DSD-INT 2024 Morphological modelling of tidal creeks along arid coasts - Luo
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The document details a PhD project focused on morphological modeling of tidal creeks in arid coastal environments, examining the complex interactions between hydrodynamics, vegetation, and morphodynamics, particularly concerning mangroves. It outlines specific research aims including understanding morphological changes and predicting future scenarios influenced by climate change. The project leverages various modeling approaches and empirical data to assess the impacts on tidal creek evolution and the resilience of intertidal habitats.
DSD-INT 2024 Morphological modelling of tidal creeks along arid coasts - Luo
- 1. Morphological modelling of tidal creeks along arid coasts Xiaoya Luo, PhD student in UWA Supervisors: Ryan Lowe, Matt Hipsey, Arnold Van Rooijen, Daniel Raj David Collaborators in Deltares: Jasper Dijkstra, Bas van Maren, Jan Boersma
- 2. Overview of my PhD project Vegetation Hydrodyn amic Morpho- dynamic interactions Mangroves in Arid climate • Intertidal habitats along arid coasts are growing at their physiological limits. • Interactions between vegetation, hydrodynamics and morphodynamics are far more complex than bare tidal flats • Climate change and industrial development bring uncertainty to the survival of these habitats Background
- 3. To develop improved understanding and predictive models that explain the ecomorphological evolution of tidal creeks in arid climates Overview of my PhD project Aim 1 • Understand the morphological change of tidal creeks Aim 2 • Quantify the influence of vegetation on tidal creeks through static vegetation approach. Aim 3 • Unravel the feedback between vegetation and hydro-morphodynamic Aim 4 • Prediction under future scenarios
- 4. Overview of my PhD project Climate in the Pilbara coast Temperature and Rainfall Evaporation Reference: 1. BOM. (2020a) Data for Onslow airport, Learnmonth Airport and Barrow Island. www.bom.gov.au ; 2. Seashore Engineering Report (2021) Aridity Index (AI)= 𝐴𝑛𝑛𝑢𝑎𝑙 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙 𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑣𝑎𝑝𝑜𝑟𝑎𝑡𝑖𝑜𝑛 = 280 𝑚𝑚 3000 𝑚𝑚 =0.09
- 5. Overview of my PhD project Oceanic forcing in the Pilbara coast Tides M2 0.58 m S2 0.31 m K1 0.21 m O1 0.14 m semidiurnal Water level variations = Mean value Waves and Cyclones Track of Tropical Cyclone Vance in 1999 Wind waves have an order of 1.5m Significant wave height during cyclone could reach 5.7m Spring Tidal Range:2.0m Neap Tidal Range: <1.0m Main constituents in Exmouth Gulf Northwestern Australia Annual mean sea level (m) SOI (El Nino) (La Nina) Strong seasonal and inter-annual sea level variability
- 6. Overview of my PhD project Vegetation in the Pilbara coast Mangrove and Algal (cyanobacterial) mats distribution *Land coverage analysis results from our teamwork Mardie Project A
- 7. 1km 1km 2.1km Sections Items Value Unit Domain Domain Size 2×15 km Grid Size 20×20 m Physical parameters Uniform friction coefficient 0.023 s/m-1/3 Layer fraction 95% sand + 5% mud Sand Specific density 2650 kg/m3 Sand sediment diameter 0.00025 m Mud critical stress for erosion 0.125 N/m2 Mud critical stress for sedimentation 1000 N/m2 Morpho-Factor 100 Open boundary Astronomical tide M2 m Delft3D FM Morpho Model Morphological modelling of tidal creeks 1 Model settings Reference: Colina Alonso, A., van Maren, D. S., van Weerdenburg, R. J. A., Huismans, Y., & Wang, Z. B. (2023). Morphodynamic Modeling of Tidal Basins: The Role of Sand‐Mud Interaction. Journal of Geophysical Research: Earth Surface, 128(9), e2023JF007391
- 8. Preliminary Scenarios: Impacts of bed slope and tidal range on tidal creeks’ formation 15km MSL 15km MSL 15km MSL Micro-tidal system Meso-tidal system Macro-tidal system Tidal Range: 1.0m, 1.5m Tidal Range: 1.8m, 2.0m, 2.5m Tidal Range: 3.0m, 4.0m, 5.0m Reference: Xie, D., Schwarz, C., Kleinhans, M. G., Zhou, Z., & van Maanen, B. (2022). Implications of Coastal Conditions and Sea-Level Rise on Mangrove Vulnerability: A Bio-Morphodynamic Modeling Study. Journal of Geophysical Research: Earth Surface, 127(3). https://doi.org/10.1029/2021JF006301 After 200 morpho-year … Morphological modelling of tidal creeks 1
- 9. Tidal range =1.8m Tidal range =1.0m Tidal range =1.5m Tidal range =2.0m Tidal range =2.5m Tidal range =3.0m Tidal range =4.0m Tidal range =5.0m Micro-tidal system Meso-tidal system Macro-tidal system Morphological modelling of tidal creeks 1
- 10. • Results analysis: Intertidal area proportion Pinter = 𝐴𝑟𝑒𝑎 (𝑀𝑆𝐿~𝐻𝑊) 𝐴𝑟𝑒𝑎(~𝐻𝑊) In micro- and meso-tide system, intertidal area proportions are around 28%. For the macro-tide system, larger tidal range leads to large intertidal area proportion. 5.0 29% (25%) 4.0 27% (21%) 3.0 18% (17%) 2.5 28% (25%) 2.0 30% (21%) 1.8 28% (19%) 1.5 28% (29%) 1.0 28% (21%) 0.00025 0.0005 0.001 Morphological modelling of tidal creeks 1 Initial slope (m/m) Tidal range (m)
