Runoff Tiago Garcia de Senna Carneiro Pedro Ribeiro
Runoff Tiago Garcia de Senna Carneiro Pedro Ribeiro de Andrade Gilberto Câmara Munster, 2014
How can geospatial data feed CA models? § § § Grid of cells Neighbourhood Finite set of discrete states Finite set of transition rules Initial state Discrete time
Brazil 2000 Espinhaço Range
Minas Gerais State 2000
Vie w
Serra do Lobo Pico do Itacolomi do Itambé
Serra do Lobo Pico do Itacolomi do Itambé m 9 k 9 km
rain Pico do Itacolomi do Itambé Serra do Lobo N
A very simple runoff model § § § Grid of cells Neighbourhood Finite set of discrete states Finite set of transition rules Initial state Discrete time
Grid of Cells – Height
Initial state – Rain in the heighest cells 1000 mm of rain in the cells above 200 m height
Neighborhood – based on height 48 34 37 25 30 33 23 21 34
Neighborhood – based on height 48 34 37 25 30 33 23 21 34 Note that the process takes place in parallel in space
States and transitions Dry Wet § What is the behavior of each state? § Are both states necessary?
Relaxing cellular automata definitions § § § Grid of cells Neighbourhood (not fixed) Finite set of discrete states Finite set of transition rules Initial state (geospatial data) Discrete time
Questions § How to verify whether the model is correctly implemented? § What are the limitations of this model?
- Slides: 17
