Natural Hazards Review

The increasing frequency of high-intensity hurricanes and associated flooding can expose residential structures to severe moisture intrusion and initiate fungal biodeterioration of structural elements. This state-of-the-art review discusses the effects of moisture intrusion from hurricane events and subsequent biodeterioration in residential wood-frame structures in the southeast United States. The review is structured to follow the timeline of progressive biodeterioration. Moisture intrusion during the hazard event is considered through post-hazard damage assessments and experimental efforts. Fungal decay inception and propagation are considered from a plant pathology view, including the limited engineering literature on the mechanical effects of decay. The long-term impacts of biodeterioration on housing and residents are explored through a social-science based discussion of pre-hazard risks and post-disaster recovery. Additionally, recommendations are suggested to prevent and mitigate wood degradation. The review found that little effort has been conducted at the scale of timber framed structures to evaluate the effects of biodeterioration from a structural engineering viewpoint, nor have existing efforts considered the long-term effects of wood rot on housing availability or health.

Projections indicate that solar energy will constitute 55% of total electricity capacity by 2050 in the US. Despite solar energy’s growing importance, few studies have analyzed the risks of countrywide deployments of solar infrastructure due to extreme weather events such as hurricanes. This paper presents a probabilistic framework to evaluate the performance of solar infrastructure to generate energy during hurricanes, which often cause significant outages in the US. Our novel framework integrates recent data-driven models that capture two critical and compounding factors: transient cloud conditions that decrease irradiance and high winds that can cause permanent panel damage. We apply the framework to the 2,694 counties in the 38 central and eastern US states to elucidate the risk landscape of solar generation during hurricanes. Our results show that hurricane impacts are significant, compounding, and strikingly disproportional in the US. We show that in Florida and Louisiana, clouds rapidly reduce solar generation to 32% and 65%, respectively, of their normal levels with a return period of 100 years. Our results also show that damage to panels can induce more acute and permanent energy losses, especially in rarer storms, e.g., causing 80% more losses than hurricane clouds 2 days after landfall for 200-year events.

Rainfall-induced flash floods are characterized by their rapid onset and small spatial scale. With little lead time for warning, floodwater can accumulate rapidly and its force can damage roads, swamp houses, destroy bridges, and scour out channels. Having data-driven estimates of potential economic losses from flash floods (before they occur) helps authorities make informed decisions about planning and prioritizing mitigation projects. This article provides a probabilistic predictive model to estimate flash flood economic damage at the census tract scale. To simplify model utilization and avoid strong assumptions about property value and replacement costs, the model predicts the total cost of property and infrastructure damages for individual census tracts (expressed in 2019 prices). The model was developed based on a flash flood data set for a 15-year period (2005–2019) in Texas. The data set was assembled by integrating disparate data from multiple platforms. The occurrence of economic damage was found to be a zero-inflated problem. Therefore, we developed a two-part mixed-effect model. The model first estimates the probability that economic damage will occur (zero-inflated part) and then predicts the dollar amount of the economic damage (continuous part). Utilization of the developed model was demonstrated in an application to Harris County, Texas.

Commercial businesses in an area affected by a natural hazard can have significant interruption and disruption because of damage and impeding factors including financing delays. The recovery of commercial buildings is a necessary but not solely sufficient condition for owners to reopen their businesses. At the community-level modeling scale for planning, the ability to model commercial recovery across the entire community, including short-term business operation disruption, is critical to understanding the interdependent recovery across engineering, economics, and social science. This study proposes a probabilistic commercial building recovery model to predict the recovery of commercial buildings over time following a hazard event and considers two critical types of downtime: (1) impeding factors such as financing delays, and (2) the repair process. A typical recovery resource portfolio available to business owners was developed and quantitatively incorporated into the proposed probabilistic commercial building recovery model. The ability to model commercial building recovery will inform decision makers of the entire recovery process for upcoming business recovery and community resilience metrics related to physical services stability, thereby improving community resilience planning.

