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CURAE – bridging the gap between regional CMEMS forecasts and coastal high-resolution applications M. Mestres 1 , J. Pein 2 , J. Schulz-Stellenfleth 2 , M. Espino 1 , J. Staneva 2 , L.B. Hansen 3 , A. Sánchez-Arcilla 1 1 Laboratori d’Enginyeria Marítima (LIM/UPC), Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona (Spain) 2 Helmholtz-Zentrum Geesthacht (HZG), Geesthacht (Germany) 3 DHI-Geographic Resources Analysis and Science (DHI-GRAS), Hørsolm (Denmark)

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Page 1: hZ t ] P ] v P Z P Á v P ] } v o D D^ ( } v } o Z ] P Z r } o µ ] } v o ] ] } v · 2020-04-30 · 'hyhorsphqw ri wzr frdvwdo lqirupdwlrq v\vwhpv edvhg rq &0(06 surgxfwv dgguhvvlqj

CURAE – bridging the gap between regional CMEMS forecasts and coastal high-resolution

applications

M. Mestres1, J. Pein2, J. Schulz-Stellenfleth2, M. Espino1, J. Staneva2, L.B. Hansen3, A. Sánchez-Arcilla1

1 Laboratori d’Enginyeria Marítima (LIM/UPC), Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona (Spain)2 Helmholtz-Zentrum Geesthacht (HZG), Geesthacht (Germany)3 DHI-Geographic Resources Analysis and Science (DHI-GRAS), Hørsolm (Denmark)

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Development of two coastal information systems, based on CMEMSproducts, addressing coastal-scale hydro-morphodynamic interactions

1.- Objectives

CMEMS Remote sensing Simulations Field data

CURAE Improved coastal BC Downscaling Interfacing

Generic conclusions on coastward evolution of CMEMS, based on two study sites with different characteristics, and different numerical approaches

Routines for flexible but robust interfacing between CMEMS and coastal applications

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2.- Motivation

Complex coastal areas Increasing anthropogenic pressures Multiple conflicting uses High socio-economic importance, but ...

CMEMS products are limited in these regions (inner shelf, ROFI..) Insufficient time/space resolution Unresolved coastal features (sediment transport, bio bed friction, ...) Boundary conditions (land discharges…)

Coastal users/managers require an enhanced forecasting and analysis ! Aquaculture (water quality) → Water / sediment / nutrient fluxes Dredging (port access, transport) → Water / sediment fluxes

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3.- The CURAE coastal pilots

Quickly-evolving seabed and coastline

Very shallow areas with vegetation

Breaching

Fangar Bay - most important bivalve culture area in Spanish Mediterranean

Highly dynamic bottom / geometry morphodynamic control

Nutrient & sediment input + Shallow bay + Closing mouth poor WQ

High water temperatures + poor water quality mortality

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Wadden Sea with Ems, Weser and Elbe estuaries

Most important port in North Sea

Highly dynamic bottom / geometry morphodynamic control

Co-existing processes (meso/macro tides, waves, river…) & scales difficult coupling

Shallow domain + river banks + dynamic mouth limited water exchanges

High impact of dredging limits to interventions Sentinel-2 RGD images

for 16 April, 2016 zoomed to Ems Estuary

3.- The CURAE coastal pilots

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In-situ data• Buoys • Oceanographic stations • HF radars• Field campaigns

Remote data• Sentinel 1a,b , 3• Sentinel 2 Numerical models

(structured / unstructured)• Hydrodynamics• Morphodynamics• Transport

CMEMS Regional

Forecasting

CMEMS remote / in situ Data

Su

staina

ble

CO

AS

TAL

man

ag

emen

t too

ls

CMEMS-based CURAE SYSTEMS

4.- The CURAE approach

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4.- The CURAE approach

Structured grid approach COAWST model (ROMS+SWAN+CSTMS) Coupled to CMEMS-IBI or CMEMS-MED Forced by MED-WAV or IBI-WAV (waves) Forced by ECMWF (atmosphere) Coastline updated from S 2 data

Unstructured grid approach SCHISM model Coupled to CMEMS-NWS Forced by CMEMS-NWS Bathymetry updated from S 2 data

