Estimating extreme flood magnitude in bedrock‐influenced channels using representative reach‐based channel resistance data
Estimating extreme flood magnitude in bedrock‐influenced channels using representative reach‐based channel resistance data
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使用代表性的基于河段的河道阻力数据估计受基岩影响的河道的极端洪水强度
DOI：
10.1111/1468-0459.00184
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
A. Birkhead
中科院分区：
文献类型：
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作者：
G. Heritage;A. Large;B. P. Moon;A. Birkhead
摘要极端洪水事件被许多研究者认为在控制半干旱基岩影响的河流系统的发展方面非常重要。然而，由于仪表在事件期间被淹没并经常损坏，因此对此类事件进行精确测量通常是不可能的。提出了一种估算基岩影响河道洪水流量的方法，该方法重建了洪峰流量的水文特性，并将其与所讨论的河道粗糙度特性联系起来。该方法是使用峰值水面坡度数据进行评估的极端洪水的2000年2月沿着萨比河和Letaba河，分别位于姆普马兰加省和北方省，南非。这些数据是以岸线测量的形式在两条河流长剖面上沿着的水力相关点采集的。所得到的数据一起使用与公布的高流量通道阻力的数字，根据渠道形态的萨比河和Letaba河，产生峰值流量估计数的位置沿着两条河流。对摩擦流量峰值流量估计值、流速-面积和峰值流量的水文估计值进行了比较。这些比较表明，该方法可以产生流量估计的精度分别为±10%和± 35%。
Abstract Extreme flood events are considered by many researchers to be very important in controlling the development of semi‐arid bedrock‐influenced river systems. Accurate gauging of such events is often impossible, however, as gauges are drowned and often damaged during the event. A methodology for estimating flood discharge for bedrock‐influenced channels is presented that reconstructs hydrometric characteristics of the peak flow and relates these to the roughness character of the river channel in question. The method is evaluated using peak water‐surface slope data relating to the extreme floods of February 2000 along the Sabie and Letaba rivers, located respectively in the Mpumalanga and Northern Provinces, South Africa. The data, in the form of strandline measurements, were taken at hydraulically relevant points along the long profile of both rivers. The resultant data are utilised together with published high flow channel resistance figures, based on the channel morphology of the Sabie and Letaba rivers, to generate peak flow estimates for a number of locations along both rivers. Comparisons are made between the frictional discharge peak flow estimates, velocity‐area and hydrologic estimates of peak flow. These comparisons indicate that the method can produce discharge estimates with an accuracy of ±10% and ± 35% respectively.