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 31 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...
RescueRequestProbability.tif Feb 21, 2022 - Output for Probability of Rescue Requests from Twitter TIFF Image - 736.5 MB - MD5: 7710911161bc7bb6dd4b706a2e01e4f8
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.
20200527_DataReport.pdf Nov 8, 2021 - Flood Hazard Modeling Output Adobe PDF - 5.7 MB - MD5: 5fdd8b7d3ab1d3f6abf660ccc20c2904
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).
Imperviousness.tif Jun 17, 2021 - Replication Data for: Flood Hazard Modeling, Impervious TIFF Image - 65.9 MB - MD5: 067f71b08b0b8f4ccf4d5677236b7d72
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...
Structures.zip Jun 16, 2021 - Flood Hazard Modeling: Structural Output Shapefile as ZIP Archive - 36.0 MB - MD5: c7a922429f0646794285f4d480e6fabf
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.

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...

RescueRequestProbability.tif

Feb 21, 2022 - Output for Probability of Rescue Requests from Twitter

TIFF Image - 736.5 MB -

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.

20200527_DataReport.pdf

Nov 8, 2021 - Flood Hazard Modeling Output

Adobe PDF - 5.7 MB -

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).

Imperviousness.tif

Jun 17, 2021 - Replication Data for: Flood Hazard Modeling, Impervious

TIFF Image - 65.9 MB -

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...

Structures.zip

Jun 16, 2021 - Flood Hazard Modeling: Structural Output

Shapefile as ZIP Archive - 36.0 MB -

FloodHazard.tif

May 13, 2020 - Flood Hazard Modeling Output

TIFF Image - 1009.7 MB -

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.

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