环境科学
小气候
植被(病理学)
城市化
节约用水
灌溉
水平衡
干旱
水资源管理
水文学(农业)
环境资源管理
地理
生态学
工程类
医学
生物
病理
考古
岩土工程
作者
Hamideh Nouri,Simon Beecham,Fatemeh Kazemi,Ali Hassanli
标识
DOI:10.1080/1573062x.2012.726360
摘要
Abstract Increasing urbanisation combined with population growth places greater demands on dwindling water supplies. This is especially the case in arid and semi-arid areas like Australia, which is known as the driest inhabited continent on earth. Sustainable irrigation management necessitates better understanding of water requirements in order to decrease environmental risks and increase water use efficiency. Although the water requirements of agricultural crops are well established in field and laboratory studies, little research has been conducted to investigate the water requirements of urban green spaces. In addition, most previous research investigations have focused on the water requirements of turf grasses and not on other landscape plant species. Landscape plants can include various species of trees, shrubs and turf grasses with different planting densities and microclimates. Such complicated environments make measuring the water requirements of urban landscapes difficult. This paper reviews previous studies and techniques for measuring the water requirements of urban landscapes and describes how optimum irrigation management strategies for urban landscape vegetation can assist in better water conservation, improved landscape quality and reduced water costs. The authors conclude that WUCOLS is a practical approach that can provide an initial estimate of urban landscape water demand but ideally this should be further refined based on the health and aesthetic condition of the urban vegetation. The authors recommend calibration of the WUCOLS estimates with an in-situ method such as a soil water balance. Keywords: evapotranspirationurban vegetationremote sensingWUCOLSnormalized difference vegetation index Acknowledgement The authors would like to acknowledge the two anonymous reviewers for their detailed and helpful comments that have greatly strengthened this manuscript. The authors are also grateful to Dr. Sattar Chavoshi Borujeni, Associate Professor David Bruce, Dr. Donald Cameron and Dr. Kevin Mills from the University of South Australia, Professor Wayne Meyer and Dr. Sigfredo Fuentes from the University of Adelaide and Dr Craig McFarlane from the University of Western Australia for their advice and support.
科研通智能强力驱动
Strongly Powered by AbleSci AI