Social media scholarship inadequately conceptualizes the online interactions between emergency managers and the communities they serve. Limited data availability, data formatting constraints, and the general failure of researchers to apply relevant social science theories contribute to the problem. This paper summarizes a framework to conceptualize and measure interactions by accounting for both the information flow between different actors (i.e., structure) and the messages’ underlying meaning (i.e., content). By combining both concepts, analysts can distinguish between profoundly different ways to inform and engage followers. The approach (1) provides precise terminology to characterize the government–public dynamic on social networking sites, microblogs, and instant messaging services; (2) draws on multiple data collection techniques and research methods; and (3) illuminates methodological pitfalls to avoid. The technical paper also contributes to a larger conversation about extracting meaning from the data found in the digital environment.

The Hazus Hurricane Model has been extended to include the US Virgin Islands and Puerto Rico. The hurricane hazard model uses the same model incorporated into the design wind speeds given in US and Caribbean wind loading provisions wind hazard maps for the US mainland and the US Caribbean islands. The validity of the hurricane hazard model for use in the Caribbean is documented herein. New damage functions were developed to model the different construction methods used in the region as compared to the mainland United States. These damage functions include new models for metal roofs developed using finite-element analyses and then parameterized for use with a range of sizes, metal thicknesses, and attachment spacings.

The implementation and validation of the Federal Emergency Management Agency’s Hazus Hurricane Model for Puerto Rico and the US Virgin Islands are presented in this paper. Previous versions of the model addressed 22 hurricane-prone states in the United States but lacked the necessary data sets for use in the Caribbean territories. To remedy this limitation, several data sets were developed and incorporated into the model, including a comprehensive building inventory, estimates of the relative frequencies of various architectural and structural features of buildings that are relevant to their performance in high winds, estimates of aerodynamic surface roughness, a simplified inventory of characteristics relevant to tree blowdown and debris management, and deterministic wind field footprints. This paper presents results produced by the enhanced model that were validated and calibrated against several metrics of damage and loss reported in the literature for Hurricanes Irma and Maria, with generally good alignment. For those metrics with less robust levels of agreement, the differences appear to be largely attributable to limitations in our knowledge of the building characteristics, limitations in the model’s census tract aggregate level of analysis, or differences between the units of analysis and observation.

The purpose of this research was to examine the effect of presenting hazard location in different formats on key warning message outcomes—understanding, personalizing, believing, deciding, and milling. We conducted two studies using experiment and focus group methods. In the experiment, we compared a standard ShakeAlert earthquake early warning message, which merely implied location, to three enhanced messages that communicated information about the earthquake epicenter via text, map, or a combined text-and-map format. Focus groups explored reactions to warning messages accompanied by different types of maps. Overall, the standard ShakeAlert message was associated with worse message outcomes compared to messages that explicitly stated the hazard location; communicating hazard location via text was associated with better message outcomes than the map or combined text-and-map format. Although participants preferred the combination text-and-map format, the text format was associated with significantly better message outcomes. Findings revealed that providing specific hazard location information leads to improvements in message outcomes; however, the format in which the information is communicated via text is the best strategy.

Fragility functions and recovery models are often used to assess lifeline systems subjected to extreme hazards. However, even though many databases for fragility and recovery models exist for essential buildings and transportation systems, fragility and recovery models for other lifelines are fragmented across the literature. This article provides a comprehensive review of the state-of-the-art seismic fragility functions and recovery models for energy (power, liquid fuel, and gas), water, and wastewater systems that can be applied in hazard risk and resiliency assessments of communities. The review focuses on fragility and recovery model parameters and summarizes the methods and validation used in developing the models. In addition, the reviewed fragility functions are compiled in an open-source database with a graphical user interface. Critical gaps in the literature are discussed to guide future research endeavors.

