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

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 SummaryIn 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.

Three‐Dimensional Distribution of Groundwater Residence Time Metrics in the Glaciated United States Using Metamodels Trained on General Numerical Simulation Models

“…Low order MOHP features are a proxy in part for drainage density, which in turn is correlated with recharge, aquifer thickness, and hydraulic conductivity. Higher order MOHP features vary with distance from major water bodies and may be a proxy for regional‐scale climate (Belitz et al., 2019). The terrane feature is based on sediment thickness and the geologic environments in which the sediments were deposited.…”

“…Response variables consist of the age metrics discussed previously for each of the 130,740 RTDs calculated from GSMs (Table S1). Two novel data sets were used, hydrogeologic terrane (Haj et al, 2018) and Multi-Order Hydrologic Position (MOHP, Belitz et al, 2019). Values of the explanatory features were interpolated to the center of every target GSM cell.…”

Using Boosted Regression Tree Models to Predict Salinity in Mississippi Embayment Aquifers, Central United States

“…SC and Cl values were higher where hdt1930 was lower (Figure ), generally corresponding to the southern extent of the study area. Multi‐order hydrologic position (MOHP) describes position within the watershed and can be used to predict depth to water (Belitz, Arnold, et al 2019; Belitz, Moore, et al 2019). In the Mississippi embayment, MOHP variables for ninth order streams (mohp9DSD and mohp9LP) represent the location of a well relative to the Arkansas and Mississippi Rivers, which serve as major groundwater flow boundaries, and were important explanatory variables of SC and Cl (Figure 5; Table ).…”

“…Explanatory variables were attributed to wells using either a point, buffer, or grid extraction in Python (Python Software Foundation 2019). Multi‐order hydrologic position (Belitz, Arnold, et al 2019; Belitz, Moore, et al 2019) was attributed to wells using a point extraction to maintain the high resolution of the source data. Other surficial explanatory variables — such as land use/land cover, recharge, and soil characteristics (physical and chemical) — were attributed to wells with 500‐m buffer using the zonepy package (Clark et al 2019) to account for spatial variation and to represent a surrogate for contributing recharge area to the well (Johnson and Belitz 2009).…”

“…Explanatory variables were attributed to wells using either a point, buffer, or grid extraction in Python (Python Software Foundation 2019). Multiorder hydrologic position Belitz, Moore, et al 2019) was attributed to wells using a point extraction to maintain the high resolution of the source data. Other surficial explanatory variablessuch as land use/land cover, recharge, Clark and Hart (2009).…”