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

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

Data and Resources

Field Value
accessLevel public
bureauCode {010:12}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
identifier USGS:5ced4d1de4b02eb068de9227
metadata_type geospatial
modified 20200826
old-spatial -125.33203125000001, 24.766784522874453, -66.79687500000001, 49.66762782262194
publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
source_datajson_identifier true
source_hash cbadd3e3c1a6c68e28644c6206bf8835ff6d3809
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]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • central-valley-california
  • ckan
  • conterminous-united-states
  • cycle-3
  • fox-wolf-peshtigo
  • geo
  • geoss
  • groundwater
  • hydrologic-position
  • hydrologic-systems
  • machine-learning
  • national
  • nawqa
  • north-america
  • united-states
  • usgs-5ced4d1de4b02eb068de9227
  • water-quality
  • wisconsin
isopen False
license_id notspecified
license_title License not specified
maintainer Kenneth Belitz
maintainer_email kbelitz@usgs.gov
metadata_created 2025-11-20T14:11:33.076390
metadata_modified 2025-11-20T14:11:33.076394
notes 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
num_resources 2
num_tags 20
title Point data for four case studies related to testing of multi-order hydrologic position