Hazard Mitigation Planning Research Articles

Modeling heat island exposure and vulnerability utilizing earth observations and social drivers: A case study for Alabama, USA

Alabama currently experiences an above-average threat from extreme heat events compared to the remaining states in the USA. More than 160,000 people living in the state (infants, elderly age groups, or poverty-ridden populations) remain vulnerable to heat events. The risk of heat-related mortalities and morbidities disproportionately impacts the growing Alabama cities due to increasing hot-weather episodes and several underlying social vulnerability factors. The exposure threat in 2050 is projected to increase by more than 90 average heat days a year and the number of heat-wave days is predicted to increase from 15 to more than 70 days a year. Although the state's hazard mitigation plan covers extreme heat issues and heat emergency plans, Alabama lacks heat adaptation plans and is conducting heat vulnerability assessments from time to time. This study focused on determining the social drivers of heat vulnerability and identifying regions within the state that experienced intense heat island effects over the course of five years (2015–2019). 15 sociodemographic factors data from the 2018 American Community Survey (ACS), and 6 health outcome variables (asthma, obesity, stroke, high blood pressure, diabetes) were analyzed to assess cumulative social vulnerability using principal component analysis (PCA). Using Spatial Autoregression (SAR) model, exposure risk was measured as a function of environmental parameters including proportional vegetation, normalized difference water index (NDWI), digital elevation model (DEM), and percent imperviousness of land surface. A heat risk index calculated as a product of social vulnerability and exposure risk was analyzed for Alabama's eight largest and growing cities (Birmingham, Huntsville, Hoover, Montgomery, Mobile, Tuscaloosa, Auburn, and Dothan) at the block-group census resolution. Spatial data depicting the physical landscape characteristics across the cities revealed differing levels of and factors in exposure to urban heat effects across the city.

Hurricanes Laura and Sally: A Case Study of Evacuation Decision-Making in the Age of COVID-19

Abstract This study examines risk perceptions and evacuation planning for those residents affected by Hurricane Laura—the first major hurricane evacuation during the COVID-19 pandemic—and Hurricane Sally, prior to the widespread availability of vaccines. Research on hurricane evacuation behavior and risk perceptions during a pandemic is critical for quantifying the intersect of these compounding threats. Analyses captured how people perceive public shelters and whether evacuation choices changed in light of the pandemic. Many study participants considered themselves vulnerable to COVID-19 (39.4%), and two-thirds believed it would be “very serious” if they or their loved ones contracted COVID-19, but this had no impact on their actual evacuation decision-making. Approximately 75% of the sample stayed at home during Hurricanes Laura or Sally, and, of these, just over 80% indicated that COVID-19 was a somewhat important deciding factor. This reflects the partial role that COVID-19 played in balancing individual and household protective action decision-making during complex disasters, whereas 15.5% wanted to evacuate but waited until it was too late. For those who evacuated to a hotel, many found that staff and guests wore masks and socially distanced in common spaces. Of particular interest is that individuals have a continued negative perception of public shelters’ ability to safeguard against COVID-19 that was coupled with a significant decrease in the number of respondents that would potentially use shelters in 2020 as compared with before the COVID-19 pandemic. These results have informed and will inform future hazard mitigation planning during the current pandemic or future pandemics or infectious disease outbreaks.

Late Holocene Tephrostratigraphy at Chokai Volcano, Northern Japan, and Contribution to Hazard Assessment

History and pattern of explosive eruptions at Chokai volcano, Japan, in the last 2500 years were investigated from tephra survey and accelerator mass spectrometry (AMS) radiocarbon dating. The tephrostratigraphy was established based on observations at eight hand-dug trenches and three outcrops. The well-correlated tephra layers were dated at c. 2.5 ka, 2.1–1.9 ka, 1.8 ka, and 1.6 ka, indicating major eruptions occurred at these ages. The tephra from the documented 871 CE eruption was also identified. Componentry analysis of ash was carried out for these five eruption deposits. The changes in tephra facies and ash components within an unbroken series of tephra layers indicate a shift from hydrothermal-dominant phreatic or phreatomagmatic eruption to magma-dominant eruptions in a single episode. Common eruption sequences were identified based on the combination of tephra facies variation and records of witnessed eruptions. Every volcanic activity begins with precursory activity of seismicity, fumaroles, and snow melting for weeks to months, then onset hydrothermal-dominant eruption happens. Then, the eruption evolves to a magma-dominant eruption, or alternatively, the hydrothermal-dominant eruption persistently continues until cessation. The eruption sizes are VEI 2 or more minor. Lahar can occur at any stage of the eruption, resulting in damage to the residential area at the base of the volcano. The eruption patterns and the extent of hazard risks elucidated by this study will be utilized to hazard mitigation plans.

