Presentation by Julien Groenenboom (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Tuesday, 14 November 2023, Delft.

1. Julien Groenenboom
Firmijn Zijl
Stendert Laan
Tammo Zijlker
Case study: DCSM-FM
Leveraging the results of a 3D
hydrodynamic model to improve the
accuracy of 2D water level forecasts
Delft3D User Days 2023
Image by Bing AI Image Creator

2. Content of presentation
• Dutch Continental Shelf Model – Flexible Mesh (DCSM-FM)
− Applications
− Model setup
− Model validation
− 2D vs. 3D
• Experiments
• Conclusions
Improved
2D
water
level
forecasting
–
DCSM-FM
2

3. Model applications
3
Ensemble Prediction
System (EPS)
Deterministic
water level forecasts
Currents and transport
Search and rescue
Oil dispersal
2D 3D
Water levels (incl. Mean
Dynamic Topography)
Geodetic applications 3D boundary conditions MetOcean
Water quality and ecology
DCSM-FM: Hydrodynamic models of the NW European Shelf used for multiple applications
➢ DCSM-FM forms basis for (coupled) Fine Sediment, Water Quality and Ecology modelling
➢ Techniques/methods developed for DCSM-FM are also used in other regional/global applications
Improved
2D
water
level
forecasting
–
DCSM-FM
DCSM-FM website: https://www.deltares.nl/en/expertise/projects/3d-dutch-continental-shelf-model-flexible-mesh

4. Model setup – Network and bathymetry
4
Yellow: 1/10° x 1/15° ~ 4 nm x 4 nm
Green: 1/20° x 1/30° ~ 2 nm x 2 nm
Blue: 1/40° x 1/60° ~ 1 nm x 1 nm
Red: 0.75’ x 0.5’ ~ 0.5 nm x 0.5 nm
→ 800 m isobath
→ 50 m isobath
→ 200 m isobath
Bathymetry Grid resolution Measurement locations
Improved
2D
water
level
forecasting
–
DCSM-FM

5. Model setup – Lateral open boundaries
5
• Lateral forcing from Global Ocean Physics Reanalysis
(Copernicus/CMEMS):
As daily mean fields (1/12° grid, 50 vertical levels, steps of about 1 m at surface)
− Temperature
− Salinity
− Velocities
− Water levels
• Water level boundaries:
− Sea surface height (from CMEMS)
− Tide (from FES2014 / GTSM / EOT20; 39 constituents)
− Storm surge (Inverse Barometer Correction; based on local, time-
varying air pressure)
Improved
2D
water
level
forecasting
–
DCSM-FM

6. Model setup – Surface forcing and
freshwater discharges
6
Meteorological forcing (ERA5 – hourly, ~30 km)
• Wind speed and atmospheric pressure
• Charnock parameter for sea surface roughness (consistent with ERA5)
• Heat fluxes computed based on:
− Solar (short wave) and thermal (long wave) radiation
− Air temperature, dew point temperature and cloud cover (and wind speed)
• Mean hourly precipitation and evaporation
Freshwater discharges
• Climatology derived from EHYPE (~900 discharges)
Monthly means derived from 2001-2013
• 7 most important Dutch rivers and discharge sluices
• 3 most important German rivers (Ems, Weser, Elbe)
Improved
2D
water
level
forecasting
–
DCSM-FM

7. Model validation
7
Sea surface temperature
Temperature stratification
Flow velocities
Salinity
Temperature
Improved
2D
water
level
forecasting
–
DCSM-FM

8. Water level validation – 3D DCSM-FM
8
total water level
• Subset of stations
• Analysis period: 2013-2017
• Harmonic analysis is used to separate total water levels into a tide and non-tidal/surge component
surge
tide
Improved
2D
water
level
forecasting
–
DCSM-FM

9. Station-
averaged
2013-2017
RMSE
tide (cm)
RMSE
surge (cm)
RMSE
total water
level (cm)
2D 6.9 4.2 8.1
3D 4.5 3.4 5.7
2D 4.8 4.1 6.3
Water level validation – 2D vs. 3D
9
2Dconverted
3D 3D minus 2Dconverted
-35%
Improved
2D
water
level
forecasting
–
DCSM-FM
release
-30% -2% -22%
-19% -30%
converted
• The 3D model performs better compared
to the directly-to-2D-converted model.
• We can improve this 2D-model and get
close to the 3D-results, see 2Drelease.
• How? See next slides.

