Articles | Volume 22, issue 10
https://doi.org/10.5194/nhess-22-3413-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-3413-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Oct 2022
Research article |
| 19 Oct 2022
| 19 Oct 2022
Wind-wave characteristics and extremes along the Emilia-Romagna coast
Umesh Pranavam Ayyappan Pillai
CORRESPONDING AUTHOR
Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
Nadia Pinardi
Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
Ivan Federico
Euro-Mediterranean Center on Climate Change, Lecce, 73100, Italy
Salvatore Causio
Euro-Mediterranean Center on Climate Change, Lecce, 73100, Italy
Francesco Trotta
Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
Silvia Unguendoli
Hydro-Meteo-Climate Service of the Agency for Prevention, Environment and Energy of Emilia-Romagna, Arpae-SIMC, Bologna, 40122, Italy
Andrea Valentini
Hydro-Meteo-Climate Service of the Agency for Prevention, Environment and Energy of Emilia-Romagna, Arpae-SIMC, Bologna, 40122, Italy
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Nat. Hazards Earth Syst. Sci., 25, 3737–3758, https://doi.org/10.5194/nhess-25-3737-2025, https://doi.org/10.5194/nhess-25-3737-2025, 2025
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This research investigates how seagrass meadows attenuate coastal waves. Our methodology integrates site measurements with numerical simulations, revealing that plant flexibility and seasonal growth cycles are crucial factors that enhance model fidelity for predicting wave damping. These insights aid ecosystem-based coastal protection and conservation of these vital habitats. Future work should address current–sediment–vegetation interactions for a more complete hydrodynamic understanding.
Kai Schröter, Pia-Johanna Schweizer, Benedikt Gräler, Lydia Cumiskey, Sukaina Bharwani, Janne Parviainen, Chahan M. Kropf, Viktor Wattin Håkansson, Martin Drews, Tracy Irvine, Clarissa Dondi, Heiko Apel, Jana Löhrlein, Stefan Hochrainer-Stigler, Stefano Bagli, Levente Huszti, Christopher Genillard, Silvia Unguendoli, Fred Hattermann, and Max Steinhausen
Nat. Hazards Earth Syst. Sci., 25, 3055–3073, https://doi.org/10.5194/nhess-25-3055-2025, https://doi.org/10.5194/nhess-25-3055-2025, 2025
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With the increasing negative impacts of extreme weather events globally, it is crucial to align efforts to manage disasters with measures to adapt to climate change. We identify challenges in systems and organizations working together. We suggest that collaboration across various fields is essential and propose an approach to improve collaboration, including a framework for better stakeholder engagement and an open-source data system that helps gather and connect important information.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
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Mahmud Hasan Ghani, Nadia Pinardi, Antonio Navarra, Lorenzo Mentaschi, Silvia Bianconcini, Francesco Maicu, and Francesco Trotta
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Salvatore Causio, Seimur Shirinov, Ivan Federico, Giovanni De Cillis, Emanuela Clementi, Lorenzo Mentaschi, and Giovanni Coppini
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Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
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Rita Lecci, Robyn Gwee, Kun Yan, Sanne Muis, Nadia Pinardi, Jun She, Martin Verlaan, Simona Masina, Wenshan Li, Hui Wang, Salvatore Causio, Antonio Novellino, Marco Alba, Etiënne Kras, Sandra Gaytan Aguilar, and Jan-Bart Calewaert
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Italo R. Lopes, Ivan Federico, Michalis Vousdoukas, Luisa Perini, Salvatore Causio, Giovanni Coppini, Maurilio Milella, Nadia Pinardi, and Lorenzo Mentaschi
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We improved a computer model to simulate coastal flooding by including temporary barriers like sand dunes. We tested it where sand dunes are built seasonally to protect the shoreline for two real storms: one that broke through the dunes and another where dunes held strong. Our model showed how important it is to design these defenses carefully since even if a small part of a dune fails, a major flooding can happen. Overall, our work helps create better tools to manage and protect coastal areas.
Mohammad Hadi Bahmanpour, Alois Tilloy, Michalis Vousdoukas, Ivan Federico, Giovanni Coppini, Luc Feyen, and Lorenzo Mentaschi
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As natural hazards evolve, understanding how extreme events interact over time is crucial. While single extremes have been widely studied, joint extremes remain challenging to analyze. We present a framework that combines advanced statistical modeling with copula theory to capture changing dependencies. Applying it to historical data reveals dynamic patterns in extreme events. To support broader use, we provide an open-source tool for improved hazard assessment.
Rodrigo Campos-Caba, Jacopo Alessandri, Paula Camus, Andrea Mazzino, Francesco Ferrari, Ivan Federico, Michalis Vousdoukas, Massimo Tondello, and Lorenzo Mentaschi
Ocean Sci., 20, 1513–1526, https://doi.org/10.5194/os-20-1513-2024, https://doi.org/10.5194/os-20-1513-2024, 2024
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Here we show the development of high-resolution simulations of storm surge in the northern Adriatic Sea employing different atmospheric forcing data and physical configurations. Traditional metrics favor a simulation forced by a coarser database and employing a less sophisticated setup. Closer examination allows us to identify a baroclinic model forced by a high-resolution dataset as being better able to capture the variability and peak values of the storm surge.