Comparing hazard mitigation and climate change adaptation planning approaches

Cities are developing hazard mitigation plans (HMP) and climate change adaptation plans (CCAP) with the common aim of proactively reducing vulnerability to future hazards. Yet, they are often treated as distinct planning instruments. Some planning scholars have suggested that HMP and CCAP should be integrated since climate change impacts are increasing and communities have limited time and resources. But, it is unclear how synergistic these two planning approaches are in practice. Focusing on flooding as a key climate-related hazard, this study examines planning frameworks and helps to address this gap by exploring HMPs and CCAPs in three US cities at the forefront of flood resilience planning. We analyze the content of standalone HMPs and CCAPs in Boston, MA, and Seattle, WA, and a combined plan in Baltimore, MD to identify overlaps in practice and explore how, and to what extent, HMPs and CCAPs are synergistic.

Spatial variability of earthquake hazard parameters, return periods and probabilities of earthquake occurrences in the eastern Himalayan seismic belt

We present a brief atlas of the geographical distribution of earthquake hazard parameters, return periods and probabilities of exceedance of medium to large earthquakes in a hazardous region of the eastern Himalayan seismic belt. A maximum likelihood procedure is applied for the purpose that allows earthquake data during 1500–2019 containing the mixed files of both the historical as well as instrumental era. The results reveal a high activity rate (λ) in eastern Nepal and Arunachal Himalaya, and a low value in the Bhutan-middle Arunachal-Assam Himalaya. A high b-value (or β-value) associated with eastern Nepal and its vicinity, Shillong Plateau and eastern Arunachal Himalaya reveals low crustal stress, while a low b-value associated with Bhutan, middle Arunachal, western Assam and east of the Lhasa reveals high differential crustal stress. The high values of Mmax (Mw > 8.0) are perceived in eastern Nepal, eastern Arunachal Himalaya, Shillong Plateau and south of Lhasa in Tibet. The return periods and probability of exceedance for magnitude Mw 6.0, 6.5 and 7.0 during the next 10-, 20- and 50-years are calculated and correlated with the prevailing tectonics and seismicity of the area. The low return periods (10–20 years) and high probabilities (>0.8) for magnitudes Mw 6.0–6.5 are associated with eastern Nepal, Shillong-Kopli Fault-Arunachal Himalaya, Eastern Himalayan Syntaxis and Yadong-Gulu rift in Tibet revealing potential regions for future earthquake hazard. These hazard maps are highly useful to prepare earthquake hazard mitigation plan in high hazard regions.

Moving Up the Ladder in Rising Waters: Community Science in Infrastructure and Hazard Mitigation Planning as a Pathway to Community Control and Flood Disaster Resilience

Public participation is the democratic gateway to more just, inclusive, and resilient communities. However, infrastructure and hazard mitigation planning tends toward top-down, expert-driven processes that fail to meaningfully include communities most at risk of disasters. In this article, we critically examine the potential of citizen science in infrastructure and hazard mitigation planning with a focus on stormwater infrastructure and extreme wet-weather events, as floods are the most common disaster in the US. We review literature on various citizen science approaches, from crowdsourcing to community science, and offer a framework that situates them within Sherry Arnstein’s foundational piece on public participation, a “Ladder of Citizen Participation.” We discuss the opportunities different participatory methods offer for meaningful public involvement, knowledge generation, and ultimately community control and ownership of stormwater and flood infrastructure. We provide case study examples across the US of how public works departments, emergency management, and related organizations have engaged communities around hazard risks and flooding challenges, and offer recommendations for how these programs can be improved. We conclude that in order to produce data needed to mitigate flood disasters and increase trust and public interest in infrastructure needs, civic participation should be grounded in community science, utilizing a multimedia and technological platform. The methods applied and data generated can be leveraged toward public safety, and provide voice, agency, and power particularly to disenfranchised communities most at risk from current hazards and looming climate change impacts.

