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Disaster recovery is considered one of the less-understood phases of the disaster cycle. In particular, the literature on lifeline infrastructure restoration modeling frequently mentions th...

Natural hazards and disaster reconnaissance investigations have provided many lessons for the research and practice communities and have greatly improved our scientific understanding of extreme events. Yet, many challenges remain for these communities, including improving our ability to model hazards, make decisions in the face of uncertainty, enhance community resilience, and mitigate risk. State-of-the-art instrumentation and mobile data collection applications have significantly advanced the ability of field investigation teams to capture quickly perishable data in post-disaster settings. The NHERI RAPID Facility convened a community workshop of experts in the professional, government, and academic sectors to determine reconnaissance data needs and opportunities, and to identify the broader challenges facing the reconnaissance community that hinder data collection and use. Participants highlighted that field teams face many practical and operational challenges before and during reconnaissance investigations, including logistics concerns, safety issues, emotional trauma, and after-returning, issues with data processing and analysis. Field teams have executed many effective missions. Among the factors contributing to successful reconnaissance are having local contacts, effective teamwork, and pre-event training. Continued progress in natural hazard reconnaissance requires adaptation of new, strategic approaches that acquire and integrate data over a range of temporal, spatial, and social scales across disciplines. U.S. National Science FoundationNational Science Foundation (NSF) [1611820] The U.S. National Science Foundation supported this work under grant number 1611820. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

In 2016, the National Science Foundation (NSF) funded a multi-institution interdisciplinary team to develop and operate the Natural Hazards Reconnaissance Facility (known as the "RAPID") as part of the Natural Hazards Engineering Research Infrastructure (NHERI) program. During the following 2 years, the RAPID facility developed its instrumentation portfolio and operational plan with input from the natural hazards community, the facility's leadership team, and an external steering committee. In September 2018, the RAPID began field operations, which continue today and include instrumentation, software, training, and support services to conduct reconnaissance research before, during, and after natural hazard and disaster events. Over the past 2 years, the RAPID has supported the data collection efforts for over 60 projects worldwide. Projects have spanned a wide range of disciplines and hazards and have also included data collection at large-scale experimental facilities in the United States and abroad. These projects have produced an unprecedented amount of high-quality field data archived on the DesignSafe cyberinfrastructure platform. This paper describes the RAPID facility's development, instrumentation portfolio (including the mobile application RApp), services and capabilities, and training activities. Additionally, overviews of three recent RAPID-supported projects are presented, including descriptions of field data collection workflows, details of the resulting data sets, and the impact of these project deployments on the natural hazard fields. NSFNational Science Foundation (NSF) [1904653, 1904327, CMMI: 1611820]; NSF through GEER [1826118]; Oregon DOT; FHWA [SPR807] The RAPID Facility operates under a cooperative agreement with the NSF under Award No. CMMI: 1611820. Research on the performance of LRLVBs in Hurricane Michael was supported by the NSF under award nos. 1904653 and 1904327. Research on the flow slide during the Palu, Indonesia earthquake was supported by the NSF through GEER under award number 1826118. Any opinions, findings, conclusions, and recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation. Funding for the Hooskadaden Landslide case study were provided by Oregon DOT and FHWA (SPR807).

The U.S. National Institute of Standards and Technology (NIST)’s Community Resilience Planning Guide uses recovery times of infrastructure functions as key metrics for disaster resilience. The existing literature also widely uses the recovery curve and the area under it to measure infrastructure resilience. Therefore, infrastructure recovery curve estimation is critical to understanding and improving disaster resilience. Unfortunately, this process is challenging in the pre-event planning context due to lack of historical data. To bridge this gap, we consider a situation where infrastructure experts are asked to estimate the time for different infrastructure systems to recover to certain functionality levels after a scenario hazard event. We propose a methodological framework to use expert-elicited data to estimate the expected recovery time curve of a particular infrastructure system. This framework uses the Gaussian process regression (GPR) to capture the experts’ estimation-uncertainty and satisfy known physical constraints of recovery processes. The framework is designed to find a balance between the data collection cost of expert elicitation and the prediction accuracy of GPR. We evaluate the framework on simulated expert-elicited data concerning two case study events, the 1995 Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake. It is shown that the framework is robust against different configurations such as the number of experts, how the quantities of interest are elicited, and uncertainty in the experts’ estimates.