José A. Jiménez, Gundula Winter, Antonio Bonaduce, Michael Depuydt, Giulia Galluccio, Bart van den Hurk, H. E. Markus Meier, Nadia Pinardi, Lavinia G. Pomarico, and Natalia Vazquez Riveiros
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The Knowledge Hub on Sea Level Rise (SLR) has done a scoping study involving stakeholders from government and academia to identify gaps and needs in SLR information, impacts, and policies across Europe. Gaps in regional SLR projections and uncertainties were found, while concerns were raised about shoreline erosion and emerging problems like saltwater intrusion and ineffective adaptation plans. The need for improved communication to make better decisions on SLR adaptation was highlighted.
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Bethany McDonagh, Emanuela Clementi, Anna Chiara Goglio, and Nadia Pinardi
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Tides in the Mediterranean Sea are typically of low amplitude, but twin experiments with and without tides demonstrate that tides affect the circulation directly at scales away from those of the tides. Analysis of the energy changes due to tides shows that they enhance existing oscillations, and internal tides interact with other internal waves. Tides also increase the mixed layer depth and enhance deep water formation in key regions. Internal tides are widespread in the Mediterranean Sea.
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
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The paper presents the Mediterranean Forecasting System evolution and performance developed in the framework of the Copernicus Marine Service.
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Revised manuscript not accepted
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Recent studies have revealed an increase in the ocean temperature and heat content in the Black Sea, where the research on marine heat waves (MHWs) is still incipient. Our study reveals long-lasting MHWs and interesting connections between surface and subsurface MHWs in the Black Sea. Our analysis is a starting point to create a monitoring system of MHWs for the Black Sea.
Giorgio Micaletto, Ivano Barletta, Silvia Mocavero, Ivan Federico, Italo Epicoco, Giorgia Verri, Giovanni Coppini, Pasquale Schiano, Giovanni Aloisio, and Nadia Pinardi
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The full exploitation of supercomputing architectures requires a deep revision of the current climate models. This paper presents the parallelization of the three-dimensional hydrodynamic model SHYFEM (System of HydrodYnamic Finite Element Modules). Optimized numerical libraries were used to partition the model domain and solve the sparse linear system of equations in parallel. The performance assessment demonstrates a good level of scalability with a realistic configuration used as a benchmark.
Georg Umgiesser, Marco Bajo, Christian Ferrarin, Andrea Cucco, Piero Lionello, Davide Zanchettin, Alvise Papa, Alessandro Tosoni, Maurizio Ferla, Elisa Coraci, Sara Morucci, Franco Crosato, Andrea Bonometto, Andrea Valentini, Mirko Orlić, Ivan D. Haigh, Jacob Woge Nielsen, Xavier Bertin, André Bustorff Fortunato, Begoña Pérez Gómez, Enrique Alvarez Fanjul, Denis Paradis, Didier Jourdan, Audrey Pasquet, Baptiste Mourre, Joaquín Tintoré, and Robert J. Nicholls
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The city of Venice relies crucially on a good storm surge forecast to protect its population and cultural heritage. In this paper, we provide a state-of-the-art review of storm surge forecasting, starting from examples in Europe and focusing on the Adriatic Sea and the Lagoon of Venice. We discuss the physics of storm surge, as well as the particular aspects of Venice and new techniques in storm surge modeling. We also give recommendations on what a future forecasting system should look like.
Katherine M. Smith, Skyler Kern, Peter E. Hamlington, Marco Zavatarelli, Nadia Pinardi, Emily F. Klee, and Kyle E. Niemeyer
Geosci. Model Dev., 14, 2419–2442, https://doi.org/10.5194/gmd-14-2419-2021, https://doi.org/10.5194/gmd-14-2419-2021, 2021
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We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The model provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of real-world data can be achieved with a small number of variables.
Cited articles
Aguirre, C., Rutllant, J. A., and Falvey, M.: Wind waves climatology of the
Southeast Pacific Ocean, Int. J. Climatol., 37, 4288–4301, 2017.
Akpinar, A. and Komurcu, M. I.: Assessment of wave energy resource of the
Black Sea based on 15-year numerical hindcast data, Appl. Energy, 101,
502–512, 2013.
Amarouche, K., Bingölbali, B., and Akpinar, A.: New wind-wave climate
records in the Western Mediterranean Sea, Clim. Dynam., 58, 1899–1922, 2022.
Ardhuin, F., O'Reilly, W. C., Herbers, T. H. C., and Jessen, P. F.: Swell
Transformation across the Continental Shelf. Part I: Attenuation and
Directional Broadening, J. Phys. Oceanogr., 33, 1921–1939, 2003.
Ardhuin, F., Bertotti, L., Bidlot, J. R., Cavaleri, L., Filipetto, V.,
Lefevre, J. M., and Wittmann, P.: Comparison of wind and wave measurements
and models in the western Mediterranean Sea, Ocean Eng., 34, 526–541, 2007.
