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Multiorder Hydrologic Position in the Conterminous United States: A Set of Metrics in Support of Groundwater Mapping at Regional and National Scales

机译:美国本土的多级水文位置:一套在区域和国家尺度上支持地下水制图的指标

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摘要

The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order "n" is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher-order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km(2)).
机译:河流网络中景观点的位置(水文位置)可能是水文学中重要的预测指标。这里的水文位置由两个指标定义:横向位置和从溪流到分水岭的距离,均水平测量。横向位置(无因次)是河流与其分水岭之间的点的相对位置。从溪流到分水岭的距离(长度单位)是流域内位置的指标:汇合处通常较小,而上游水源区域通常较大。分水岭和分水岭分界在这里是由蒂森多边形定义的,而不是地形分界。从水文顺序的角度还定义了从溪流到分水岭的侧向位置和距离。水文“ n”阶被定义为n阶或更高阶的河流网络和相关的分界线。考虑到一个点在不同的水文位置中可以具有不同的位置,因此使用多级水文位置(MOHP)来描述水文位置的集合。 MOHP被绘制成横跨整个美国的9个水文命令,其空间分辨率为30 m(约87亿像素)。每个像素有18个指标。提出了四个案例研究,这些案例使用MOHP指标作为随机森林机器学习模型中的解释因素。案例研究表明,低阶MOHP指标可以作为水文过程的指标,而高阶MOHP指标可以作为位置的指标。事实证明,MOHP在较大范围的尺度(50,000至8,000,000 km(2))中可用作预测变量。

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