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You are in Geoinformatics - Creative Commons :: Introduction to Earth System Science Modelling :: Classes 2020

CST-323: Introduction to Earth System Modelling/CAP-465: Modelling and Simulation of Earth Systems (INPE Course 2020)

  • Professors: Mariane Coutinho, Pedro R. Andrade, Gilberto Câmara
  • Lectures: Mondays and Tuesdays, 14h00-17h00, Zoom


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) Environmental spatially-explicit models; © Social simulation. 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 examples from the book of Andrew Ford (“Modelling the Environment”). In the third part, we focus on agent-based modelling, taking some examples from the literature (such as the Sugarscape model).

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.


“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.





Title Models Scenarios Concepts Exercises
1 Lógica de Programação
2 Lua for TerraME nil, number, boolean, string, table, function Lua exercises
3 Systems Dynamics Tub (sysdyn) tub-scenarios (sysdyn) Model, Event, Timer, Chart
4 Feedbacks Coffee, PopulationGrowth (sysdyn) coffee-scenarios, population-scenarios-1, population-scenarios-2 (sysdyn) Environment, instance of Model Water in the Dam
5 Epidemics SIR (sysdyn) infection-scenarios-1, infection-scenarios-2, infection-scenarios-3 (sysdyn)
6 Chaos ChaoticGrowth (sysdyn), Lorenz (sysdyn)
7 Daisyworld Daisyworld (sysdyn) daisy (calibration) MultipleRuns (calibration)
8 Cellular Automata Life (ca) Cell, CellularSpace, Neighborhood, Map, Random
9 Fire in the Forest Fire (ca) Fire in the Forest
10 Runoff Runoff (gis)
11 Cellular Data Cabeca de Boi (gis)
12 Deforestation Deforestation (base)
13 Agent-Based Modelling GrowingSociety, Disease (logo) Agent, Society, SocialNetwork
14 Sugarscape Sugarscape (logo)
15 Summary

Final Project

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 (students from CAP must choose an agent-based model). There are some suggestions below.

System Dynamics

Cellular Automata

S. G. Berjak, J. W. Hearne (2002) An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system. Ecological Modelling 148(2):133–15
Erica, Gisele, Murilo S. Hoya White, A. Martín del Rey, G. Rodríguez Sánchez(2007), Modeling epidemics using cellular automata. Applied Mathematics and Computation, 186(1):193-202
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.
Colasanti, R. L., R. Hunt, and L. Watrud. “A simple cellular automaton model for high-level vegetation dynamics.” Ecological Modelling 203.3 (2007): 363-374.
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.
White, R. and Engelen, G., 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and planning A, 25(8), pp.1175-1199.
Alda, Dione, Marcelo Barredo, J.I., Kasanko, M., McCormick, N. and Lavalle, C., 2003. Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata. Landscape and urban planning, 64(3), pp.145-160.
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

Any paper from Journal of Artificial Societies and Social Simulation
Pe'er et al. Virtual Corridors for Conservation Management, Conservation Biology (2005): 1997–2003
Garcia et al. Predicting evolution of insect resistance to transgenic crops in within field refuge configurations, based on larval movement. Ecol. Complex. 28, 94–103 (2016).
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.
cst-317/classes2020.1599530254.txt.gz · Last modified: 2020/09/07 22:57 by pedro