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UNIVERSITY of ROME “TOR VERGATA” XXV Doctoral Program

UNIVERSITY of ROME “TOR VERGATA” XXV Doctoral Program A study of the Tiber R iver dynamics and coastal primary production with satellite data, circulation and primary production models Institute of Atmospheric Sciences and Climate

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UNIVERSITY of ROME “TOR VERGATA” XXV Doctoral Program

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  1. UNIVERSITY of ROME “TOR VERGATA” XXV Doctoral Program A study of the Tiber River dynamics and coastalprimary production with satellite data, circulation and primary production models Instituteof AtmosphericSciences and Climate of the Italian National ResearchCouncil (ISAC-CNR). Cinzia Pizzi

  2. Motivation The algalbiomassactivityismodified from rivers’ load: NEGATIVE EFFECT POSITIVE EFFECT ROME Rome TYRRHENIAN SEA TIBER PLUME NASA MODIS - Sediment plume from the Tiber River, Italy (http://modis.gsfc.nasa.gov/)

  3. Motivation POSITIVE EFFECT NUTRIENTS INCREASE ALGAL PRODUCTIVITY SUPPORTS MARINE FOOD WEB

  4. Motivation NEGATIVE EFFECT • POLLUTANTS • (heavy metal, hydrocarb., etc.) • NUTRIENTS SURPLUS • HARMFUL ALGAL BLOOMS RED TIDE BLOOM • DANGEROUS • MARINE ECOSYSTEM • TOURISM • FISHING (www.centroricerchemarine.it)

  5. OBJECTIVES Development of a COASTAL MONITORING TOOL for the Tyrrhenian Sea (Tiber river) Rome COASTAL & OFFSHORE AREAS MORE EXPOSED TO THE TIBER LOAD • TIBER PLUME DYNAMICS MODULATION ON PRIMARY PRODUCTION • TIBER PLUME EFFECT

  6. Analyzed periods WINTER CASE STUDY Exceptional Tiber River discharge (1660 m3/s) since 1965 DECEMBER 2008 Typical Tiber River discharge (860 m3/s) NOVEMBER 2010 SUMMER CASE STUDY Typical Tiber River discharge (210 m3/s) JULY 2010

  7. Dataset1) SATELLITE DATASET (MODIS/AQUA) • (December 2008, July and November 2010) • 1. SurfaceChlorophyll-a (chl- mg m-3) 2. Diffuse light attenuationcoefficientat 490 nm (K490; m-1) • 3. Turbid water flag (L2flag – CASE 1 or open seawater & CASE 2 or coastal water)

  8. output output Dataset2) MODEL DATASET (Dr. Inghilesi, ISPRA runs) • (December 2008, July and November 2010) • current, temperature, salinityfields • POM (Princeton Ocean Model) • LAM (Limited Area Model) wind to force POM circulation • Trajectory/distribution of syntheticparticlesreleasedat Tiber estuary • 2) Lagrangian model (nested in POM) POM salinity/current fields Lagrangian diffusion particles

  9. Dataset 3) Wind data LAM WIND MODEL DATA (POM FORCING) • ASCAT WIND SATELLITE DATA • (LAM WIND VALIDATION)

  10. Results 1) LAM WIND VALIDATION WITH ASCAT • (December 2008, July and November 2010)

  11. Dataset4) Primary Production (PP) from VGPNN model (Vertically Generalized Production Neural Network; Scardi, 2001) • (December 2008, July and November 2010) • Data input: • SATELLITE DATA (MYOCEAN products): • Surfacechlorophyll(chl- mg m-3) • Sea Surface Temperature (SST – C°) • Photosynthetically AvailableRadiation(PAR-E m-2 day-1) • MODEL DATA (Circulation POM) • Mixed Layer Depth (MLD) Data output: Primary Production (PP - g C m2day-1) for the Tyrrhenian Sea

  12. Results 2) River plume dynamics Current & particledistribution (DECEMBER 2008) 30 Dec. 5 Dec. • DECEMBER 2008 • Coastal - offshore interaction dynamics IMPORTANT • Coastal circulation driven by offshore oceanographic features, NOT by wind • Tiber plume moves northwestwards Sea Surface Temperature (DECEMBER 2008) Wind & particleconcentration (DECEMBER 2008) 5 Dec. 30 Dec. 19 Dec. wind 45° Ekman Ekman 90°

  13. Results 2) River plume dynamics Current & Particledistribution (NOVEMBER 2010) 25 Nov. 5 Nov. • NOVEMBER 2010 • Coastal - offshore dynamics is • partlycoupled to Tyrrhenian Sea cycloniceddy (e.g. Nov 25) • partlywinddriven(e.g. Nov5) • THEREFORE: Tiber plume moves both northwestwards & southeastwards Sea Surface Temperature (NOVEMBER 2010) 25 Nov. 5 Nov. 12 Nov. wind 45° Ekman Ekman 90° Wind & particleconcentration (NOVEMBER 2010)

  14. Results 2) River plume dynamics Current & particledistribution (JULY 2010) 14 Jul. 30 Jul. • JULY 2010 • The cold cyclonic gyre is absent • Wind driven circulation • Tiber plume moves northwestwards, • southeastwards & offshore • Plume is more mobile because the summer MLD is shallower i.e. plume is thinner. Sea Surface Temperature (JULY 2010) Wind & particleconcentration(JULY 2010) 30 Jul. 14 Jul. wind 45° Ekman Ekman 90° 24 Jul.