Ardhuin, F., Rogers, W. E., Babanin, A. V., Filipot, J., Magne, R., Roland,
A., van der Westhuysen, A., Queffeulou, P., Lefevre, J., Aouf, L., and
Collard, F.: Semiempirical dissipation source functions for ocean waves.
Part I: Definition, calibration, and validation, J. Phys. Oceanogr., 40,
1917–1941, 2010.
Arkhipkin, V. S., Gippius, F. N., Koltermann, K. P., and Surkova, G. V.: Wind waves in the Black Sea: results of a hindcast study, Nat. Hazards Earth Syst. Sci., 14, 2883–2897, https://doi.org/10.5194/nhess-14-2883-2014, 2014.
Armaroli, C. and Duo, E.: Validation of the coastal storm risk assessment
framework along the emilia-romagna coast, Coast. Eng., 134, 159–167, 2018.
Armaroli, C., Ciavola, P., Masina, M., and Perini, L.: Run-up computation
behind emerged breakwaters for marine storm risk assessment, J. Coast. Res.,
56, 1612–1616, 2009.
Armaroli, C., Ciavola, P., Perini, L., Calabrese, L., Lorito, S., Valentini,
A., and Masina, M.: Critical storm thresholds for significant morphological
changes and damage along the Emilia-Romagna coastline, Italy, Geomorphology,
143–144, 34–51, 2012.
Armaroli, C., Duo, E., and Viavattene, C.: From Hazard to Consequences:
Evaluation of Direct and Indirect Impacts of Flooding Along the Emilia-Romagna Coastline, Italy, Front. Earth Sci., 7, 203, https://doi.org/10.3389/feart.2019.00203, 2019.
Babanin, A. V.: Breaking and dissipation of ocean surface waves, Cambridge
University Press, 480 pp., ISBN 9780511736162, https://doi.org/10.1017/CBO9780511736162., 2011.
Barbariol, F., Davison, S., Falcieri, F. M., Ferretti, R., Ricchi, A., Sclavo, M., and Benetazzo, A.: Wind Waves in the Mediterranean Sea: An ERA5
Reanalysis Wind-Based Climatology, Front. Mar. Sci., 8, 760614, https://doi.org/10.3389/fmars.2021.760614, 2021.
Battjes, J. A. and Janssen, J. P. F. M.: Energy loss and set-up due to
breaking of random waves, in: 16th International Conference on Coastal Engineering, 27 August–3 September 1978, Hamburg, Germany, 569–587,
https://doi.org/10.1061/9780872621909.034, 1978.
Benetazzo, A., Francesco, B., Paolo, P., Staneva, J., Behrens, A., Davison,
S., Bergamasco, F., Sclavo, M., and Cavaleri, L.: Towards a unified framework for extreme sea waves from spectral models: rationale and applications, Ocean Eng., 219, 108263, https://doi.org/10.1016/j.oceaneng.2020.108263, 2021.
Bertotti, L., Canestrelli, P., Cavaleri, L., Pastore, F., and Zampato, L.: The Henetus wave forecast system in the Adriatic Sea, Nat. Hazards Earth Syst. Sci., 11, 2965–2979, https://doi.org/10.5194/nhess-11-2965-2011, 2011.
Bertotti, L., Cavaleri, L., Loffredo, L., and Torrisi, L.: Nettuno: Analysis
of a Wind and Wave Forecast System for the Mediterranean Sea, Mon. Weather
Rev., 141, 3130–3141, 2013.
Biolchi, L. G., Unguendoli, S., Bressan, L., and Valentini, A.: Recent
developments in the forecasting chain at Arpae-Simc for the Emilia-Romagna
(Northeast Italy) coastal areas, in: 9th EuroGOOS International conference,
Shom, Ifremer, EuroGOOS AISBL, May 2021, Brest, France, hal-03328370, 2021.
Biolchi, L. G., Unguendoli, S., Bressan, L., Giambastiani, B. M. S., and Valentini, A.: Ensemble technique application to an XBeach-based coastal Early Warning System for the Northwest Adriatic Sea (Emilia-Romagna region, Italy), Coast. Eng., 173, 104081, https://doi.org/10.1016/j.coastaleng.2022.104081, 2022.
Bonaldo, D., Bucchignani, E., Ricchi, A., and Carniel, S.: Wind storminess
in the Adriatic Sea in a climate change scenario, Acta Adriat., 58, 195–208, 2017.
Bosserelle, C., Pattiaratchi, C., and Haigh, I.: Inter-annual variability
and longer-term changes in the wave climate of western Australia between
1970 and 2009, Ocean Dynam., 62, 63–76, 2012.
Carter, D. J. T., Foale, S., and Webb, D. J.: Variations in global wave
climate throughout the year, Int. J. Remote Sens., 12, 1687–1697, 1991.
Cavaleri, L.: The oceanographic tower Acqua Alta – activity and prediction of sea states at Venice, Coast. Eng., 39, 29–70, 2000.
