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An integrated approach for wind fields assessment in coastal areas, based on bioindicators, CFD modeling, and observations

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Abstract

Wind-deformed trees can be good bioindicators of the mean wind speed and prevailing wind directions. The current research used bioindicators, computational fluid dynamics (CFD), and linear models to assess the wind fields in the windy coastal area of Cascais/Portugal. The main objectives of this research are to assess mean speed and directions of winds by using bioindicators and modeling techniques and to correlate both results in order to assess the best methods. The results obtained with the bioindicators showed that carpeting, the most severe deformation, was observed near the shoreline showing that the highest wind speeds are felt in this sector. Inland, where the winds have lower mean speeds, flagging forms are more frequent. When correlated with the bioindicators, the linear model gave better results than CFD models. We can conclude that in areas with good wind potential, the use of bioindicators can be a good alternative in the absence of wind data.

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Acknowledgments

The authors would like to thank Prof. Dr. Maria João Alcoforado and the anonymous reviewers that contributed to the improvement of this paper. We are also thankful to Luís Miguel Faria for his help in the fieldwork.

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Authors and Affiliations

  1. Institute of Geography and Spatial Planning, Center of Geographical Studies (RISKam Research Group), Universidade de Lisboa. Ed. IGOT, Rua Branca Edmée Marques, 1600-276, Lisbon, Portugal

    Bruno M. Meneses

  2. Institute of Geography and Spatial Planning, Center of Geographical Studies (ZEPHYRUS/Climate Change and Environmental Systems Research Group), Universidade de Lisboa. Ed. IGOT, Rua Branca Edmée Marques, 1600-276, Lisbon, Portugal

    António Lopes

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  1. Bruno M. Meneses
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  2. António Lopes
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Correspondence to Bruno M. Meneses.

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Meneses, B.M., Lopes, A. An integrated approach for wind fields assessment in coastal areas, based on bioindicators, CFD modeling, and observations. Theor Appl Climatol 128, 301–310 (2017). https://doi.org/10.1007/s00704-015-1707-4

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  • DOI: https://doi.org/10.1007/s00704-015-1707-4

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