Cargando…

Field surveying data of low-cost networked flood sensors in southeast Texas

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the regio...

Descripción completa

Detalles Bibliográficos
Autores principales: Hariri Asli, Hossein, Brake, Nicholas, Kruger, Joseph, Haselbach, Liv, Adesina, Mubarak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480587/
https://www.ncbi.nlm.nih.gov/pubmed/37680348
http://dx.doi.org/10.1016/j.dib.2023.109504
_version_ 1785101821051666432
author Hariri Asli, Hossein
Brake, Nicholas
Kruger, Joseph
Haselbach, Liv
Adesina, Mubarak
author_facet Hariri Asli, Hossein
Brake, Nicholas
Kruger, Joseph
Haselbach, Liv
Adesina, Mubarak
author_sort Hariri Asli, Hossein
collection PubMed
description Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA).
format Online
Article
Text
id pubmed-10480587
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-104805872023-09-07 Field surveying data of low-cost networked flood sensors in southeast Texas Hariri Asli, Hossein Brake, Nicholas Kruger, Joseph Haselbach, Liv Adesina, Mubarak Data Brief Data Article Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA). Elsevier 2023-08-22 /pmc/articles/PMC10480587/ /pubmed/37680348 http://dx.doi.org/10.1016/j.dib.2023.109504 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Hariri Asli, Hossein
Brake, Nicholas
Kruger, Joseph
Haselbach, Liv
Adesina, Mubarak
Field surveying data of low-cost networked flood sensors in southeast Texas
title Field surveying data of low-cost networked flood sensors in southeast Texas
title_full Field surveying data of low-cost networked flood sensors in southeast Texas
title_fullStr Field surveying data of low-cost networked flood sensors in southeast Texas
title_full_unstemmed Field surveying data of low-cost networked flood sensors in southeast Texas
title_short Field surveying data of low-cost networked flood sensors in southeast Texas
title_sort field surveying data of low-cost networked flood sensors in southeast texas
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480587/
https://www.ncbi.nlm.nih.gov/pubmed/37680348
http://dx.doi.org/10.1016/j.dib.2023.109504
work_keys_str_mv AT haririaslihossein fieldsurveyingdataoflowcostnetworkedfloodsensorsinsoutheasttexas
AT brakenicholas fieldsurveyingdataoflowcostnetworkedfloodsensorsinsoutheasttexas
AT krugerjoseph fieldsurveyingdataoflowcostnetworkedfloodsensorsinsoutheasttexas
AT haselbachliv fieldsurveyingdataoflowcostnetworkedfloodsensorsinsoutheasttexas
AT adesinamubarak fieldsurveyingdataoflowcostnetworkedfloodsensorsinsoutheasttexas