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Increasing resilience to flooding is a complex process that requires horizontal and vertical coordination between institutions in policy making and implementation. This paper explores the effect of institutional coordination on managing flood risk in two cities plagued by flooding. Our results show that efforts on building urban flood resilience can be undermined by lack of proper coordination between urban development, water management and land use planning. We find that this complexity is magnified by the emergence of the concept of resilience as an urban development goal that is increasingly pursued by various authorities, but that is inherently contested in practice. [ABSTRACT FROM AUTHOR]

Addicks and Barker reservoirs were built in the 1940s to protect downtown Houston from flooding and have generally worked very well until 2017 when Hurricane Harvey devastated much of Houston and surroundings with up to 40 inches (102 cm) of rainfall causing flooding of 154,000 homes in over 22 watersheds in Houston/Harris County alone. However, the story of how Addicks and Barker flooded upstream residential areas from a hydrologic standpoint is a harsh lesson in flood infrastructure policy and funding. This failure to protect both downstream properties in Buffalo Bayou and upstream areas behind the dams ended up with tens of thousands of flooded homes and properties, with many having flood waters for over 10 days. This paper explores the main causes for the flooding and addresses the hydrologic issues upstream in both reservoirs. The main causes of flooding were not just related to a massive rainfall event, but also explosive urban expansion of land use upstream of reservoirs, altered and updated reservoir design issues, and lack of governmental action in the years leading up to the disaster. Potential long‐term solutions to the flooding and design problems are addressed in this article as well. [ABSTRACT FROM AUTHOR]

In view that cities will continue to house the majority of the world's population at an increasing rate in the face of climate change, this paper studies urban resilience by examining the response history of the mean-square displacement of the citizens of large cities prior to and upon historic natural hazards strike. The recorded mean-square displacements of large numbers of cellphone users from the cities of Houston, Miami, and Jacksonville when struck by hurricanes Harvey 2017, Irma 2017, and Dorian 2019 together with the recorded mean-square displacements of the citizens of Dallas and Houston when experiencing the 2021 North American winter storm suggest that large cities of average population density when struck by natural hazards are inherently resilient. The recorded mean-square displacements presented in this study also validate a mechanical model for cities, previously developed by the authors, that is rooted in Langevin dynamics and predicts that following a natural hazard, large cities revert immediately to their initial steady-state behavior and resume their normal, preevent activities. The inherent ability of large American cities to revert to their normal, preevent, steady-state response as evidenced in this study by the recorded mean-square displacement of their citizens needs to be further explored for other cities around the world with different resources, and socioeconomic structures. [ABSTRACT FROM AUTHOR]

This paper examines the vulnerability of road networks to two types of disruptions by modeling the percolation dynamics in road networks under different disruption scenarios. The objective of this paper is threefold: (1) to examine if the theoretical network robustness measure proposed in the literature is applicable for measuring the integrity of road networks during disruptions; (2) to unveil the impacts of network size on the overall vulnerability of road networks; and (3) to compare the performance profile of road networks to random and nonrandom types of disruptions. To that end, this study first modeled the road system in a community as a planar graph. Then, the percolation dynamic in the road network during the flood is captured by assigning different removal probabilities to nodes in the road network according to Bayes' rule that take floodplain types, node-elevation, and street-grade as inputs. In the end, an overall road network robustness measure and its temporal changes were obtained and for random and nonrandom scenarios, using road networks with different sizes. The results were compared in order to characterize the vulnerability of road networks under different scenarios. The proposed method was applied to the road network in central Houston during Hurricane Harvey. The results show that: (1) the theoretical network robustness measure is applicable to assess the road network robustness; (2) compared to the random percolation model, the probability (Bayes' rule) based percolation could lead to a greater decrease in the network robustness; and (3) the percolation profiles of the road networks with different sizes are not significantly different. The findings of this study could not only inform resilience enhancing decisions by the stakeholders but also could serve as a foundation for future vulnerability related research. [ABSTRACT FROM AUTHOR]

