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Streamflow Permanence Probability (SPP) rasters (PROSPER)

Probabilities of annual streamflow permanence throughout the Pacific Northwest


Publication Date
Start Date
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Sando, R., and Hockman-Wert, D.P., 2019, Streamflow Permanence Probability (SPP) rasters (PROSPER) (ver. 2.0, February 2019): U.S. Geological Survey data release,


Streamflow Permanence Probability (SPP) rasters represent the raw streamflow permanence probabilities produced by the PRObability of Streamflow PERmanence (PROSPER) model, annually for years 2004 through 2016, and overall mean and standard deviation. The PROSPER model is a GIS raster-based empirical model of probabilistic predictions of a stream channel having year-round flow for any unregulated and minimally-impaired stream channel in the Pacific Northwest region, U.S. The model provides predictions of annual streamflow permanence probabilities at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions and static physiographic variables associated with the upstream basin. Predictions correspond [...]

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Attached Files

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SPP_2004.tif 184.2 MB
SPP_2005.tif 184.87 MB
SPP_2006.tif 184.06 MB
SPP_2007.tif 183.2 MB
SPP_2008.tif 184.14 MB
SPP_2009.tif 184.77 MB
SPP_2010.tif 185.45 MB
SPP_2011.tif 185.78 MB
SPP_2012.tif 184.02 MB
SPP_2013.tif 183.12 MB
SPP_2014.tif 183.73 MB
SPP_2015.tif 183.58 MB
SPP_2016.tif 182.91 MB
SPP_MEAN.tif 251.12 MB
SPP_STD.tif 269.2 MB
Streamflow Permanence Probability (SPP) rasters, 2004-2016, Region 17_Rev2.xml
Original FGDC Metadata

20.87 KB
RevisionHistory2.0_SPP_PROSPER.txt 913 Bytes


The Streamflow Permanence Probability (SPP) rasters represent the raw output from the PROSPER model. The values range from zero to one and are directly correlated to the probability that there will be year-round streamflow at a given location; for example, the greater the SPP value, the greater the likelihood of there being year-round streamflow at a given location. While the raw SPP rasters are the basic output of the PROSPER model and the foundation of the prediction of streamflow permanence conditions in the Pacific Northwest, the spatial complexity and variability of the model output means that the raw values need further interpretation in order to be classified as "wet" or "dry." See the related ScienceBase pages, "Streamflow Permanence Class rasters" and "Threshold and confidence interval rasters" for more information on how the raw SPP values can be translated into bias-corrected wet or dry predictions.


Spatial Services

ScienceBase WMS


  • National and Regional Climate Adaptation Science Centers
  • Northwest CASC



Data source
Input directly

Additional Information


Type Scheme Key
DOI doi:10.5066/F7QN661W

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