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DSD-INT 2019 Ecomorphodynamic applications of Delft3D Flexible Mesh - Latella

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Presentation by Melissa Latella, Polytechnic University of Turin, Italy, at the Delft3D - User Days (Day 4: Water quality and ecology), during Delft Software Days - Edition 2019. Thursday, 14 November 2019, Delft.

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DSD-INT 2019 Ecomorphodynamic applications of Delft3D Flexible Mesh - Latella

  1. 1. Delft, November 14th 2019 Ecomorphodynamic applications of Delft3D Flexible Mesh Melissa Latella, Fabio Sola, Carlo Camporeale Envirofluidgroup - Department of Environment, Land and Infrastructure Engineering - Polytechnic of Turin, Italy melissa.latella@polito.it
  2. 2. Ecomorphodynamics How different processes shape the riparian environment Latella, Sola, & Camporeale Delft, November 14th 2019 1 Flow variability Sediment transport Vegetation dynamics
  3. 3. Delft3D Flexible Mesh for morphodynamic applications Key functionalities and potential uses Latella, Sola, & Camporeale Delft, November 14th 2019 2 • Flexibile mesh to describe river sinuosity and obstacles in the floodplain • Trachytopes to simulate the contribution of vegetation to local roughness • Simulation over both steady and evolving morphology ➢ Calibration of theoretical models (Case Study 1) ➢ Design of interventions for river restoration (Case Study 2)
  4. 4. Case Study 1 Study site & field activities Latella, Sola, & Camporeale Delft, November 14th 2019 3 Sediment sampling Tree measurements
  5. 5. Case Study 1 Study site & field activities Latella, Sola, & Camporeale Delft, November 14th 2019 4 Allometric relations & vegetation biomass Phase 1: Processing LiDAR data for vegetation height statistics Phase 2-3: Field-based measurements and biomass computation Phase 1: Wolman Pebble Count Phase 3: d50, d90 computation Δpi = (weight of fraction i)/(weight of total sample); Δqi = (number of pebbles in fraction i)/(total amount of pebbles); dmi = mean diameter of fraction i. Phase 4: Manning computation Phase 2: BaseGrain processing Grain distribution & Manning coefficient
  6. 6. Flexible mesh: - High resolution curvilinear grid for the main channel good representation of the bathymetry - Triangular grid with varying resolution for bars resolution can be increased on the most relevant areas and coarsened elsewhere lower computational time Latella, Sola, & Camporeale Delft, November 14th 2019 5 Grid generation Traditional grid: - Similar grid resolution throughout the whole domain - Cells not always aligned with flow direction - Not realistic representation of boundaries Case Study 1 Delft3D FM for the computation of the site-dependent probability of inundation
  7. 7. Latella, Sola, & Camporeale Delft, November 14th 2019 6 Trachytopes Phase 2: Areas with homogenous vegetation (QGIS) Bed and bare soil Phase 1: Vegetation height from LiDAR data (FUSION/LDV) Phase 3: Definition of .arl and .ttd input file for Delft3D Vegetated areas Case Study 1 Delft3D FM for the computation of the site-dependent probability of inundation
  8. 8. Latella, Sola, & Camporeale Delft, November 14th 2019 7 Hydrodynamic simulations and Probability of inundation Case Study 1 Delft3D FM for the computation of the site-dependent probability of inundation
  9. 9. Case Study 1 Calibration of a stochastic model for riparian vegetation dynamics Latella, Sola, & Camporeale Delft, November 14th 2019 8 Stochastic model and calibration strategy 𝑑ν 𝑑𝑡 = 𝜈 𝑚 (𝛽 − 𝜈) 𝑝 , ℎ < η 𝑑ν 𝑑𝑡 = −𝛼𝜈 𝑛 , ℎ ≥ η η topographic level N normalization constant τ integral scale of the process Pi probability of inundation Camporeale & Ridolfi, 2006 ν vegetation biomass α decay factor β carrying capacity m,n,p vegetation parameters h water level 𝑝 ν = 𝑁 α ν 𝛽 1−ατ − 𝛼+𝛽 𝑃 𝐼 αβτ 𝛽 − 𝜈 𝑃 𝐼 βτ −1 (α + β − ν) Biomass estimation LiDAR and field-based relationships Delft3D FM Calibration through least square minimization between real and computed pdfs
  10. 10. Case Study 1 Calibration of a stochastic model for riparian vegetation dynamics Latella, Sola, & Camporeale Delft, November 14th 2019 9 Stochastic model and calibration strategy ν vegetation biomass η topographic level Pi probability of inundation k decay rate β carrying capacity Optimised pdfs Calibrated parameters
  11. 11. Case Study 1 Calibration of a stochastic model for riparian vegetation dynamics Latella, Sola, & Camporeale Delft, November 14th 2019 10 Results and uses Real biomass first moment Computed first moment First moment for a 40% flow rate reduction
  12. 12. Case Study 2 Study site & intervention areas Latella, Sola, & Camporeale Delft, November 14th 2019 11 1 2
  13. 13. Latella, Sola, & Camporeale Delft, November 14th 2019 12 Curvilinear ~ 20x30 m Curvilinear ~ 2x5 m Triangular Case Study 2 Flexible mesh
  14. 14. Latella, Sola, & Camporeale Delft, November 14th 2019 13 River surveyor boat Drone Rectangularization Rectangularization Case Study 2 LiDAR ground points & bathymetry
  15. 15. Latella, Sola, & Camporeale Delft, November 14th 2019 14 Cloud Raster Case Study 2 Vegetation & Trachytopes
  16. 16. Case Study 2 Delft3D - Vegetation Latella, Sola, & Camporeale Delft, November 14th 2019 15
  17. 17. Latella, Sola, & Camporeale Delft, November 14th 2019 16 Flow rate: 582 m³/s Return time: 2 years Highway Intense bank erosion Case Study 2 Intervention area 1
  18. 18. Latella, Sola, & Camporeale Delft, November 14th 2019 17 Depth Velocity Case Study 2 Intervention area 1: Flow rate 582 m³/s , Return time 2 years
  19. 19. Latella, Sola, & Camporeale Delft, November 14th 2019 18 Bank erosion Case Study 2 Intervention area 2 Flow rate: 582 m³/s Return time: 2 years
  20. 20. Latella, Sola, & Camporeale Delft, November 14th 2019 19 Case Study 2 Intervention area 2: Flow rate 582 m³/s , Return time 2 years Depth Velocity
  21. 21. Delft3D Flexible Mesh for morphodynamic applications Conclusions & Future steps Latella, Sola, & Camporeale Delft, November 14th 2019 20 • Delft3D FM can be a useful tool to study ecomorphodynamic processes • It can be applied to the calibration of theoretical models • It can be applied to the design of real interventions ➢ Adding of morphological evolution for the Case Study 2 ➢ Implementation of the dynamic vegetation module
  22. 22. Delft, November 14th 2019 Thanks for your attention Melissa Latella, Fabio Sola, Carlo Camporeale Envirofluidgroup - Department of Environment, Land and Infrastructure Engineering - Polytechnic of Turin, Italy melissa.latella@polito.it

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