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4.1- CMEMS downscaling

Downscaling ratio 1:5 and 1:3

Grid AA for SWAN only, since x for CMEMS wave product is 4 km

Grids A, B and C are common for both ROMS and SWAN

Grid sizes AA (224x157x1)A (162x152x10) – 350 mB (147x107x10) – 70 mC (272x122x10) – 23 m

a) Fangar Bay

4.1- CMEMS downscaling

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4.1- CMEMS downscaling

IBI_ANALYSIS_FORECAST_PHYS_005_001

MEDSEA_ANALYSIS_FORECAST_PHY_006_013

MEDSEA_ANALYSIS_FORECAST_WAV_006_017

INI (A, B, C)BRY (A)

INI (AA, A, B, C)BRY (AA)

ATM FRC

WIND

IBI_ANALYSIS_FORECAST_WAV_005_005

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4.1- CMEMS downscaling

NORTHWESTSHELF_ANALYSIS_FORECAST_PHYS_004_001

AMM15 (1.5 km) interpolated as boundary forcing: (2D), T, S, (3D)

Seamlessly refined grid from 1.5km to 50m across estuaries to resolve coastal/estuarine baroclinic processes.

DWD COSMO-EU

Weir river discharge(from authorities)

9·105 elements 4.8·105 nodesx = [50 m, 1.5 km]Nz = 21 S-layerst = 120 sHourly, daily forcing

b) German Bight

One way nest

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Morphodynamic feedback constantly reshape nearshore morphology

Evaluate role of up to date nearshore morphology: CURAE has incorporated S 2 derived bathymetry (SDB)

The integrated EMODnet 2018 + Sentinel- 2 Bathymetry has been compared to the HR topography data already used in the SCHISM model.

Differences show the high variability in the area of tidal channels and flats.

SDB in shallow zones in HR coastal models can reduce errors and improve reliability.

4.1- CMEMS downscaling

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5.- Results

5.1 Fangar Bay

a) Currents - Offshore validation with HFR

• Spatially variable correlation, but in general slightly larger for nested suite (domain A) than for CMEMS (r2 = 0.65 vs. 0.55)

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b) Currents – In-bay validation with currentmeters

• Good agreement, especially in energetic NW wind events, but also trends with weak currents ( ~ 5 cm/s)

5.- Scientific / technical results

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c) Water temperature – In-bay

• Good agreement, but highly sensitive to freshwater discharges from the canals! influence on open sea dynamics

5.- Scientific / technical results

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d) Waves

• Slight differences in terms of the atmosphericforcing selected

• In open sea, ECMWF is better than AEMET

• Significant differences in terms of theatmospheric forcing selected

• In the bay, ECMWF resoution is inadequate to simulate wave fields!

• Hs underestimated

Wind forcing used in the simulations

5.- Scientific / technical results

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e) Sediment – erosion/accretion in the bay for idealized wind cases

5.- Scientific / technical results

3% of time 0.8% of time

1.5% of time 0.2% of time

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5.2 German Bight

5.- Scientific / technical results

a) Comparison SCHISM vs. AMM15

• Slightly better performance for nested model, due to improved bathymetry• RMSE reduces by 10 - 20 cm• Correlation increases around 0.05 • SCHISM/AMM15 tend to over/underestimate variability

SSH at different tidal stations (black– SCHISM; red – AMM15)

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b) T and S from FerryBox data

Salinity:• Better salinity front with SCHISM• Otherwise similar performance

Temperature• Similar negative bias• Better spatial correlation in AMM15• SCHISM is closer in the estuary

5.- Scientific / technical results

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c) Dependence of tidal and SPM dynamics on density variations

5.- Scientific / technical results

• Control run and barotropic experiment impact of density on tides and sediment distribution?

• 3 estuaries (Ems, Wesser, Elbe, different runoff)• 8 classes of non-cohesive sediment - 0.06 to 2 mm

• Sorting by grain size and gradients Maxima of grain size classes: distributed

differently along estuary Salinity front traps sediments: maxima

located further seaward than in barotropicexperiment.