While much research investigates how social capital relates to mental health after disasters, less work employs a multi‐scalar, multi‐dimensional social capital framework. This study applies such a construct to an analysis of novel survey data of approximately 1,000 rural and urban Texans after Hurricane Harvey struck the United States in August 2017. On the individual level, it finds that greater social support is linked to fewer mental health impacts, but that greater civic and organisational engagement is connected to greater mental health impacts. At the community level, it finds that neither a density of bridging social capital organisations nor of bonding social capital organisations is associated with poorer mental health, although a greater number of bonding organisations is related to negative mental health impacts on rural residents. The paper concludes by focusing on how individual and community social capital relationships with mental health are contingent on measurement, scale, and rural or urban location. [ABSTRACT FROM AUTHOR]

Over the past years, the frequency and scope of disasters affecting the United States have significantly increased. Government agencies have made efforts in improving the nation's disaster response framework to minimize fatalities and economic loss due to disasters. Disaster response has evolved with the emergency management agencies incorporating systematic changes in their organization and emergency response functions to accommodate lessons learned from past disaster events. Technological advancements in disaster response have also improved the agencies' ability to prepare for and respond to natural hazards. The transportation and logistics sector has a primary role in emergency response during and after disasters. In this light, this paper seeks to identify how effective policy changes and new technology have aided the transportation and logistics sector in emergency response and identify gaps in current practices for further improvement. Specifically, this study compares and contrasts the transportation and logistical support to emergency relief efforts during and after two major Hurricane events in the U.S., namely Hurricane Katrina (which affected New Orleans in 2005) and Hurricane Harvey (which affected Houston in 2017). This comparison intends to outline the major steps taken by the government and the private entities in the transportation and logistics sector to facilitate emergency response and the issues faced during the process. Finally, the paper summarizes the lessons learned from both the Hurricane events and provides recommendations for further improvements in transportation and logistical support to disaster response. [ABSTRACT FROM AUTHOR]

Late Dr. Sam Mannan promoted in his last year the concept of the Safety Triad as a basis for effective process safety. As he mentioned himself, nobody will be surprised that this triad consists of Prevention, Mitigation, and Response. If everything were perfect an accident would not occur, but in hindsight after an accident experience teaches that at least one of these pillars of the triad is missing or defective. Also, given the recent hurricane Harvey experience in the Houston region, socio-technical associated hazard threats emerge not only from inside a plant but can be external: NaTech or technological accidental occurrences evoked by natural forceful events. Hence to be acceptably safe, the robustness and reliability of the triad components must be well designed, and their performance over time must be periodically verified. This objective supposes a risk-based approach both for design and for reliable operation. However, risk assessment itself is fallible. Over the years it has been shown that there are many limitations, but fortunately during the last few years we have seen an advent of new approaches, methods, and techniques to perform a more realistic scenario definition of what can go wrong, enabling to cope with uncertainties in models and parameter values, to analyze the risk dynamics of situations, such as reliability and availability degradation, but also process risk evolving in continuous processes. In all this, not only the technical aspects but also the associated human and organizational factors must be included. The latter dominate the scene in many situations. Last but not least is the concept of resilience in case risks are overlooked due to lack of knowledge or even to the notorious "black swan" incidents. Preparing for unexpected events should provide not only error tolerant design but early warning agility, capability to improvise, and means to recover operations. It is business continuity that will be at stake! The paper will elaborate both the triad and Sam Mannan's view on risk assessment. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) remote sensing has been widely used for flood mapping and monitoring. Nevertheless, flood detection in urban areas still proves to be particularly challenging by using SAR. In this paper, we assess the roles of SAR intensity and interferometric coherence in urban flood detection using multi-temporal TerraSAR-X data. We further introduce an active self-learning convolution neural network (A-SL CNN) framework to alleviate the effect of a limited annotated training dataset. The proposed framework selects informative unlabeled samples based on a temporal-ensembling CNN model. These samples are subsequently pseudo-labeled by a multi-scale spatial filter. Consistency regularization is introduced to penalize incorrect labels caused by pseudo-labeling. We show results for a case study that is centered on flooded areas in Houston, USA, during hurricane Harvey in August 2017. Our experiments show that multi-temporal intensity (pre- and co-event) plays the most important role in urban flood detection. Adding multi-temporal coherence can increase the reliability of the inundation map considerably. Meanwhile, encouraging results are achieved by the proposed A-SL CNN framework: the к statistic is improved from 0.614 to 0.686 in comparison to its supervised counterpart. [ABSTRACT FROM AUTHOR]