Cavaleri, L. and Malanotte-Rizzoli, P.: Wind-wave prediction in shallow
water: Theory and applications, J. Geophys. Res., 86, 10961–10973, 1981.
Cavaleri, L., Bertotti, L., and Lionello, P.: Extreme storms in the Adriatic
Sea, In: Edge, B. L. Ed., in: Proceedings 22nd Int. Conf. on Coastal Eng., 2–6 July 1990, Delft, the Netherlands, 218–226, 3305, 1991.
Cavaleri, L., Abdalla, S., Benetazzo, A., Bertotti, L., Bidlot, J.-R., Breivik, Ø., Carniel, S., Jensen, R. E., Portilla-Yandun, J., Rogers, W. E., Roland, A., Sanchez-Arcilla, A., Smith, J. M., Staneva, J., Toledo, Y., van Vledder, G. P. and van der Westhuysen, A. J.: Wave modelling in coastal and inner seas, Prog. Oceanogr., 167, 164–233, 2018.
Cavaleri, L., Bajo, M., Barbariol, F., Bastianini, M., Benetazzo, A.,
Bertotti, L., Chiggiato, J., Davolio, S., Ferrarin, C., Magnusson, L., Papa,
A., Pezzutto, P., Pomaro, A., and Umgiesser, G.: The October 29, 2018 storm
in Northern Italy-An exceptional event and its modeling, Prog. Oceanogr.,
178, 102178, https://doi.org/10.1016/j.pocean.2019.102178, 2019.
Cavaleri, L., Barbariol, F., Bastianini, M., Benetazzo, A., Bertotti, L., and Pomaro, A.: An exceptionally high wave at the CNR-ISMAR oceanographic tower in the Northern Adriatic Sea, Sci. Data, 8, 37, https://doi.org/10.1038/s41597-021-00825-x, 2021.
Ciavola, P., Harley, M. D., and den Heijer, C.: The RISC-KIT storm impact
database: a new tool in support of DR, Coast. Eng., 134, 24–32, 2017.
Clementi, E., Oddo, P., Drudi, M., Pinardi, N., Korres, G., and Grandi, A.: Coupling hydrodynamic and wave models: first step and sensitivity experiments in the Mediterranean Sea, Ocean Dynam., 67, 1293–1312, 2017.
Cox, A. T. and Swail, V. R.: A global wave hindcast over the period 1958–1997: validation and climate assessment, J. Geophys. Res., 106,
2313–2329, 2001.
De Leo, F., De Leo, A., Besio, G., and Briganti, R.: Detection and quantification of trends in time series of significant wave heights: an
application in the Mediterranean Sea, Ocean Eng., 202, 107155, https://doi.org/10.1016/j.oceaneng.2020.107155, 2020.
De Leo, F., Besio, G., and Mentaschi, L.: Trends and variability of ocean
waves under RCP8.5 emission scenario in the Mediterranean Sea, Ocean Dynam.,
71, 97–117, 2021.
de Rosnay, P., Browne, P., de Boisséson, E., Fairbairn, D., Hirahara, Y., Ochi, K., Schepers, D., Weston, P., Zuo, H., Alonso-Balmaseda, M., Balsamo, G., Bonavita, M., Borman, N., Brown, A., Chrust, M., Dahoui, M., De Chiara, G., English, S., Geer, A., Healy, S., Hersbach, H., Laloyaux, P., Magnusson, L., Massart, S., McNally, A., Pappenberger, F., and Rabier, F.: Coupled data assimilation at ECMWF: current status, challenges and future developments, Q. J. Roy. Meteorol Soc., 148, 2672–2702, https://doi.org/10.1002/qj.4330, 2022.
DHI group: MIKE 21 Spectral Wave Module, Scientific Documentation, Danish
Hydraulic Institute (DHI), Holsholm, Denmark, p. 56, https://manuals.mikepoweredbydhi.help/2017/Coast_and_Sea/M21SW_Scientific_Doc.pdf
(last access: 20 March 2022), 2017.
Dodet, G., Bertin, X., and Taborda, R.: Wave climate variability in the
North-East Atlantic Ocean over the last six decades, Ocean Model., 31,
120–131, 2010.
Donatini, L., Lupieri, G., Contento, G., Feudale, L., Pedroncini, A., Cusati, L. A., and Crosta, A.: A high resolution wind and wave forecast model chain for the Mediterranean and Adriatic Sea, in: Vol. 1, Towards Green Marine Technology and Transport: Proceedings of the 16th International Conference of the International Maritime Association of the Mediterranean (IMAM2015),
21–24 September 2015, Pula, Croatia, edited by: Guedes Soares, C., Dejhalla, R., and Pavletic, D., University of Trieste, Trieste, Italy, CRC Press, 859–866, ISBN 9780429225604, https://doi.org/10.1201/b18855, 2015.
Donelan, M. A., Babanin, A. V., Young, I. R., and Banner, M. L.: Wave
follower measurements of the wind-input spectral function. Part II.
