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An automated and rapid method for identifying dam wall locations and estimating reservoir yield over large areas

机译:一种自动,快速的方法来识别大坝墙的位置并估算大面积的水库产量

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Global demand for water, food and energy has seen the construction and planning of large dams continue at a steady pace in many parts of the world. However, the process of 'exhaustively' examining all potential dams sites within an area as part of an initial scoping study is still largely undertaken using manual methods. This paper describes DamSite, a series of novel algorithms that construct 'virtual' dam walls at every pixel along every channel within a catchment, including saddle dams where required by the terrain. By repetitively calculating dam and reservoir dimensions, reservoir yield and dam costs along a river network and for incrementally higher dam walls at each location it is possible to identify both optimal dam wall locations and optimal dam wall height at a given location. The DamSite model was tested in two catchments in northern Australia and accurately pin-pointed previously identified potential dam locations. (C) 2017 Elsevier Ltd. All rights reserved.
机译:全球对水,食物和能源的需求已经使大型水坝的建设和规划在世界许多地方持续稳定地发展。但是,作为初步范围研究的一部分,“穷尽”地检查区域内所有潜在的水坝位置的过程仍主要是使用手动方法进行的。本文介绍了DamSite,这是一系列新颖的算法,可在流域内每个通道的每个像素处构建“虚拟”水坝墙,包括地形所需的鞍式水坝。通过重复计算沿河网的水坝和水库尺寸,水库产量和水坝成本,以及在每个位置递增增加的水坝壁,可以确定给定位置的最佳水坝壁位置和最佳水坝壁高度。 DamSite模型在澳大利亚北部的两个流域进行了测试,并精确指出了先前确定的潜在大坝位置。 (C)2017 Elsevier Ltd.保留所有权利。

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