The number and intensity of flood events have been on the rise in many regions of the world. In some parts of the U.S., for example, almost all residential properties, transportation networks, and major infrastructure (e.g., hospitals, airports, power stations) are at risk of failure caused by floods. The vulnerability to flooding, particularly in coastal areas and among marginalized populations is expected to increase as the climate continues to change, thus necessitating more effective flood management practices that consider various data modalities and innovative approaches to monitor and communicate flood risk. Research points to the importance of reliable information about the movement of floodwater as a critical decision-making parameter in flood evacuation and emergency response. Existing flood mapping systems, however, rely on sparsely installed flood gauges that lack sufficient spatial granularity for precise characterization of flood risk in populated urban areas. In this paper, we introduce a floodwater depth estimation methodology that augments flood gauge data with user-contributed photos of flooded streets to reliably estimate the depth of floodwater and provide ad-hoc, risk-informed route optimization. The performance of the developed technique is evaluated in Houston, Texas, that experienced urban floods during the 2017 Hurricane Harvey. A subset of 20 user-contributed flood photos in combination with gauge readings taken at the same time is used to create a flood inundation map of the experiment area. Results show that augmenting flood gauge data with crowdsourced photos of flooded streets leads to shorter travel time and distance while avoiding flood-inundated areas. [ABSTRACT FROM AUTHOR]

Hurricanes can have adverse effects on residential communities and pose a significant risk to their economic prosperity. The ingress of water into a building due to wind-driven rain (WDR) and inland flooding can cause significant damage leading to downtime or temporary loss of housing. Existing frameworks focus on estimating the amount of water ingress due to WDR and inland flooding separately. This paper provides a comprehensive framework that considers both WDR and inland flooding when estimating the amount of water ingress into residential buildings due to hurricane rainfall. The framework estimates the water ingress due to WDR by combining the WDR intensity with the perforated area of the building envelopes. The intensity of the WDR is quantified using an Eulerian Multi-phase Model. The buildings' envelope is considered susceptible to damage from the impact of windborne debris and excessive dynamic wind pressure. The framework to characterize and quantify inland flooding uses a coupled hydrologic-hydrodynamic model to estimate the inundation depth at each building. A case study consisting of a residential community in Houston, TX, which is subject to Hurricane Harvey illustrates the ability of the framework to capture the influence of WDR and inland flooding when quantifying water ingress. • A framework is presented for building water ingress estimation due to hurricane rainfall. • Both wind driven rain and inland flooding are considered simultaneously. • Wind driven rain is modeled through an Eulerian multiphase model. • Inland flooding is estimated using a coupled hydrologic-hydrodynamic model. • The importance of both WDR and inland flooding when estimating losses is shown. [ABSTRACT FROM AUTHOR]

Twitter has significant potential as a source of Volunteered Geographic Information (VGI), as its content is updated at high frequency, with high availability thanks to dedicated interfaces. However, the diversity of content types and the low average accuracy of geographic information attached to individual tweets remain obstacles in this context. The contributions in this paper relate to the general goal of extracting actionable information regarding the impact of natural hazards on a specific region from social platforms, such as Twitter. Specifically, our contributions describe the construction of a model classifying whether given spatio-temporal coordinates, materialized by raster cells in a remote sensing context, lie in a flooded area. For training, remotely sensed data are used as the target variable, and the input covariates are built on the sole basis of textual and spatial data extracted from a Twitter corpus. Our contributions enable the use of trained models for arbitrary new Twitter corpora collected for the same region, but at different times, allowing for the construction of a flooded area measurement proxy available at a higher temporal frequency. Experimental validation uses true data that were collected during Hurricane Harvey, which caused significant flooding in the Houston urban area between mid-August and mid-September 2017. Our experimental section compares several spatial information extraction methods, as well as various textual representation and aggregation techniques, which were applied to the collected Twitter data. The best configuration yields a F1 score of 0.425, boosted to 0.834 if restricted to the 10% most confident predictions. [ABSTRACT FROM AUTHOR]