IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i5p1470-d145114.html
   My bibliography  Save this article

Predicting Migratory Corridors of White Storks, Ciconia ciconia , to Enhance Sustainable Wind Energy Planning: A Data-Driven Agent-Based Model

Author

Listed:
  • Francis Oloo

    (Department of Geoinformatics (Z_GIS), University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria)

  • Kamran Safi

    (Max Planck Institute for Ornithology, Vogelwarte Radolfzell, Schlossalee 2, 78315 Radolfzell, Germany)

  • Jagannath Aryal

    (School of Technology, Environments and Design, Discipline of Geography and Spatial Sciences, University of Tasmania, Churchill Avenue, Hobart, Tasmania 7001, Australia)

Abstract

White storks ( Ciconia ciconia ) are birds that make annual long-distance migration flights from their breeding grounds in the Northern Hemisphere to the south of Africa. These trips take place in the winter season, when the temperatures in the North fall and food supply drops. White storks, because of their large size, depend on the wind, thermals, and orographic characteristics of the environment in order to minimize their energy expenditure during flight. In particular, the birds adopt a soaring behavior in landscapes where the thermal uplift and orographic updrafts are conducive. By attaining suitable soaring heights, the birds then use the wind characteristics to glide for hundreds of kilometers. It is therefore expected that white storks would prefer landscapes that are characterized by suitable wind and thermal characteristics, which promote the soaring and gliding behaviors. However, these same landscapes are also potential sites for large-scale wind energy generation. In this study, we used the observed data of the white stork movement trajectories to specify a data-driven agent-based model, which simulates flight behavior of the white storks in a dynamic environment. The data on the wind characteristics and thermal uplift are dynamically changed on a daily basis so as to mimic the scenarios that the observed birds experienced during flight. The flight corridors that emerge from the simulated flights are then combined with the predicted surface on the wind energy potential, in order to highlight the potential risk of collision between the migratory white storks and hypothetical wind farms in the locations that are suitable for wind energy developments. This work provides methods that can be adopted to assess the overlap between wind energy potential and migratory corridors of the migration of birds. This can contribute to achieving sustainable trade-offs between wind energy development and conservation of wildlife and, hence, handling the issues of human–wildlife conflicts.