This is the first in a set of companion papers that seek to compare homeowners’ expectations and engineering predictions of damage to timber-framed houses before and after undertaking seismic structural strengthening. By combining social and engineering sciences, this multidisciplinary and innovative research contributes to ongoing work on building resilient communities. Part I analyzes the seismic vulnerability of wooden-framed houses located on slopes in the Wellington Region, New Zealand. Data collected using a structural survey in Wellington was used to define common structural parameters in wood-framed houses—slope, plan shape orientation relative to the slope, and wall distribution—and evaluate the influence these parameters have on the levels of earthquake damage. Data from the survey was used to develop a set of representative case-study houses which were modeled using validated nonlinear analysis techniques. The models were subjected to a suite of site-specific ground motions at a range of expected seismic hazards in Wellington, and damage was estimated using based on the maximum drifts recorded at the floor and roof level, and torsion ratio between up and downslope displacement. The results of this paper, Part I, demonstrated a high correlation between damage—measured as torsion and large interstory drifts—and the plan-shape orientation of the houses relative to the slope, while the correlation between damage and both slope and superstructure wall distribution was moderate to low. The structural strengthening of houses and the homeowners’ expectations of structural damage are addressed in Part II, the companion paper.

This paper is the second of two companion papers that seeks to compare homeowners’ expectations of damage and engineering predictions of damage to timber-framed houses before and after undertaking seismic structural strengthening. Part I analyzed the seismic vulnerability of wooden-framed houses located on slopes in Wellington, New Zealand, investigating factors of plan shape relative to the slope, slope variations, and wall distribution, and how they influence the final seismic performance of houses. A structural survey provided data on the form and typical details for the subfloor bracing, and this was then used as the basis for a simple strengthening solution that is numerically investigated here. This companion paper analyzes the improvement of seismic performance after undertaking strengthening to the subfloor structure using a multiple stripe analysis. In the last phase of this work, engineering-based predictions of performance as determined using the numerical models were compared to homeowners’ expectations of damage before and after undertaking strengthening. This comparison paper found that although strengthened timber-framed houses located on slopes satisfy the minimum requirement of New Zealand design codes, there is still a probability of damage occurring, which would not satisfy owners’ expectations of performance in all scenarios.

Abstract

Reliable sensing of roadway conditions during flooding is a long-standing, challenging problem with societal importance for roadway safety. Tools that provide real-time data on road conditions during floods can facilitate safer mobility, reduce vehicle-related drownings, enhance flood response efficiency, and support emergency response decision-making. Following the tenets of user-centered design, such tools ideally should address the needs of diverse stakeholders involved in flood response. Currently, the existing literature lacks a thorough understanding of stakeholder needs to guide situational awareness tool development in the area of roadway mobility during flood events. This paper addresses this gap by studying the needs of stakeholders responsible for managing flood response in Houston. Semi structured one-on-one interviews were conducted with stakeholders from different Houston-based organizations responsible for managing and responding to flood hazard events in the downtown metropolitan area. Interview responses were systematically analyzed to identify (1) data needs for facilitating efficient and safe emergency response, (2) the most and least valuable information available during flooding, (3) communication and visualization strategies, (4) factors influencing stakeholder trust, and (5) factors influencing occupational stress during flood response. Finally, interview insights were used to develop a conceptual situational awareness framework and a prototype map-based tool that provides real-time road condition data during flood events. This study elucidates vital information for improving existing tools and providing preliminary guidance for future mobility-centric situational awareness tools that promote safer mobility and facilitate emergency response decision-making during flooding. Although the study focused on Houston, insights gained may be useful for comparable flood-prone regions.

Practical Applications

In developed countries, 40%–60% of flood fatalities are attributed to vehicle-related incidents. Flooded roads and lack of real-time road condition data pose safety risks to first responders and reduce emergency response efficiency. Understanding stakeholder needs and developing tools that address them are essential for improving the safety and efficiency of emergency response, especially considering a potential increase in flood risk to urban mobility due to climate change and other factors. Following the tenets of the user-centered design process, this study identified stakeholder needs, conceptualized a framework for sensing road conditions, and developed an open-source prototype tool in the context of flood response in Houston. Insights gained in this study can improve the efficacy of existing mobility-centric situational awareness tools and provide preliminary guidance for quick prototyping of new situational awareness tools. Furthermore, organizations can use the insights presented here to help reduce work-related stress among emergency response personnel, thereby improving emergency response efficiency and organizational resilience.

