Supporting data for and predictions from streamflow permanence modeling in Mount Rainier National Park and surrounding area, Washington, 2018-2020

This data release contains spatially gridded geospatial data (rasters), R scripts, and supporting files to run Random Forest models to predict the probability of late summer surface flow in Mount Rainier and surrounding area in Washington State for 2018–20. Gridded geospatial data that describes the physical conditions of Mount Rainier National Park and surrounding area are used to refine the existing PRObability of Streamflow PERmanence (PROSPER) model (Jaeger and others, 2019).

All data processing and analysis were scripted with R (version 4.0.4; https://www.r-project.org/) and was executed from the RStudio GUI (version 1.4.1103; https://www.rstudio.com/). R scripts to prepare the geospatial data, develop random forest models, and provide predictions are contained within “MORA_Source_Code.zip”. Geospatial data and supporting files used in these scripts are contained within "MORA_Model_Inputs.zip". Predictions and a suitability grid are contained within "MORA_Model_Outputs.zip."

Jaeger K, Sando R, McShane R, Dunham J, Hockman-Wert D, Kaiser K, Hafen K, Risley J, Blasch K. 2019. Probability of Streamflow Permanence Model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific Northwest. Journal of Hydrology X, 2: 100005.

First posted - 2022-05-13
Revision posted - xxxxxxx

Changes in Version 2.0

This dataset includes changes to the following files
1) MORA_Model_Inputs that include replacement of monthly climatic FCPGs with seven-month summary of climatic FCPGs,
2) MORA_Source_Code to account for these changed model inputs, and additional covariate correlation analysis, and
3) MORA_Model_Outputs include all new probability prediction rasters from the revised model.