Parameterization of the wind input, J. Phys. Oceanogr., 36, 1672–1689, 2006.
Farda, A., Štěpánek, P., Halenka, T., Skalák, P., and Belda,
M.: Model ALADIN in climate mode forced with ERA-40 reanalysis (coarse
resolution experiment), Meteorol. J., 10, 123–130, 2007.
Fedor, N. G. and Stanislav, A. M.: Black Sea wind wave climate with a focus
on coastal regions, Ocean Eng., 218, 108199, https://doi.org/10.1016/j.oceaneng.2020.108199, 2020.
Ferrarin, C., Valentini, A., Vodopivec, M., Klaric, D., Massaro, G., Bajo, M., De Pascalis, F., Fadini, A., Ghezzo, M., Menegon, S., Bressan, L., Unguendoli, S., Fettich, A., Jerman, J., Ličer, M., Fustar, L., Papa, A., and Carraro, E.: Integrated sea storm management strategy: the 29 October 2018 event in the Adriatic Sea, Nat. Hazards Earth Syst. Sci., 20, 73–93, https://doi.org/10.5194/nhess-20-73-2020, 2020.
Fiori, E., Zavatarelli, M., Pinardi, N., Mazziotti, C., and Ferrari, C. R.: Observed and simulated trophic index (TRIX) values for the Adriatic Sea basin, Nat. Hazards Earth Syst. Sci., 16, 2043–2054, https://doi.org/10.5194/nhess-16-2043-2016, 2016.
Gaeta, M. G., Samaras, A. G., Federico, I., Archetti, R., Maicu, F., and Lorenzetti, G.: A coupled wave–3-D hydrodynamics model of the Taranto Sea (Italy): a multiple-nesting approach, Nat. Hazards Earth Syst. Sci., 16, 2071–2083, https://doi.org/10.5194/nhess-16-2071-2016, 2016.
Gaeta, M. G., Bonaldo, D., Samaras, A. G., Carniel, S., and Archetti, R.:
Coupled Wave-2D Hydrodynamics Modeling at the Reno River Mouth (Italy) under
Climate Change Scenarios, Water, 10, 1380, https://doi.org/10.3390/w10101380, 2018.
Gorman, R. M., Bryan, K. R., and Laing, A. K.: Wave hindcast for the New Zealand region: deep-water wave climate, NZ J. Mar. Freshw. Res., 37, 589–612, 2003.
Harley, M. D., Valentini, A., Armaroli, C., Perini, L., Calabrese, L., and Ciavola, P.: Can an early-warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, northern Italy, Nat. Hazards Earth Syst. Sci., 16, 209–222, https://doi.org/10.5194/nhess-16-209-2016, 2016.
Hasselmann, K., TBarnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E.,
Enke, K., Ewing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P., Meerburg, A., Muller, P., Olbers, D. J., Richter, K., Sell, W., and Walden,
H.: Measurements of wind-wave growth and swell decay during the Joint North
Sea Wave Project (JONSWAP), Ergänzungsheft zur Deutschen Hydrographischen Zeitschrift, Reihe A 12, 95 pp., http://resolver.tudelft.nl/uuid:f204e188-13b9-49d8-a6dc-4fb7c20562fc (last access: 20 March 2022), 1973.
Hemer, M. A., Church, J. A., and Hunter, J. R.: Variability and trends in
the directional wave climate of the Southern Hemisphere, Int. J. Climatol.,
30, 475–491, 2010.
Hemer, M. A., Wang, X. L., Weisse, R., and Swail, V. R.: Advancing wind-waves climate science, B. Am. Meteorol. Soc., 93, 791–796, 2012.
IDROSER: Progetto di Piano per la difesa del mare e la riqualificazione ambientale del litorale della Regione Emilia-Romagna, Regione Emilia-Romagna, Bologna, Italia, 365 pp., 1996.
IPCC: Climate Change 2007: The Physical Science Basis, in: Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge
University Press, Cambridge, UK, 996 pp., ISBN 978-0-521-88009-1, 2007.
IPCC: Climate Change 2021: The Physical Science Basis, in: Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2391 pp.,
https://doi.org/10.1017/9781009157896, 2021.
Kamranzad, B., Etemad-shahidi, A., and Chegini, V.: Assessment of wave energy variation in the Persian Gulf, Ocean Eng., 70, 72–80, 2013.
Katalinić, M., Ćorak, M., and Parunov, J.: Analysis of wave heights and wind speeds in the Adriatic Sea, in: Maritime Technology and Engineering Vol. 1, Proceedings of the MARTECH 2014, 2nd International Conference on Maritime Technology and Engineering, 15–17 October 2014, Lisabon, Portugal,
edited by: Soares, C. G. and Santos, T. A., CRC Press/Balkema, Leiden, the
Netherlands, 1389–1394, 2015.
Komen, G. J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., and
Janssen, P. A. E. M.: Dynamics and Modelling of Ocean Waves, Cambridge
University Press, 532 pp., ISBN 9780511628955, https://doi.org/10.1017/CBO9780511628955, 1994.
