Presentation by Bregje van Wesenbeeck (Deltares) at the Seminar Models and decision-making in the wake of climate uncertainties, during the Deltares Software Days South-East Asia 2023. Wednesday, 22 February 2023, Singapore.

1. Bregje van Wesenbeeck
@BregjevW
Uncertainties in modelling
flood risk reduction
capacities of vegetation
bregjevanwesenbeeck

2. Nature-based solutions
2
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

3. Assisting implementation of nature-based solutions
3
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

4. Short-term functional performance
4
• Functions:
− Flood risk reduction
− Habitat
− Nutrient and carbon cycling
− Sediment sink
− Recreation
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

5. Long-term dynamics and performance
• NbS extent:
− SLR
− Sediment supply
− Wave climate
− Vegetation cover
5
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

6. NBS modeling tools
• Evaluation of wave attenuation function, ‘static’:
− XBeach (Mendez & Losada formulation)
− SWAN / Delft3D-WAVE (also Mendez & Losada, but
recently included spectral dissipation of Jacobsen et al
2019)
− M-Flat: transect-based model
• Flood risk reduction of NBS in a 2D spatial context
− Delft3D FM Suite with D-Flow FM and D-Waves
modules
• Landscape development, ‘dynamic’:
− NBS dynamics (Python)
− D-Flow FM + Python
→ 2D: Trachytope (Baptist et al. 2007) roughness +
reduced bed shear stress
→ 3D: k-epsilon ‘rigid rod’ model
6
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

7. Static wave attenuation with SWAN for design
7
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
-20 -10 0 10 20 30 40 50 60 70 80 90 100
x (m)
Wave
Height
(m)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
Ground
level
(m)
NAP
VF000
HS1
HS2
HS3
Van Wesenbeeck et al. 2017
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

8. Global static transect modelling with SWAN
8
Van Zelst et al. 2022
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

9. Data for model calibration under extremes
9
Model
Calibration
Validation
Br
jev
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

10. 10

11. Van Wesenbeeck et al. 2022
Br
jev

12. Calibrating numerical models
12
Van Wesenbeeck et al. 2022
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

13. Flood risk reduction of NBS in a 2D spatial context
13
Offshore conditions RP50 Hs = 2,6 m, Tp = 10,4 s, SS = 2,3 m + MSL

14. Model components
14
Delft3D FM
D-Flow FM model
Grid Bathymetry
Boundary
conditions
Bed roughness Mangroves
Delft3D 4
Delft3D-WAVE model
OUTPUT
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

15. Mangrove extent
• Base: GlobalMangroveMap (Bunting et al., 2022)
• Estimate of future mangrove extent based on elevation and expected inundation level
15
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

16. Mangrove schematization
• Fieldwork complemented with data from literature
• CD – KC relationship from van Wesenbeeck et al. (2022)
16
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

17. Results
17
• Fringing mangroves effectively reduce incoming
storm waves
• Increased reduction in wave heights with restoration
measures
• Flood depths are hardly affected by mangrove
presence (median depth 0,84 m)
• 76% of the area is flooded in an event with RP of 50
years
• 16780 peope are exposed to flooding in a 1 in 50
year storm
• 26% and higher lowering in erosional forces due to
mangrove presence
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

18. Dynamic process-based modelling of NBS/ecosystems
18
• Use of widely used, generic models
D-Flow FM and SWAN allows for link with
larger environment (estuary)
• Delft3D Flexible Mesh
• Vegetation module (Temmerman et al., 2007;
Geology)
• other biophysical models can be included
• Coupling via BMI (Hutton et al., 2020; JOSS)
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

19. Dynamic process-based modelling of NBS/ecosystems
19
Basic ecological model in Python. Codes for:
• marshes
• mangroves
• rivers
• seagrass
• coral
• benthic fauna
Also facilitates post-processing (evaluation).
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

20. Ongoing work
20
• Spatially varying vegetation characteristics
(aside from stem density)
• Input option to provide combined CD
- KC value
• Looking into dissipation of the wave spectrum
and applying non-hydrostatic mode
• Dynamic Cd
• Flexibility of vegetation and effect on wave
dissipation
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

21. Design and management of Nature based Solutions
21
• M-Flat: contact Mick van der Wegen Mick.vanderWegen@deltares.nl
• NBS dynamics
− Bob Smits Bob.Smits@deltares.nl
− Jasper Dijkstra Jasper.Dijkstra@deltares.nl
Vincent
van Zelst
Pim
Willemsen
Bob
Smits
Jasper
Dijkstra
Peter
Herman
Bregje van
Wesenbeeck
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation

22. How to get started?
• XBeach:
− Code: http://xbeach.readthedocs.io/
− Example with vegetation: https://github.com/openearth/xbeach-
docs/blob/master/docs/tutorials/
vegetation_lab/index.rst
• Delft3D FM Suite: D-Flow FM & Python:
− Code is available Open Source:
https://svn.oss.deltares.nl/repos/delft3d/trunk/src/
− Executable through Deltares Service Package
− Python code: available on GitHub:
https://github.com/Deltares/NBSDynamics
DSD-SEA
2023
Uncertainties
in
modelling
flood
risk
reduction
capacities
of
vegetation
22
On GitHub

23. Contact
www.deltares.nl
info@deltares.nl
@deltares
@deltares
linkedin.com/company/deltares
facebook.com/deltaresNL