KEY POINTS

• Massive amounts of water quality data, biological surveys, and habitat condition assessments have been collected by natural resource organizations throughout the U.S.
• The National Stream Internet (NSI) project developed an analytical infrastructure that can be used consistently anywhere with existing stream databases.
• Status and trend assessments for the nation’s aquatic resources can be greatly enhanced through application of NSI technologies at relatively low cost.

BACKGROUND

Accurate, high resolution information does not exist regarding the status and trends of water quality and aquatic biota throughout the 5,000,000 kilometers of river and stream networks in the U.S. Without this information, prioritization of limited conservation resources within and among resource agencies proceeds inefficiently. Massive amounts of water quality data, biological surveys, and habitat condition assessments have been collected by state, federal, tribal, and private organizations across the U.S. Those data could be used to develop huge amounts of new information and precise status/trend assessments if they were used with new spatial-statistical network (SSN) that enable a suite of sophisticated analyses.

STREAM INTERNET COMPONENTS

The National Stream Internet (NSI) project was funded by the USFWS LCC program and led by researchers from USFS, CSIRO, NOAA, and USGS. The project developed a national analytical infrastructure for stream data that can be applied consistently anywhere in the country to develop new information at low cost. To create that infrastructure, the NSI project developed compatibility among key digital stream geospatial data and analysis tools. Those included the EPA/USGS NHD-Plus v.2 stream hydrography layer (Cooter et al. 2010), sets of stream reach descriptors (Wang et al. 2011), and tools for implementing spatial statistical network models (STARS/SSN website, Ver Hoef et al. 2014).

IMPLICATIONS

The NSI enables consistent application of sophisticated analysis tools to many types of stream data throughout the U.S. Moreover, the spatial-statistical network models can be applied to databases characterized by non-random, clustered locations, which provides a strong incentive to develop comprehensive, inter-agency databases (Isaak et al. 2014). The spatial models outperform traditional techniques applied to stream data and enable predictions at ungaged/unmonitored sites, which facilitates development of high-resolution status maps throughout full river networks (for a regional example of NSI technology applications, please visit the NorWeST website). Like the real Internet, a Stream Internet requires a user-base, so free statistical software have been developed (Peterson and Ver Hoef 2014; Ver Hoef et al. 2014) and annual training workshops are conducted in Boise, Idaho. A workshop for leaders of national aquatics programs was also held in 2015 to discuss potential future NSI applications. It is hoped that as better information is developed about stream resources and ecosystems, it empowers resource agencies and managers to make more efficient use of conservation resources and be more effective resource stewards.

MORE INFORMATION

For more information, please visit the project website: www.fs.fed.us/rm/boise/AWAE/projects/NationalStreamInternet.html

KEY REFERENCES

Cooter et al. 2010. A nationally consistent NHDPlus framework for identifying interstate waters: Implications for integrated assessments and interjurisdictional TMDLs. Environmental Management 46:510-524.

Isaak et al. 2014. Applications of spatial statistical network models to stream data. Wiley Interdisciplinary Reviews –Water 1:277-294.

National Stream Internet website: www.fs.fed.us/rm/boise/AWAE/projects/NationalStreamInternet.html

NorWeST: An interagency stream temperature database, model, and climate scenarios for streams and rivers in the western U.S. website: www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html

Peterson and Ver Hoef. 2014. STARS: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. Journal of Statistical Software 56(2):1-17.

SSN/STARS: Tools for Spatial Statistical Modeling on Stream Networks. Website: www.fs.fed.us/rm/boise/AWAE/projects/SpatialStreamNetworks.html.

Ver Hoef et al. 2014. SSN: An R package for spatial statistical modeling on stream networks. Journal of Statistical Software 56(3):1-45.

Wang et al. 2011. A hierarchical spatial frame-work and database for the national river fish habitat condition assessment. Fisheries 36: 436-449.