  15. Results 2) River plume dynamics ModelTiberplume 20 Dec. SatelliteTiberplume • Model/plumecirculationwellreproducesreality asseen from satellite data CHL K490 (water transparency) Tw(coastal and offshore water) 20 Dec. 20 Dec. 20 Dec.

  16. Results 3) Plumeeffects on primary production Comparisonbetween Primary production and dailyTiber discharge S. Marinella – Anzio

  17. Results 3) Plumeeffects on primary production time lag=8 days Algal biomass seems to be favoured by Tiber river discharge

  18. Results2) Plumeeffects on primary production Particle concentration (Cp) model output December 2008 satellite chl (mg m-3) P. Production PP (g C m-2 day-1) High PP atgyre edge: favored by submesoscale dynamics (Lévy et al., 2001)

  19. Results3) Plumeeffects on primary production

  20. Hypotheses on observed summer PP variability 1) Nutrients are not limiting in the control box ZOOPLANKTON GRAZING (TOP - DOWN CONTROL) 2) Nutrients are limiting in the control box: NUTRIENTS SUPPLY FROM: • SUMMER AGRICULTURAL • FERTILIZING COASTAL UPWELLING

  21. Conclusions • Model/plumecirculationreasonablywellreproduces reality asseenfrom satellite validation • THIS WORK: PROTOTYPE TOOL FOR COASTAL MONITORING • APPLICATIONS: WFD (Water Framework Directive) • MFSD (Marine Framework Strategy Directive) WINTER: plume is heavily influenced by offshore structures (Tyrrhenian eddy) basin - wide dynamics important for coastal monitoring SUMMER: plume is wind mesoscale driven (absence of organized offshore dynamic structures impact in the coast) PRIMARY PRODUCTION: seems more directly connected to winter peak Tiber discharge; summer correlation not likely (to be verified) • FUTURE WORK • Extension to multi-year satellite/model datasets • Integration with in situ data (ISAC-CNR Tyrrhenian sea 2010- 2013 cruises)

  22. REFERENCES Barale V. and D. Larkin (1998). “Optical remote sensing of coastalplumes an run-off in the mediterranean region”. Journal of CoastalConservation, 4: 51-68 Bignami, F., Sciarra, R., Carniel, S., and R. Santoleri (2007). “Variabilityof Adriatic Sea coastalturbid watersfrom SeaWiFS Imagery”. Journal of GeophysicalResearch, vol. 112, C03S10, doi:10.1029/2006JC003518 Fong, D. A. and W. R. Geyer(2002). “The AlongshoreTransport of Freshwater in a Surface-Trapped River Plume”. Journal of PhysicalOceanography, 32: 957-972 Lévy, M., Klein, P. and A. M. Treguier (2001). "Impacts of sub-mesoscale physics on phytoplankton production and subduction“. Journal Marine Research, 59: 535-565 Pinardi, N., Allen, I., Demirov, E., De Mey, P., Korres, G., Lascaratos, A., Traon, P. Y., Maillard, C., Manzella, G. and C. Tziavos (2003). “The Mediterranean oceanforecastingsystem: first phase of implementation(1998-2001)”.Annals of Geophysics, 21: 3-20 Ruti, P. M., Marullo, S., D'Ortenzi, F. and M. Tremant(2008) “ Comparisonof analyzed and measured windspeedsin the perspective of oceanicsimulations over the Mediterranean basin: Analyses, QuikSCATand buoydata”. Journal of Marine Systems 70: 33–48 Santoleri, R., Banzon, V., Marullo, S., Napolitano, E., D’Ortenzio, F. and R. Evans (2003). “Year-to-year variabilityof the phytoplanktonbloom in the southernAdriatic Sea (1998–2000): Sea-viewing Wide Field-of-viewSensor observations and modelingstudy”. Journal of GeophysicalResearch, 108, (c9), 8122, doi:10.1029/2002jc001636 Sorgente, R., Drago, A. F. and A. Ribotti (2003). “Seasonalvariability in the Central Mediterranean Sea circulation”. Annales Geophysicae 21: 299–322 Scardi, M., (2001). “Advancesin neural network modeling of phytoplanktonprimaryproduction ”. EcologicalModelling 146: 33-45

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