Changes in levels and patterns of physical activity might be a mechanism to assess and inform disaster recovery through the lens of wellbeing. However, few studies have examined disaster impacts on physical activity or the potential for physical activity to serve as an indicator of disaster recovery. In this exploratory study, we examined daily bicycle and pedestrian counts from four public bicycle/pedestrian trails in Houston, before and after Hurricane Harvey landfall, to assess if physical activity returned to pre-Harvey levels. An interrupted time series analysis was conducted to examine the immediate impact of Harvey landfall on physical activity, t-tests were performed to assess if trail usage returned to pre-Harvey levels. Hurricane Harvey was found to have a significant negative impact on daily pedestrian and bicycle counts for three of the four trails. Daily pedestrian and bicycle counts were found to return to pre-Harvey or higher levels at 6 weeks post-landfall at all locations studied. We discuss the potential for further research to examine the trends, feasibility, validity, and limitations of using bicycle and pedestrian use levels as a proxy for disaster recovery and wellbeing among affected populations.

A growing body of literature suggests that restrictive public health measures implemented to control COVID-19 have had negative impacts on physical activity. We examined how Stay Home orders in Houston, New York City, and Seattle impacted outdoor physical activity patterns, measured by daily bicycle and pedestrian count data. We assessed changes in activity levels between the period before and during Stay Home orders. Across all three cities, we found significant changes in bicycle and pedestrian counts from the period before to the period during Stay Home orders. The direction of change varied by location, likely due to differing local contexts and outbreak progression. These results can inform policy around the use of outdoor public infrastructure as the COVID-19 pandemic continues.

Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain-and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining simulation modeling with human-centered design and refer it as human-centered simulation modeling. Human-centered simulation modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers. We qualitatively analyzed several resilience planning initiatives, post-disaster recovery assessments, and relevant journal articles to understand experts and decision-makers' perspectives. We propose a conceptual design framework for creating human-centered simulation models for critical infrastructure disaster recovery planning. This framework consists of three constructs: 1) user interaction with design features that end-users interact with, including model parameters assignment, decision-making support, task queries, and usability; 2) system representation that refers to system components, system interactions, and system state variables; and 3) computation core that represents computational methods required to perform processes.

The devastating 2017 Puebla quake provides an opportunity to assess how citizens perceive and use the Mexico City earthquake early warning system.

An integrated community resilience index (CRI) quantifies the status, exposure, and recovery of the physical, economic, and socio-cultural capital for a specific target community. However, most CRIs do not account for the recovery of ecosystem functioning after extreme events, even though many aspects of a community depend on the services provided by the natural environment. The primary goal of this study was to monitor the recovery of ecosystem functionality (ecological capital) using remote sensing-derived gross primary production (GPP) as an indicator of ‘ecosystem-wellness’ and assess the effect of resilience of ecological capital on the recovery of a community via an integrated CRI. We developed a measure of ecosystem resilience using remotely sensed GPP data and applied the modeling prototype ResilUS in a pilot study for a four-parish coastal community in southwestern Louisiana, USA that was impacted by Hurricane Rita in 2005. The results illustrate that after such an extreme event, the recovery of ecological capital varies according to land use type and may take many months to return to full functionality. This variable recovery can potentially impact the recovery of certain businesses that rely heavily on ecosystem services such as agriculture, forestry, fisheries, and tourism.

A new comprehensive areal model of earthquake-induced landslides (CAMEL) has been developed to assist in planning decisions related to disaster risk reduction. CAMEL provides an integrated framework for modeling all types of earthquake-induced landslides using fuzzy logic systems and geographic information systems. CAMEL is designed to facilitate quantitative and qualitative representation of terrain conditions and knowledge about these conditions on the likely areal concentration of each landslide type. CAMEL has been empirically evaluated with respect to disrupted landslides (Category I) using a case study of the 1989 M = 6.9 Loma Prieta, CA earthquake. In this case, CAMEL performs best in comparison to disrupted slides and falls in soil. For disrupted rock fall and slides, CAMEL's performance was slightly poorer. The model predicted a low occurrence of rock avalanches, when none in fact occurred. A similar comparison with the Loma Prieta case study was also conducted using a simplified Newmark displacement model. The area under the curve method of evaluation was used in order to draw comparisons between both models, revealing improved performance with CAMEL. CAMEL should not however be viewed as a strict alternative to Newmark displacement models. CAMEL can be used to integrate Newmark displacements with other, previously incompatible, types of knowledge.

A tool that has proliferated within civil engineering in recent years is geographic information systems (GIS). The goal of a tool is to supplement ability and knowledge that already exists, not to ...