To reduce disaster response failures, controlling disaster preparedness faults is essential. Emergency management departments should analyze the lack of capability as soon as an early warning is released and make adaptive improvements based on disaster risk information. However, because of time constraints and experience scarcity, it frequently is challenging for decision makers to identify faults precisely and develop suitable fault-control measures. Current studies lack attention to this aspect. Case-based reasoning (CBR) can rapidly acquire relevant knowledge for target case by learning from historical cases. Therefore, this paper adopts CBR to support fault-control decision-making when a disaster is approaching. We introduce the cause–effect bow-tie diagram to reconstruct an ontology-supported fault-control scenario model. Based on the structured scenario, a multiphase scenario retrieval is presented to recommend similar scenarios. To generate the target fault-control measures, a hybrid scenario reuse is carried out to adjust the previous fault-control measures in similar scenarios. Finally, a case study illustrates the proposed model’s use in a typhoon situation. The results show that scenario-driven CBR is a proactive and agile approach for providing fault-control suggestions.

Abstract

In February 2021, a winter storm brought snow, ice, and freezing temperatures, which caused severe interruptions in the electric power and water supply systems in Texas and surrounding areas. In this paper, we use survey data to investigate the ways that households adapted and reacted to those outages. The analysis aimed to determine (1) how common different household adaptations were, (2) how adaptations varied with outage and household characteristics, (3) how adaptations tended to occur together, (4) how unhappy households were as a result, (5) how household unhappiness varied with outage and household characteristics, and (6) what concerns influenced the unhappiness level. Results are compared with findings from a study that used an almost identical survey instrument but was based on a larger data set from Los Angeles. Findings from both studies suggest that almost everyone implemented at least one adaptation; most implemented several. They also agreed the most common adaptations were using candles, a flashlight, and/or a lantern; charging the cell phone in the car; purchasing bottled water; and delaying or reducing consumption. Both studies indicate that households experienced varied levels of unhappiness, which were similar for electric power and water interruptions. The reported levels of unhappiness were notably higher in Texas than Los Angeles, however, possibly because the outages had relatively long durations and were recent. Financial, time/effort, health, and stress concerns all were found to have a substantial influence on the extent of unhappiness, in both the Texas and Los Angeles analyses, suggesting that it is critical to consider all of them. Analysis of the Texas study introduced the new finding that repeated service outages during an event is associated with both increased adaptation implementation and greater unhappiness. Analyzing larger data sets from additional events in different locations would be helpful to further understanding of household experiences in service outages.

Practical Applications

Several practical applications emerge from this work. First, by knowing how people are likely to adapt, officials can better prepare to support their constituents during a crisis. Anticipating the distribution of unhappiness could point infrastructure operators to consider other measures for service quality besides downtime alone, as important as that is. Knowing the differential effects of outages on different populations can improve officials’ understanding of social vulnerabilities in their communities, which is needed for planning, education, and outreach. Electric and water outages accompany many kinds of hazard events; the extent to which a local population can adapt, and how officials and infrastructure operators can enhance those adaptations, will be important elements of local community resilience.

Abstract

The hurricane-forecast-evacuation system is complex and dynamic, making it difficult to diagnose potential challenges and implement effective intervention strategies to ensure successful evacuations for everyone. Here we use an agent-based modeling framework to explore how changing different components of the system affects key evacuation outcomes. Called the forecasting laboratory for exploring the evacuation-system (FLEE), this modeling framework integrates high-level representations of the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between elements (forecasts and warning information, traffic). Using FLEE, we investigate the simulated effects of changing the number of cars on the road network (changing evacuation demand), implementing approximations to different evacuation management strategies and policies (contraflow, evacuation order timing), and changing population characteristics (population growth and distribution), all for two real scenarios (Irma, Dorian) and one simulated storm (rapid-onset version of Irma). After comparing and validating FLEE’s evacuation outcomes with real-world empirical data, we use FLEE to explore how simulated changes impact evacuation success overall, how the changes compare, and how impacts from the changes vary across forecast scenarios and regions. Through the experiments, we demonstrate the power of these types of frameworks as tools for exploring the forecast-evacuation system across many scenarios, and identify potential next steps to better support researchers, practitioners, and policymakers.

