This is the Mid Columbia stream network used in the application section of the journal article. This dataset is associated with the following publication: Ver Hoef, J., M. Dumelle, M. Higham, E. Peterson, and D. Isaak. Indexing and Partitioning the Spatial Linear Model for Large Data Sets. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 18(11): e0291906, (2023).

Explore the historical Whois records related to spatialstream.net (Domain). Get insights into ownership history and changes over time.

High resolution spatial stream network (SSN) models are needed to predict stream temperature distributions across large basins at a fine scale, to identify thermal refuge areas for conservation and protection, and to predict the effects of weather variation and management actions on coldwater habitat. EPA has been working with the Penobscot tribe and Maine Temperature Monitoring Working Group to plan development of a fine scale temperature model for the Penobscot River basin in Maine. This suite of datasets with supporting Python code provides calibration and prediction covariates for a fine resolution SSN model for the Penosbscot. Included are an SSN object with catchment covariates, associated Python code and metadata. At this point model development has not been initiated.

large spatial stream network data set used in following publications,Isaak, D.J., Wenger, S.J., Peterson, E.E., Ver Hoef, J.M., Nagel, D.E., Luce, C.H., Hostetler, S.W., Dunham, J.B., Roper, B.B., Wollrab, S.P., Chandler, G.L., Horan,D.L., Parkes-Payne, S. 2017. The NorWeST Database and Modeled Summer Temperature Scenarios: Massive Crowd-sourcing and New Geospatial Tools Reveal Broad Climate Warming of Rivers and Streams in the Western U.S. Water Resources Research: 53(11): 9181–9205. DOI: 10.1002/2017WR020969.andVer Hoef, J.M., Dumelle, M., Higham, M., Peterson, E.E., and Isaak, D.J. 2023. Indexing and Partitioning the Spatial Linear Model for Large Data Sets. PLOS ONE 18(11): e0291906. PLOS ONE 18(11): e0291906

These files contain data for relating stream total phosphorus concentration, a nutrient, to land cover and land use variables in the East Fork of the Little Miami River watershed near Cincinnati, Ohio. Water quality grab samples were collected from June 26, 2012 to September 11, 2012, and total phosphorus concentrations were measured on those samples. The files in the jawr12543-sup-002-R_code_and outputs folder are htmls, which can be opened with any browser to view the data and work flow of the data analysis. The files in the jawr12543-sup-003-SSN_file_objects contains the dataset as an R object, which can be opened in the open-source R software.

This dataset is associated with the following publication: Scown, M., M. McManus, J. Carson, and C. Nietch. Improving predictive models of in-stream phosphorus based on nationally-available spatial data coverages in a Southwestern Ohio watershed. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 53(4): 944-960, (2017).

The rate at which new information about stream resources is being created has accelerated with the recent development of spatial stream-network models (SSNMs), the growing availability of stream databases, and ongoing advances in geospatial science and computational efficiency. To further enhance information development, the National Stream Internet (NSI) project was developed as a means of providing a consistent, flexible analytical infrastructure that can be applied with many types of stream data anywhere in the country. A key part of that infrastructure is the NSI network, a digital GIS layer which has a specific topological structure that was designed to work effectively with SSNMs. The NSI network was derived from the National Hydrography Dataset Plus, Version 2 (NHDPlusV2) following technical procedures that ensure compatibility with SSNMs. The SSN models outperform traditional statistical techniques applied to stream data, enable predictions at unsampled locations to create status maps for river networks, and work particularly well with databases aggregated from multiple sources that contain clustered sampling locations. The NSI project is funded by the U.S. Fish & Wildlife Service's Landscape Conservation Cooperative program and has two simple objectives: 1) refine key spatial and statistical stream software and digital databases for compatibility so that a nationally consistent analytical infrastructure exists and is easy to apply; and 2) engage a grassroots user-base in application of this infrastructure so they are empowered to create new and valuable information from stream databases anywhere in the country. This website is a hub designed to connect users with software, data, and tools for creating that information. As better information is developed, it should enable stronger science, management, and conservation as pertains to stream ecosystems. Resources in this dataset:Resource Title: Website Pointer to the National Stream Internet. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects/NationalStreamInternet.html The National Stream Internet (NSI) is a network of people, data, and analytical techniques that interact synergistically to create information about streams. Elements and tools composing the NSI, including STARS, NHDPlusV2, and SSNs, enable integration of existing databases (e.g., water quality parameters, biological surveys, habitat condition) and development of new information using sophisticated spatial-statistical network models (SSNMs). The NSI provides a nationally consistent framework for analysis of stream data that can greatly improve the accuracy of status and trend assessments. The NSI project is described, together with an analytical infrastructure for using the spatial statistical network models with many types of stream datasets.

