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Carbonate coasts as complex systems: A Case Study from Andros Island, Bahamas

Coastal Sediments ‘03. Carbonate coasts as complex systems: A Case Study from Andros Island, Bahamas. Gene Rankey and Brigitte Vlaswinkel University of Miami. Thanks to sponsors: NASA, National Science Foundation, Petroleum Research Fund, Contributors to the Comparative Sedimentology Lab

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Carbonate coasts as complex systems: A Case Study from Andros Island, Bahamas

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  1. Coastal Sediments ‘03 Carbonate coasts as complex systems: A Case Study from Andros Island, Bahamas Gene Rankey and Brigitte Vlaswinkel University of Miami Thanks to sponsors: NASA, National Science Foundation, Petroleum Research Fund, Contributors to the Comparative Sedimentology Lab Center for Southeastern Tropical Advanced Remote Sensing (CSTARS)

  2. Purposes • Quantify and describe spatial patterns on a modern carbonate tidal flat • Explore for the presence of chaos • Highlight potential implications for interpretation of modern and ancient Earth surface systems Focus of talk: Using quantitative morphometrics to provide insights on sedimentary system

  3. Outline • Background • Geomorphology of the tidal flat • Systemic facies patterns • Network structure • Implications

  4. Study Locations Miami Andros Island

  5. Tidal Flat Subenvironments 1 km • carbonate • microtidal • humid • Readily discernable on remote sensing data TM bands 4,2,1

  6. Tidal Flat Subenvironments Algal marsh Channel Mangrove pond Beach ridge

  7. Outline • Background • Geomorphology of the tidal flat • Systemic facies patterns • Subfacies size • Network structure • Implications

  8. 1 km 1 km Thematic Map ‘Spectral Lithotopes’ TM bands 4,2,1 Remote Sensing Data Analysis Similar spectral signatures, NOT necessarily similar facies

  9. Interpretation Calibration:Ikonos data existing mapsfield observations 1 km …more than ‘just a pretty picture’ Thematic Map ‘Spectral Lithotopes’

  10. Composition - Patch Size Exceedance probability (E.P.) is the cumulative probability (P [Y ≥ x]) of a given patch of area Y having an area > x Rank/(n+1)

  11. Composition - Patch Size Exceedance probability (E.P.) is the cumulative probability (P [Y ≥ x]) of a given patch of area Y having an area > x Exceedance probability has a power-lawdependence on the size of patch  Data is scale invariant or statistically self-similar Spatial configuration (lacunarity) also scales w/power-law

  12. Composition - Patch size For all intertidal facies: • Data are statistically described by power laws • Patch sizes have fractal distribution Inflection suggests 2 different geomorphic processes ‘the spectrometer is not a geomorphologist’

  13. Outline • Background • Geomorphology of Bahamian tidal flat • Facies patterns • Creek network structure • Creek & network morphometrics • Temporal character/evolution • Implications

  14. Tidal Creek Networks • Creeks most dominant components wrt sediment distribution and overall morphology Focus: • Network attributes (composition & configuration) • Temporal dynamics – compare active and inactive networks - testing for divergence(chaos…maybe) Distribution?

  15. Active networks Stabilized networks Tidal Creek Networks 1 km

  16. Tidal Creek Networks Horton (1945) stream numbering – fluvial channel segments Order 1 Order 2 Order 3

  17. Tidal Creek Segments Segment length by network – exponential Suggests stochastic processes, change in ‘rules’ at a certain length scale

  18. Tidal Creek Network Structure Entropy: measure of network disorder rij = probability of transition from a creek of order i into one of order j. E = 0 indicates a perfectly ordered system (streams of order i flow only into streams of order i+1).

  19. Tidal Creek Network Structure E > 0 E = 0

  20. Einactive = 0.403 All active networks are more disordered than inactive Tidal Creek Network Structure Entropy: measure of network disorder Among active networks, entropy increases exponentially from north to south Presence of divergencethrough time …chaos (?)…

  21. Historical Changes 1943 2001 • Creeks extending headwards, new creeks forming • …Observations consistent with predictions based on analysis

  22. Tidal Creek Flow Structure Data from UM/MIT field trip, March 2003 Flood tide; ‘predictable’

  23. Summary Nature of ‘Predictability’ varies depending on scale • Subfacies size - power-law • Creek lengths - stochastic • Creek flow structure – ‘deterministic’ • Entropy of active networks > inactive networks: •  • tidal creek networks more elaborate and diverge

  24. Interpretation – Chaos? “Mix of randomness and order” Predicts non-linearity Geomorphic evidence? Shoreline jumps Stratigraphic record? (TBA)

  25. System Dynamics “Mix of randomness and order” Macro-scale: System Fractal Facies patterns Meso-scale: Compartments Tidal creeks Stochastic Micro-scale: Constituents Deterministic Grain transport Important questions: Interfaces, characteristics, spatio-temporal domains

  26. Take-Home Messages • Nature and domains of co-existing order and disorder • Divergent behavior (chaos) • Implications for predictability

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