Practical Applications

Because hurricane evacuations involve many factors and uncertainties, it is often difficult to diagnose potential challenges and implement intervention strategies to ensure everyone can evacuate safely. This research provides a new attempt to help address these needs, by integrating the many physical-social factors into a single modeling framework where we can explore potential challenges and interventions from a new perspective. Using the model, we look at how evacuation outcomes vary with the number of cars on the road network, evacuation management strategies and policies, changes in population characteristics, and different forecast scenarios. We look at which changes are most important to evacuation success, if that changes across forecast scenarios, and if some areas in the model are more impacted than others. Throughout the analyses, we demonstrate how the modeling framework is a powerful research tool capable of studying evacuations across many scenarios. We also discuss next steps for improving the models to support researchers and practitioners working to improve evacuations and save lives.

For residential structures in America’s coastal communities, resilience is an essential system quality. Such resilience is best conceptualized as a given system’s ability to anticipate, respond to, recover from, and adapt to disruption. Practically, the quantification of parcel-scale resilience requires detailed temporal data and the ability to assess various indicators of system performance variability. This paper considers expanded datasets related to wave and storm surge damage for two catastrophic disruptions: Hurricane Ike in Galveston County, Texas, and Hurricane Irma in Monroe County, Florida. Data mining suggests that appraised property values for single-family residences may be used to characterize a given parcel’s post-disruption economic response and rebound behavior. Such rebound varies, and the authors offer four potential indicators of such variability for future study: the severity of the initial disruption, a parcel’s location, a parcel’s construction, and a given property’s flood insurance coverage. In general, following a hurricane, recorded property values appear to serve as another feasible measure of a given disaster-affected system’s resilient performance. Ultimately, these findings not only offer another mechanism by which community leaders can effectively manage resilience but also suggest that FEMA must continue to improve the National Flood Insurance Program.

Abstract

When large-scale disasters occur, people often are left on their own to seek critical resources: food, water, medications, and other important items. Historically, government agencies have developed disaster preparedness strategies focused primarily on either the level of the individual or household or on the ability of government agents to provide relief to affected areas. Such approaches do not consider the potential for community members to share needed resources with one another—a crucial factor in survival when earthquakes, floods, landslides, and other disruptions to transportation and communications cut off whole communities from external aid. In this study, we used a simple random sample survey to measure households’ actual and perceived preparedness and assess individuals’ willingness to share essential resources following a large disaster using survey data gathered from three communities in the Pacific Northwest of the US ($N=638$; overall response rate 20.1%) and Nagoya, Japan ($N=\mathrm{1,043}$; response rate 13.6%), two regions that expect to experience a magnitude 9.0 megaquake. Analysis of the survey data using an ordered response probit model found that the strength of social ties and levels of social trust strongly influence willingness to share in both regions. Differences between the Japanese and American responses suggest different dependencies on and roles for government agencies in the two societies, as well as differences in the types of resources that community members are willing to share, and with whom. Trust emerges as the most important factor across both study regions and for all resources. Willingness to share may be enhanced through trust-building interventions and should be regarded as an effective focus for preparedness efforts, especially if it is shown to be beneficial for a variety of social purposes.

Practical Applications

The impacts of natural disasters worldwide have increased in recent years, often leading to situations in which communities must fend for themselves and make do with what resources they have while waiting for outside assistance to arrive. This research considered the potential benefits of community members sharing resources with one another during disaster. We surveyed households in both US and Japanese communities facing extreme earthquake threats to understand how willing they would be to share resources with others in a disaster and what factors might affect their willingness to share. Our results show that although there are some differences, trust is the most important factor for sharing resources in both regions. We argue that building trust within communities can improve disaster outcomes while also providing everyday benefits for society.