Korres, G., Papadopoulos, A., Katsafados, P., Ballas, D., Perivoliotis, L.,
and Nittis, K.: A 2-year intercomparison of the WAM-CYCLE4 and the WAVEWATCH-III wave models implemented within the Mediterranean Sea, Mediterr. Mar. Sci., 12, 129–152, 2011.
Korres, G., Ravdas, M., Zacharioudaki, A., Denaxa, D., and Sotiropoulou, M.:
Mediterranean Sea Waves Analysis and Forecast (CMEMS MED-Waves, MedWAM3 system) (Version 1) set, CMEMS – Copernicus Monitoring Environment Marine
Service, https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORE, 2021.
Le Cozannet, G., Oliveros, C., Brivois, O., Giremus, A., Garcin, M., and
Lavigne, F.: Detecting Changes in European Shoreline Evolution Trends Using
Markov Chains and the Eurosion Database, Front. Mar. Sci., 7, 326, https://doi.org/10.3389/fmars.2020.00326, 2020.
Lionello, P.: The Climate of the Mediterranean Region, Elsevier, Amsterdam,
https://doi.org/10.1016/C2011-0-06210-5, 2012.
Lionello, P. and Sanna, A.: Mediterranean wave climate variability and its
links with NAO and Indian monsoon, Clim. Dynam., 25, 611–623, 2005.
Lionello P., Abrantes, F., Congedi, L., Dulac, F., Gacic, M., Gomis, D.,
Goodess, C., Hoff, H., Kutiel, H., Luterbacher, J., Planton, S., Reale, M.,
Schröder, K., Struglia, M. V., Toreti, A., Tsimplis, M., Ulbrich, U., and
Xoplaki, E.: Introduction: Mediterranean Climate: Background Information in
The Climate of the Mediterranean Region. From the Past to the Future, edited by: Lionello, P., Elsevier, Amsterdam, the Netherlands, XXXV–lXXX,
ISBN 9780124160422, 2012.
Lobeto, H., Menendez, M., and Losada, I. J.: Projections of Directional
Spectra Help to Unravel the Future Behavior of Wind Waves, Front. Mar. Sci.,
8, 655490, https://doi.org/10.3389/fmars.2021.655490, 2021.
Luijendijk, A., Hagenaars, G., Ranasinghe, R., Fedor, B., Gennadii, D., and
Stefan, A.: The State of the World's Beaches, Sci. Rep., 8, 6641, https://doi.org/10.1038/s41598-018-24630-6, 2018.
Mahmoudi, E., Meshkat, R. S., Kargar, B., and Kundu, D.: The Extended Exponentiated Weibull Distribution and its Applications, Statistica, 78,
363–396, 2018.
Mentaschi, L., Vousdoukas, M. I., Pekel, J.-F., Voukouvalas, E., and Feyen,
L.: Global long-term observations of coastal erosion and accretion, Sci.
Rep., 8, 12876, https://doi.org/10.1038/s41598-018-30904-w, 2018.
Morales-Márquez, V., Orfila, A., Simarro, G., and Marcos, M.: Extreme waves and climatic patterns of variability in the eastern North Atlantic and Mediterranean basins, Ocean Sci., 16, 1385–1398, https://doi.org/10.5194/os-16-1385-2020, 2020.
Muraleedharan, G., Unnikrishnan Nair, N., and Kurup, P. G.: Characteristics of long-term distributions of wave heights and periods in the eastern Arabian
Sea, Indian J. Mar. Sci., 22, 21–27, 1993.
Muraleedharan, G., Kurup, P. G., and Unnikrishnan Nair, N.: Weibull model for shallow water wave height distribution and prediction, in: National Conference on Current Trends in Ocean Predictions with Special Reference to Indian Seas, Naval Physical and Oceanographic Laboratory, 22–23 December 1998, Kochi, Kerala, India, 80–85, 1998.
Muraleedharan, G., Unnikrishnan Nair, N., and Kurup, P. G.: Application of
Weibull model for redefined significant wave height distributions, Proc.
Indian Acad. Sci. Earth Planet. Sci., 108, 149–153, 1999.
Murphy, A. H.: What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting, Weather Forecast., 8, 281–293, 1993.
Pandzic, K. and Likso, T.: Eastern Adriatic typical wind field patterns and
large-scale atmospheric conditions, Int. J. Climatol., 25, 81–98, 2005.
Perini, L., Calabrese, L., Luciani, P., Olivieri, M., Galassi, G., and Spada, G.: Sea-level rise along the Emilia-Romagna coast (Northern Italy) in 2100: scenarios and impacts, Nat. Hazards Earth Syst. Sci., 17, 2271–2287, https://doi.org/10.5194/nhess-17-2271-2017, 2017.
Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Data/codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled “Wind-Wave Characteristics and extremes along the Emilia-Romagna coast” by Pranavam Ayyappan Pillai et al., 2022 (Version V1), Zenodo [code and data set], https://doi.org/10.5281/zenodo.6360348, 2022.
