Advertisement

Models in Geosciences

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

Keywords

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.
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. 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--270Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    G.K. Gilbert: Report on the Geology of the Henry Mountains (Government Printing Office, Washington 1877)CrossRefGoogle Scholar
  4. 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. 6–21CrossRefGoogle Scholar
  5. 5.
    W.M. Davis: The systematic description of land forms, Geogr. J. 34, 300–318 (1909)CrossRefGoogle Scholar
  6. 6.
    W.M. Davis: The geographical cycle, Geogr. J. 14, 481–504 (1899)CrossRefGoogle Scholar
  7. 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 1755CrossRefGoogle Scholar
  8. 8.
    P.-S. Laplace: Exposition du Système du Monde (Cambridge Univ. Press, Cambridge 2009), originally published in 1796CrossRefzbMATHGoogle Scholar
  9. 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. 93–124CrossRefGoogle Scholar
  10. 10.
    A. Daubrée: Études Synthétiques de Géologie Expérimentale (Dunod, Paris 1879), in FrenchGoogle Scholar
  11. 11.
    A. Bokulich: How the tiger bush got its stripes: How possibly versus how actually model explanations, Monist 97(3), 321–338 (2014)CrossRefGoogle Scholar
  12. 12.
    M.K. Hubbert: Strength of the earth, Bull. Am. Assoc. Petroleum Geol. 29(11), 1630–1653 (1945)Google Scholar
  13. 13.
    R. Bagnold: The Physics of Blown Sand and Desert Dunes (Dover, Mineola 2005), originally published in 1941Google Scholar
  14. 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. 3Google Scholar
  15. 15.
    M. Weisberg: Simulation and Similarity (Oxford Univ. Press, Oxford 2013)CrossRefGoogle Scholar
  16. 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. 17.
    M. Kirkby, P. Naden, T. Burt, D. Butcher: Computer Simulation in Physical Geography (Wiley, New York 1987)Google Scholar
  18. 18.
    G. Tucker: Models. In: Encyclopedia of Geomorphology, Vol. 2, ed. by A. Goudie (Routledge, London 2004) pp. 687–691 Google Scholar
  19. 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. 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)CrossRefGoogle Scholar
  21. 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. 22.
    CMIP5: World Climate Research Programme’s Coupled Model Intercomparison Project, Phase 5 Multi-Model Dataset, http://cmip-pcmdi.llnl.gov/cmip5/ (2011)
  23. 23.
    J. Katzav, W. Parker: The future of climate modeling, Clim. Change 132, 475–487 (2015)CrossRefGoogle Scholar
  24. 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. 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)CrossRefGoogle Scholar
  26. 26.
    A.B. Murray, C. Paola: A cellular model of braided rivers, Nature 371, 54–57 (1994)CrossRefGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 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. 151–165Google Scholar
  29. 29.
    B.T. Werner: Complexity in natural landform patterns, Science 284, 102–104 (1999)CrossRefGoogle Scholar
  30. 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. 31.
    A.B. Murray: Reducing model complexity for explanation and prediction, Geomorphology 90, 178–191 (2007)CrossRefGoogle Scholar
  32. 32.
    S. Hall: At fault?, Nature 477, 264–269 (2011)CrossRefGoogle Scholar
  33. 33.
    J. Wainwright, M. Mulligan: Mind, the gap in landscape evolution modelling, Earth Surf. Process. Landf. 35, 842–855 (2010)CrossRefGoogle Scholar
  34. 34.
    T. Kuhn: The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago 2012), [1962]CrossRefGoogle Scholar
  35. 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. 251–261Google Scholar
  36. 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, 1965CrossRefGoogle Scholar
  37. 37.
    E. Rykiel: Testing ecological models: The meaning of validation, Ecol. Model. 90, 229–244 (1996)CrossRefGoogle Scholar
  38. 38.
    P. Edwards: Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming (MIT Press, Cambrindge 2010)Google Scholar
  39. 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)CrossRefGoogle Scholar
  40. 40.
    R. Benson, P. Mannion: Multi-variate models are essential for understanding vertebrate diversification in deep time, Biol. Lett. 8(1), 127–130 (2012)CrossRefGoogle Scholar
  41. 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 PublicationGoogle Scholar
  42. 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. 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. 67–106Google Scholar
  44. 44.
    N. Oreskes: Models all the way down (review of Edwards A Vast Machine), Metascience 21, 99–104 (2012)CrossRefGoogle Scholar
  45. 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. 46.
    H. Chang: Inventing Temperature: Measurement and Scientific Progress (Oxford Univ. Press, Oxford 2004)CrossRefGoogle Scholar
  47. 47.
    N.K.S. Oreskes: Frechette, K. Belitz: Verification, validation, and confirmation of numerical models in the earth sciences, Science 263, 641–646 (1994)CrossRefGoogle Scholar
  48. 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. 23–42Google Scholar
  49. 49.
    N. Oreskes: Evaluation (not validation) of quantitative models, Environ. Health Perspect. 106(supp. 6), 1453–1460 (1998)CrossRefGoogle Scholar
  50. 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. 1–18Google Scholar
  51. 51.
    J. Hutchinson, M. Garcia: Tyrannosaurus was not a fast runner, Nature 415, 1018–1021 (2002)CrossRefGoogle Scholar
  52. 52.
