Reach-scale flow classifications (ephemeral , intermittent, and perennial) based on direct hydrologic data and field (biological and geomorphological) and geospatial (climate, geographical) indicators for northeastern and southeastern United States used to build random forest models to predict flow duration class. This dataset is associated with the following publication: Gross, S., M. Eddy, K. Fritz, B. Topping, T. Nadeau, R. Edgerton, R. Mazor, and K. Nicholas. Data Supplement to Development and Evaluation of the Beta Streamflow Duration Assessment Methods for the Northeast and Southeast. U.S. Environmental Protection Agency, Washington, DC, USA, 2023.

Hydrological, biological, geomorphological, and geospatial datasets collected from the Western Mountains of the United States used to develop the beta SDAM for the Western Mountains.

Hydrological, biological, geomorphological, and geospatial datasets collected from the Western Mountains of the United States used to develop the beta SDAM for the Western Mountains.

Hydrological, biological, geomorphological, and geospatial datasets collected from the Arid West used to develop the Beta SDAM for the Arid West.

GP_betasdam Final data and code. This dataset is associated with the following publication: Eddy, M., S. Gross, K. Fritz, B. Topping, T. Nadeau, R. Edgerton, and J. Kelso. Data Supplement to Development and Evaluation of the Beta Streamflow Duration Assessment Method (SDAM) for the Great Plains (GP). U.S. Environmental Protection Agency, Washington, DC, USA, 2022.

GP_betasdam Final data and code. This dataset is associated with the following publication: Eddy, M., S. Gross, K. Fritz, B. Topping, T. Nadeau, R. Edgerton, and J. Kelso. Data Supplement to Development and Evaluation of the Beta Streamflow Duration Assessment Method (SDAM) for the Great Plains (GP). U.S. Environmental Protection Agency, Washington, DC, USA, 2022.

Our objective was to model the risk of becoming intermittent under drier climate conditions on small, ungaged streams in the Upper Colorado River Basin. Modeling streamflows is an important tool for understanding landscape-scale drivers of flow and estimating flows where there are no gaged records. We focused our study in the Upper Colorado River Basin, a region that is not only critical for water resources but also projected to experience large future climate shifts toward a drier climate. We used a conditional inference modeling approach to model the relation between intermittency status on gaged streams (115 gages) and selected mean and minimum flow metrics. We then projected intermittency status and if a stream reach would be "threatened by intermittency" under a drier climate to ungaged reaches in the Upper Colorado River Basin using predicted minimum flow coefficient of variation (CV) and specific mean annual flow for each stream reach in the basin. This data layer shows modeled values of stream intermittency based on minimum flow CV and specific mean annual flow for each stream reach in the basin.

Our objective was to model the risk of becoming intermittent under drier climate conditions on small, ungaged streams in the Upper Colorado River Basin. Modeling streamflows is an important tool for understanding landscape-scale drivers of flow and estimating flows where there are no gaged records. We focused our study in the Upper Colorado River Basin, a region that is not only critical for water resources but also projected to experience large future climate shifts toward a drier climate. We used a conditional inference modeling approach to model the relation between intermittency status on gaged streams (115 gages) and selected mean and minimum flow metrics. We then projected intermittency status and if a stream reach would be "threatened by intermittency" under a drier climate to ungaged reaches in the Upper Colorado River Basin using predicted minimum flow coefficient of variation (CV) and specific mean annual flow for each stream reach in the basin. This data layer shows modeled values of stream intermittency based on minimum flow CV and specific mean annual flow for each stream reach in the basin.

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.