This dataset is a combination of annual Probability of Streamflow Permanence (PROSPER) predictions, Northwest Stream Temperature (NorWeST) predictions of monthly mean stream temperatures for August of each year, and predicted monthly streamflow discharge for stream reaches in the Pacific Northwest from the USGS database of natural monthly streamflow estimates, U.S., 2004-2015. The PROSPER model provides predictions of the annual probability of a 30-meter stream segment maintaining year-round streamflow. The NorWeST model provides annual predictions of monthly mean stream temperature for August for 1-kilometer stream segments. Finally, predictions of natural monthly streamflow were combined with NorWeST and PROSPER to provide information on the volume of water in a given system. The data are merged using the Medium Resolution National Hydrography Dataset, which serves as the foundation for the stream lines that the data are represented with. The intent of this merged dataset is to analyze the availability of current and future aquatic habitat.

This dataset is a combination of annual Probability of Streamflow Permanence (PROSPER) predictions, Northwest Stream Temperature (NorWeST) predictions of monthly mean stream temperatures for August of each year, and predicted monthly streamflow discharge for stream reaches in the Pacific Northwest from the USGS database of natural monthly streamflow estimates, U.S., 2004-2015. The PROSPER model provides predictions of the annual probability of a 30-meter stream segment maintaining year-round streamflow. The NorWeST model provides annual predictions of monthly mean stream temperature for August for 1-kilometer stream segments. Finally, predictions of natural monthly streamflow were combined with NorWeST and PROSPER to provide information on the volume of water in a given system. The data are merged using the Medium Resolution National Hydrography Dataset, which serves as the foundation for the stream lines that the data are represented with. The intent of this merged dataset is to analyze the availability of current and future aquatic habitat.

This dataset is a combination of annual Probability of Streamflow Permanence (PROSPER) predictions, Northwest Stream Temperature (NorWeST) predictions of monthly mean stream temperatures for August of each year, and predicted monthly streamflow discharge for stream reaches in the Pacific Northwest from the USGS database of natural monthly streamflow estimates, U.S., 2004-2015. The PROSPER model provides predictions of the annual probability of a 30-meter stream segment maintaining year-round streamflow. The NorWeST model provides annual predictions of monthly mean stream temperature for August for 1-kilometer stream segments. Finally, predictions of natural monthly streamflow were combined with NorWeST and PROSPER to provide information on the volume of water in a given system. The data are merged using the Medium Resolution National Hydrography Dataset, which serves as the foundation for the stream lines that the data are represented with. The intent of this merged dataset is to analyze the availability of current and future aquatic habitat.

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 Mt. Rainier and surrounding area in Washington State for 2018–20. Gridded geospatial data that describes the physical conditions of Mt. 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.

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 Mt. Rainier and surrounding area in Washington State for 2018–20. Gridded geospatial data that describes the physical conditions of Mt. 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.

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 Mt. Rainier and surrounding area in Washington State for 2018–20. Gridded geospatial data that describes the physical conditions of Mt. 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.

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 Mt. Rainier and surrounding area in Washington State for 2018–20. Gridded geospatial data that describes the physical conditions of Mt. 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.

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 Mt. Rainier and surrounding area in Washington State for 2018–20. Gridded geospatial data that describes the physical conditions of Mt. 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.

This dataset includes inputs and results for parameterizing the USGS Thornthwaite Monthly Water Balance Model (MWBM) to simulate annual stream permanence on National Hydrography Dataset (NHD) stream reaches. Also included are results from sensitivity analysis of MWBM parameters to final stream permanence classification (permanent or nonpermanent). The dataset includes files that link PRISM climate grids to NHD catchments and flowlines. Data tables describe the sensitivity of MWBM stream permanence classifications to each of the altered MWBM parameters. Suitable MWBM parameter sets, which resulted in accuracy of at least 65% when compared to observed surface water conditions, for modeling stream permanence are presented with precision estimates for MWBM stream permanence classifications for NHD flowlines.

This dataset includes inputs and results for parameterizing the USGS Thornthwaite Monthly Water Balance Model (MWBM) to simulate annual stream permanence on National Hydrography Dataset (NHD) stream reaches. Also included are results from sensitivity analysis of MWBM parameters to final stream permanence classification (permanent or nonpermanent). The dataset includes files that link PRISM climate grids to NHD catchments and flowlines. Data tables describe the sensitivity of MWBM stream permanence classifications to each of the altered MWBM parameters. Suitable MWBM parameter sets, which resulted in accuracy of at least 65% when compared to observed surface water conditions, for modeling stream permanence are presented with precision estimates for MWBM stream permanence classifications for NHD flowlines.