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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781 to 790 of 821 Results

Inundation_Probability.zip Jun 8, 2020 - Environmental Variables Used in Spatial Analysis Shapefile as ZIP Archive - 2.2 MB - MD5: bcc65403593eb55054d70ceae4914972 Consists of three classes that represent levels informed by the inundation probability within the Mission River Floodplain. Inundation probability was based on mean daily discharge record (1939-2016) using inundation extents from Castillo et.al. (under review) as indexes. All ind...
slope_10.zip Jun 8, 2020 - Environmental Variables Used in Spatial Analysis Shapefile as ZIP Archive - 1.1 MB - MD5: ded915aa43a5616590528ffc4a92a87c Polygon shapefile that consists of four classes that represent levels informed by the generalized slope within the Mission River Floodplain. The generalized slope raster map is derived from resampled elevation values at a resolution of 10m using bilinear interpolation. Then, we u...
Environmental Variables Used in Maxent Machine Learning Approach Jun 8, 2020 - Environmental Analyses FLUD Research Group, 2020, "Environmental Variables Used in Maxent Machine Learning Approach", https://doi.org/10.18738/T8/MJNKOW, Texas Data Repository, V1 Six environmental variables are represented as raster maps, and they are used in conjunction with locations of downed trees to inform the variable importance and relationships derived from results of the Maxent ML approach. These variables are processed as ESRI ASCII raster maps...
EDFR.zip Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach ZIP Archive - 413.3 MB - MD5: a7b5e614246f8eb859cc6394cb036597 Euclidean distance (in meters) from the main river channel and major tributaries is calculated with the Euclidean Distance algorithm in ArcGIS Desktop® 10.5. Only those distance values within the model evaluation domain are used.
Elevation.zip Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach ZIP Archive - 314.1 MB - MD5: fae27aed541194dbb3778e8bc6d648f8 Elevation values taken from topography featured in Castillo et.al. (under review). Only those elevation values within the model evaluation domain are used.
Generalized_Slope.zip Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach ZIP Archive - 34.3 MB - MD5: 0e27c4894b9c37b0e0f9e5a34139f11c Derived from resampled elevation values at a resolution of 20m using bilinear interpolation. Then, we use the Zevenbergen and Thorne (1987) slope approach in QGIS Desktop 3.4 to calculate slope values as degrees with a resolution of 20m. The raster map is then upsampled back to 1...
Inundation_Probability.zip Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach ZIP Archive - 5.9 MB - MD5: c12f5ef9c22b4f20bf912851ae24dbcb Calculated inundation probability within the Mission River Floodplain. Inundation probability was based on mean daily discharge record (1939-2016) using inundation extents from Castillo et.al. (under review) as indexes. All indices (as raster maps of inundation extent) are sorted...
Soil_Type_Class.zip Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach ZIP Archive - 783.0 KB - MD5: 1200a0ae831ca4b35ff44947f67dcfd7 Raster maps representing soil type is derived from polygon geometries that represent patches from the Ecological Modeling Systems of Texas (Elliott et.al. 2014). Each patch is assigned a soil type classification are based on the EcoName fields within this dataset, respectively. O...
Vegetation_Community_Class.zip Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach ZIP Archive - 1.6 MB - MD5: 1e6f76fdf7012966cc702c301b3e04eb Raster map representing vegetation community is derived from polygon geometries that represent patches from the Ecological Modeling Systems of Texas (Elliott et.al. 2014). Each patch is assigned a vegetation community classification is based on the CommonName fields within this d...
Digitized Downed/Standing Trees in the Mission River Floodplain Jun 8, 2020 - Environmental Analyses FLUD Research Group, 2020, "Digitized Downed/Standing Trees in the Mission River Floodplain", https://doi.org/10.18738/T8/UUAQT7, Texas Data Repository, V1 Downed trees (N=9505) were digitized by members of the FLUD Research group (faculty and student researchers). Student researchers consist of graduate and undergraduate students. Undergraduate student researchers worked at the supervision and expertise of faulty and graduate stude...

Inundation_Probability.zip

Jun 8, 2020 - Environmental Variables Used in Spatial Analysis

Shapefile as ZIP Archive - 2.2 MB -

Consists of three classes that represent levels informed by the inundation probability within the Mission River Floodplain. Inundation probability was based on mean daily discharge record (1939-2016) using inundation extents from Castillo et.al. (under review) as indexes. All ind...

slope_10.zip

Jun 8, 2020 - Environmental Variables Used in Spatial Analysis

Shapefile as ZIP Archive - 1.1 MB -

Polygon shapefile that consists of four classes that represent levels informed by the generalized slope within the Mission River Floodplain. The generalized slope raster map is derived from resampled elevation values at a resolution of 10m using bilinear interpolation. Then, we u...

Environmental Variables Used in Maxent Machine Learning Approach

Jun 8, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Environmental Variables Used in Maxent Machine Learning Approach", https://doi.org/10.18738/T8/MJNKOW, Texas Data Repository, V1

Six environmental variables are represented as raster maps, and they are used in conjunction with locations of downed trees to inform the variable importance and relationships derived from results of the Maxent ML approach. These variables are processed as ESRI ASCII raster maps...

EDFR.zip

Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach

ZIP Archive - 413.3 MB -

Euclidean distance (in meters) from the main river channel and major tributaries is calculated with the Euclidean Distance algorithm in ArcGIS Desktop® 10.5. Only those distance values within the model evaluation domain are used.

Elevation.zip

Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach

ZIP Archive - 314.1 MB -

Elevation values taken from topography featured in Castillo et.al. (under review). Only those elevation values within the model evaluation domain are used.

Generalized_Slope.zip

Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach

ZIP Archive - 34.3 MB -

Derived from resampled elevation values at a resolution of 20m using bilinear interpolation. Then, we use the Zevenbergen and Thorne (1987) slope approach in QGIS Desktop 3.4 to calculate slope values as degrees with a resolution of 20m. The raster map is then upsampled back to 1...

Inundation_Probability.zip

Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach

ZIP Archive - 5.9 MB -

Calculated inundation probability within the Mission River Floodplain. Inundation probability was based on mean daily discharge record (1939-2016) using inundation extents from Castillo et.al. (under review) as indexes. All indices (as raster maps of inundation extent) are sorted...

Soil_Type_Class.zip

Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach

ZIP Archive - 783.0 KB -

Raster maps representing soil type is derived from polygon geometries that represent patches from the Ecological Modeling Systems of Texas (Elliott et.al. 2014). Each patch is assigned a soil type classification are based on the EcoName fields within this dataset, respectively. O...

Vegetation_Community_Class.zip

Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach

ZIP Archive - 1.6 MB -

Raster map representing vegetation community is derived from polygon geometries that represent patches from the Ecological Modeling Systems of Texas (Elliott et.al. 2014). Each patch is assigned a vegetation community classification is based on the CommonName fields within this d...

Digitized Downed/Standing Trees in the Mission River Floodplain

Jun 8, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Digitized Downed/Standing Trees in the Mission River Floodplain", https://doi.org/10.18738/T8/UUAQT7, Texas Data Repository, V1

Downed trees (N=9505) were digitized by members of the FLUD Research group (faculty and student researchers). Student researchers consist of graduate and undergraduate students. Undergraduate student researchers worked at the supervision and expertise of faulty and graduate stude...

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