Collaboration in Hazard Mitigation Planning: Case Studies in Washington State, USA.

Hazard mitigation plans can support communities' resilience in the context of natural hazards and climate change. The quality of these plans can be evaluated using established indicators; however, research is also needed regarding the perceptions of participants in planning processes, to understand aspects of the planning processes that may not be evident in the plan documents. This study builds on previously reported plan quality scores and survey data, to investigate whether selected collaboration dynamics (principled engagement and capacity for joint action) occurred during counties' hazard planning processes. Semi-structured interviews were conducted with 20 hazard planning professionals who were involved in preparing county-level hazard mitigation plans in Washington State, USA. Findings (for cases with both high- and low-scoring plans) include evidence of collaboration dynamics, although important participants (e.g., members of the local community) were reportedly missing from some planning processes, raising concerns about the extent to which the plans reflect local needs. These results are consistent with previous literature, which has demonstrated that members of the public often view hazard mitigation as inaccessible and disconnected from their daily lives. The paper concludes with recommendations for how practitioners might go about bolstering participation from important participants, potentially leading to higher-quality plans and helping to protect communities from hazards.

An assessment of the extremes and impacts of the February 2021 South-Central U.S. Arctic outbreak, and how climate services can help

In February 2021, a widespread cold-air outbreak, with two associated winter storm systems, impacted the South-Central United States. After a comprehensive summary of the synoptic setup and a day-by-day analysis of the event, we assess the significance of the storm from a climatological perspective. Concerning winter precipitation, there were isolated instances of record snowfall accumulations. While freezing rain and freezing drizzle both occurred, total freezing precipitation accumulations did not exceed a one-in-50 year event. The duration of the cold was notable — many stations across the region broke records for the highest number of consecutive days below freezing. When analyzing hourly temperature observations, we found that the February 2021 event was the record longest duration of hours below freezing for 12 stations. Nearly 6,000 daily temperature records were broken by this event. We next summarize significant impacts of this event. While we find that this event was extreme, most aspects of this storm were not unprecedented. Even in the context of a warming climate, cold events such as this should be considered when assessing risk and hazard mitigation planning. The magnitude of impacts associated with this event suggests a lack of preparedness that needs to be addressed. We discuss the importance of using climate services in planning for future extreme events. While there are documented benefits to users engaging with climate service providers and integrating climate information into their decision-making, the February 2021 event serves as an example of the failures that can occur when climate services have not been integrated into planning. We recommend the use of climate services when assessing risk and planning for future climate and weather extremes.

Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model

• We aim to develop a robust method for predicting future waterlogging-prone areas. • Maximum entropy algorithm and Future Land Use Simulation model were combined. • Dominant driver of waterlogging (land use change) was considered in the prediction. • A high proportion of impervious surfaces may face huge waterlogging risks in 2030. • This method could support future urban design and waterlogging risk prevention. Urban waterlogging is a severe hazard that can directly damage environmental quality and human well-being. It would be desirable for hazard mitigation planning and sustainable urban design if potential waterlogging-prone areas under dynamic land use change could be appropriately predicted. However, previous related studies did not simultaneously consider the reliability of negative samples and the future influence of fine-scale land use change. To fill the knowledge gap, this research has developed a robust method for predicting future waterlogging-prone areas by coupling the maximum entropy (MAXENT) and the Future Land Use Simulation (FLUS) model. The former can ensure that no extra sampling bias will be introduced, while the latter can accurately forecast the spatio-temporal pattern of land use. This case study has confirmed the accuracy and feasibility of this method. It was found that the proportion of impervious surfaces, population density, and proportion of green areas are key spatial drivers behind urban waterlogging issues. In addition, the future hazard potential map provided by the MAXENT and FLUS implies that a large proportion of impervious surfaces will face huge waterlogging risks. Therefore, policymakers should focus more on places with a higher probability of urban waterlogging. In summary, this research is expected to offer a practical tool for future urban design and waterlogging risk prevention.

