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Point data for four case studies related to testing of multi-order hydrologic position

Metadata Updated: January 20, 2026

The location of a point (or pixel) within the conterminous U.S. can be assigned based on its position relative to the Nation’s stream network. Two metrics are recognized: lateral position (LP) and distance from stream to divide (DSD). And given that a point can have different positions in different hydrologic orders the term multi-order hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. LP and DSD were developed for nine hydrologic orders across the conterminous U.S. (Belitz and others, 2019; Moore and others, 2019). Four case studies are presented here that were used for evaluating the utility of MOHP in the context of random forest machine learning (Belitz and others, 2019). Two of the case studies evaluate categorical response variables: geomorphic province in the Central Valley of California (Faunt, 2009) and physiographic province in the conterminous U.S. (Fenneman and Johnson, 1946). The other two case studies evaluated depth to the water table (DTW), which is a continuous variable. DTW for these two cases were determined from: 1) a numerical simulation model of the groundwater flow system in the Fox-Wolf-Peshtigo area located to the west of Lake Michigan (Juckem and others, 2017); and 2) observed values in Wisconsin (Fan and others, 2013). The point data for each of the four case studies include: land surface elevation, the 18 MOHP metrics (LP and DSD for nine hydrologic orders), and the appropriate response variable. Latitude and longitude are also included for the purposes of plotting. The case studies show that some MOHP metrics serve as indicators of hydrologic process and others 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 square kilometers). Four comma-separated values (.csv) data tables are included in this data release: 1) CVAL_sampsites_mohp.csv -- Central Valley, California 2) FPR_sampsites_mohp.csv -- Fenneman Physiographic Regions 3) FWP_sampsites_mohp.csv -- Simulated depth-to-water in the Fox-Wolf-Peshtigo model area 4) WIOBS_sampsites_mohp.csv -- Observed depth-to-water throughout Wisconsin

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date January 13, 2026
Metadata Updated Date January 20, 2026

Metadata Source

Harvested from DOI USGS DCAT-US

Additional Metadata

Resource Type Dataset
Metadata Created Date January 13, 2026
Metadata Updated Date January 20, 2026
Publisher U.S. Geological Survey
Maintainer
Identifier http://datainventory.doi.gov/id/dataset/USGS_5ced4d1de4b02eb068de9227
Data Last Modified 2020-08-26T00:00:00Z
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://ddi.doi.gov/usgs-data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Datagov Dedupe Retained 20260120105240
Harvest Object Id 1e435531-1ca5-4d65-8f1e-a22d98c62937
Harvest Source Id 2b80d118-ab3a-48ba-bd93-996bbacefac2
Harvest Source Title DOI USGS DCAT-US
Metadata Type geospatial
Old Spatial {"type": "Polygon", "coordinates": -125.33203125000001, 24.766784522874453, -125.33203125000001, 49.66762782262194, -66.79687500000001, 49.66762782262194, -66.79687500000001, 24.766784522874453, -125.33203125000001, 24.766784522874453}
Source Datajson Identifier True
Source Hash 4b0691ae3d9ca15d513f6d763744a66443a3c735d95cb9cd43070abfd32dec58
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -125.33203125000001, 24.766784522874453, -125.33203125000001, 49.66762782262194, -66.79687500000001, 49.66762782262194, -66.79687500000001, 24.766784522874453, -125.33203125000001, 24.766784522874453}

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