Secondary maxima in upriver direction are damped

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• Tidal amplitudes Increasing river runoff

increases tidal amplitude and reduces bottom friction (stratification)

Density affects tidal amplitude: Ems ≈ 6 cm and in Elbe > 10 cm of M2 amplitude (5%)

Density affects tidal asymmetry in frontal area (contribution similar to M4 magnitude)

5.- Scientific / technical results

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• Atmospheric forcing sensitivity usinga) Different forcings (CMEMS ERA5 vs G. Weather Service DWD)

b) Tuned cloud parameterization for incoming short wave radiation

• Strong correlation with observed SST (DWD & CMEMS)

• Reduced negative CMEMS bias. Overall RMSE from 1.54ºC to 0.98ºC

• Results can be systematicallyimproved with new observations

FerryBox Validation

5.- Scientific / technical results

d) Sensitivity to atmospheric forcing

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e) Sensitivity to river forcing (QR)

• QR from weirs (CR) vs. QR from LAMBDA (HM)

• HM trend to over-predict QR,increasing buoyancy forcing in Elbe plume and N. Frisian WaddenSea

• Velocity changes with QR

comparable to monthly mean in CR

• River forcing has a strong effect on simulated S and circulation fields

<Salinity (CR)> <Velocity (CR)>

LAMBDA- CR LAMBDA - CR

5.- Scientific / technical results

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• Strong morphodynamics control• Multi decadal data available to derive different

bathymetries• Deepening trend, increasing tidal amplitude (up to

20 cm sea side) due to reduced friction/increased volume inflow

• Relative M2 increase wrt M4 (tidal channels) indicating decrease in tidal asymmetry

Elbe salinity front unaffected in location but temporal changes in salinity range

5.- Scientific / technical results

e) Sensitivity to morphodynamic changes

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5.3 Satellite derived bathymetry

• Historical data and EMODnet outdated errors in dynamic regions• Merging of remote-sensing (active areas) with EMODnet (stable areas)• Data challenge: merging different scales (what to keep and how to decide)

Sentinel 2 mosaic EMODnet Intertidal bathymetry Data artefacts

EMODnet SDB

5.- Scientific / technical results

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• Relevance of bathymetric/shoreline updating for impact/risk analyses• Coastal zone info: coastline, erosion rates, beach width, dominant land

cover/use

18 Jan. 2020

23 Jan. 2020

Storm Gloria, January 2020

5.- Scientific / technical results

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• Robust agreement of metocean downscaled variables, providing a CMEMS coastal extension for structured/unstructured grids in 2 complementary sites.

• Density field controls hydro-morphodynamics, SSH and tidal asymmetry in the German Bight.

• In low energy (microtidal) cases, Eurlerian+Lagrangian validation required for SPM/ circulation fields

6.- Conclusions

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• Combined in-situ and RS products for coastal models

• … need for expertise on sensor/satellite• … SDB limited by physical processes (turbidity)

and numerical issues (merging)

Accretion

Erosion

• Local CMEMS predictions may differ from observations due to local winds, land discharges, bed friction needfor adequate characterization (winds, bathymetry, granulometry, coastline, land solid/liquid discharges…)

6.- Conclusions

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1) Limited compatibility/quality of input data, BCs and SDB/shorelines

2) Inability to rank error contributions from

• Land river and distributed discharges (freshwater/sediment/ nutrients) with a strong seasonal modulation

7.- Identified issues for CMEMS coastward evolution

• Short duration events with operational forcing publicly available (ECMWF, 6 hours) insufficient for observed behaviour S2 Catalan coast

Storm Gloria (23/01/2020)

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3) Limited multiple variable in-situ data for ground truthing numerical and RS data

4) Calibration/validation for low-energy hydrodynamics (restricted domains) in Eulerian frame: Evolve to new metrics and approaches

5) Artefacts and interpolation coastal errors for SDB + in-situ data (sun glint/turbidity), reducing flexibility for capturing specific events

• Tidal flat areas (WaddenSea) with tidal resuspension

• Chronic high turbidity in microtidal bays (Ebro delta)

7.- Issues for CMEMS evolution

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CURAE – bridging the gap between regional CMEMS forecasts and coastal high-resolution

applications


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