10. 3D vs 2D
(directly-to-2D-converted model)
• The 3D model is more accurate as it
includes more physical processes.
• However, it comes with a drawback of
being computationally expensive
compared to a 2D model.
• Runtime is a key factor in operational
forecasting.
• The largest tidal error in the converted
2D model is in the SA-component.
Improved
2D
water
level
forecasting
–
DCSM-FM
10
3D DCSM-FM 2D (converted)
Component Average
amplitude
error [cm]
Average
phase
error [º]
RMS VD
[cm]
Component Average
amplitude
error [cm]
Average
phase
error [º]
RMS VD
[cm]
M2 1.0 -1.6 4.6 SA -4,2 58,5 6,5
S2 0.1 1.1 2.3 M2 1,2 -1,9 4,7
SA 0.3 -2.4 1.1 S2 0,0 -0,1 2,2
K1 -0.4 2.8 1.1 K1 -0,5 1,6 2,0
N2 0.2 -1.6 1.0 SSA 0,0 -38,1 1,6
3D DCSM-FM 2D (converted)
Number of vertical
layers
50 z-sigma-layers
(more info in link below) 1
Transport of salinity
and temperature included excluded
River discharges included excluded
Baroclinic
contribution to water
level open boundary
forcing
included excluded
Meteorological
forcing parameters
Air pressure, windx,
windy, Charnock,
dew point temperature,
air temperature, short
wave (solar) radiation,
long wave radiation,
rainfall, evaporation
Air pressure, windx,
windy, Charnock
Delft3D User Days 2020 – 3D Hydrodynamics – z-sigma-layering: https://vimeo.com/482624454/cb82e4d727

11. Experiments
• SA / seasonal variation in water levels
• Mean Dynamic Topography (MDT)
• Internal tide dissipation
11
Improved
2D
water
level
forecasting
–
DCSM-FM

12. Station-
averaged
2013-2017
RMSE
tide (cm)
RMSE
surge (cm)
RMSE
total water
level (cm)
Vector
difference
(cm)
SA
2D 6.9 4.2 8.1 6.5
3D 4.5 3.4 5.7 1.1
2D + Sa 5.3 4.2 6.9 2.5
Improved
2D
water
level
forecasting
–
DCSM-FM
12
2D
3D
2D+SA
Adding Sa on open boundaries
Impact of SA on tide
• Positive impact
on tides (focus is
on Dutch coast)
+SA

13. 13
Station-
averaged
2013-2017
RMSE
tide (cm)
RMSE
surge (cm)
RMSE
total water
level (cm)
Vector
difference
(cm)
SA
Vector
difference
(cm)
SSA
2D 6.9 4.2 8.1 6.5 1.6
2D+CMEMS 5.2 4.1 6.6 2.3 0.6
3D 4.5 3.4 5.7 1.1 0.5
Adding CMEMS’ sea surface heights on open boundaries
• Compared to SA on open boundaries, there is
a larger positive impact on international
stations as well.
• Also a positive effect on SSA (solar semi-annual).
Improved
2D
water
level
forecasting
–
DCSM-FM
Impact of CMEMS’ ssh on tide

14. Ongoing research: periodic SA-forcing
• SA-forcing not only on open boundaries, but also in the model domain.
• This approach can also be used for SSA.
14
Improved
2D
water
level
forecasting
–
DCSM-FM

