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Modeling the ocean from the open sea to the coasts requires an adequate choice of the vertical coordinate system.A category of generalized vertical coordinates, the so-called z∗ coordinates ([Stacey et al 1995; Adcroft and Campin 2004]), has been implemented into an unstructured grid, finite elements and layer integrated model which was previously z−geopotential coordinate based.Idealized and realistic test cases are shown to assess the vertical coordinate impact on key physical processes, i.e. the internal tide generation at a continental slope, a river plume shape and intensity and the salt water intrusion.To our knowledge this is the first work that uses the z∗ coordinates in an estuary and river plume with an unstructured grid model. Other studies have used the z∗ coordinates with unstructured grid models covering the global ocean ([Ringler et al 2013, Scholz et al 2019] as examples), but without assessing their performance for the small scale dynamics of estuaries and river plumes. Coastal ocean models are currently mostly based on the terrain following coordinates, used within optimised or hybrid approaches ([Zhang et al 2015; Bruciaferri et al 2018, Fofonova et al 2021, Wise et al 2022] as examples).The results show that the z∗ coordinate model produces, with respect to z−geopotential coordinates, a stronger water column stratification. An idealized twin experiment configuration of the model to simulate the production of internal tides at the continental slope confirms that the z∗ coordinate model reproduces the expected internal tide fields while the z−geopotential model fails. A set of realistic experiments in the Po river delta coastal region, demonstrates that the representation of the dynamical processes at the coastal scales benefits from a very fine resolution of a few tens of centimeters at the surface (four layers of 25 cm are considered in the upper water column), that is only allowed by the z∗ formulation.

Urban settlements near to coastal environments are exposed to ocean and cryosphere change, such as sea level rise and extreme sea levels. High-resolution sea level prediction systems have become fundamental tools for taking preventive measures in the face of extreme events, mainly in the most vulnerable coastal locations. Techniques such as Machine Learning (ML) are at the forefront of the development in this sector, as they can reduce the computational time needed to reproduce the results of costly high resolution dynamic models. In this line, different authors have reported results for the prediction of oceanographic variables using ML approaches (Camus et al., 2019; Costa et al., 2020; Zust et al., 2021), mainly for significant wave height, sea level and surge component of sea level. Generally, these works use global and/or regional databases as training data for ML tools.With the aim of developing a data-driven system for sea level downscaling, by means of very high-resolution circulation model output used as a training data for a ML framework, in this work the results of a long-term numerical modeling of sea level are presented, carried out in the Northern Adriatic. The numerical model implemented correspond to SURF-SHYFEM, a 3-D finite element hydrodynamic model that solves the primitive equations under hydrostatic and Boussinesq approximations. As atmospheric forcing, mean sea level pressure, and meridional and zonal components of wind speed have been included, both from the ERA5 database. For the boundary conditions, sea level has been considered from two databases, the Copernicus Mediterranean Forecasting System (available from November 2020 to present, with tides included in sea level) and the Copernicus Mediterranean Sea Physics Reanalysis (available from 1987 to June 2021, without tides in sea level). Both databases were used on initial analysis in the representation of surge component of sea level when tides are or not included in the boundary condition. The validation of the results has been carried out by comparison with tide gauges located near the Venice Lagoon, from ISPRA[1] and PSMSL[2].The results show that the model reproduces accurately the sea level (correlation 94% and RMSE 0.09 [m]) and the surge component of sea level (correlation 91% and RMSE 0.06 [m]) measured at the location of the tide gauge. The next step will consist of using such output as a training set for ML-based techniques, with the aim of developing an accurate and cost-effective downscaling tool. [1] Istituto Superiore per la Protezione e la Ricerca Ambientale. Available at: https://www.mareografico.it/ [2] Permanent Service for Mean Sea Level. Available at: https://psmsl.org/ REFERENCES Camus, P., Herrera, S., Guitiérrez, J.M. and Losada, I.J. (2019). Statistical downscaling of seasonal wave forecast. Ocean Modelling 138, 1-12. Costa, W., Idier, D., Rohmer, J., Menendez, M. and Camus, P. (2020). Statistical prediction of extreme storm surges based on a fully supervised weather-type downscaling model. J. Mar. Sci. Eng. 8, 1028. Zust, L., Fettich, A., Kristan, M. and Licer, M. (2021). HIDRA 1.0: Deep-Learning-Based ensemble sea level forecasting in the Northern Adriatic. Geosci. Model. Dev. 14, 2057-2074.

Nature Based solutions (NBS) have been presented in the recent past as a potential solution to natural and climate change adverse effects on human well-being and socio-economic activities. In this study, we present a simulation design methodology for NBS that can mitigate the effect of storm surges and coastal erosion. The chosen NBS is marine seagrass and it will be applied to the coastal strip of the Emilia-Romagna coasts. Within the framework of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project, the seagrass NBS is presented within a simulation design methodology consisting of the comparison between validated wave numerical simulations for the present climate and modified wave simulations with marine seagrass. In this context, the unstructured version of WAVEWATCH III (WW3) model has been implemented for simulating the wave characteristics across the Emilia-Romagna coastal strip with and without seagrass.The calibration/validation of WW3 was carried out and sensitivity experiments using the various wind-input dissipation source packages and bottom friction formulations were also attempted to evaluate the model performances (validation results presented here are for the entire 2017 year). The ST6 physics along with SHOWEX bottom friction formulations were chosen ideal for the study area. To evaluate the model results a directional wave rider buoy data was utilized. The model simulated significant wave parameters namely Hs (significant wave height), Tm (mean wave period) were compared with buoy observations and high correlations (0.93) were found with Hs comparison. Further the WW3 model was modified by including the modified bottom dissipation stress due to submerged vegetation, thereby incorporating the NBS as a potential mechanism for wave amplitude reduction. The seagrass species ‘Zostera marina’ was chosen in this study and comparisons showed that seagrass is capable to reduce the wave energy in the study area. Furthermore, the dependence on seagrass plant high-density and low-density scenarios, together with seagrass parameters (height and width of the seagrass) and species show the sensitivity of the results even on reduction of wave energy as obtained with different degrees by all NBS scenarios. Keywords: Nature-based solutions, WW3, marine seagrass, storm surge, Zostera marina.

The adoption of Nature-Based Solutions (NBSs) represents a novel means to mitigate natural hazards. In the framework of the OPERANDUM project, this study introduces a methodology to assess the efficiency of the NBSs and a series of Open-Air Laboratories (OALs) regarded as a proof-of-concept for the wider uptake of NBSs. The OALs are located in Finland, Greece, UK, Italy, and Ireland. The methodology is based on a wide modeling activity, incorporated in the context of future climate scenarios. Herein, we present a series of models’ chains able to estimate the efficiency of the NBSs. While the presented models are mainly well-established, their coupling represents a first fundamental step in the study of the long-term efficacy and impact of the NBSs. In the selected sites, NBSs are utilized to cope with distinct natural hazards: floods, droughts, landslides, salt intrusion, and nutrient and sediment loading. The study of the efficacy of NBSs to mitigate these hazards belongs to a series of works devoted to the implementation of NBSs for environmental purposes. Our findings prove that land management plays a crucial role in the process. Specifically, the selected NBSs include intensive forestry, the conversion of urban areas to grassland, dunes, marine seagrass, water retention ponds, live cribwalls, and high-density plantations of woody vegetation and deep-rooted herbaceous vegetation. The management of natural resources should eventually consider the effect of NBSs on urban and rural areas, as their employment is becoming widespread.