Fundamental to species conservation efforts is the development of accurate distribution models, but doing so is challenging for many stream organisms, where limited funding often necessitates the compilation of incidental observations from multiple sources, which lack an overall sampling design and may be spatially clustered. We demonstrate the application of specialized spatial-statistical-network models (SSNMs), which incorporate autocorrelation among observations and significantly outperform non-spatial models when used to develop distribution models for the Idaho giant salamander (IGS; Dicamptodon aterrimus). The study was located in the Rocky Mountains in west-central North America. We compiled a comprehensive presence-absence dataset for IGS from previous studies, natural resource agencies, museum collections, and new surveys and linked these data to geospatial habitat covariates. The dataset was modeled using a suite of candidate SSNMs and results were compared to generalized lin..., A presence-absence dataset for Idaho giant salamander that consisted of 707 unique sampling locations was collected using electrofishing and eDNA surveys. Many of the surveys were aggregated from existing sources such as previous peer-reviewed studies, grey literature reports, state and federal agency databases, and natural resource museum records. The survey locations were attached to reaches within stream networks across the species range, linked to geospatial habitat covariates, and processed using the open-source SSNbler R package into a landscape network object suitable for spatial-stream-network model analysis using the SSN2 R package. , , # Data from: Improving species distribution models for stream networks by incorporating spatial autocorrelation in multi-sourced datasets: An assessment of Idaho giant salamander status and future risk

https://doi.org/10.5061/dryad.h18931zxb

Description of the data and file structure

This dataset was used in a manuscript published in Diversity and Distributions and consists of several elements: 1) an annotated R script for running an SSNM species distribution model analysis of Idaho giant salamanders (AnnotatedRscript_IGSAnalysis_SDM_n707.R), 2) a zipfile which contains a .ssn directory of files with the observations, covariates, range-wide prediction points, and other helper files needed to conduct a spatial-stream-network model analysis (IGS_LSN3.ssn.zip), 3) the master database of presence-absence surveys as an Excel file (Isaak2025D_D_MasterDataset_IGS-Observations.xlsx), 4) a high-resolution .pdf map showing the survey locations used...,

This data compares spatial autocovariance models used in the modeling and prediction of specific conductivity over 8 sampling periods in an Eastern Kentucky watershed. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

This study presents the application of a dendritic network model, specifically Spatial Stream Network Models, for predicting variations in strontium concentrations [Sr] and isotope ratios (87Sr/86Sr) in large river systems. We applied the model to the Taku River, a significant river system located within the accreted volcanic arc terranes of the northern Cordillera of North America. The model strongly fits the observed data with RMSE=0.05: r2=0.67 for [Sr] and RMSE=0.0003: r2=0.87 for 87Sr/86Sr. Our multidisciplinary data product offers a comprehensive tool with applications across the biosphere, hydrosphere, and geosphere. This model can address diverse research questions ranging from assessing the ecology of wild salmon fisheries to calculating Sr ocean budgets and evaluating silicate weathering feedback.