15. Mean Dynamic Topography (MDT)
• Mean Dynamic Topography (MDT): Represents the long-term difference between Mean Sea Level
(MSL) and the geoid, which is the shape of the ocean surface under Earth's gravity alone.
• MDT Influences: MDT is significantly affected by spatial variations in density, making it challenging
to represent accurately in 2D models.
• MDT correction: mean water level 3D model minus mean water level 2D model (period 2013-2016).
• The resulting MDT field is converted to a ‘pseudo’ atmospheric pressure field, according to:
𝑃𝑀𝑆𝐿 [𝑁/𝑚
2
] = 𝑀𝐷𝑇 [𝑚] ∗ 𝑔 [𝑚/𝑠
2
] ∗ 𝜌𝑤𝑎𝑡𝑒𝑟 [𝑘𝑔/𝑚
3
]
• Due to use of Inverse Barometer Correction
further adjustments of open boundary conditions
are not required
15
Improved
2D
water
level
forecasting
–
DCSM-FM

16. Impact on tide
• MDT contributes to a better representation of water depth.
• The water depth has influence on propagation of tide and surge.
• Change of M2 tide along Dutch coast:
− Amplitude: -0.1 cm
− Fase: +0.5°
16
Improved
2D
water
level
forecasting
–
DCSM-FM

17. Validation (NAP-referenced observations)
17
2D 2D incl. MDT
3D
• MDT correction makes simplification of the operational system possible: no need for
operational ‘bias-correction’.
• Further MDT correction is possible through use of detailed 3D models or manual
adjustments of correction field.
Improved
2D
water
level
forecasting
–
DCSM-FM

18. Internal tide dissipation (ITD)
• Imposing internal tide friction → enhancing the energy dissipation → reducing the station-averaged
M2 tidal amplitude and phase error.
Improved
2D
water
level
forecasting
–
DCSM-FM
18
Station-
averaged
2013-2017
RMSE
tide (cm)
RMSE
surge (cm)
RMSE
total water
level (cm)
Vector
difference
(cm)
M2
2D 6.9 4.2 8.1 3.8
2D + ITD 6.6 4.2 7.8 3.6
3D 4.5 3.4 5.7 3.8

19. Comparison of 2Dconverted, 2Drelease and 3D
Improved
2D
water
level
forecasting
–
DCSM-FM
19
Station-
averaged
2013-2017
RMSE
tide (cm)
RMSE
surge (cm)
RMSE
total water
level (cm)
Vector
difference
(cm)
M2
Vector
difference
(cm)
SA
Vector
difference
(cm)
SSA
2D 6.9 4.2 8.1 3.8 6.5 1.6
2D 4.8 4.1 6.3 3.5 2.3 0.7
3D 4.5 3.4 5.7 3.8 1.1 0.5
converted
release

20. Conclusion
• 3D DCSM-FM provides more accurate water level forecasts (both tides and surge) compared to 2D.
• For operational forecasting, a (much faster) 2D-model is used.
− Instead of using the 3D-model, a 2D-model including additional refinements in Dutch coastal waters is used
• By incorporating information on 3D phenomena such as Mean Dynamic Topography and seasonal
water level variations, we enhance the 2D model's capabilities to forecast water levels.
• The enhanced 2D model resembles the 3D model in terms of simulated mean water levels and tides.
− We haven't yet explored improvements for the surge component in the 2D-model (see next slide).
Improved
2D
water
level
forecasting
–
DCSM-FM
20

21. Future work
• The enhanced 2D-model still contains some
low-frequency residuals.
• Potential methods to improve this:
− Periodic (SA) forcing
− Bias Kalman filter to correct for low-frequency
corrections using data-assimilation (OpenDA)
Improved
2D
water
level
forecasting
–
DCSM-FM
21
2Drelease
3D

22. Julien Groenenboom
Firmijn Zijl
Stendert Laan
Tammo Zijlker
Case study: DCSM-FM
Leveraging the results of a 3D
hydrodynamic model to improve the
accuracy of 2D water level forecasts
Delft3D User Days 2023
Image by Bing AI Image Creator