DOI: 10.4211/hs.1d1ed97e40024409a866d2164e3e001c
title: Southeast Texas Networked Flood Monitoring Sensors

description: 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 important. Southeast Texas is still at substantial 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. To make these data more useful for flood monitoring and mapping, Lamar University developed a program to measure elevation and coordinates for the various sensor locations. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various thresholds at critical points used by flood decision-makers for reference at each site. These sensors, in the first phase of this program, were deployed throughout a 7-county region spanning nearly 6000 square miles in Southeast Texas. The latitude and longitude of the sensors, along with their elevations, were determined using survey-grade Global Navigation Satellite System (GNSS) technology.  This is an accurate, rapid, and relatively low-cost surveying method. Various Continually Operating Reference Stations (CORS) were examined during post-processing to achieve the most accurate horizontal and vertical results.  After differential corrections were applied, accuracies of 0.4 in. (or better) were achieved. Each site's critical points and thresholds were also established using this method. The thresholds, elevations, and positions of these sensors and their surrounding critical points are transmitted to various dashboards on websites.  These data are 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). This data may also be used in the development of flood hydrological models in Southeast Texas watersheds and sub-basins. This program currently involves the Flood Coordination Study team which is part of the Center for Resiliency at Lamar University in collaboration with various entities such as the U.S. Department of Homeland Security Science and Technology Directorate, the Southeast Texas Flood Control District, and various other regional agencies, municipalities, and industries.

Steps to reproduce:
A Trimble GEOX7 Global Navigation Satellite System (GNSS) handheld device, which employs Trimble H-StarTM technology, and a ZIPLEVEL PRO-2000 High Precision Altimeter was used to determine the coordinates and elevations of the sensors and surrounding critical points. Post-processing of the GNSS data used the Trimble GPS Pathfinder Office software. The closest CORS base stations were used for differential corrections and the NAD 1983 (2011) (epoch 2010.00) horizontal datum was used as the geographic coordinate system. Furthermore, orthometric heights were calculated using GEOID 18 which is referenced to the North American Vertical Datum of 1988 (NAVD 88).  ArcGIS Pro 3 was used to create a map of the sensors and critical points, as well as a watershed delineation relative to Southeast Texas landmarks. 
Data were gathered in Southeast Texas watersheds and sub-watersheds in order to monitor and map the elevation and movement of water in the drainages. Vertical and horizontal positions of the 74 flood sensors installed in the first phase of the project and their surrounding critical points, including the node (solar panels, battery, and transmission device), the bottom of the posts that nodes attached (bottom of the node from now on), top of the bank, the bottom of the ditch, the bottom of the bridge's deck, and the center of the road and edges, have been gathered accordingly. Also, the relative elevations between these points are important and were collected.