Applications of Data Assimilation in MarineEcosystems

Ghada El Serafy, many many others

Ghada.elserafy@deltares.nl

Introduction Application to Data AssimilationData Assimilation

Stat

e

Time

Model OutputMeasurements

Reality

Estimate

Stat

e

Time

Model OutputMeasurements

Reality

Estimate

State Estimation

Introduction Application to Data AssimilationData Assimilation

Parameter Estimation

Model-Supported Monitoring of SPM

• Integration and assimilation of EO data into sedimenttransport model

• Reconstruction of autonomous reference conditions forprolonged time

• More accurate trend detection of suspended matter(SPM)

Impact assessment of large scale land reclamation on

turbidity in coastal waters

MoS2│ 2007-2014Introduction Application DA in Ecosystems

Data Assimilation of RS for Forecasting & AssessmentMoS2: Model-supported Monitoring of SPM in the Dutch coastal zone

Data Assimilation of RS for Forecasting & AssessmentMoS2: Model-supported Monitoring of SPM in the Dutch coastal zone

Validation: Comparison of time series

instantaneous &monthly moving average

A successful station

Validation: Comparison of time series

instantaneous &monthly moving average

A moderately successful station

Validation: Comparison of time series

instantaneous &monthly moving average

A less successful station

Data Assimilation of RS for Forecasting & AssessmentMoS2: Model-supported Monitoring of SPM in the Dutch coastal zone

Data Assimilation of RS for Forecasting & AssessmentMoS2: Model-supported Monitoring of SPM in the Dutch coastal zone

Warp (CEFAS)

Data Assimilation of RS for Forecasting & AssessmentMoS2: Model-supported Monitoring of SPM in the Dutch coastal zone

Oester Grounds (CEFAS)

17 juni 2016

Strategy proposed for DA

Sensitivity Analysis: Identified Parameters

(TauShields, FactResPup)Critical Shield stress for resuspension & factor for resuspensionpickup from second layer (directly proportional)

(VSedIMi, FRMiSedS2)Sedimentation velocity for fraction i & Fraction of total settling fluxinto second layer (inversly proportional)

(TaucRS1IMi, VResIMi)Critical resuspension stress layer S1 and resuspension rate functionfor fraction i (directly proportional)

17 juni 2016

Results of the Sensitivity Analysis (DD)

Rank(index)

Parameter Pair (2003-2007DD, 2007 corase)

1 (2) VsedIm1 - FrIm1SedS22 (1) TauShields - FactRespup3 (5) VResIM1 - TaucRS1IM14 (4) VsedIm2 – FrIm2SedS25 (3) VsedIm3 – FrIm3SedS2

17 juni 2016

1. Sedimentation velocity for medium fine &Fraction of total settling flux into second layer(sandy layer)

2. Critical Shield stress for resuspension & factorfor resuspension pickup from second layer

3. Critical resuspension stress layer S1 (fluffylayer) and resuspension rate function formedium fine fraction

Simulated Annealing

Step 1: Initializing – Start with an initialparameter value (predefined or random)

Step 2: Updating– Perturb the value (usually asmall distortion)

Step 3: Computing the objective function –calculate the change in the objective due to themove made.

Step 4: Decision step – Depending on thechange in the objective function, accept or rejectthe move.Ø If the perturbation causes a decrease ΔE it is

acceptedØ If it causes an increase it might be accepted,

depending on a probability

Step 5: Update and repeat– Update the value bygradual lowering.

Go back to Step 2.

The process continues until the terminationcriteria are satisfied...

Initialize parameter

Update parameter

Min Obj

17 juni 2016

The characteristics of SPM behaviormostly depends on bathymetry, weatherinduced variability, presence of sourcesor fresh water outflows. (Vos et al)

Parameter Estimation Regions

Based on a combination of hydrodynamicswaters are well mixed, seasonally stratifiedor permanently stratified and riverine influenceof nutrients and fresh water (Ospar Regions).

