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EROS (Crave & Davy, 2001) “Stochastic model of erosion– sedimentation processes, based on cellular automata, which mimics the natural variability of climatic.

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Presentation on theme: "EROS (Crave & Davy, 2001) “Stochastic model of erosion– sedimentation processes, based on cellular automata, which mimics the natural variability of climatic."— Presentation transcript:

1 EROS (Crave & Davy, 2001) “Stochastic model of erosion– sedimentation processes, based on cellular automata, which mimics the natural variability of climatic events with deterministic transport processes” + Very simple concept: model based on water flux conservation and mass balance + Not rigid: properties such as channel geometry emerge from the action of stochastic processes + Produces realistic discharge patterns and fluvial network morphologies - Landscape evolution is driven primarily by sediment transport  “transport-limited” - It is somehow difficult to relate the model parameters to measurable quantities

2 + User friendly + Includes a wide range of fluvial incision laws and tectonic scenarios + Uses a Triangulated Irregular Network + Stochastic rainfall variability - No landsliding algorithm - Some properties are relatively rigid, e.g. channel geometry defined by hydraulic scaling relationships CHILD (Tucker at al., 2001)

3 CHILD (Tucker at al., 2001) Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008) Uplift rate increased 1 Ma ago

4 CHILD (Tucker at al., 2001) Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

5 Profile shape and landscape morphology Time = 0.0 My Fiamignano Fault Uplift Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008) Questions: can we reproduce the catchments’ morphology using a simple detachment-limited fluvial erosion law (model testing)? What is the effect of “dynamic channel adjustment” (sensitivity analysis)?

6 Profile shape and landscape morphology Time = 0.2 My Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

7 Profile shape and landscape morphology Time = 0.4 My Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

8 Profile shape and landscape morphology Time = 0.6 My Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

9 Profile shape and landscape morphology Time = 0.8 My Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

10 Profile shape and landscape morphology Time = 1.0 My Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

11 Profile shape and landscape morphology Time = 1.2 My Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

12 Profile shape and landscape morphology Time = 1.4 My Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

13 Profile shape and landscape morphology Time = 1.6 My Slope < 0 Basin internally drained Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

14 Profile shape and landscape morphology Time = 1.8 My Slope < 0 Basin internally drained Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

15 Profile shape and landscape morphology Time = 2.0 My Slope < 0 Basin internally drained Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)

16 Profile shape and landscape morphology Time = 2.2 My Slope < 0 Basin internally drained Example 1: landscape response to tectonic perturbation; application to the Central Apennines, Italy (Attal et al., 2008)  Simple DL model makes relatively good predictions.  Role of CHANNEL NARROWING (morphology + response time)

17 CHILD (Tucker at al., 2001) Example 2 (sensitivity analysis): effect of vegetation and wildfires on landscape development; application to the Oregon Coast Range (Istanbulluoglu & Bras, 2005) No vegetation Static vegetation cover  Vegetation cover and changes in cover through time (landslides, wild fires) strongly affects landscape morphology (relief, drainage density) (Roering et al., 1999)

18 Modelling landscape evolution Where are we? Models are getting more and more complex, include more and more processes and parameters, but… Some essential key issues need to be addressed:  Role of sediment (fluvial erosion),  Role of “life” (vegetation, bioturbation, etc.),  Role of climate

19 Climate and landscape evolution  Climate is highly variable at geological time scales  Climate (e.g. temperature, rainfall, storminess) strongly influences erosion rates and processes Hillslopes: landsliding, freeze-thaw cycles Rivers: erosion occurs during discrete events = floods which mobilize sediments

20 LANDSCAPE EVO. MODELS Climate and landscape evolution Problem: most studies involving landscape evolution modelling assume that climate parameters are constant over millions of years! Solution: coupling landscape evolution models with climate models Problem: SCALE!!! CLIMATE MODELS TIME STEP = 10s hours to 1000s year TIME STEP = 15 minutes to a few hours GRID RESOLUTION = 10s to 1000s meters MIN GRID RESOLUTION = 100 km! Climatic Research Unit, UEA Norwich

21 Vision for the future? Climatic Research Unit, UEA Norwich The aim: Solving the scale problems to couple climate and landscape development models  realistic predictions of landscape evolution as a result of climate variability + feedbacks between topography and climate  predictions at varied time and space scales (from minutes to millions of years, from a small stream to whole countries!)

22 Tectonics CHILD The Channel-Hillslope Integrated Landscape Development (CHILD) model (Tucker et al., 2001) Initial topography Climate parameters Hillslope transport + landslide threshold Fluvial sediment transport + deposition + bedrock erosion Additional parameters and algorithms: fluvial hydraulic geometry, bedrock and sediment characteristics, role of vegetation, etc. PAUSE

23 The Channel-Hillslope Integrated Landscape Development (CHILD) model (Tucker et al., 2001)

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31 If OPTDETACHLIM = 1  E = KA m S n

32 The Channel-Hillslope Integrated Landscape Development (CHILD) model (Tucker et al., 2001) E = k b (τ – τ c ) pb τ = k t q mb S nb = k t (Q/W) mb S nb Calculated by CHILD To set in the.in file We will consider: Erosion  Specific Stream power (Law 2): E  Ω/ W  E = K Q S / W  mb = 0.6; nb = 0.7; pb = 1.5; τ c = 0 kt = 1197 (typical value for bedrock rivers) kb or kr = poorly constrained parameters

33 The Channel-Hillslope Integrated Landscape Development (CHILD) model (Tucker et al., 2001)

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35 CHILD user guide

36 The Channel-Hillslope Integrated Landscape Development (CHILD) model (Tucker et al., 2001) Channel Width is calculated at each time step for every node: W = k w Q ωs Q b (ωb-ωs) = k w Q ωs if ωs = ωb

37 The Channel-Hillslope Integrated Landscape Development (CHILD) model (Tucker et al., 2001)

38 Visualizing the output using Matlab ctrisurf(‘filename’, ts, 0)

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