"What makes for a good model?" In various forms, this question is a question that, undoubtedly, many people, businesses, and institutions ponder with regards to their particular domain of modeling. One particular domain that is wrestling with this question is the multidisciplinary field of environmental modeling. Examples of environmental models range from models of contaminated ground water flow to the economic impact of natural disasters, such as earthquakes. One of the distinguishing claims of the field is the relevancy of environmental modeling to policy and environment-related decision-making in general. A pervasive view by both scientists and decision-makers is that a "good" model is one that is an accurate predictor. Thus, determining whether a model is "accurate" or "correct" is done by comparing model output to empirical observations. The expected outcome of this process, usually referred to as "validation" or "ground truthing," is a stamp on the model in question of "valid" or "not valid" that serves to indicate whether or not the model will be reliable before it is put into service in a decision-making context. In this paper, I begin by elaborating on the prevailing view of model validation and why this view must change. Drawing from concepts coming out of the studies of science and technology, I go on to propose a contextual view of validity that can overcome the problems associated with "ground truthing" models as an indicator of model goodness. The problem of how we talk about and determine model validity has much to do about how we perceive the utility of environmental models. In the remainder of the paper, I argue that we should adopt ideas of pragmatism in judging what makes for a good model and, in turn, developing good models. From such a perspective of model goodness, good environmental models should facilitate communication, convey  not bury or "eliminate"  uncertainties, and, thus, afford the active building of consensus decisions, instead of promoting passive or self-righteous decisions.

Researchers, emergency response and lifeline managers, and municipal planners are beginning to recognize the utility of seismic landslide hazard zonation. With this recognition, the decisions made based on resulting maps could have widespread social and economic impact in the event of a large earthquake. This report compares several popular permanent displacement models for assessing seismic slope-performance. The approaches are implemented in a raster GIS to expose potential differences and assess the effects of using a particular approach within a decision-making context. It is observed that each approach forecasts notably different levels of slope-performance. Thus, considering the variety of spatial seismic landslide analysis approaches and the effect of basing a decision on a map created using a single one of them, it is suggested that less reliance be put on the traditional paper map format. Instead, multiple approaches can be used to investigate many scenario earthquakes under a variety of conditions in a computer-based spatial decision support system. INTRODUCTION Keefer (1984) observed that earthquakes of moderate to high magnitude can cause landslides over an area as large as 500,000 km2 . These landslides also have large damage potential as illustrated by the recent effects of the 1989 Loma Prieta and 1994 Northridge earthquakes (Harp and Jibson, 1995; Keefer, 1998). Accordingly, researchers, emergency response and lifeline managers, and municipal planners are beginning to recognize the utility of seismic landslide hazard and risk zonation. With this recognition, the decisions made based on resulting hazard or risk maps could have widespread social and economic impact in the event of a large earthquake. Therefore, investigating and comparing several popular techniques for seismic slopeperformance zonation is important. The state of the art in seismic landslide hazard zonation using geographic information systems (GIS) was summarized by Ho and Miles (1997), who suggested several potential approaches using dynamic permanent-displacement models. In the short time since then, considerable effort has been spent improving seismic landslide hazard zonation techniques using spatial technologies (Miles and Ho, 1999; Jibson and others, 1998; McCrink and Real, 1996). This report extends the study of Ho and Miles (1997) by implementing several seismic slope-performance models using raster GIS to expose any differences between the approaches and assess the potential effects of using a particular approach within a decision-making context. The report begins by summarizing the approaches that exist for determining seismic landslide hazard. The general procedure of a permanent-displacement analysis the class of approaches chosen in the report for investigation is then described. The report concludes by discussing the implementation of each individual approach and the differences among these approaches. PERMANENT-DISPLACEMENT ANALYSIS Three basic approaches exist for conducting seismic landslide hazard analysis. These consist of the statistical, pseudo-static, and permanent-displacement approaches. A statistical approach assesses hazard by assuming the past predicts the future. Hazard is assessed through correlation of past landslides with several influential factors. Results of a statistically based analysis can range from an estimated probability of failure to some index indicating degrees of hazard. Pseudo-static analysis employs a traditional static slope-stability analysis with the addition of a horizontal force component that models the effects of earthquake-induced ground-motions. A pseudo-static analysis yields a factor of safety against seismic slope failure. This effectively provides a simple binary index of whether a slope is expected to fail or not at a given level of seismic acceleration. Permanent-displacement techniques provide information regarding actual slope-performance through calculation of some index of relative or actual displacement based on commonly accepted characterizations of earthquake-shaking severity. Permanent-displacement analysis is chosen for investigation because of its higher information content, better modeling of ground-motion, and increasing acceptance in the earthquake engineering community. Newmark's Sliding Block Analogy In his landmark paper, Newmark (1965) noted that the transient effects of earthquake motions can cause permanent deformation of slopes prior to complete failure. Newmark proposed modeling a slope subjected to earthquake-induced accelerations as a friction block resting on an inclined plane subjected to the same accelerations as the modeled slope (Figure 1). Therefore, in each instance when the sum of the static and dynamic forces exceed the shear resistance of the sliding interface the block will displace. The interface shear resistance is commonly characterized by the critical acceleration (ac) of the modeled slope, which is the base acceleration needed to overcome the shear resistance. Newmark (1965) defined the following relationship to calculate critical acceleration in the case of planar slip