In the wake of a disaster, the price of essential goods and services, including reconstruction materials and labor, sharply increases. Price gouging refers to sellers and supply companies charging exorbitant prices for necessary items to take advantage of spikes in demand. Thirty-seven states out of fifty in the US have legislation regulating price gouging, regarded as an unfair or deceptive trade practice during a disaster or emergency. Consumers, academics, and practitioners have mixed opinions about the effectiveness of this anti-price gouging law. Most existing studies focus on the impact of general price control qualitatively and theoretically. This study aims to empirically examine the effect of the anti-price gouging law on the speed of reconstruction in Virginia and Maryland in the aftermath of Hurricane Sandy. The difference-in-differences approach was used to estimate the effect of the anti-price gouging law (treatment) on postdisaster reconstruction speed. This approach allows us to estimate the average treatment effect on the treated group by comparing the pre-to-post changes in the average number of monthly building permits in counties in Virginia (treatment group) with those of counties in Maryland (control group), while at the same time controlling for time-invariant county-specific heterogeneity and some other factors that may affect the monthly building permits for both groups in the absence of treatment. The findings show that the anti-price gouging law decreased the speed of postdisaster reconstruction by 18 units of monthly building permits (additional units in the treatment group due to treatment), indicating that the number of new housing units authorized by monthly building permits in Virginia is 18 units less than that of Maryland. The findings of this research are expected to assist policymakers and decision-makers in understanding the effect of the anti-price gouging law on reconstruction speed and enhancing their postdisaster reconstruction strategies and policies.

Urban flood inundation is spatially uncertain. To quantify this uncertainty, it is necessary to explore the spatial probability of urban flood inundation in different return periods. In this study, an urban flood spatial inundation probability assessment method based on an improved Bayesian model is proposed, which comprises three parts: data reconstruction based on undersampling; optimal Bayesian sample planning; and spatial inundation probability assessment. A case study of the central urban area of Jingdezhen City, China, is presented in this paper. The results indicate that (1) the inundation probabilities generated based on various return periods (20-, 50-, and 100-year return periods) are accurately determined and can provide more detailed inundation information. (2) The adoption of the random undersampling data reconstruction method solves the problem of an imbalanced number of inundations/noninundations during Bayesian modeling and substantially enhances the prediction accuracy compared with the traditional Bayesian modeling approach. (3) A sensitivity analysis reveals that inundation probability is sensitive to the drainage network and elevation rather than soil water retention and distance to river. With an increase in the return period, the inundation probability gradually increases. As the proposed method can quantify flood inundation uncertainty, it is valuable in supporting specific flood risk assessments.

Accurate forecasts and estimates of disaster debris are critical for effective debris management planning. However, detailed postdisaster waste data to validate and improve debris predictions is often unavailable. In this study, a postdisaster waste dataset collected in Beaumont, Texas, following widespread flooding from Hurricane Harvey that included debris tonnages and coordinate locations of each debris removal in residential areas was investigated. The dataset was utilized to quantify the amount of debris produced, identify the factors that influenced debris generation in different areas of the city, and compare Beaumont debris tonnages to predictive models. The study found that the type of flooding (riverine versus urban) had the highest influence on debris generation. Riverine flooded areas generated twice the debris tonnage as urban flooded areas. Elevation appeared to influence debris generation when considered with the type of flooding. FEMA’s correlation for flooded personal property was within the same magnitude of debris quantities, while other methods significantly overpredicted debris quantities (up to one order of magnitude) due to their generality. However, they can be adapted for flood-generated debris. Urban flooding is an increasingly prevalent issue following a natural hazard and generates considerable amounts of debris, but is not addressed in current disaster management plans. Estimation methods that consider urban flooding should be developed.

Abstract

Climate change and land use land cover (LULC) change are two major factors influencing river basin hydrology. This study explored these drivers’ isolated and combined impacts on the ecologically relevant flow in the Achencoil basin, Kerala, India. The LULC classification in the study is carried out with the Random Forest (RF) algorithm in the Google Earth Engine (GEE) platform, and Land Change Modeler (LCM) is incorporated for change detection and projection. The future climate data from the National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) is used for climate change impact assessment. The Soil and Water Assessment Tool (SWAT) is employed to simulate streamflow under LULC and climate change scenarios. The historical and projected future LULC change in the basin revealed an increase in the built-up and barren land, with a significant decrease in agricultural and forest areas. The results show that the projected future precipitation will decrease under the RCP 4.5 and increase under the RCP 8.5 scenario. The projected average maximum and minimum temperature are expected to increase under both scenarios in the basin. The LULC 2050 scenario shows the most significant rise in average annual streamflow, at 7.5%. Whereas in the climate change scenarios, the average annual flow decreases under RCP 4.5 and increases under RCP 8.5. The combined impacts of climate change and LULC change are relatively higher than the isolated effects of these drivers in the basin. The study outcomes are expected to help policymakers consider the effect of climate change and LULC change on the river’s hydrology so as to implement the management activities that account for the riverine ecosystem.

