Center for Texas Beaches and Shores (CTBS)

The Center for Texas Beaches and Shores (CTBS) at Texas A&M University at Galveston was established in 1993 by the Texas Legislature to address beach erosion and wetlands loss throughout the state. CTBS is dedicated to the conservation and protection of the Texas shoreline, bays and waterways through innovative research in cooperation with government and private sector agencies. Our focus is to develop comprehensive, holistic approaches to Texas coastal research and restoration solutions while incorporating natural, economic and political processes.

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1 to 10 of 28 Results

Flood Hazard Modeling Output May 13, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Flood Hazard Modeling Output", https://doi.org/10.18738/T8/FVJFSW, Texas Data Repository, V2 The results from a flood hazard study using the Random Forest Classification method to predict the probability of flooding at 30-m resolution for a 30,523 km2 area. We generate flood hazard maps for twelve USGS 8-digit watersheds along the coast in southeast Texas.
FloodHazard.tif May 13, 2020 - Flood Hazard Modeling Output TIFF Image - 1009.7 MB - MD5: b3b798e87551d84fae9a8bdcda8f4be8
Replication Data for: Flood Hazard Modeling, TWI May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, TWI", https://doi.org/10.18738/T8/85LBLA, Texas Data Repository, V1 TWI is calculated by the following equation: TWI= Ln ((flow_accumulation * 900) + 1 )/(Tan((slope*π)/180 )) Where high values of TWI are associated with areas that are concave, low gradient areas where water often accumulates and pools making them more vulnerable to floods.
TWI.tfw May 4, 2020 - Replication Data for: Flood Hazard Modeling, TWI Unknown - 91 B - MD5: de3c9e3526e87b573154fd0006d7a267
TWI.tif May 4, 2020 - Replication Data for: Flood Hazard Modeling, TWI TIFF Image - 265.4 MB - MD5: a765d3c0f3696ed50569aa6a8c67e784
FloodHazard.tfw May 4, 2020 - Flood Hazard Modeling Output Unknown - 91 B - MD5: de3c9e3526e87b573154fd0006d7a267
Replication Data for: Flood Hazard Modeling, Average Roughness May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Average Roughness", https://doi.org/10.18738/T8/5ZQMXV, Texas Data Repository, V1 Roughness values were assigned to each NLCD land cover class using the values suggested by Kalyanapu (2009), and, like KSAT, was averaged across the contributing upstream area for each raster cell for 2016. Kalyanapu, A.J., Burian, S.J. & McPherson, T.N., 2010. Effect of land use...
AverageRoughness.tfw May 4, 2020 - Replication Data for: Flood Hazard Modeling, Average Roughness Unknown - 91 B - MD5: de3c9e3526e87b573154fd0006d7a267
AverageRoughness.tif May 4, 2020 - Replication Data for: Flood Hazard Modeling, Average Roughness TIFF Image - 811.4 MB - MD5: 8c83f15e08dcfdccdfddf18334e7012f
Replication Data for: Flood Hazard Modeling, Distance to Coast May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Distance to Coast", https://doi.org/10.18738/T8/YYWO9P, Texas Data Repository, V1 Distance to coast were calculated using Euclidean Distance based on the National Hydrography Dataset (NHD) stream and coastline features.

Flood Hazard Modeling Output

May 13, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas

William Mobley, 2020, "Flood Hazard Modeling Output", https://doi.org/10.18738/T8/FVJFSW, Texas Data Repository, V2

The results from a flood hazard study using the Random Forest Classification method to predict the probability of flooding at 30-m resolution for a 30,523 km2 area. We generate flood hazard maps for twelve USGS 8-digit watersheds along the coast in southeast Texas.

FloodHazard.tif

May 13, 2020 - Flood Hazard Modeling Output

TIFF Image - 1009.7 MB - MD5: b3b798e87551d84fae9a8bdcda8f4be8

Replication Data for: Flood Hazard Modeling, TWI

May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas

William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, TWI", https://doi.org/10.18738/T8/85LBLA, Texas Data Repository, V1

TWI is calculated by the following equation: TWI= Ln ((flow_accumulation * 900) + 1 )/(Tan((slope*π)/180 )) Where high values of TWI are associated with areas that are concave, low gradient areas where water often accumulates and pools making them more vulnerable to floods.

TWI.tfw

May 4, 2020 - Replication Data for: Flood Hazard Modeling, TWI

Unknown - 91 B - MD5: de3c9e3526e87b573154fd0006d7a267

TWI.tif

May 4, 2020 - Replication Data for: Flood Hazard Modeling, TWI

TIFF Image - 265.4 MB - MD5: a765d3c0f3696ed50569aa6a8c67e784

FloodHazard.tfw

May 4, 2020 - Flood Hazard Modeling Output

Unknown - 91 B - MD5: de3c9e3526e87b573154fd0006d7a267

Replication Data for: Flood Hazard Modeling, Average Roughness

May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas

William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Average Roughness", https://doi.org/10.18738/T8/5ZQMXV, Texas Data Repository, V1

Roughness values were assigned to each NLCD land cover class using the values suggested by Kalyanapu (2009), and, like KSAT, was averaged across the contributing upstream area for each raster cell for 2016. Kalyanapu, A.J., Burian, S.J. & McPherson, T.N., 2010. Effect of land use...

AverageRoughness.tfw

May 4, 2020 - Replication Data for: Flood Hazard Modeling, Average Roughness

Unknown - 91 B - MD5: de3c9e3526e87b573154fd0006d7a267

AverageRoughness.tif

May 4, 2020 - Replication Data for: Flood Hazard Modeling, Average Roughness

TIFF Image - 811.4 MB - MD5: 8c83f15e08dcfdccdfddf18334e7012f

Replication Data for: Flood Hazard Modeling, Distance to Coast

May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas

William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Distance to Coast", https://doi.org/10.18738/T8/YYWO9P, Texas Data Repository, V1

Distance to coast were calculated using Euclidean Distance based on the National Hydrography Dataset (NHD) stream and coastline features.

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