Modelling & Data Assimilation for Estuary Water Quality

1. Contributes of Modelling and Data Assimilation Techniques forWater Quality Operational Modelling in Estuaries: The Case of the Tagus Estuary Ângela Canas

2. Table of Contents • 1. Introduction • 2. Research Plan (objective, research question and methodology) • 3. State of the Art • 4. Improvement of Model Configuration • 5. Implementation of Data Assimilation Module • 6. Test of Data Assimilation Module in Improving TEPOMS • 7. Preliminary Conclusions

3. 1. Introduction • Relevance of estuaries: case of Tagus Estuary Diverse services: Tagus River Economical (transport, resources, disposal of waste) Natural Reserve of Tagus Estuary Lisbon Guia wastewater underwateroutfall Social (aesthetic and recreation services) industrial belt Almada Environmental (high primary production, nursing and habitat for ecological communities) Estoril coast beaches primary production navigation Conflicts affect services sustainability

4. 1. Introduction • Operational oceanography: “making, disseminating, and interpreting measurements of the seas and oceans in order to provide forecasts of future conditions” (Prandle, 2000) Operational forecast systems Adequate answer to coastal and estuarine areas management needs (Water Framework Directive) Prandle, 2000, Coastal Engineering, 41, 3-12

5. 1. Introduction • Tagus Estuary Pre-Operational Modelling System (TEPOMS) 3 nested domains: Fernandes (2005) at Maretec (IST) 72h 1 - 2D, hydrodynamics 162x162 (300m) 2 - 2D, 3D (11), hydrodynamics, water quality Objective: monitorization of Guia outfall pollution on estuary and beaches water quality Tagus Estuary 3 - 3D (11), hydrodynamics, outfall pollution dillution and dispersion Beaches 301x105 (10m) outfall 100x60 (35m) 168x223 (2km) + + Meteo station (Guia) Hydrometic station (Ómnias) Water sampling Sensor data Forcing: Meteo (Meteo IST) Tide (FES 95) River flow (INAG) Main end-user: SANEST S.A. (outfall operator) + Validation and posting (http://www.mohid.com/tejo-op/)

6. 1. Introduction • TEPOMS forecast strenghts: • Model using state of the art technology (MOHID Water): • Accounts for relevant processes (tide, wind, river) • Easily actualized (modular code) (Prandle, 2000) • Easily runnable (MPI, several OS) (James, 2002) • TEPOMS known forecast defficiencies: • Large scale circulation not adequate (e.g. slope current 250-1500m) • Meteo forcing not accounting topography • Poor grid resolution: • Horizontal: inside estuary • Vertical: at outfall • Lack of data assimilation

7. 2. Research Plan • Objective: • Improve the TEPOMS numerical modelling tool (MOHID Water) to help meeting a Tagus Estuary operational water quality forecast system needs • Research Question: • “Is it possible to improve the existing TEPOMS skill on forecasting hydrodynamic variables through an alternative model configuration or the use of data assimilation techniques?” • Methodology: • Assess TEPOMS face to face with state of the art operational systems for coastal and estuarine areas • Test hypothesis of TEPOMS improvement through advanced modelling techniques • Test hypothesis of TEPOMS improvement through data assimilation techniques

8. Data assimilation Numeric model Validation posting Measurement network 3. State of the Art: Operational Forecast Systems • Aim: • Water level / storm surge: North Sea (DMI, DCSM), North America (GoMMOOS, PORTS) • Water quality (new): Mediterranean Sea (MFS) • Structure: • Modelling approaches: • 2D: circulation and water level forecasts • 3D: biological/water quality forecasts • Larger scale boundary conditions: nesting in other systems (e.g. MFS, MERCATOR, HYCOM) • Atmospheric pressure effect: • Coupling with meteorological models level = - (Pmb – Prefmb).(0.01m/1000mb) (Inverted barometer, e.g. Cañizares, 1999)