    M. Weisberg: Robustness analysis, Phil. Sci. 73, 730–742 (2006)MathSciNetCrossRefGoogle Scholar
  53. 53.
    B. Calcott: Wimsatt and the robustness family: Review of Wimsatt’s re-engineering philosophy for limited beings, Biol. Phil. 26, 281–293 (2011)CrossRefGoogle Scholar
  54. 54.
    A. Saltelli, K. Chan, M. Scott: Sensitivity Analysis (Wiley, West Sussex 2009)zbMATHGoogle Scholar
  55. 55.
    J. Hutchinson: On the inference of structure using biomechanical modelling and simulation of extinct organisms, Biol. Lett. 8(1), 115–118 (2012)CrossRefGoogle Scholar
  56. 56.
    D. Hamby: A review of techniques for parameter sensitivity analysis of environmental models, Environ. Monit. Assess. 32, 135–154 (1994)CrossRefGoogle Scholar
  57. 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)zbMATHGoogle Scholar
  58. 58.
    R. Snieder, J. Trampert: Inverse problems in geophysics. In: Wavefield Inversion, ed. by A. Wirgin (Springer, New York 1999) pp. 119–190Google Scholar
  59. 59.
    G. Backus, J. Gilbert: Numerical applications of a formalism for geophysical inverse problems, Geophys. J. R. Astron. Soc. 13, 247–276 (1967)CrossRefGoogle Scholar
  60. 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. 61.
    W. Sandham, D. Hamilton: Inverse theory, artificial neural networks. In: Encyclopedia of Solid Earth Geophysics, ed. by H. Gupta (Springer, Dordrecht 2011) pp. 618–625CrossRefGoogle Scholar
  62. 62.
    G. Belot: Down to earth underdetermination, Phil. Phenomenol. Res. XCI 2, 456–464 (2015)CrossRefGoogle Scholar
  63. 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. 64.
    E. Tal: The Epistemology of Measurement: A Model-Based Account, Ph.D. Thesis (Univ. Toronto, London 2012)Google Scholar
  65. 65.
    S. Schumm: To Interpret the Earth: Ten Ways to be Wrong (Cambridge UP, Cambridge 1998)Google Scholar
  66. 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, 2003Google Scholar
  67. 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)CrossRefGoogle Scholar
  68. 68.
    W. Parker: Predicting weather and climate: Uncertainty, ensembles, and climate, Stud. Hist. Phil. Mod. Phys. 41, 263–272 (2010)CrossRefGoogle Scholar
  69. 69.
    R. Frigg, S. Bradley, H. Du, L. Smith: Laplace’s demon and the adventures of his apprentices, Phil. Sci. 81, 31–59 (2014)MathSciNetCrossRefGoogle Scholar
  70. 70.
    E.L. Thompson: Modelling North Atlantic Storms in a Changing Climate, Ph.D. Thesis (Imperial College, London 2013)Google Scholar
  71. 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. 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), L02306CrossRefGoogle Scholar
  73. 73.
    T. Chamberlin: The method of multiple working hypotheses, Science 15(366), 92–96 (1890)Google Scholar
  74. 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. 331–343CrossRefGoogle Scholar
  75. 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. 76.
    C. Cleland: Prediction and explanation in historical natural science, Br. J. Phil. Sci. 62, 551–582 (2011)CrossRefGoogle Scholar
  77. 77.
    A.B. Murray: Cause and effect in geomorphic systems: Complex systems perspectives, Geomorphology 214, 1–9 (2014)CrossRefGoogle Scholar
  78. 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)CrossRefGoogle Scholar
  79. 79.
    P. Duhem: The Aim and Structure of Physical Theory (Princeton Univ. Press, Princeton 1954), trans. P. Weiner, 1906zbMATHGoogle Scholar
  80. 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. 81.
    K. Beven: Prophecy, reality and uncertainty in distributed hydrological modelling, Adv. Water Resour. 16(1), 41–51 (1993)CrossRefGoogle Scholar
  82. 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)CrossRefGoogle Scholar
  83. 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. 289–313Google Scholar
  84. 84.
    K. Beven, A. Binley: GLUE: 20 years on, Hydrol. Process. 28(24), 5897–5918 (2014)CrossRefGoogle Scholar
  85. 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. 173–223CrossRefGoogle Scholar
  86. 86.
    N. Odoni: Exploring Equifinality in a Landscape Evolution Model, Ph.D. Thesis (Univ. Southhampton, School of Geography, Southhampton 2007)Google Scholar
  87. 87.
    R.L. Slingerland, G. Tucker: Erosional dynamics, flexural isostasy, and long-lived escarpments, J. Geophys. Res. 99, 229–243 (1994)Google Scholar
  88. 88.
    R. Knutti, J. Sedlácek: Robustness and uncertainties in the new CMIP5 climate model projections, Nat. Clim. Change 3, 369–373 (2013)CrossRefGoogle Scholar
  89. 89.
    E. Lloyd: Confirmation and robustness of climate models, Phil. Sci. 77, 971–984 (2010)CrossRefGoogle Scholar
  90. 90.
    W. Parker: When climate models agree: The significance of robust model predictions, Phil. Sci. 78, 579–600 (2011)MathSciNetCrossRefGoogle Scholar
  91. 91.
    W. Parker: Ensemble modeling, uncertainty, and robust predictions, WIREs Clim. Change 4, 213–223 (2013)CrossRefGoogle Scholar
  92. 92.
    J. Lenhard, E. Winsberg: Holism, entrenchment, and the future of climate model pluralism, Stud. Hist. Phil. Mod. Phys. 41, 253–262 (2010)CrossRefGoogle Scholar
  93. 93.
    D. Masson, R. Knutti: Climate model genealogy, Geophys. Res. Lett. 38, L08703 (2011)CrossRefGoogle Scholar
  94. 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)CrossRefGoogle Scholar
  95. 95.
    A. Bokulich: How scientific models can explain, Synthese 180(1), 33–45 (2011)CrossRefGoogle Scholar
  96. 96.
    A. Bokulich: Models and explanation. In: Handbook of Model-Based Science, ed. by L. Magnani, T. Bertolotti (Springer, Dordrecht 2016)Google Scholar

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

Personalised recommendations