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

被引:21
作者
Belitz, Kenneth [1 ]
Moore, Richard B. [2 ]
Arnold, Terri L. [3 ]
Sharpe, Jennifer B. [3 ]
Starn, J. J. [4 ]
机构
[1] US Geol Survey, Carlisle, MA 01741 USA
[2] USGS New England Water Sci Ctr, Pembroke, NH USA
[3] USGS Cent Midwest WSC, Urbana, IL USA
[4] USGS New England WSC, E Hartford, CT USA
关键词
hydrology; groundwater; machine learning; mapping; GIS; CATCHMENT TRANSIT-TIME; CENTRAL VALLEY; THEORETICAL-ANALYSIS; LANDSCAPE POSITION; GLOBAL PATTERNS; RANDOM FOREST; BASE-FLOW; NITRATE; MODELS; WATER;
D O I
10.1029/2019WR025908
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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)).
引用
收藏
页码:11188 / 11207
页数:20
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