Hurricane Harvey Report:A Fact-Finding Effort in the Direct Aftermath of Hurricane Harvey in the Greater Houston Region

On August 25, 2017, Hurricane Harvey made landfall near Rockport, Texas as a Category 4 hurricane with maximum sustained winds of approximately 200 km/hour. Harvey caused severe damages in coastal Texas due to extreme winds and storm surge, but will go down in history for record-setting rainfall totals and flood-related damages. Across large portions of southeast Texas, rainfall totals during the six-day period between August 25 and 31, 2017 were amongst the highest ever recorded, causing flooding at an unprecedented scale. More than 100,000 residential properties are estimated to have been affected in southeast Texas. It is likely that Harvey will rank among the costliest storms in U.S. history. In the wake of Hurricane Harvey, Delft University of Technology has initiated a Harvey Research Team to undertake a coordinated multidisciplinary investigation of the events with a focus on the greater Houston area. This ‘fact-finding’ research is based on information available from public sources during and in the first weeks after the event. Results are therefore preliminary, but aim to provide insight into lessons that can be learned for both Texas and the Netherlands. As part of the investigations, a hackathon with more than 80 participants was organized to collect and analyze available public information. Houston was especially hard hit by flooding. During the event, all 22 watersheds in the greater Houston area experienced flooding. Many of Houston’s creeks and bayous exceeded their channel capacities, reaching water levels never before recorded. Across large portions of Harris County, rainfall totals exceeded the 1000-year return period. In addition, the water from the two reservoirs protecting downtown Houston (Addicks and Barker) were opened on August 28 to prevent catastrophic damages to the dams and further flooding in upstream communities. The releases exacerbated flooding in the areas downstream of the dams and an estimated 4,000 homes in neighborhoods downstream of the dams were impacted by flooding. The consequences of the event in the greater Houston area have been characterized in terms of economic damages, loss of life and impacts on critical infrastructure, airports and industry. In total, more than 100,000 homes were affected more than 70 fatalities were reported in the greater Houston area. The event highlighted the vulnerability of industrial facilities, as several cascading impacts (releases of toxic materials and explosions) were reported. Emergency response has been assessed. No large-scale mandatory evacuation was ordered before or during Harvey. However, it appeared that several local evacuations were ordered for areas with specific risks and circumstances. During the event, many people were trapped by rising waters necessitating a major rescue operation. In total, more than 10,000 rescues were made by professional and volunteer rescuers. Social media played an important role during the event and recovery, as an additional source of information, to inform emergency managers and as a means to organize community response e.g. for clean-up. Also, messages were conveyed through social media, e.g. a report of a levee breach that appeared to be incorrect afterwards. Major flooding is a problem that has multiple causes from both physical and social origin. Based on the investigations, recommendations for future research and lessons for flood management have been formulated. A better understanding of the issues studied in this report is expected to contribute to a knowledge basis for further in-depth investigations and future directions for flood risk reduction. Data collection and Report production funded by DIMI and DSys Special Case 'Houston Galveston Bay Region, Texas, USA' Project 'Harvey hackathon' and follow-up researc

Understanding The Decision-Making Process of Local Level Emergency Managers and Future Impacts of Social Data

During the course of a natural disaster, affected populations turn to different avenues to attempt to communicate their needs and locations while emergency managers are faced with the task of making quick decisions to aid in the response effort. The decisions that emergency managers face are affected by factors such as available resources, responder safety, and source of information. In this research, we interview emergency managers about the 2009 North American Ice Storm and a flooding event in late April of 2017 to understand the decisions made and the factors that affected these decisions. Using these interviews, a list of interview questions using the Critical Decision Method were created that could be used to more deeply understand the decisions and decision-making process of a local-level emergency manager during a disaster response event. Additionally, animations were created to illustrate the comparative effectiveness of disaster response routing plans developed with and without the consideration of social data based on data inspired by a real event