17 juni 2016

Parameter Estimation Regions & Coverage

Perventagecoverage figure

_ _ _2

1 1 1

1 ( )_

Nr regions Nr days Nr segments

MODEL MERISr d s

f C CNr days= = =

= -å å å

12/03/2013MoS2-II final update

Illustration of parameter response

TauShields (determines sand mobilisation which releases silt from sandy layer)

(baseline = 0.8 Pa)

12/03/2013MoS2-II final update

Illustration of parameter response

TauShields (determines sand mobilisation which releases silt from sandy layer)

(baseline = 0.8 Pa)

12/03/2013MoS2-II final update

Noordwijk 10 km offshore: increased storm response

Increased storm response

Decreased storm response

Optimization Results : Error Statistics

17 juni 2016

Optimization Results: Residuals Area of Interest

17 juni 2016

• Regional operational system for Algal Bloom forecasting inEurope’s coastal waters

• Integrate ocean color remote sensing (water transparency, Chl-a)with biogeochemical models

• Multi-model approach: system of systems with service portal• Use the model to interpolate & extrapolate the RS data

http://cobios.waterinsight.nl/viewer/

CoBiOS│2012-2014Introduction Application DA in Ecosystems

Integrate satellite products and ecological models into a operationalinformation service on algae blooms in Europe’s coastal waters.

:Ghada El Serafy,Sandra Gaytan Aguilar, Dana Stuparu, Meinte Blaas, DianaLucatero, Zhang Wang, Lisanne Rens, Tijsa Daggersand plenty of others

Calibration of Algae Bloom Forecasting SystemUsing Remote Sensing Images and concepts of Data Science

Coastal BiomassObservatory

Services

Ecological COastalStrategies and Toolsfor Resilient EuropeanSocietieS

Operational Forecasting System for the Coastal Waters

Main water quality issues of concern are related to:• Algal blooms• High turbidity• Bacterial contamination• Fish kill

Dutch water Service Line

Source: NASA (16-APR-2003)

• Blooms Observation Service Portal

• Integrating Earth Observation

• Prediction of 3D ecosystem state

Dutch waters Service Line

Import River Loads and boundary conditions

AmmoniumNitrateSilicatePhosphate

Import and pre-processing HIRLAM input data

HIRLAM meridional wind speed HIRLAM air pressure

HIRLAM cloud coverHIRLAM air temperature

Validation of EO products

Gapfilling of EO (Dineof based on EOF)

Noordwijk 20

Magnitude

P:C ratio Diatoms P

Chla:C ratio Diatoms P

Timing

Spec. Ext. Inor. Susp. Matter

Max Growth Diatoms P

A B

Probabilistic modelling of the bloom

Step 1: Initializing – Start with an initialparameter value (predefined or random)

Step 2: Updating– Perturb the value (usually asmall distortion)

Step 3: Computing the objective function –calculate the change in the objective due to themove made.

Step 4: Decision step – Depending on thechange in the objective function, accept or rejectthe move.Ø If the perturbation causes a decrease ΔE it is

acceptedØ If it causes an increase it might be accepted,

depending on a probability

Step 5: Update and repeat– Update the value bygradual lowering.Go back to Step 2. The process continues until the termination

criteria are satisfied...

Initial parameter

Update parameter

Min Obj

Calibration of the model (Simulated Annealing)

Calibration of model (BBN)

Nutrients: Nitrogen; Phosphorus; Silicate

Algae Species: Diatoms, Flagellets, Dinoflagellets, Phaeosystis

Parameters: Extinction coeff; Burial Rate; de-nitrification rate;max. growth rate

Validation of Products

Validation of Products

10 December 201337

Data Science for ecosystem services

Link existing GMES +non-GMES WebGISServices

Download GMES +non-GMES dataand provide asWebGIS services(local cache)

++

++

=

Dynamic multi-hazardmap

WebGIS Viewer + GUI

Python framework forWeb processing services

Multi-spatialanalysis

Web based territorialmanagement system

Database

• Examine feasibility & cost-benefit of risk preventionrelated to the use ofwetlands, coastal systemsand dry lands to mitigateflood.

• Combined Sentinel dataw/ field studies andstatistics in GIS

• Thematic maps &analyses for coastalregions such as WaddenSea and Northern Adriaticcoast.

• Training to implementalgorithm for risk analysis& “Building with Nature”

ECOSTRESS│2014-2015Introduction Application DA in Ecosystems