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

This data release consists of 5390 streamflow gages within the conterminous United States that will serve as version 1.0 of streamflow benchmark locations for hydrologic model evaluation and benchmarking.

Multi Order Hydrologic Position (MOHP) raster datasets: Distance from Stream to Divide (DSD) and Lateral Position (LP) have been produced nationally for the 48 contiguous United States at 30-meter and 90-meter cell resolution for stream orders 1 through 9. These data are available for testing as predictor variables for various regional and national groundwater-flow and groundwater-quality statistical models. For quicker downloads, these data are available here nationally at a 90-meter cell resolution, as well as on the National Spatial Data Infrastructure (NSDI) Node at the higher 30-meter cell resolution ( https://water.usgs.gov/GIS/metadata/styles/landingPage/national_MOHP_Predictor.xml ). The concept behind MOHP is that for any given point on the earth’s surface there is the potential for longer and longer groundwater flow paths as one goes deeper and deeper beneath the land surface. These increasing depths correspond to increasing stream orders. Or in other words, with increasing depth these paths of groundwater flow travel further from divides to point of discharge which are to increasingly larger streams of higher stream order. DSD – Raster – Distance from Stream to Divide (DSD) rasters have cell values equal to the sum of the shortest distance to the stream or associated waterbody plus the shortest distance to the matching Thiessen divide. There are 9 rasters for streams orders 1 through 9. Units are in meters. LP – Raster -- the lateral position (LP) raster has cell values equal to the shortest distance to the stream or associated waterbody divided by the DSD. There are 9 rasters for streams orders 1 through 9. Combined, these two factors, DSD and LP, provide a measure or description of potential distance of groundwater flow to any location along the groundwater flow path.

This dataset consists of culvert verification images from site visits to confirm hydrologic connection in areas of uncertainty in U.S. Geological Survey 3D Elevation Program (3DEP) lidar digital elevation models (DEM) and aerial leaf off orthoimagery to observe ground conditions in the Upper Shawsheen River Basin, Massachusetts.

Three streamflow measurements are used to demonstrate the use of equations developed in Mueller (in review). All three measurements are from various locations on the Mississippi River. These data were not collected for the purpose of this paper but provide practical examples of the effect of heading errors. The use of data from the Mississippi River allows the collection of 500 or more ensembles in each transect which reduce the overall effect of random errors that could complicate the identification of effects due to heading errors. In addition, by using wide cross sections, the effect of GPS errors due to vegetation near the boundaries of the river are minimized. All measurements were collected with WinRiver II (Teledyne RD Instruments, 2016) and processed with QRev (Mueller, 2016). These three data sets represent three different situations: 1) availability of heading data from a GPS compass (Mississippi River near Hickman, KY), 2) transects intentionally collected at different speeds (Mississippi River near Vicksburg, MS), and 3) GPS data collected where there is minimal influence from a moving bed (Mississippi River near Clinton, IA). All data were collected using Teledyne RD Instruments Rio Grande ADCPs. All data were collected with Teledyne RD Instrument WinRiver II (Teledyne RD Instruments, 2016) and processed with QRev version 3.43 (Mueller, 2016). Due to the complexity of an ADCP data file and the various algorithms applied to compute the streamflow from ADCP data, these data are most useful in either 1) their original raw data format which can be opened and processed in either WinRiver II or QRev or 2) their processed format that can be opened and processed by QRev or opened by Matlab or any software that can read Matlab formatted files. Both WinRiver II and QRev are distributed free. WinRiver II can be obtained from: http://www.teledynemarine.com/rdi/support# QRev can be obtained from: https://hydroacoustics.usgs.gov/movingboat/QRev.shtml Each measurement consists of: 1) *.mmt file is an xml configuration file used by WinRiver II for setup, specific measurement data entry, and filenames of the raw transect data files (pd0) 2) *.pd0 files are the raw binary data collected by WinRiver II. The format for these files is defined in Teledyne RD Instruments (2016). 3) *.txt files contain raw ASCII data from external sensors such as GPS receivers. These data are not used by WinRiver II or QRev but provide the raw external data strings sent by the GPS receiver. 4) *.mat files are the saved data processed by QRev. These files can be opened and processed by QRev or loaded into Matlab or software that can read Matlab formatted files. The variable definitions are documented in Mueller (2016). 5) *.xml are summaries of the data processed by QRev. The variable definitions are documented in Mueller (2016).

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. One of the major goals of the NAWQA project is to determine how water-quality conditions change over time. To support that goal, long-term consistent and comparable monitoring has been conducted on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water-quality conditions. Data from these multiple sources have been combined to support one of the most comprehensive assessments conducted to date of water-quality trends in the United States. Ultimately, these data will provide insight into how natural features and human activities have contributed to water-quality changes over time in Nation’s streams and rivers. This USGS data release contains all of the input and output files necessary to reproduce the results of the Seasonal Kendall trend tests described in the associated U.S. Geological Survey Scientific Investigations Report. Data preparation for input to the model is also fully described in the above-mentioned report.

This data release provides coupling of Conterminous United States and several Canadian operational reservoirs used in recent hydrologic modeling studies to authoritative national hydrographic datasets used to identify, calibrate, model, and assess streamflow, water quantity, quality, and ecological resources. The National Inventory of Dams (NID) provides linkages to dams operated in the United States, GRanD provides linkages to Global Dams and Reservoirs, and the National Hydrography Dataset Plus (NHDPlus) provides linkages to the stream network and waterbodies to easily couple with National Hydrologic Models and landscape parameters. The Geospatial Fabric for National Hydrologic Modeling, version 1.1 (GFv1.1) provides linkages to existing National Hydrologic Model (NHM) spatial modeling units. All crosswalks, linkages, and other information is provided in a comma-separated value (csv) file.