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Radar-Based Flood Detection and Real-Time Alert System

Solution Overview

Houston researchers developed flood detection sensors designed for installation on light poles and elevated surfaces to transmit real-time data and alert the public of dangerous conditions.[12] This technology-enabled approach addresses the city's recurring flood challenges through multiple interconnected systems that provide early warnings and situational awareness during severe weather events. The implementation builds on decades of flood monitoring research and represents a comprehensive effort to protect critical infrastructure and vulnerable communities across Houston's major watersheds.

Technical Components

The Flood Information and Response System (FIRST) was developed for the City of Houston as an end-to-end radar-based flood assessment and mapping tool for critical infrastructure.[1] The system continuously ingests 15-minute radar rainfall data across four major watersheds in Houston, including Brays Bayou, White Oak Bayou, Hunting Bayou and Sims Bayou, and instantly displays flood risks in color through intuitive visual maps.[6] FIRST integrates real-time radar with precision floodplain mapping to deliver forecasts that account for how rain moves through watersheds, analyzing how it interacts with infrastructure and how it puts people at risk.[5][6] The system provides data every 10 to 15 minutes and instantly displays flood risk in an easy-to-use visual map.[5] If any region records over 5 inches of rain in a 24-hour period, FIRST pulls from an extensive floodplain map library (FPML) to simulate and display the expected inundation zones.[6] The FPML contains 49 advanced flood scenarios per watershed, built using state-of-the-art modeling tools, allowing for precision flood forecasts based on both upstream and downstream rainfall levels ranging from 5 to 18 inches.[6] SSPEED hydrologists built over 350 flood plain maps for FIRST showing all different combinations of rainfall, creating a flood plain map library using high-speed servers running hundreds of simulations to determine how quickly and where water will rise during intense rainfall events.[7]

A team of senior engineering students at Rice University developed a real-time, web-enabled system to monitor flood levels throughout Houston, which has suffered three damaging floods in recent years, topped by the devastation of Hurricane Harvey.[4][8][9] The students developed wireless stations that communicate with a base to report on flooding at their locations, consisting of a solar-powered wireless transmitter that can ride high atop a utility pole, with a rubber conduit that stretches down the side and connects the station to a water-level rain gauge and pressure sensor.[8][9] The flood monitoring system uses off-the-shelf components and students designed and built several simple PC boards to connect sensors and solar cells to battery packs and a wireless transmitter.[4] The individual sensors store and send information to a central location using radio, and that location will then parse through and send the data off through a cellular connection, which can then be accessed from any web interface.[4][8][9] Initially, the sensor nodes are set to report local conditions every five minutes, but that can be adjusted as needed.[4][8][9]

Rice University engineers Jamie Padgett and Pranavesh Panakkal developed an automated data fusion framework called OpenSafe Fusion (Open Source Situational Awareness Framework for Mobility using Data Fusion) that leverages existing individual reporting mechanisms and public data sources to sense quickly evolving road conditions during urban flooding events.[2] The framework uses data from sources like traffic alerts, cameras and traffic speed, and leverages machine learning and data fusion to predict whether a road is flooded or not.[2] Other data sources that could be used in the OpenSafe Fusion framework include water-level sensors, citizen portals, crowdsourcing, social media, flood models and a factor referred to as 'human-in-the-loop.'[2] The OpenSafe Fusion framework builds off of and is inspired by longtime collaboration with colleagues in the SSPEED Center at Rice, who have been developing state-of-the-art flood alert systems.[2]

FIRST was designed by flood expert Dr. Philip Bedient at Rice University, with assistance from Dr. Nick Fang and Dr. Baxter Vieux.[1] SSPEED's Nick Fang, associate professor of civil engineering at the University of Texas at Arlington, was instrumental in developing FIRST's web interface and software for incorporating radar data.[7] The FIRST system was developed over decades in collaboration with fellow researchers, including Nick Fang at the University of Texas at Arlington.[5] FAS3 provides specific local flood warnings by monitoring key watersheds using a combination of cameras, rain gauges, radar, and digital modeling, and is interactive with real time updates every five minutes.[10]

Implementation Details

An upgraded FIRST system was designed for the city of Houston and implemented in 2020.[6] Initially developed for the Brays Bayou Watershed to protect the Texas Medical Center 20 years ago, the new FIRST system was developed for the city of Houston in 2020.[1][6] The SSPEED Center is researching Houston's major flood-prone watersheds through partnerships with the Texas Medical Center, the City of Houston, and community organizations.[1] SSPEED currently has flood monitoring projects on White Oak, Hunting, Brays, and Sims Bayous in Houston.[1] Initially created to protect Houston's Texas Medical Center following Tropical Storm Allison in 2001, the system has expanded to cover four major watersheds in the Houston area and could be scaled to serve vulnerable communities across Texas.[5] FIRST covers several flood-prone and at-risk communities in Houston, including Kashmere Gardens, Gulfton and Sunnyside.[7]

