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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.

Item Type:	Refereed Article
Keywords:	agent-based models, collision risk, data-driven models, sustainability, wind energy
Research Division:	Engineering
Research Group:	Geomatic Engineering
Research Field:	Photogrammetry and Remote Sensing
Objective Division:	Environment
Objective Group:	Environmental and Natural Resource Evaluation
Objective Field:	Environmental Management Systems
UTAS Author:	Aryal, J (Dr Jagannath Aryal)
ID Code:	125945
Year Published:	2018
Web of Science® Times Cited:	2
Deposited By:	Geography and Spatial Science
Deposited On:	2018-05-16
Last Modified:	2019-03-14
Downloads:	27 View Download Statistics