How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey

Social media platforms, such as Twitter, have been increasingly used by people during natural disasters to share information and request for help. Hurricane Harvey was a category 4 hurricane that devastated Houston, Texas, USA in August 2017 and caused catastrophic flooding in the Houston metropolitan area. Hurricane Harvey also witnessed the widespread use of social media by the general public in response to this major disaster, and geographic locations are key information pieces described in many of the social media messages. A geoparsing system, or a geoparser, can be utilized to automatically extract and locate the described locations, which can help first responders reach the people in need. While a number of geoparsers have already been developed, it is unclear how effective they are in recognizing and geo-locating the locations described by people during natural disasters. To fill this gap, this work seeks to understand how people describe locations during a natural disaster by analyzing a sample of tweets posted during Hurricane Harvey. We then identify the limitations of existing geoparsers in processing these tweets, and discuss possible approaches to overcoming these limitations

Mitigating Roadway Disasters in Extreme Flooding Events: A Critical Case Study of Flood Fatalities in Harris County, Texas

From 1959 – 2009, Texas has annually led the nation in the number of flood fatalities. On average the number of deaths in Texas was three times the amount of the second leading state (Sharif, Hossain, Jackson & Bin-Shafique, 2012). Quantitative studies have attempted to construct the definition of when, how and the likelihood that a person dies in a flood event, why they made the fatal decision to drive, and who is particularly vulnerable to making this decision. This dissertation used a qualitative approach to explore this occurrence in a deeper and more meaningful context. This dissertation includes four research questions. What factors govern drivers’ decision-making during a flood? What social norms about driving and flooding contribute to the risk factor decision? What mitigation measures has the local government implemented to prevent driver fatalities during extreme flooding? What experiences from drivers are missing in current Flood Warning Systems (FWS)? This qualitative dissertation used a historical narrative approach to provide a critical case study of fatalities in Harris County, Texas. The selected storms were Great Flood of 1994, Tropical Storm Allison of 2001, Memorial Day Flood of 2015, Tax Day Flood of 2016, Hurricane Harvey of 2017, and Tropical Storm Imelda of 2019. Data was collected from live broadcasts, online and printed media sources, 97 semi-structured interviews, public government documents and reports as well as social media posts and comments from Twitter and Facebook. Street observations were conducted at the known locations where fatalities occurred for those that either died in their car or abandoned a car and died. This research resulted in the creation of a database of the victims demographics and reasons for driving as well as a database creation of street design characteristics at the fatality locations. All fatalities that occurred in Harris County were mapped, including non-vehicle related fatalities. This dissertation concludes that both driver error and road design error contribute to fatalities. Social norms influence the lack of adequate mitigation from local governments and better Flood Warning Systems could prevent deaths from occurring

@Houstonpolice: an exploratory case of Twitter during Hurricane Harvey

Abstract Purpose The purpose of this paper is to examine the Houston Police Department (HPD)’s public engagement efforts using Twitter during Hurricane Harvey, which was a large-scale urban crisis event. Design/methodology/approach This study harvested a corpus of over 13,000 tweets using Twitter’s streaming API, across three phases of the Hurricane Harvey event: preparedness, response and recovery. Both text and social network analysis (SNA) techniques were employed including word clouds, n-gram analysis and eigenvector centrality to analyze data. Findings Findings indicate that departmental tweets coalesced around topics of protocol, reassurance and community resilience. Twitter accounts of governmental agencies, such as regional police departments, local fire departments, municipal offices, and the personal accounts of city’s police and fire chiefs were the most influential actors during the period under review, and Twitter was leveraged as de facto a 9-1-1 dispatch. Practical implications Emergency management agencies should consider adopting a three-phase strategy to improve communication and narrowcast specific types of information corresponding to relevant periods of a crisis episode. Originality/value Previous studies on police agencies and social media have largely overlooked discrete periods, or phases, in crisis events. To address this gap, the current study leveraged text and SNA to investigate Twitter communications between HPD and the public. This analysis advances understanding of information flows on law enforcement social media networks during crisis and emergency events

CrisisMMD: Multimodal Twitter Datasets from Natural Disasters

During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this on- line information, if processed timely and effectively, is ex- tremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.Comment: 9 page