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CST-323: Introduction to Earth System Modelling (INPE Course 2017)

  • Professors: Mariane Coutinho, Pedro R. Andrade,Gilberto Câmara
  • Lectures: Mondays and Thursdays, 14h00-16h30, Room 12, 1st floor, CCST building


Title Models Scenarios Concepts Exercises
1 Introduction
2 Modelling Programming Basics
Lua for TerraME Lua scripts nil, number, boolean, string, table, function Lua exercises
3 System Dynamics
Systems Theory
Systems Dynamics Tub (sysdyn) tub-scenarios (sysdyn) Model, Event, Timer, Chart Water in the Dam
Feedbacks Coffee, PopulationGrowth (sysdyn) coffee-scenarios, population-scenarios-1, population-scenarios-2 (sysdyn) Environment, instance of Model
Water Flows in Mono Lake Mono Lake Code in TerraME
Daisyworld Daisyworld model
4 Celular Automata
Cellular Automata Life (ca) Cell, CellularSpace, Neighborhood, Map, Random
Fire in the Forest Fire (ca) Fire in the Forest
5 Spatial Models
Runoff Runoff (terralib)
Geospatial data Project, Layer (terralib)
Deforestation Trajectory Deforestation, database
6 Climate Change Scenarios
Kaya Identity
Methane vs. CO2
7 Climate models

Additional Reading

Papers for Final Projects

The final project consists of an implementation and discussion of one of the models available here or the following papers.

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
G.Ch Sirakoulis, I. Karafyllidis, A. Thanailakis (2000) A cellular automaton model for the effects of population movement and vaccination on epidemic propagation. Ecological Modelling 133(3): 209–223
C. Beauchemina, J. Samuelb, J. Tuszynskia (2005) A simple cellular automaton model for influenza A viral infections. Journal of Theoretical Biology 232(2) 223–234
Medeiros, L. C., Castilho, C. A. R., Braga, C., de Souza, W. V., Regis, L., Monteiro, A. M. V. (2011). Modeling the dynamic transmission of dengue fever: investigating disease persistence. PLOS neglected tropical diseases, 5(1), e942.
M Janssen and N.D. Rollins (2012). Evolution of cooperation in asymmetric commons dilemmas. Journal of Economic Behavior and Organization, 81: 220-229. Available in CoMSES Computational Model Library).
S. Hoya White, A. Martín del Rey, G. Rodríguez Sánchez(2007), Modeling epidemics using cellular automata. Applied Mathematics and Computation, 186(1):193-202
Almeida, Rodolfo Maduro, and Elbert EN Macau. "Stochastic cellular automata model for wildland fire spread dynamics." Journal of Physics: Conference Series. Vol. 285. No. 1. IOP Publishing, 2011.
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.
Belousov–Zhabotinsky reaction
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.
Scherer A. & McLean A., (2002) Mathematical models of vaccination, British Medical Bulletin 2002;62 187-199.
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.
Energy scenarios for Brazil (in portuguese)

Papers for Final Projects: Secondary Choices

cst-317/classes2017.txt · Last modified: 2018/03/20 21:32 by pedro