The Effects of Enhanced Information Utilization in Collaborative Hazard Mitigation Planning

Problem, research strategy, and indings Natural hazard mitigation plans are prepared in communities throughout the United States. Like many complex, multijurisdictional issues, the plans should be developed collaboratively using the best available data to reduce risks. In this study we assessed a program that enhanced data utilization for collaborative hazard mitigation planning to determine its impact on plans, processes, and implementation. We used surveys, plan analysis, interviews, and forums to compare three Oregon counties that participated in the program with three that partially participated and four that did not participate. We found that the program had limited effect because professionals encountered mismatched perceptions of risk, difficulties with data translation and interpretation, lack of integration, and limited engagement. This research highlights implications for packaging risk data, the role of information in collaboration, and the challenge of collaborative implementation, but the focus on the Oregon experience may limit the generalizability to other contexts. Takeaway for practice To overcome the decision biases common in risk-based decision making, practitioners need to articulate specific outcomes connected to emotion, social norms, and peer attitudes. Data and analysis should be part of an interactive process to promote shared understanding among decision makers. Practitioners will be more effective when they combine these strategies with opportunities to galvanize the public and decision makers toward action.

Hurricane Hazards, Evacuations, and Sheltering: Evacuation Decision-Making in the Prevaccine Era of the COVID-19 Pandemic in the PRVI Region

Abstract Although research relating to hurricane evacuation behavior and perceptions of risk has grown throughout the years, there is very little understanding of how these risks compound during a pandemic. Utilizing the U.S. territories of Puerto Rico and the U.S. Virgin Islands (PRVI) as a study region, this work examines risk perceptions and evacuation planning during the first hurricane season following the coronavirus disease 2019 (COVID-19) pandemic before vaccines were widely available. Analyses of how people view public shelters and whether evacuation choices will change in light of COVID-19 concerns were conducted, and results reflect major changes in anticipated evacuation behavior during the 2020 hurricane season. Key findings include that over one-half of the sample considered themselves vulnerable to COVID-19. When asked about their intended actions for the 2020 hurricane season, a significant number of individuals who would have previously evacuated to a shelter said that they would choose not to during the pandemic, reflecting that public shelter usage has the potential to decrease when the decision is coupled with COVID-19 threats. In addition, individuals were shown to have a negative perception of public shelter options. Approximately one-half of the respondents had little faith in shelters’ ability to protect them, and three-quarters of respondents found the risks of enduring a hurricane to be less than those posed by public shelters. These results will inform future hazard mitigation planning during a disease outbreak or pandemic.

Optimized Hot Spot and Directional Distribution Analyses Characterize the Spatiotemporal Variation of Large Wildfires in Washington, USA, 1970\u22122020

Spatiotemporal analysis of fire activity is vital for determining why wildfires occur where they do, assessing wildfire risks, and developing locally relevant wildfire risk reduction strategies. Using various spatial statistical methods, we determined hot spots of large wildfires (> 100 acres) in Washington, the United States, and mapped spatiotemporal variations in large wildfire activity from 1970 to 2020. Our results found that all hot spots are located east of the crest of the Cascade Range. Our spatiotemporal analysis found that the geographic area wherein most of the state’s acres burned has shrunk considerably since 1970 and has become concentrated over the north-central portion of the state over time. This concentration of large wildfire activity in north-central Washington was previously unquantified and may provide important information for hazard mitigation efforts in that area. Our results highlight the advantages of using spatial statistical methods that could aid the development of natural hazard mitigation plans and risk reduction strategies by characterizing previous hazard occurrences spatially and spatiotemporally.

