SMH Explorer Stream Network Attributes

DeRolph, Chris; Bevelhimer, Mark; Witt, Adam

doi:10.21951/SMHAttributeData/1575385

Title: SMH Explorer Stream Network Attributes

Dataset
Other Related Research

Authors:

DeRolph, Chris; Bevelhimer, Mark; Witt, Adam

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); ORNL

Publication Date:: Fri Nov 01 04:00:00 UTC 2019

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Water Power Technologies Office (EE-4WP)

Subject:: 13 HYDRO ENERGY

OSTI Identifier:: 1575385

DOI:: https://doi.org/10.21951/SMHAttributeData/1575385

Citation Formats


                    DeRolph, Chris, Bevelhimer, Mark, and Witt, Adam. SMH Explorer Stream Network Attributes.  United States: N. p., 2019. 
        Web.  doi:10.21951/SMHAttributeData/1575385.

Copy to clipboard


                    DeRolph, Chris, Bevelhimer, Mark, & Witt, Adam. SMH Explorer Stream Network Attributes.  United States.  doi:https://doi.org/10.21951/SMHAttributeData/1575385

Copy to clipboard


                    DeRolph, Chris, Bevelhimer, Mark, and Witt, Adam. 2019.  
"SMH Explorer Stream Network Attributes".  United States.  doi:https://doi.org/10.21951/SMHAttributeData/1575385.  https://www.osti.gov/servlets/purl/1575385. Pub date:Fri Nov 01 04:00:00 UTC 2019

Copy to clipboard


                    
@article{osti_1575385,

  title        = {SMH Explorer Stream Network Attributes},

  author       = {DeRolph, Chris and Bevelhimer, Mark and Witt, Adam},

  abstractNote = {},

  doi          = {10.21951/SMHAttributeData/1575385},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Nov 01 04:00:00 UTC 2019},

  month        = {Fri Nov 01 04:00:00 UTC 2019}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.21951/SMHAttributeData/1575385

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

SMH Explorer Stream Network Clustering Attributes

Dataset DeRolph, Chris ; Bevelhimer, Mark ; Witt, Adam
3D Seismic Reflection Attributes of a Geothermal Area

Dataset Mellors, Robert J.

Images from a 3D seismic reflection survey across a geothermal area are shown. Images of coherency and acoustic impedance are shown. Well tracks and MEQ location are overlain.
Toward more mechanistic representations of biogeochemical processes in stream and river networks: Implementation and demonstration of a multiscale model : Modeling Archive

Dataset Jan, Ahmad ; Coon, Ethan ; Painter, Scott

This Modeling Archive supports the ORNL-SFA and IDEAS-Waterhsed publication submitted to Environmental Modeling and Software. A recently introduced multiscale model for representing the combined effect of hyporheic exchange flows and small-scale hyporheic-zone biogeochemical processes is extended from the reach scale to river network scales. The ADELS (Advection Dispersion Equation with Lagrangian Subgrid) model uses a one-dimensional advection-dispersion-reaction equation for the channel network and couples that equation at each channel location to a one-dimensional advection-reaction subgrid model representing an ensemble of streamlines that are diverted into the hyporheic zone before returning to the channel. The subgrid model is written in amore »« less
Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset

Dataset Cromwell, Erol ; Shuai, Pin ; Jiang, Peishi ; ...

This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph atmore »« less
VotE-Dams: a compilation of global dams' locations and attributes (v1)

Dataset Prior, Elizabeth ; Schwenk, Jon ; Rowland, Joel

This dataset represents a compilation of two global and three USA-specific datasets of dam locations and their attributes. The major hurdle toward developing this compilation was the identification of duplicates within the source datasets, especially given the variable precision of dam location coordinates. The most immediately-useful product in this dataset is a spreadsheet (VotE-Dams_v1.csv) that documents the unique dams found across the datasets, their coordinates, and their ids within the respective source datasets. We do not reproduce the source datasets (GRaND, GOODD, GeoDAR, NID, and EHA) here, but their download locations are provided in the README files ('Overview' tab). Somemore »« less

Similar Records