- 11. 5.0 0.0030 4.0 0.0041 3.0 0.0032 2.5 0.0029 2.0 0.0035 1.8 0.0031 1.5 0.0018 1.0 0.0023 0.00025 0.0005 0.001 Initial slope (m/m) Tidal range (m) Averaged Drainage Density (m/m2) • Results analysis: Drainage Density Morphological modelling of tidal creeks 1 y = 0.0004x1.146 R² = 0.89 1,0E+00 1,0E+01 1,0E+02 1,0E+03 1,0E+04 1,0E+05 1,0E+04 1,0E+05 1,0E+06 1,0E+07 Channel Length (m) Watershed Area (m2)
- 12. Morphological modelling of tidal creeks 1 • Results analysis: Drainage Density compared with Giralia networks y = 0,0004x1,0566 1,0E+03 1,0E+04 1,0E+05 1,0E+06 1,0E+07 1,0E+08 Total channel length (m) Watershed area (m2) 0 0,0002 0,0004 0,0006 0,0008 0,001 0,0012 0,0014 0 5 10 15 20 Drainage Density No. of tidal networks Averaged Density: 0.00105 Channel drainage density in modelled results is higher than that in the vegetated Giralia networks
- 13. Morphological modelling of tidal creeks 1 • Results analysis: Unchanneled Path 5.0 191 4.0 109 3.0 74 2.5 239 2.0 140 1.8 113 1.5 790 1.0 410 0.00025 0.0005 0.001 Tidal range (m) Average unchanneled length l (m) Initial slope (m/m) Unchanneled path represents the distance of a particle water at a point on the platform travels before reaching a channel. Unchanneled path increases with tidal range and decreases with initial bed slope.
- 14. Morphological modelling of tidal creeks 1 • Results analysis: Channel Efficiency The Hortonian length (the inverse of drainage density) divided by the mean unchanneled path: lH/l Capture the branching and meandering characteristics of the channel network. 5 1.75 4 2.24 3 4.22 2.5 1.44 2 2.04 1.8 2.85 1.5 0.70 1 1.06 0.00025 0.0005 0.001 Channel efficiency increases with initial bed slope and decreases with tidal range . Initial slope (m/m) Tidal range (m) Micro-tidal system Tidal range =1.5m Macro-tidal system Tidal range =4.0m Reference: 1. Marani, M., Belluco, E., D’Alpaos, A., Defina, A., Lanzoni, S., & Rinaldo, A. (2003). On the drainage density of tidal networks. Water Resources Research, 39(2). 2. Kearney, W. S., & Fagherazzi, S. (2016). Salt marsh vegetation promotes efficient tidal channel networks. Nature Communications, 7. https://doi.org/10.1038/ncomms12287 Channel patterns comparing examples Straight channel Dendritic channel
- 15. Morphological modelling of tidal creeks 1 • Next steps: Impact of Bed erodibility Sample name Site 1 Site 2 Site 3 D50(𝜇𝑚) 25.85 50.06 271 Clay content 12% 13% - Silt content 50% 37% - Sand content 38% 50% 100% Field work sites Examples of sediment property D50 varies by an order and fraction varies from mixed to sandy environment
- 16. • How will tidal creeks evolve in presence of arid mangroves species? what’s the role of mangroves? • How tidal creeks support mangroves’ survival and migration? Will mangrove adapt to future sea level rise? • The character of algal mats adjacent to creeks’ ends is little known, especially when affected by undulating terrain. • Can algal mats survive under the block of industrial banks? Future works: Habitats modelling 2 But a morphological model alone cannot answer these questions: Mangroves Tidal creeks Trap sediments Promote channel incision Reduce currents And more … Modify inundation and shear stress on mangroves
- 17. Future works: Habitats modelling 2 Empirical model • Correlate mangrove distribution with environmental factors • For example, inundation and exposure period, salinity limits • Based on static vegetation models (Trachytopes method in Delft3D FM) • Update hydro and morpho manually Fully coupled model • Apply physical process-based vegetation models with Delft3D FM, • Information exchange between hydrodynamic, morphodynamic and vegetation model will be automatically conducted by BMI(Basic Model Interface) Reference: Willemsen, P. W. J. M., Smits, B. P., Borsje, B. W., Herman, P. M. J., Dijkstra, J. T., Bouma, T. J., & Hulscher, S. J. M. H. (2022). Modeling Decadal Salt Marsh Development: Variability of the Salt Marsh Edge Under Influence of Waves and Sediment Availability. Water Resources Research, 58(1). https://doi.org/10.1029/2020WR028962 Workflow of coupling model
- 18. Future works: Habitats modelling 2 Empirical model Preliminary results from our teamwork Mardie Project B PROPAGULE AN CHORING PROPAGULE DES SICATION SEEDLING ESTABLISHMENT ADULT DROWNING Surface exposure time 48h 10day - - Surface temperature f(T) f(T) - - Inundation hydroperiod 0 0 1 tidal cycle (12hr) >0.1m 10 days > 0.5m Bottom stress - - - - Substrate SAND, MUD - - - Surface salinity / salt- crust - - < 80 psu -
- 19. Future works: Prediction 3 Sea Level Rise Predict tidal creek evolution and the survival of mangroves and algal mats under low, medium and high emission scenarios Salt Pond development Evaluate the impact of artificial levees on the expansion and survival of algal mats Different conceptual models for mangroves under sea level rise Existing Mortality under SLR Migration under SLR Adaptation and Migration under SLR Adaptation and Migration under accretion and SLR
- 20. Thanks for listening & Welcome to Perth!