Percentage of the residual error structures in the final spatial stream temperature models attributable to tail-up, tail-down, Euclidean, and nugget portions of the covariance structure.

The NorWeST webpage hosts stream temperature data and climate scenarios in a variety of user-friendly digital formats for streams and rivers across the western U.S. The temperature database was compiled from hundreds of biologists and hydrologists working for >100 resource agencies and contains >200,000,000 hourly temperature recordings at >20,000 unique stream sites. Those temperature data were used with spatial statistical network models to develop 36 historical and future climate scenarios at 1-kilometer resolution for >1,000,000 kilometers of stream. Temperature data and model outputs, registered to NHDPlus stream lines, are posted to the website after QA/QC procedures and development of the final temperature model within a river basin. Open access to the data and the availability of accurate stream temperature scenarios will foster new research and collaborative relationships that enhance management and conservation of aquatic resources. Funding for the project was provided by the GNLCC and NPLCC with additional funds and in-kind support from the USFS, USGS, USFWS, NFWF, California Fish Passage Forum, and NASA. Resources in this dataset:Resource Title: Website Pointer to NorWeST Stream Temperature Regional Database and Model. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html The NorWeST webpage hosts stream temperature data and climate scenarios in a variety of user-friendly digital formats for streams and rivers across the western U.S. The temperature database was compiled from hundreds of biologists and hydrologists working for >100 resource agencies and contains >200,000,000 hourly temperature recordings at >20,000 unique stream sites. Those temperature data were used with spatial statistical network models to develop 36 historical and future climate scenarios at 1-kilometer resolution for >1,000,000 kilometers of stream. Temperature data and model outputs, registered to NHDPlus stream lines, are posted to the website after QA/QC procedures and development of the final temperature model within a river basin.

The data is provided as Landscape Network (lsn.ssn) objects, which contain the spatial files for the predictions as well as the occurrences.

NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the > 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The scenarios include: 1) Adobe PDF format maps depicting historical and future modeled mean August stream temperatures, 2) graphs (JPG format) demonstrating the accuracy of the temperature model, and 3) GIS shapefiles (SHP format) representing the spatially modeled stream temperatures. The GIS shapefiles include stream lines and associated mid-points representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two shapefile formats are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the shapefile attribute tables. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.These data were originally intended to be used for managing biological resources and predicting species distributions affected by August mean stream temperature.For more information on the NorWeST stream temperature project see: https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST.html

This data publication originally became available via the FS Research Data Archive on 11/17/2016. On 7/27/2022 the metadata was updated to correct old URLs.

NorWeST is an interagency stream temperature database and model for the western United States containing data from over 20,000 unique stream locations. Temperature observations were solicited from state, federal, tribal, private, and municipal resource organizations and processed using a custom cleaning script developed by Gwynne Chandler. Summaries of daily, weekly, and monthly means, minima, and maxima are provided for observation years. The data summaries and location information are available in user-friendly file formats that include: 1) a map (PDF) depicting the locations of in-stream thermographs (temperature sensors) for each processing unit, 2) a GIS shapefile (SHP) containing the location of these sensors for each processing unit, and 3) a tabular file (XLSX) containing observed temperature database summaries for data generally ranging from 1993 to 2015, dependent on the processing unit. Each point shapefile extent corresponds to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs). The tabular data can be joined to the observation point shapefile using the ID field OBSPRED_ID. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Semi-variograms of the residuals from the final maximum weekly maximum temperature (MWMT) and summer mean spatial stream temperature models.

Comparisons of the accuracy of multi-stream with different input modalities on the NTU-RGB+D 60 dataset.

Comparisons of the accuracy with different input modalities on the NTU-RGBD 60 dataset.

This layer indicates the location of the observed stream temperature records used for the NorWeST database summaries. NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the greater than 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This layer represents modeled stream temperatures derived from the NorWeST point feature class (NorWest_TemperaturePoints). NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the > 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Summary of multi-stream techniques utilized in human action recognition.