9. Kalman filter (Kalman, 1960) Optimal: Linear MLarge cost (N) Sub-optimal: Non linear M: x Small cost (r+1) SEEK (Pham et al., 1998) P=LULt EOFs Sub-optimal: Non linear M: x,L Small cost (r+1) SEIK (Pham et al., 1998) 3. State of the Art: Data Assimilation • Sequential assimilation: time = t1 time = t2 Data assimilation scheme Model Model state forecast (xf, Nx1) Model state forecast Correction(K) Contrast(y-Hxf) Analysis (xa) ... ... Measurements (y) + + + error error (R) error (P)

10. Research hypothesis 1: “Yes, through the spatial expansion of large scale domain.” Better currents description: N N Bathymetry (324x218): - Old - MeteoGalicia - ETOPO 2’ 0.04ºx0.04º Velocity modulus (m/s) MOHID Water (tide only) Similar contrast with tide prediction: 0.02ºx0.02º Peniche Cascais Sines Correl V1 0.99932 0.99933 0.98673 Correl V2 0.99933 0.99934 0.98690 RMSE V1 0.345 m 0.361m 0.1308 m RMSE V2 0.427m 0.426 m 0.1314 m 4. Improvement of Model Configuration Basis for other Portuguese systems and for a 3D version (account slope current)

11. Contrast with measured detided water level: Tide gauges measurements 1 – Peniche 2 – Cascais 3 – Paço d’Arcos 4 – Trafaria 5 – Cacilhas 6 – Lisboa 7 – Seixal 8 – Montijo 9 – Alfeite 10 – Cabo Ruivo 11 – Sesimbra 12 – Alcochete 13 – Ponta da Erva 14 – Póvoa Santa Iria 15 – Vila Franca Inverted barometer mean sea level time series (Level 1 boundary, ERA40 data) 2D forced only with tide 4. Improvement of Model Configuration • Research hypothesis 2: “Yes, through the incorporation of the inverted barometer effect.”

12. Spatial distribution Stratification 4. Improvement of Model Configuration • Research hypothesis 3: “Yes, through the use of a boundary condition for stratification.” Methodology: Relaxation to Levitus (1982) T/S climatology in boundary 33 layers resolution Other improvements (advection, bathymetry) Validation data: SST images (MODIS) CTD near outfall (01/02/05)

13. cartesian geometry sigma geometry surface temperature 4. Improvement of Model Configuration • Research hypothesis 4: “Yes, through the use of 3D nested domains with different vertical resolution when and where needed.” Study of sediment transport in Nazare canyon Methodology: Implementation in MOHID Water code one-way connection with different 3D vertical resolution

14. Model historical data: Covariance calculation and EOF analysis: Hydrodynamic_#.hdf5 ... WaterProperties_#.hdf5 Assimilation PreProcessor Measurements (time series) EOF set Eigenvalues MOHID Water Sequential Assimilation (SFEK, SEEK) Model Modules Hydrodynamic WaterProperties 5. Implementation of Data Assimilation Module • Research hypothesis 5: “Yes, through data assimilation, with SFEK/SEEK method, of water level measurements.” • Methodology: • - Assimilation PreProcessor • Sequential Assimilation Module • Test in schematic 1D channel • Test in TEPOMS twin test • Test in TEPOMS assimilating tide gauges water level

15. 6. Test Data Assimilation Module in Improving TEPOMS EOF analysis: • Twin test: • True model: improved TEPOMS (without inverted barometer) • Wrong model: improved TEPOMS with 0.1 standard deviation error at mean sea level

16. 6. Test Data Assimilation Module in Improving TEPOMS Measurement location EOFs become smaller and smaller: Validation location (Hoteit, 2001)

17. 7. Preliminary Conclusions • Operating TEPOMS approaches state of the art modelling technology but defficiencies remain; • Changes to TEPOMS model configuration provide improvements and affect MARETEC work (http://data.mohid.com/data.xml, support tools) • Water level forecast inside estuary remains an important problem

18. 7. Preliminary Conclusions • An advanced data assimilation scheme was implemented in MOHID Water: • AssimilationPreProcessor tool can be used for validation or data assimilation • Sequential Assimilation Module usable for data assimilation aimed at: • initial modelling conditions (Objective Analysis) • on-line correction of model hydrodynamic and water properties forecasts (time series of observations) • Data assimilation in TEPOMS still does not provide acceptable results