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. Monitoring networks aims at capturing the spatial and temporal variability ofone or several environmental variables in a specific environment. The optimalplacement of sensors in an ocean or coastal observatory should maximize theamount of collected information and minimize the development and operationalcosts for the whole monitoring network. In this study, the problem of the designand optimization of ocean monitoring networks is tackled throughout theimplementation of data assimilation techniques in the Shallow water HYdrodynamicFinite Element Model (SHYFEM). Two data assimilation methods – nudging andensemble square root filter – have been applied and tested in the Lagoon ofVenice (Italy), where an extensive water level monitoring network exists. Atotal of 29 tide gauge stations were available, and the assimilation of theobservations results in an improvement of the performance of the SHYFEM model, which went from an initial root mean square error (RMSE) on the water level of5.8 cm to a final value of about 2.1 and 3.2 cm for each of the two data assimilationmethods. In the monitoring network optimization procedure, byexcluding just one tide gauge at a time and always the station that contributesless to the improvement of the RMSE, a minimum number of tide gauges can befound that still allow for a successful description of the water levelvariability. Both data assimilation methods allow identifying the number ofstations and their distribution that correctly represent the state variable inthe investigated system. However, the more advanced ensemble square root filterhas the benefit of keeping a physically and mass-conservative solution of thegoverning equations, which results in a better reproduction of the hydrodynamicsover the whole system. In the case of the Lagoon of Venice, we found that, withthe help of a process-based and observation-driven numerical model, two-thirdsof the monitoring network can be dismissed. In this way, if some of the stationsmust be decommissioned due to a lack of funding, an a priori choice can be made,and the importance of a single monitoring site can be evaluated. The developedprocedure may also be applied to the continuous monitoring of other oceanvariables, like sea temperature and salinity.

Model-driven optimization of coastal sea observatories through data assimilation in a finite element hydrodynamic model (SHYFEM v. 7_5_65)