Modeling Interdependencies between the Building Portfolio, Transportation Network, and Healthcare System in Community Resilience

A community resilience model that takes into account the mutual interdependencies between the building portfolio, transportation network, and healthcare system both during and after a seismic event is presented. The model is modularized into independent simulators to facilitate modeling their interdependencies. The transportation network model accounts for the capacity reduction attributed to bridge damage and links blocked by debris from collapsed buildings. It also addresses the increased demand from ambulance trips ferrying injured people to healthcare facilities and trucks hauling away debris. The transportation network model is incorporated into a discrete event simulation environment that models the response of the healthcare system as well as the debris removal process in the aftermath of a seismic event. Measures are proposed to quantify and improve the seismic resilience of each individual system as well as the whole community considering the three systems’ mutual interdependencies. The capability of the proposed model to support hazard mitigation planning is demonstrated through a case study that highlights the effects of interdependencies between the three systems under consideration. Mitigation strategies to improve seismic resilience of a prototype community are proposed and assessed.

Content analysis of state hazard mitigation plans and their ability to address pandemics

Hazard mitigation plans address hazards such as earthquakes, hurricanes, landslides, tornados, flooding more explicitly than pandemics or other public health crises. This paper analyzes and identifies pandemic-related mitigation strategies in state-level hazard mitigation plans across the United States. This study identifies common and uncommon strategies that could be included in plan updates such as: use of personal protective equipment (PPE), self-isolation/quarantine, hygiene, restricted gatherings, vaccination, vulnerability assessment, economic consequences analysis, acknowledging the need to address pandemics, and referring Centers for Disease Control and Prevention (CDC) guidelines. The 54 state-level plans are compared based on their inclusion of the nine strategies. Findings show that vaccination, self-isolation/ quarantine, and vulnerability assessment are the strategies most listed in plans. Additional concerns made evident by the 2020 coronavirus pandemic, such as contact tracing, environmental impact, and public services, were limited across the plans. It was concluded that long-term mitigation strategies for reducing the risk and speed of spread such as identifying strategic locations for testing, vaccination, and quarantine should be devised and implemented. Pandemics should be included as a distinct class of hazards while formulating state hazard mitigation plans. Strategies for addressing the impact of long working hours on the mental health of healthcare workers should be explored. Economic and psychological impact of unavailability of essential public services, such as transport and groceries, should be investigated and strategies should be formulated in the mitigation plans accordingly.

The Role of Natural Hazard Mitigation Plans in an Age of Pandemics

The Role of Natural Hazard Mitigation Plans in an Age of Pandemics

Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping

Natural disaster impact assessment is of the utmost significance for post-disaster recovery, environmental protection, and hazard mitigation plans. With their recent usage in landslide susceptibility mapping, deep learning (DL) architectures have proven their efficiency in many scientific studies. However, some restrictions, including insufficient model variance and limited generalization capabilities, have been reported in the literature. To overcome these restrictions, ensembling DL models has often been preferred as a practical solution. In this study, an ensemble DL architecture, based on shared blocks, was proposed to improve the prediction capability of individual DL models. For this purpose, three DL models, namely Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), together with their ensemble form (CNN–RNN–LSTM) were utilized to model landslide susceptibility in Trabzon province, Turkey. The proposed DL architecture produced the highest modeling performance of 0.93, followed by CNN (0.92), RNN (0.91), and LSTM (0.86). Findings proved that the proposed model excelled the performance of the DL models by up to 7% in terms of overall accuracy, which was also confirmed by the Wilcoxon signed-rank test. The area under curve analysis also showed a significant improvement (~4%) in susceptibility map accuracy by the proposed strategy.

Jazz on Tulsa Time: The Remarkable Story of the Network of Flood Mitigation Champions behind the Tulsa Turnaround

AbstractDecades of scholarship and practitioner reflection point to factors that promote local hazard mitigation planning and implementation, collectively forming the standard model of local hazard...