Suggested Citation

  • Francis Oloo & Kamran Safi & Jagannath Aryal, 2018. "Predicting Migratory Corridors of White Storks, Ciconia ciconia , to Enhance Sustainable Wind Energy Planning: A Data-Driven Agent-Based Model," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1470-:d:145114
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/5/1470/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/5/1470/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joan Garriga & John R B Palmer & Aitana Oltra & Frederic Bartumeus, 2016. "Expectation-Maximization Binary Clustering for Behavioural Annotation," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-26, March.
    2. H Couclelis, 1989. "Macrostructure and Microbehavior in a Metropolitan Area," Environment and Planning B, , vol. 16(2), pages 141-154, June.
    3. Mentis, Dimitrios & Hermann, Sebastian & Howells, Mark & Welsch, Manuel & Siyal, Shahid Hussain, 2015. "Assessing the technical wind energy potential in Africa a GIS-based approach," Renewable Energy, Elsevier, vol. 83(C), pages 110-125.
    4. Pegels, Anna, 2010. "Renewable energy in South Africa: Potentials, barriers and options for support," Energy Policy, Elsevier, vol. 38(9), pages 4945-4954, September.
    5. Rodman, Laura C. & Meentemeyer, Ross K., 2006. "A geographic analysis of wind turbine placement in Northern California," Energy Policy, Elsevier, vol. 34(15), pages 2137-2149, October.
    6. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    7. Catherine Linard & Marius Gilbert & Robert W Snow & Abdisalan M Noor & Andrew J Tatem, 2012. "Population Distribution, Settlement Patterns and Accessibility across Africa in 2010," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
    8. Leslie New & Emily Bjerre & Brian Millsap & Mark C Otto & Michael C Runge, 2015. "A Collision Risk Model to Predict Avian Fatalities at Wind Facilities: An Example Using Golden Eagles, Aquila chrysaetos," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-12, July.
    9. Ahmed Shata, A.S. & Hanitsch, R., 2006. "Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt," Renewable Energy, Elsevier, vol. 31(8), pages 1183-1202.
    10. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    11. Daniel G. Brown & Rick Riolo & Derek T. Robinson & Michael North & William Rand, 2005. "Spatial process and data models: Toward integration of agent-based models and GIS," Journal of Geographical Systems, Springer, vol. 7(1), pages 25-47, October.
    12. Dennhardt, Andrew J. & Duerr, Adam E. & Brandes, David & Katzner, Todd E., 2015. "Modeling autumn migration of a rare soaring raptor identifies new movement corridors in central Appalachia," Ecological Modelling, Elsevier, vol. 303(C), pages 19-29.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nick Malleson & Kevin Minors & Le-Minh Kieu & Jonathan A. Ward & Andrew West & Alison Heppenstall, 2020. "Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-3.
    2. Camelia Delcea & Liviu-Adrian Cotfas & Ramona Paun, 2018. "Agent-Based Evaluation of the Airplane Boarding Strategies’ Efficiency and Sustainability," Sustainability, MDPI, vol. 10(6), pages 1-26, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sandhu, Rimple & Tripp, Charles & Quon, Eliot & Thedin, Regis & Lawson, Michael & Brandes, David & Farmer, Christopher J. & Miller, Tricia A. & Draxl, Caroline & Doubrawa, Paula & Williams, Lindy & Du, 2022. "Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights," Ecological Modelling, Elsevier, vol. 466(C).
    2. Rianne Duinen & Tatiana Filatova & Wander Jager & Anne Veen, 2016. "Going beyond perfect rationality: drought risk, economic choices and the influence of social networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 335-369, November.
    3. Njoh, Ambe J. & Etta, Simon & Ngyah-Etchutambe, Ijang B. & Enomah, Lucy E.D. & Tabrey, Hans T. & Essia, Uwem, 2019. "Opportunities and challenges to rural renewable energy projects in Africa: Lessons from the Esaghem Village, Cameroon solar electrification project," Renewable Energy, Elsevier, vol. 131(C), pages 1013-1021.
    4. Gabra, Samuel & Miles, John & Scott, Stuart A., 2019. "Techno-economic analysis of stand-alone wind micro-grids, compared with PV and diesel in Africa," Renewable Energy, Elsevier, vol. 143(C), pages 1928-1938.
    5. Ouammi, Ahmed & Ghigliotti, Valeria & Robba, Michela & Mimet, Abdelaziz & Sacile, Roberto, 2012. "A decision support system for the optimal exploitation of wind energy on regional scale," Renewable Energy, Elsevier, vol. 37(1), pages 299-309.
    6. Wallentin, Gudrun, 2017. "Spatial simulation: A spatial perspective on individual-based ecology—a review," Ecological Modelling, Elsevier, vol. 350(C), pages 30-41.
    7. Olaofe, Z.O., 2018. "Review of energy systems deployment and development of offshore wind energy resource map at the coastal regions of Africa," Energy, Elsevier, vol. 161(C), pages 1096-1114.
    8. Mahdy, Mostafa & Bahaj, AbuBakr S., 2018. "Multi criteria decision analysis for offshore wind energy potential in Egypt," Renewable Energy, Elsevier, vol. 118(C), pages 278-289.
    9. Abdullahi Abubakar Mas’ud & Asan Vernyuy Wirba & Jorge Alfredo Ardila-Rey & Ricardo Albarracín & Firdaus Muhammad-Sukki & Álvaro Jaramillo Duque & Nurul Aini Bani & Abu Bakar Munir, 2017. "Wind Power Potentials in Cameroon and Nigeria: Lessons from South Africa," Energies, MDPI, vol. 10(4), pages 1-19, March.
    10. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    11. Neupane, Deependra & Kafle, Sagar & Karki, Kaji Ram & Kim, Dae Hyun & Pradhan, Prajal, 2022. "Solar and wind energy potential assessment at provincial level in Nepal: Geospatial and economic analysis," Renewable Energy, Elsevier, vol. 181(C), pages 278-291.
    12. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    13. Patrick Mukumba & Shylet Y. Chivanga, 2023. "An Overview of Renewable Energy Technologies in the Eastern Cape Province in South Africa and the Rural Households’ Energy Poverty Coping Strategies," Challenges, MDPI, vol. 14(1), pages 1-12, March.
    14. Marco A. Janssen & Lilian N. Alessa & C. Michael Barton & Sean Bergin & Allen Lee, 2008. "Towards a Community Framework for Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-6.
    15. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    16. Jagadish, Arundhati & Dwivedi, Puneet & McEntire, Kira D. & Chandar, Mamta, 2019. "Agent-based modeling of “cleaner” cookstove adoption and woodfuel use: An integrative empirical approach," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    17. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    18. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    19. Jascha-Alexander Koch & Jens Lausen & Moritz Kohlhase, 2021. "Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms," Journal of Business Economics, Springer, vol. 91(9), pages 1387-1430, November.
    20. Masebinu, S.O. & Akinlabi, E.T. & Muzenda, E. & Aboyade, A.O., 2017. "Techno-economics and environmental analysis of energy storage for a student residence under a South African time-of-use tariff rate," Energy, Elsevier, vol. 135(C), pages 413-429.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1470-:d:145114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.