Qian, C., Jiang, H., Wang, X., and Chen, G.: Climatology of Wind-Seas and Swells in the China Seas from Wave Hindcast, J. Ocean Univ. China, 19, 90–100, https://doi.org/10.1007/s11802-020-3924-4, 2020.
Queffeulou, P. and Bentamy, A.: Analysis of Wave Height Variability Using
Altimeter Measurements: Application to the Mediterranean Sea, J. Atmos. Ocean. Tech., 24, 2078–2092, 2007.
Ravdas, M., Zacharioudaki, A., and Korres, G.: Implementation and validation of a new operational wave forecasting system of the Mediterranean Monitoring and Forecasting Centre in the framework of the Copernicus Marine Environment Monitoring Service, Nat. Hazards Earth Syst. Sci., 18, 2675–2695, https://doi.org/10.5194/nhess-18-2675-2018, 2018.
Reistad, M., Breivik, Ø., Haakenstad, H., Aarnes, O. J., Furevik, B. R.,
and Bidlot, J. R.: A high-resolution hindcast of wind and waves for the North Sea, the Norwegian Sea, and the Barents Sea, J. Geophys. Res., 116, C05019, https://doi.org/10.1029/2010JC006402, 2011.
Rogers, W. E., Babanin, A. V., and Wang, D. W.: Observation-consistent input
and whitecapping dissipation in a model for wind-generated surface waves:
Description and simple calculations, J. Atmos. Ocean. Tech., 29, 1329–1346, 2012.
Romagnoli, C., Sistilli, F., Cantelli, L., Aguzzi, M., De Nigris, N., Morelli, M., Gaeta, M. G., and Archetti, R.: Beach Monitoring and Morphological Response in the Presence of Coastal Defense Strategies at Riccione (Italy), J. Mar. Sci. Eng., 9, 851, https://doi.org/10.3390/jmse9080851, 2021.
Russo, A., Coluccelli, A., Carniel, S., Benetazzo, A., Valentini, A., and
Paccagnella, T.: Operational Models Hierarchy for Short Term Marine
Predictions: The Adriatic Sea Example, in: IEEE MTS/IEEE OCEANS, Bergen, 1–6, https://doi.org/10.1109/OCEANS-Bergen.2013.6608139, 2013.
Sanuy, M., Duo, E., Jäger, W. S., Ciavola, P., and Jiménez, J. A.: Linking source with consequences of coastal storm impacts for climate change and risk reduction scenarios for Mediterranean sandy beaches, Nat. Hazards Earth Syst. Sci., 18, 1825–1847, https://doi.org/10.5194/nhess-18-1825-2018, 2018.
Sekovski, I., Armaroli, C., Calabrese, L., Mancini, F., Stecchi, F., and Perini, L.: Coupling scenarios of urban growth and flood hazards along the Emilia-Romagna coast (Italy), Nat. Hazards Earth Syst. Sci., 15, 2331–2346, https://doi.org/10.5194/nhess-15-2331-2015, 2015.
Semedo, A., Suselj, K., Rutgersson, A., and Sterl, A.: A global view on the
wind sea and swell climate and variability from ERA-40, J. Climate, 24,
1461–1479, 2011.
Sepulveda, H. H., Queffeulou, P., and Ardhuin, F.: Assessment of SARAL/AltiKa wave height measurements relative to buoy, Jason-2, and Cryosat-2 data, Mar. Geod., 38, 449–465, 2015.
Sikiric, M. D., Damir, I., Roland, A., Ivatek-Shahdan, S., and Tudor, M.:
Operational Wave Modelling in the Adriatic Sea with the Wind Wave Model, Pure Appl. Geophys., 175, 3801–3815, 2018.
Steppeler, J., G. Doms, U. Shatter, Bitzer, H. W., Gassmann, A., Damrath, U., and Gregoric, G.: Meso-gamma scale forecasts using the nonhydrostatic model LM, Meteorol. Atmos. Phys., 82, 75–96, 2003.
Sterl, A. and Caires, S.: Climatology, variability and extrema of ocean waves: The web-based KNMI/ERA-40 wave atlas, Int. J. Climatol., 25, 963–977, 2005.
Sterl, A., Komen, G. J., and Cotton, P. D.: Fifteen years of global wave
hindcasts using winds from the European centre for medium-range weather
forecasts reanalysis: validating the reanalyzed winds and assessing the wave
climate, J. Geophys. Res., 103, 5477–5492, 1998.
Stopa, J. E. and Cheung, K. F.: Periodicity and patterns of ocean wind and
wave climate, J. Geophys. Res.-Oceans, 119, 5563–5584, 2014.
Tolman, H. L.: A genetic optimization package for the Generalized Multiple
DIA in WAVEWATCH III, Tech. Note 289, Ver. 1.0, NOAA/NWS/NCEP/MMAB, 21 pp.,
https://polar.ncep.noaa.gov/mmab/papers/tn289/MMAB_289_v1.0.pdf (last access: 20 March 2022), 2010.
