CST-323: Introduction to Earth System Modelling/CAP-465: Modelling and Simulation of Earth Systems

Professors: Pedro R. Andrade, Gilberto Câmara, Francisco Gilney Bezerra

Lectures: Mondays and Wednesdays, 13h30-16h30

Outline

Earth System Science is an interdisciplinary area that deals with the different aspects of interaction between society and nature. At the broadest level, Earth System Science models deal with natural systems (Climate, Ecosystems, Biogeochemical cycles, Hydrology) and its interaction with society (Economics, Sociology, Energy, Agriculture, Urbanisation, Demographics). Since Earth System Science covers a broad area of expertise, this course covers the basic fundamentals of nature-society interactions, by describing some of the foundational models in the area.

The course covers three main areas of expertise: (a) System Dynamics; (b) Cellular Automata; (c) Agent-based modeling. In the first part, we cover the basis of systems dynamics, following the Donella Meadows book, which is a good introduction to the field. In the second, we draw on some basic examples from the literature and use real data for creating models. In the third part, agent-based modelling, we implement some examples from the literature, some of them being more complex examples of models implemented using system dynamics.

Considering the broad nature of the field, the course does not require a background on Natural Sciences. It tries to present the basics of modelling through examples taken from the literature.

Motivation

"The biggest problem with models is the fact that they are made by humans who tend to shape or use their models in ways that mirror their own notion of what a desirable outcome would be." (John Firor, formed director of NCAR, cited in Myanna Lahsen's paper "Seductive Simulations".

"There are certain similarities between a work of fiction and a model: Just as we may wonder how much the characters in a novel are drawn from real life and how much is artifice, we might ask the same of a model; How much is based on observation and measurement of accessible phenomena, how much is based on informed judgment, and how much is convenience?" (Naomi Oreskes, professor of History of Science, also cited by Myanna Lahsen).

“A model is clear, decisive, and positive, but it is believed by no one but the man who created it. Observations, on the other hand, are messy, inexact things, which are believed by everyone except the man who did that work” (Harlow Shapley, American astronomer)

Conclusion: to understand what models are, a scientist needs to be able to develop models himself. He needs to master computer programs that allow him to grasp the basics of modelling activity. He needs to be understand the different techniques used in modelling and their limitations.

References

• Thinking in Systems. Donella Meadows, Chelsea Green Publishing, 2008.
• Modeling the Environment (2nd edition). Andrew Ford, Island Press, 2010.

Software

• TerraME
• ZeroBrane
• Configuring ZeroBrane to run TerraME

Classes

1. Introduction to Computing
2. Lua for TerraME: A Short Introduction
3. System Dynamics
4. Feedbacks
5. Epidemics
6. Chaos
7. Predator-Prey
8. Daisyworld
9. Cellular Automaton
10. Fire in the forest
11. Runoff
12. Cellular Data
13. Deforestation
14. Agent-based modeling
15. Predator-Prey
16. Summary

Assignments

1. Water in the dam (deadline TBD, 23h59 BRT)
2. Fire in the forest, Fire.lua, fire-average.lua (deadline TBD, 23h59 BRT)

Final Project

Deadline: TBD, 23h59

The final project consists of an implementation and discussion of a model described in a scientific paper that uses one of the paradigms presented during the course. There are some suggestions below. In case of replicating a paper, it is possible to submit the results to ReScience C.

Final projects

System Dynamics

• Any model from Andrew Ford's Modeling the Environment
• Simple climate models (some are not available anymore, check with the teacher)
• Scherer A. & McLean A., (2002) Mathematical models of vaccination, British Medical Bulletin 2002;62 187-199.
• Energy scenarios for Brazil (in portuguese)
• R. Seppelta, O. Richter. "It was an artefact not the result": A note on systems dynamic model development tools. Environmental Modelling & Software 20 (2005) 1543-1548.

Cellular Automata

• Fisch, Robert, Janko Gravner, and David Griffeath. "Threshold-range scaling of excitable cellular automata." Statistics and Computing 1.1 (1991): 23-39.
• Fisch, Robert. "Clustering in the one-dimensional three-color cyclic cellular automaton." The Annals of Probability (1992): 1528-1548.
• Li, Wentian. "Complex patterns generated by next nearest neighbors cellular automata." Computers & Graphics 13.4 (1989): 531-537.
• Chate, H. & Manneville, P. (1990). Criticality in cellular automata. Physica D (45), 122-135.
• Li, W., Packard, N., & Langton, C. (1990). Transition Phenomena in Cellular Automata Rule Space. Physica D (45), 77-94.
• S. Yassemi, S. Dragićevića, M. Schmidt(2008), Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour, Ecological Modelling, 210(1–2), 71–84
• Araujo and Celani (20166), Exploring Weaire-Phelan through Cellular Automata: A proposal for a structural variance-producing engine
• Rickert, M., Nagel, K., Schreckenberg, M. and Latour, A., 1996. Two lane traffic simulations using cellular automata. Physica A: Statistical Mechanics and its Applications, 231(4), pp.534-550.
• Karafyllidis, I. and Thanailakis, A., 1997. A model for predicting forest fire spreading using cellular automata. Ecological Modelling, 99(1), pp.87-97.
• Ermentrout, G.B. and Edelstein-Keshet, L., 1993. Cellular automata approaches to biological modeling. Journal of theoretical Biology, 160(1), pp.97-133.
• Alarcón, T., Byrne, H.M. and Maini, P.K., 2003. A cellular automaton model for tumour growth in inhomogeneous environment. Journal of theoretical biology, 225(2), pp.257-274.
• Yuan, W. and Tan, K.H., 2007. An evacuation model using cellular automata. Physica A: Statistical Mechanics and its Applications, 384(2), pp.549-566.
• Dormann, S. and Deutsch, A., 2002. Modeling of self-organized avascular tumor growth with a hybrid cellular automaton. In silico biology, 2(3), pp.393-406.
• Bersini, H. and Detours, V., 1994, July. Asynchrony induces stability in cellular automata based models. In Artificial Life IV (pp. 382-387). MIT Press, MA.

Agent-based Modeling

• Dou, Y., Millington, J.D.A., Bicudo Da Silva, R.F., McCord, P., Viña, A., Song, Q., Yu, Q., Wu, W., Batistella, M., Moran, E., & Liu, J. (2019). Land-use changes across distant places: Design of a telecoupled agent-based model. Journal of Land Use Science, 14(3), 191–209.)[https://doi.org/10.1080/1747423X.2019.1687769]
• Dou, Y., Yao, G., Herzberger, A., Da Silva, R.F.B., song, Q., Hovis, C., Batistella, M., Moran, E., Wu, W., & Liu, J. (2020). Landuse changes in distant places: Implementation of a telecoupled agent-based model. Journal of Artificial Societies and Social Simulation, 23(1), 11.)[https://doi.org/10.18564/jasss.4211]
• Any paper from Journal of Artificial Societies and Social Simulation
• Pe'er et al. Virtual Corridors for Conservation Management, Conservation Biology (2005): 1997–2003
• Malaquias et al. Larval Dispersal of Spodoptera frugiperda Strains on Bt Cotton: A Model for Understanding Resistance Evolution and Consequences for its Management. Scientific reports. 2017 Nov 23;7(1):16109.
• Brown, C.; Bakam, I.; Smith. P.; Matthews, R.B., (2016) An agent-based modelling approach to evaluate factors influencing bioenergy crop adoption in north-east Scotland., Global Change Biology Bioenergy, 8, 226-244.