Impacts of Downed Trees on hydrologic surface connectivity within the Mission River floodplain in Southeast Texas

This dataverse includes datasets as a result of NSF RAPID Award #1760717 that covers the ecological role and impact of downed trees on hydrologic surface connectivity within the Mission River floodplain.

There are two dataverses that have data inputs and products related to Objectives 1 and 2. These are:

Objective 1: Environmental Analyses

Objective 2: Roughness Scenarios

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11 to 20 of 34 Results

Flow Inundation Extents: Baseline Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Flow Inundation Extents: Baseline Roughness Scenario", https://doi.org/10.18738/T8/KBN60V, Texas Data Repository, V1 Polygon shapefiles that represent flow inundation extents. These extents are derived from water surface elevations from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0 – 11.63m) at 0.25m interval (N=44). These flow stages were output...
Flow Inundation Extents: Landcover +5% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +5% Roughness Scenario", https://doi.org/10.18738/T8/Z2CPJ1, Texas Data Repository, V1 Polygon shapefiles that represent flow inundation extents. These extents are derived from water surface elevations from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0 – 11.63m) at 0.25m interval (N=44). These flow stages were output...
Flow Inundation Extents: Landcover +10% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +10% Roughness Scenario", https://doi.org/10.18738/T8/YCKBGY, Texas Data Repository, V1 Polygon shapefiles that represent flow inundation extents. These extents are derived from water surface elevations from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0 – 11.63m) at 0.25m interval (N=44). These flow stages were output...
Flow Inundation Extents: Landcover +25% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +25% Roughness Scenario", https://doi.org/10.18738/T8/MZQ3O8, Texas Data Repository, V1 Polygon shapefiles that represent flow inundation extents. These extents are derived from water surface elevations from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0 – 11.63m) at 0.25m interval (N=44). These flow stages were output...
Flow Inundation Extents: Landcover +50% Roughness Scenario Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +50% Roughness Scenario", https://doi.org/10.18738/T8/AQL6EC, Texas Data Repository, V1 Polygon shapefiles that represent flow inundation extents. These extents are derived from water surface elevations from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0 – 11.63m) at 0.25m interval (N=44). These flow stages were output...
Maximum Entropy machine learning model output using all digitized downed trees 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 all digitized downed trees with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GK5DBZ, Texas Data Repository, V1 This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...
Maximum Entropy machine learning model output using all digitized downed trees 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 all digitized downed trees with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/EQUZO5, Texas Data Repository, V1 This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...
Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0", https://doi.org/10.18738/T8/WHS3BR, Texas Data Repository, V1 This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...
Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 1.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/4TVY7S, Texas Data Repository, V1 This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes all environmental variables (...
Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 2.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 2.0", https://doi.org/10.18738/T8/VMKLOK, Texas Data Repository, V1 This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes all environmental variables (...

Flow Inundation Extents: Baseline Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Flow Inundation Extents: Baseline Roughness Scenario", https://doi.org/10.18738/T8/KBN60V, Texas Data Repository, V1

Polygon shapefiles that represent flow inundation extents. These extents are derived from water surface elevations from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (1.0 – 11.63m) at 0.25m interval (N=44). These flow stages were output...

Flow Inundation Extents: Landcover +5% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +5% Roughness Scenario", https://doi.org/10.18738/T8/Z2CPJ1, Texas Data Repository, V1

Flow Inundation Extents: Landcover +10% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +10% Roughness Scenario", https://doi.org/10.18738/T8/YCKBGY, Texas Data Repository, V1

Flow Inundation Extents: Landcover +25% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +25% Roughness Scenario", https://doi.org/10.18738/T8/MZQ3O8, Texas Data Repository, V1

Flow Inundation Extents: Landcover +50% Roughness Scenario

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Flow Inundation Extents: Landcover +50% Roughness Scenario", https://doi.org/10.18738/T8/AQL6EC, Texas Data Repository, V1

Maximum Entropy machine learning model output using all digitized downed trees 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 all digitized downed trees with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GK5DBZ, Texas Data Repository, V1

This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...

Maximum Entropy machine learning model output using all digitized downed trees 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 all digitized downed trees with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/EQUZO5, Texas Data Repository, V1

Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0", https://doi.org/10.18738/T8/WHS3BR, Texas Data Repository, V1

Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 1.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/4TVY7S, Texas Data Repository, V1

Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 2.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 2.0", https://doi.org/10.18738/T8/VMKLOK, Texas Data Repository, V1

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