Multiorder Hydrologic Position in the Conterminous United States: A Set of Metrics in Support of Groundwater Mapping at Regional and National Scales. Issue 12 (26th December 2019)

• Record Type: Journal Article
• Authors: Belitz, Kenneth; Moore, Richard B.; Arnold, Terri L.; Sharpe, Jennifer B.; Starn, J. J.
• Is Part Of: Water resources research. Volume 55:Issue 12(2019)
• Page Start: 11188
• Page End: 11207
• Publication Date: 2019-12-26
• Subjects: hydrology -- groundwater -- machine learning -- mapping -- GIS; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR025908
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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 ). Plain Language Summary: In hydrology, as in other endeavors, location matters. This study presents a new type of data that describes the location of a point on the landscape in the context of the network of streams that are present across the continental United States. The new data are presented as maps, and different patterns can be recognized in different areas of the United States. The patterns that can be seen also vary as one looks more or less closely at an area. The patterns that are present in these maps are shown to be useful for the purposes of mapping water resources. Key Points: Multiorder hydrologic position (lateral position and distance from stream to divide) quantifies the location of a point on the landscape Multiorder hydrologic position is mapped for nine hydrologic orders at a 30‐m resolution for the conterminous United States Multiorder hydrologic position is used in machine learning models for the purposes of physiographic and hydrologic mapping