1. UNIVERSITY of ROME “TOR VERGATA” XXV Doctoral Program A study of the Tiber River dynamics and coastal primary production with satellite data, circulation and primary production models Institute of Atmospheric Sciences and Climate of the Italian National Research Council (ISAC-CNR). Cinzia Pizzi

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

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 DANGEROUS MARINE ECOSYSTEM TOURISM FISHING (www.centroricerchemarine.it) RED TIDE BLOOM

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

6. Analyzed periods WINTER CASE STUDY SUMMER CASE STUDY DECEMBER 2008 NOVEMBER 2010 Typical Tiber River discharge (860 m 3 /s) Exceptional Tiber River discharge (1660 m 3 /s) since 1965 Typical Tiber River discharge (210 m 3 /s) JULY 2010

7. Dataset 1) SATELLITE DATASET (MODIS/AQUA) 1. Surface Chlorophyll-a (chl - mg m -3 ) 2. Diffuse light attenuation coefficient at 490 nm (K490; m -1 ) 3. Turbid water flag (L2flag – CASE 1 or open sea water & CASE 2 or coastal water) (December 2008, July and November 2010)

8. Dataset 2) MODEL DATASET (Dr. Inghilesi, ISPRA runs) output Lagrangian diffusion particles POM salinity/current fields 1)POM (Princeton Ocean Model) current, temperature, salinity fields 2) Lagrangian model (nested in POM) Trajectory/distribution of synthetic particles released at Tiber estuary LAM (Limited Area Model) wind to force POM circulation (December 2008, July and November 2010)

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

10. Wind Speed dataset Interc.SlopeMBE (m/s) RMSE (m/s) CorrCN. of pair ASCAT-LAM2.250.78-0.592.480.7741363 Results 1) LAM WIND VALIDATION WITH ASCAT (December 2008, July and November 2010)

11. Dataset 4) Primary Production (PP) from VGPNN model (Vertically Generalized Production Neural Network; Scardi, 2001) Data output: Primary Production (PP - g C m 2 day -1 ) for the Tyrrhenian Sea Data input: SATELLITE DATA (MYOCEAN products): Surface chlorophyll (chl - mg m -3 ) Sea Surface Temperature (SST – C°) Photosynthetically Available Radiation (PAR - E m -2 day -1 ) MODEL DATA (Circulation POM) Mixed Layer Depth (MLD) (December 2008, July and November 2010)

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

13. 25 Nov.5 Nov. 25 Nov. Sea Surface Temperature (NOVEMBER 2010) Current & Particle distribution (NOVEMBER 2010) Wind & particle concentration (NOVEMBER 2010) 90° 45° wind Results 2) River plume dynamics 12 Nov. NOVEMBER 2010 Coastal - offshore dynamics is partly coupled to Tyrrhenian Sea cyclonic eddy (e.g. Nov 25) partly wind driven (e.g. Nov 5) THEREFORE: Tiber plume moves both northwestwards & southeastwards

14. Current & particle distribution (JULY 2010) 14 Jul.30 Jul. Sea Surface Temperature (JULY 2010) 14 Jul. 30 Jul. Wind & particle concentration (JULY 2010) 24 Jul. Results 2) River plume dynamics 90° 45° wind 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.

15. 20 Dec. Results 2) River plume dynamics ModelTiber plume CHL K490 (water transparency) Tw (coastal and offshore water) SatelliteTiber plume 20 Dec. Model/plume circulation well reproduces reality as seen from satellite data

16. Results 3) Plume effects on primary production Comparison between Primary production and daily Tiber discharge S. Marinella – Anzio

17. PPRT-st.P Discharge t- 70.722.020.07 t- 80.742.030.06 time lag=8 days Results 3) Plume effects on primary production Algal biomass seems to be favoured by Tiber river discharge

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

19. PPRT-st.P Discharge t- 30.672.3400.04 t- 100.572.00.06 Results 3) Plume effects on primary production

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

21. Conclusions 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) Model/plume circulation reasonably well reproduces reality as seen from satellite validation 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) THIS WORK: PROTOTYPE TOOL FOR COASTAL MONITORING APPLICATIONS: WFD (Water Framework Directive) MFSD (Marine Framework Strategy Directive)

22. REFERENCES Barale V. and D. Larkin (1998). “ Optical remote sensing of coastal plumes an run-off in the mediterranean region ”. Journal of Coastal Conservation, 4: 51-68 Bignami, F., Sciarra, R., Carniel, S., and R. Santoleri (2007). “ Variability of Adriatic Sea coastal turbid waters from SeaWiFS Imagery ”. Journal of Geophysical Research, vol. 112, C03S10, doi:10.1029/2006JC003518 Fong, D. A. and W. R. Geyer (2002). “ The Alongshore Transport of Freshwater in a Surface-Trapped River Plume ”. Journal of Physical Oceanography, 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 ocean forecasting system: first phase of implementation (1998-2001) ”. Annals of Geophysics, 21: 3-20 Ruti, P. M., Marullo, S., D'Ortenzi, F. and M. Tremant (2008) “ Comparison of analyzed and measured wind speeds in the perspective of oceanic simulations over the Mediterranean basin: Analyses, QuikSCAT and buoy data ”. 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 variability of the phytoplankton bloom in the southern Adriatic Sea (1998 – 2000): Sea-viewing Wide Field-of-view Sensor observations and modeling study ”. Journal of Geophysical Research, 108, (c9), 8122, doi:10.1029/2002jc001636 Sorgente, R., Drago, A. F. and A. Ribotti (2003). “ Seasonal variability in the Central Mediterranean Sea circulation ”. Annales Geophysicae 21: 299 – 322 Scardi, M., (2001). “ Advances in neural network modeling of phytoplankton primary production ”. Ecological Modelling 146: 33-45