanities, University of Pavia, Piazza Botta 6, 27100, Pavia, Italy

    Lorenzo Magnani  & Tommaso Bertolotti  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bokulich, A., Oreskes, N. (2017). Models in Geosciences. In: Magnani, L., Bertolotti, T. (eds) Springer Handbook of Model-Based Science. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-30526-4_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30526-4_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30525-7

  • Online ISBN: 978-3-319-30526-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation