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Publication Year: 2020 Author Name: FLUD Research Group

1 to 10 of 32 Results

Water Surface Elevations: Landcover +50% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Water Surface Elevations: Landcover +50% Roughness Scenario", https://doi.org/10.18738/T8/PYIEPU, Texas Data Repository Dataverse, V1 Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the scenario that...
Water Surface Elevations: Landcover +5% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Water Surface Elevations: Landcover +5% Roughness Scenario", https://doi.org/10.18738/T8/UEQZTC, Texas Data Repository Dataverse, V1 Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (0.1m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the scenario that...
Water Surface Elevations: Landcover +25% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Water Surface Elevations: Landcover +25% Roughness Scenario", https://doi.org/10.18738/T8/EHFCMN, Texas Data Repository Dataverse, V1 Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the scenario that...
Water Surface Elevations: Landcover +10% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Water Surface Elevations: Landcover +10% Roughness Scenario", https://doi.org/10.18738/T8/LBWRDQ, Texas Data Repository Dataverse, V1 Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (0.1m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the scenario that...
Water Surface Elevations: Baseline Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Water Surface Elevations: Baseline Roughness Scenario", https://doi.org/10.18738/T8/G7T78B, Texas Data Repository Dataverse, V1 Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (0.1m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the baseline scena...
Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m", https://doi.org/10.18738/T8/CES19C, Texas Data Repository Dataverse, V1 Shapefiles of Computational Meshes for HEC-RAS Models for that have an average point spacing of 7.5m, 10.0m, 12.5m, and 15.0m (original). Created with the export feature of RAS-Mapper for HEC-RAS v 5.0.7 (Brunner, 2016) Brunner, G.W. (2016). HEC-RAS: River Analysis System, 2D Mod...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0", https://doi.org/10.18738/T8/ILRKFQ, Texas Data Repository Dataverse, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/F2U3QV, Texas Data Repository Dataverse, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GMU4XY, Texas Data Repository Dataverse, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0 Jun 8, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/0PQLIB, Texas Data Repository Dataverse, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes all environmental variables (veget...

Water Surface Elevations: Landcover +50% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Water Surface Elevations: Landcover +50% Roughness Scenario", https://doi.org/10.18738/T8/PYIEPU, Texas Data Repository Dataverse, V1

Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the scenario that...

Water Surface Elevations: Landcover +5% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Water Surface Elevations: Landcover +5% Roughness Scenario", https://doi.org/10.18738/T8/UEQZTC, Texas Data Repository Dataverse, V1

Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (0.1m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the scenario that...

Water Surface Elevations: Landcover +25% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Water Surface Elevations: Landcover +25% Roughness Scenario", https://doi.org/10.18738/T8/EHFCMN, Texas Data Repository Dataverse, V1

Water Surface Elevations: Landcover +10% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Water Surface Elevations: Landcover +10% Roughness Scenario", https://doi.org/10.18738/T8/LBWRDQ, Texas Data Repository Dataverse, V1

Water Surface Elevations: Baseline Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Water Surface Elevations: Baseline Roughness Scenario", https://doi.org/10.18738/T8/G7T78B, Texas Data Repository Dataverse, V1

Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m", https://doi.org/10.18738/T8/CES19C, Texas Data Repository Dataverse, V1

Shapefiles of Computational Meshes for HEC-RAS Models for that have an average point spacing of 7.5m, 10.0m, 12.5m, and 15.0m (original). Created with the export feature of RAS-Mapper for HEC-RAS v 5.0.7 (Brunner, 2016) Brunner, G.W. (2016). HEC-RAS: River Analysis System, 2D Mod...

Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0", https://doi.org/10.18738/T8/ILRKFQ, Texas Data Repository Dataverse, V1

This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...

Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/F2U3QV, Texas Data Repository Dataverse, V1

Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GMU4XY, Texas Data Repository Dataverse, V1

Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0

Jun 8, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/0PQLIB, Texas Data Repository Dataverse, V1

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