Rice University's Houston Solutions Lab announced funding in June 2018 to support three projects focusing on flooding issues and a sustainability plan for the city's fleet of vehicles.[3] The Houston Solutions Lab, a partnership between Rice's Kinder Institute for Urban Research and the city of Houston, is sponsoring the initiative being developed as a senior capstone project at the university's Oshman Engineering Design Kitchen.[4][8][9] A team from the civil and environmental engineering department created low-cost flood sensors that were tested on Rice's campus in spring 2019.[11][13] The sensors are currently being tested on the campus of Rice University with the goal to scale to areas with frequent localized street flooding.[12] The team created relatively low-price sensors and pitched the system to several entities interested in having real-time street-level flooding information.[11] The flood sensor researchers will work with Steve Costello, Houston's chief resiliency officer, to ensure the sensor platform is robust and meets requirements for broader city deployment.[3]

Each station shouldn't cost more than a few hundred dollars, including waterproof casing to protect the electronics – and less if the product is someday made in bulk quantities.[4][8] The students plan to have a small set of sensors set up around campus by the end of the spring semester, and hope to deploy a larger set in a Houston neighborhood prone to flooding, like Meyerland, over the summer.[4][8][9] Most of the FIRST flood plain map library was created by Rice undergraduates in fall 2020, after Bedient trained them in summer 2020 on how to build map libraries using hydrologic software.[7] The project started when two Rice professors, civil engineer Leonardo Dueñas-Osorio and computer scientist Devika Subramanian, looked in vain for commercial versions of what the students have developed for their own projects.[4]

To test the OpenSafe Fusion process, researchers used historical flooding data observed during Hurricane Harvey in 2017 to recreate the scenario in the framework, consisting of around 62,000 roads in the Houston region.[2] During Hurricane Harvey in 2017, many people in Houston including emergency responders resorted to manually examining data sources to infer probable road conditions due to the lack of reliable real-time road condition data.[2]

Benefits and Impacts

FIRST is an integrated system that predicts inundation levels caused by heavy rainfall events in order to provide lead-time for flood-response decisions.[1] Philip Bedient, designer of the FIRST system, director of the SSPEED Center and the Herman Brown Professor of Engineering at Rice, stated that FIRST was designed to give decision-makers the earliest possible warnings, ideally hours before flash flooding takes place.[6] The system is designed to assist the City of Houston in addressing emergency management and operations, including emergency closures, evacuation, as well as rescue operations.[1] FIRST was created to serve hospitals, nursing homes, fire stations and communities vulnerable to flooding, helping city and county officials and emergency responders determine where and when to issue warnings, close roads or evacuate neighborhoods.[6] FIRST's maps show the location of hospitals, nursing homes and fire stations in the four watersheds, and the system includes tools that allow emergency managers to establish warning thresholds so they have access to flood level information during rain events.[7]

The FIRST system performed well during thunderstorms in May 2021, and SSPEED engineers planned to refine its performance with data from those and other storms.[7] FAS accurately predicted the peak surge of nearby Brays Bayou during and immediately after Hurricane Ike, showing that the Texas Medical Center building was not at risk of flooding and saving the medical center from a costly and unnecessary evacuation.[10] The OpenSafe Fusion study considered flooding impacts on community access to critical facilities such as hospitals and dialysis centers during a natural disaster, giving community members or emergency responders an understanding of which roads are flooded and how to safely navigate to a location.[2] The ultimate goal of the piloted flood sensor array is to collect flood level data in real-time for neighborhood streets, which can be used to aid evacuation and transportation in storms and to refine models for future flooding.[3]

Climate Adaptation Relevance

Houston has experienced recurring severe flooding events, including three damaging floods in recent years topped by the devastation of Hurricane Harvey in 2017.[4][8][9] In mid-April 2016, a low-pressure system produced record-breaking rain in and around Houston, Texas, resulting in widespread and severe flooding that was the worst since Tropical Storm Allison in 2001, with eight people losing their lives and nearly 1,200 homes and apartments inundated with water.[10] The integrated flood monitoring and early warning systems address these extreme precipitation and flooding hazards by providing real-time situational awareness and advance warnings to protect critical infrastructure and vulnerable communities during severe weather events.

Business Analysis

Rice University and the City of Houston have joined forces to create Houston Solutions Lab, building on other recent efforts to bring together research and policy.[11] The Houston Solutions Lab partnership between Rice's Kinder Institute for Urban Research and the city of Houston provides institutional support for developing and deploying flood monitoring technologies.[4][8][9] The initiative received funding announced in June 2018 to support three projects focusing on flooding issues.[3] The low-cost sensor approach developed by students uses off-the-shelf components and simple PC boards, with each station costing no more than a few hundred dollars including waterproof casing, and potentially less if produced in bulk quantities.[4][8] This cost structure makes the technology accessible for broader deployment across flood-prone neighborhoods. The collaboration model between university researchers, student teams, and city officials demonstrates a public-private partnership approach that leverages academic research capacity to address municipal resilience challenges while maintaining affordability through innovative design and component selection.

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