Practical Applications

Changes in land use land cover and meteorological parameters are important environmental issues that must be pointed out. These changes will affect the river flow and eventually affect the river ecosystem. The current research investigates the combined effects of land use land cover and climate change in the flow of the Achencoil basin in India. This study area is one of the most frequently flooded in the state. As a result, the projected climate and land use land cover provide an idea of future streamflow in the basin. This research enables a better understanding of the response of streamflow components to climate variability and land use land cover changes in the Achencoil basin, which might help policymakers develop strategies for regional water resource management. Implementing these policies and management strategies will necessitate collaboration and coordination across various levels of government, private sector, and local communities in order to mitigate the risk and support a sustainable riverine ecosystem.

Road networks play an essential role in economic activities and are required to have high level of reliability. Heavy precipitation could lead to disruptions to road networks, causing service failure and associated economic and social impacts. In this study, we proposed a framework for assessing the reliability of road networks to precipitation based on historical recorded disruption data. We first developed a vulnerability disruption model, which describes the relationship between precipitation intensity and the probability of road disruption, as well as the amount of disruptions, based on recorded data. The vulnerability of roads shows a nonlinear increasing trend with increasing precipitation. Based on the vulnerability model and Monte Carlo approach, the travel time reliability of road network of Fujian province, China was assessed. Two precipitation events in Fujian road network were selected to validate the proposed framework. The travel time reliability of 11.12% city pairs in Fujian Province decreased to below 0.85 with an average precipitation of 73.32 mm. The proposed vulnerability model and the framework for assessing reliability can provide guidance to highway departments so that they can prepare for possible adverse impacts caused by precipitation on roads and plan for road network service.

Floods have a devastating impact on developing nations. The focus of current resilience models is predominantly developed countries, and less is known regarding how they might perform in other contexts. This paper presents a comparative case study in Bangladesh using two established hazard resilience models: the Disaster Resilience of Place (DROP) Model and the Spatially Explicit Resilience-Vulnerability (SERV) model. Using publicly available data, we applied the two models at the district level in the context of flooding, the most extensive hazard in Bangladesh. The results of our model implementations were evaluated by Bangladeshi emergency managers and researchers regarding their potential utility. Whereas emergency managers preferred the SERV model results, researchers thought the DROP model results were preferable. This suggests an opportunity to create a model that would serve the flood resilience needs of managers and researchers.

Houston, Texas, has highly engineered its waterways to reduce flooding. However, this has been shown to be inadequate during recent storms in 2015 through 2017, including Hurricane Harvey. This study analyzed the efficacy of a large-scale structural mitigation project, Project Brays, on reducing flood levels in the Brays Bayou watershed given updated rainfall standards released in 2018. In addition, we investigated the water surface elevation (WSE) reduction of three detention basins that were constructed as a part of this mitigation project. We propose other possible detention basins to mitigate flood hazard in the highly flood-prone regions of the watershed. We found that Project Brays will decrease WSE in the upstream half of the watershed by around 1.2 m at the expense of increasing WSE by 0.52 m downstream. Additional detention basins could further decrease WSE by an average of 0.1 m over the area impacted during the Atlas 14 100-year storm. These impacts are very localized directly downstream of the detention locations and are no longer seen after approximately 300 m. Future mitigation should contain more localized detention directly upstream of flood-prone areas to provide additional targeted reductions in floodplain extent. Project Brays is a case study that demonstrates how future climate scenarios may be necessary for the large-scale design works of the future, given that such projects often take decades to construct and it is not typically standard practice to design for climate conditions potentially existing upon completion.