Tolman, H. L.: A Generalized Multiple Discrete Interaction Approximation for
resonant four-wave nonlinear interactions in wind wave models with arbitrary
depth, Ocean Model., 70, 11–24, 2013.
Tolman, H. L.: A genetic optimization package for the Generalized Multiple
DIA in WAVEWATCH III, Tech. Note 289, Ver. 1.4, NOAA/NWS/NCEP/MMAB, 21 pp. + Appendix, https://polar.ncep.noaa.gov/mmab/papers/tn289/MMAB_289_v1.4.pdf (last access: 20 March 2022), 2014.
Tolman, H. L., Balasubramaniyan, B., Burroughs, L. D., Chalikov, D. V.,
Chao, Y. Y., Chen, H. S., and Gerald, V. M.: Development and Implementation
of Wind-Generated Ocean Surface Wave Models at NCEP, Weather Forecast., 17, 311–333, 2002.
Torrence, C. and Compo, G. P.: A practical guide to wavelet analysis, B. Am. Meteorol. Soc., 79, 61–78, 1998.
Umgiesser, G., Ferrari, C., Cucc, A., De Pascalis, F., Bellafiore, D.,
Ghezzo, M., and Bajo, M.: Comparative hydrodynamics of 10 Mediterranean lagoons by means of numerical modeling, J. Geophys. Res.-Oceans, 119, 2212–2226, 2014.
Umgiesser, G., Bajo, M., Ferrarin, C., Cucco, A., Lionello, P., Zanchettin, D., Papa, A., Tosoni, A., Ferla, M., Coraci, E., Morucci, S., Crosato, F.,
Bonometto, A., Valentini, A., Orlić, M., Haigh, I. D., Nielsen, J. W.,
Bertin, X., Fortunato, A. B., Pérez Gómez, B., Alvarez Fanjul, E.,
Paradis, D., Jourdan, D., Pasquet, A., Mourre, B., Tintoré, J., and
Nicholls, R. J.: The prediction of floods in Venice: methods, models and
uncertainty (review article), Nat. Hazards Earth Syst. Sci., 21, 2679–2704,
https://doi.org/10.5194/nhess-21-2679-2021, 2021.
Valentini, A., Delli Passeri, L., Paccagnella, T., Patruno, P., Marsigli, C., Cesari, D., Deserti, M., Chiggiato, J., and Tibaldi, S.: The sea state forecast system of ARPA-SIM, Boll. Geofis. Teor. Appl., 48, 333–349, 2007.
Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Bianchi, A., Dottori, F.,
and Feyen, L.: Climatic and socioeconomic controls of future coastal flood
risk in Europe, Nat. Clim. Change, 8, 776–780, 2018.
Weibull, W.: A statistical distribution function of wide applicability, J.
Appl. Mech., 18, 293–297, 1951.
Woolf, D. K., Challenor, P. G., and Cotton, P. D.: Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107,
3145–3158, 2002.
WW3DG – WW3 Development Group: User manual and system documentation of WW3 v.5.16, NOAA, http://polar.ncep.noaa.gov/waves/wavewatch/manual.v5.16.pdf (last access: 20 March 2022), 2016.
Yamaguchi, M.: Approximate expressions for integral properties of the
JONSWAP spectrum, Proc. Jpn. Soc. Civ. Eng., 345, 149–152, 1984.
Young, I. R.: Seasonal variability of the global ocean wind and wave climate, Int. J. Climatol., 19, 931–950, 1999.
Young, I. R. and Donelan, M. A.: On the determination of global ocean wind and wave climate from satellite observations, Remote Sens. Environ., 215,
228–241, 2018.
Young, I. R. and Holland, G. J.: Atlas of the Oceans: Wind and Wave Climate, Pergamon Press, 241 pp., ISBN 13:978-0080425191, 1996.
Young, I. R., Zieger, S., and Babanin, A. V.: Global trends in wind speed and
wave height, Science, 332, 451–455, 2011.
Young, I. R., Fontaine, E., Liu, Q., and Babanin, A. V.: The wave climate of
the Southern Ocean, J. Phys. Oceanogr., 50, 1417–1433, 2020.
Zheng, C. W. and Li, C. Y.: Variation of the wave energy and significant wave
height in the China Sea and adjacent waters, Renew. Sustain. Energ. Rev., 43, 381–387, 2015.
Zheng, K., Sun, J., Guan, C., and Shao, W.: Analysis of the global swell and
wind sea energy distribution using WAVEWATCH III, Adv. Meteorol., 2016, 1–9, https://doi.org/10.1155/2016/8419580, 2016.
Zieger, S., Babanin, A. V., Rogers, W. E., and Young, I. R.: Observation-based source terms in the third-generation wave model WAVEWATCH, Ocean Model., 96, 2–25, 2015.
Short summary
The study presents the application of high-resolution coastal modelling for wave hindcasting on the Emilia-Romagna coastal belt. The generated coastal databases which provide an understanding of the prevailing wind-wave characteristics can aid in predicting coastal impacts.
The study presents the application of high-resolution coastal modelling for wave hindcasting on...
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