Landslide Scarp Assessments by Means of an Ellipse-Referenced Idealized Curved Surface

The importance of scenario investigation in landslide-related hazard mitigation planning has long been recognized, where numerical simulation with physics-based models plays a crucial role because of its quantitative information. However, a plausible failure surface is a prerequisite in conducting the numerical simulation, but it often has a high degree of uncertainty due to the complex geological structure. The present study is devoted to proposing a methodology to mimic the plausible landslide failure surface (with some uncertainty) for investigating the consequent flow paths when failure takes place. Instead of a spherical shape, an idealized curved surface (ICS) is used, where two constant curvatures are, respectively, assigned in the down-slope and cross-slope directions. A reference ellipse is introduced for constructing the associated ICS with a specified failure depth regarding these two curvatures. Through translating, rotating, or side-tilting the reference ellipse, the most appropriate ICS is figured out with respect to the assigned constraints (failure area, volume of released mass, depth of sliding interface, etc.). The feasibility and practicability of this ellipse–ICS method are examined by application to a historical landslide event and one landslide-prone area. In application to the historical event, the fitness versus the landslide scarp area and its impacts on the consequent flow paths are investigated. For the landslide-prone area, five scenarios are arranged based on the surface features and the records of gaging wells. The most plausible failure scenario is therefore suggested as the prerequisite for mimicking the consequential flow paths.

Earthquake vulnerability in the Himalaya by integrated multi-criteria decision models

Himalayan mountains are one of the most seismo-tectonically active zones on the surface of the earth. Recurring moderate and high magnitude earthquakes are not uncommon in this region. This paper maps the earthquake vulnerability in the region using integrated multi-criteria decision models. Factors which may influence vulnerability in a region can be categorized in social, geotechnical, structural, and physical parameters. We have used the analytical hierarchy process (AHP) approach to determine the weights of various parameters, which were further used to develop earthquake vulnerability maps for the study area using the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Grey Relational Analysis (GRA) methods. There is good correlation between the vulnerability estimated through AHP-VIKOR and AHP-GRA methods. Our analysis indicates that more than 12% area may be under high to very-high vulnerability, whereas more than 44% population, and about 43% buildings are highly vulnerable to hazards due to earthquakes. The results would be useful for various hazard mitigation and infrastructure planning agencies working in the region.

Numerical study on impacts of a concurrent storm-tide-tsunami event in Macau and Hong Kong

Intensified activities of tropical cyclones in the Western North Pacific have imposed increasing threats to coastal cities in the context of global climate change. A storm surge superimposed with astronomical high tide, i.e., a storm-tide event, often causes severe flooding in many coastal cities in South China Sea (SCS) region. Meanwhile, the potential tsunami hazard associated with the megathrust in Manila subduction zone has become a serious concern in this region. These two kinds of coastal hazard have been studied independently in the past since they are caused by different physical mechanisms in nature. However, there is no scientific reason to rule out the possibility of concurrence of a storm-tide-tsunami event, which is admittedly rare. This study simulates a group of synthetic events assuming that tsunami waves are generated by a Mw 9 earthquake in the Manila subduction zone during a typhoon, which has the same characteristics as the 2017 Typhoon Hato. A numerical model package originally developed for storm-tide calculation has been modified to simulate a concurrent storm-tide-tsunami event. A variety of scenarios are considered as the tsunamis are superimposed at different phases of the storm-tide event. Their compound impacts on Macau and Hong Kong in Pearl River Delta, China, are investigated. Specifically, the results of water level, arrival time of the maximum water level, flow velocity, and inundation depth are discussed. The worst-case scenarios have been identified at Macau and Hong Kong, respectively. The scenario with tsunami being initiated at 07:00 on 23-Aug-2017 leads to an inundated area of 12.0 km2 in Macau. On the other hand, the scenario with tsunami being started at 08:10 on the same day generates an inundation area of 8.9 km2 in the vicinity of Kai Tak terminal region in Hong Kong. In addition, the efficacy of the linear superposition of results obtained separately for each hazard (i.e., typhoon and tsunamis) is also discussed. Generally speaking, the differences between the linearly superimposed solutions and fully coupled results vary temporally and spatially and could be either positive or negative. However, the linearly superimposed solutions consistently underestimate the maximum water elevation and yield delays on the arrival times of peak flooding stage in the Pearl River estuary. It is concluded that for coastal protection and hazard mitigation planning in this region, these extreme situations need to be considered. A fully coupled numerical model package is now available for conducting such studies.