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 12 Results

Output for Probability of Rescue Requests from Twitter Feb 21, 2022 Mobley, William, 2022, "Output for Probability of Rescue Requests from Twitter", https://doi.org/10.18738/T8/OOJL5D, Texas Data Repository, V1 Rescue requests during large-scale urban flood disasters can be difficult to validate and prioritize. High-resolution aerial imagery is often unavailable or lacks the necessary geographic extent, making it difficult to obtain real-time information about where flooding is occurrin...
Flood Hazard Modeling Output Nov 8, 2021 - 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, V3 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.
Replication Data for: Flood Hazard Modeling, Impervious Jun 17, 2021 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Impervious", https://doi.org/10.18738/T8/S7NFPI, Texas Data Repository, V2 Percent impervious was measured using the percent developed impervious surface raster from the National Land Cover Database (NLCD).
Flood Hazard Modeling: Structural Output Jun 16, 2021 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2021, "Flood Hazard Modeling: Structural Output", https://doi.org/10.18738/T8/IPWHEL, Texas Data Repository, V1 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. The shapefile contains structures within the study area. Two columns are available Flood Probability represents the...
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.
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...
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.
Replication Data for: Flood Hazard Modeling, Average Ksat May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Average Ksat", https://doi.org/10.18738/T8/UKAGX0, Texas Data Repository, V1 KSAT values were assigned to soil classes obtained from the Natural Resources Conservation Service’s (NRCS) Soil Service Geographic Database (SSURGO) , and then averaged across the upstream area for each cell. (2020-04-28)
Replication Data for: Flood Hazard Modeling, Elevation May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Elevation", https://doi.org/10.18738/T8/BPARY8, Texas Data Repository, V1 Elevation was calculated using the National Elevation Dataset (NED), which was provided as a seamless raster product via the LANDFIRE website at a 30-m resolution. For more information on the initial dataset see : https://www.landfire.gov/elevation.php
Replication Data for: Flood Hazard Modeling, Distance to Stream May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Distance to Stream", https://doi.org/10.18738/T8/VLIS6E, Texas Data Repository, V1 Distance to Stream was calculated using Euclidean Distance based on the National Hydrography Dataset (NHD) stream and coastline features.

Output for Probability of Rescue Requests from Twitter

Feb 21, 2022

Mobley, William, 2022, "Output for Probability of Rescue Requests from Twitter", https://doi.org/10.18738/T8/OOJL5D, Texas Data Repository, V1

Rescue requests during large-scale urban flood disasters can be difficult to validate and prioritize. High-resolution aerial imagery is often unavailable or lacks the necessary geographic extent, making it difficult to obtain real-time information about where flooding is occurrin...

Flood Hazard Modeling Output

Nov 8, 2021 - 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, V3

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.

Replication Data for: Flood Hazard Modeling, Impervious

Jun 17, 2021 - Measuring, Mapping, and Managing Flood Risk in Texas

William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Impervious", https://doi.org/10.18738/T8/S7NFPI, Texas Data Repository, V2

Percent impervious was measured using the percent developed impervious surface raster from the National Land Cover Database (NLCD).

Flood Hazard Modeling: Structural Output

Jun 16, 2021 - Measuring, Mapping, and Managing Flood Risk in Texas

William Mobley, 2021, "Flood Hazard Modeling: Structural Output", https://doi.org/10.18738/T8/IPWHEL, Texas Data Repository, V1

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. The shapefile contains structures within the study area. Two columns are available Flood Probability represents the...

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.

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...

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.

Replication Data for: Flood Hazard Modeling, Average Ksat

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

William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Average Ksat", https://doi.org/10.18738/T8/UKAGX0, Texas Data Repository, V1

KSAT values were assigned to soil classes obtained from the Natural Resources Conservation Service’s (NRCS) Soil Service Geographic Database (SSURGO) , and then averaged across the upstream area for each cell. (2020-04-28)

Replication Data for: Flood Hazard Modeling, Elevation

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

William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Elevation", https://doi.org/10.18738/T8/BPARY8, Texas Data Repository, V1

Elevation was calculated using the National Elevation Dataset (NED), which was provided as a seamless raster product via the LANDFIRE website at a 30-m resolution. For more information on the initial dataset see : https://www.landfire.gov/elevation.php

Replication Data for: Flood Hazard Modeling, Distance to Stream

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

William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Distance to Stream", https://doi.org/10.18738/T8/VLIS6E, Texas Data Repository, V1

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

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