The functionality of interdependent infrastructure and resilience to seismic hazards has become a topic of importance across the world. The ability to optimize an engineered solution and support informed decision-making is highly dependent on the availability of comprehensive datasets and requires substantial effort to ingest into community-scale models. In this article, a comprehensive seismic resilience modeling methodology is developed, with detailed multi-disciplinary datasets, and is explored using the state-of-the-science algorithms within the interdependent networked community resilience modeling environment (IN-CORE). The methodology includes a six-step chained/linked process consists of: (a) community data and information, (b) spatial seismic hazard analysis using next-generation attenuation, (c) interdependent community model development, (d) physical damage and functionality analysis, (e) socio-economic impact analysis and (f) structural health monitoring (SHM) and emerging technologies (ET). An illustrative case study is presented to demonstrate the seismic functionality and resilience assessment of Shelby County in Memphis, Tennessee, in the United States. From the discussion of results, it is then concluded that data from structural health monitoring and emerging technologies is a viable approach to enhance characterising the seismic hazard resilience of infrastructure, enabling rapid and in-depth understanding of structural behaviour in emergency situations. Moreover, considering the momentum of the digitalization era, setting an holistic framework on resilience that includes SHM and ET will allow reducing uncertainties that are still a challenge to quantify and propagate, supported by sequential updating techniques from Bayesian statistics.

In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform. Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of "what if" scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.

Byblos, a Lebanese City, a UNESCO world heritage and one of the oldest continuously inhabited cities, is a member of the 100 Resilient Cities project. To help mitigate its stresses and chocks, Byblos is implementing its resilience strategy enclosing the five pillars: Connected, Resourceful, Peaceful, Cultural, and Thriving. Since the earthquake threat is not selected in this strategy, this study proposes to complement the 100 RC efforts and aims to urge Byblos Municipality authorities and governmental institutions to act based on concrete results offered, to provide emergency, prevention and recovery plans for the city and to enhance the civil protection. Therefore, first, we present the Byblos resilience strategy, identifying the city stresses and shocks, discussing its seismic threat and presenting complementary measures. Second, we model structural building damage, casualties, and buildings direct economic damage, including structural and nonstructural damage. Finally, results and recommendations are offered for an improved resilience strategy.

This work focuses on developing methods to better manage significant imbalances between water supply and demand during droughts. A service-driven approach (Model as a Service, or MaaS) is used to couple river modeling services with optimization services for determining optimal water allocation strategies under daily drought scenarios. It demonstrates the promise of coupling simulation-optimization model services to improve real-time water management in a service driven framework, which should be beneficial to many other water resource applications. The approach is implemented using the DataWolf workflow tool and AzureML Cloud machine learning services and applied to an April 2015 drought event in the Upper Guadalupe River Basin, Texas. Weather and water demand uncertainty are considered through scenario-based optimization. The optimization objective is to minimize the daily total curtailment hours across all groups of permit holders. The scenario analysis shows that the current permit grouping system has a significant impact on the optimal water allocation strategy. The scenarios also demonstrate that noncompliance of junior water users is predicted to have a much greater effect on the river system than noncompliance of senior water users. The resulting framework can be deployed for water allocation in any area by updating water user information, water allocation policy constraints, and river data that can be obtained from publicly available sources.

This paper discusses why research software is important, and what sustainability means in this context. It then talks about how research software sustainability can be achieved, and what our experiences at NCSA have been using specific examples, what we have learned from this, and how we think these lessons can help others.

Tornadoes occur at a high frequency in the United States compared with other natural hazards but have a substantially small footprint. A single high-intensity tornado can result in high casualty rates and catastrophic economic and social consequences, particularly for small- to medium-size communities. Comprehensive community resilience assessment and improvement requires the analyst to develop a model of interacting physical, social, and economic systems and to measure outcomes that result from specific decisions made. These outcomes often are in the form of metrics such as the number of people injured or the number of households and/or businesses without water, but it has been recognized that most community resilience metrics have socioeconomic characteristics. In this study, for the first time, a fully quantitative interacting model is used to examine the effect of a tornado damaging physical infrastructure (buildings and electrical power network) and the effects on the population and the local economy for a real community. Then, three residential building retrofit strategies are considered as alternatives to improve community resilience, and the metrics for the physical, economic, and social sectors are computed. An illustrative example is presented for the 2011 Joplin tornado in a new open-source Interdependent Networked Community Resilience Modeling Environment (IN-CORE), with a computable general equilibrium (CGE) economics model that computes household income, employment, and domestic supply before and after the tornado. Detailed demographic data was allocated to each structure to enable the calculation of resilience metrics related to population dislocation impacts from the tornado. The results of these analyses stemming from building damage estimation have a logical trend, but the substantial contribution of this work is that, for the first time, the effect of retrofit strategies for tornado loading can be quantified in terms of their effects on socioeconomic metrics.

The seismic damage to buried pipelines could be disruptive in terms of safety, life quality, and socioeconomic impacts. In this paper, we investigate the extensive list of available buried pipelines fragilities: we select a series of typical and interesting fragilities in terms of different ground motions, diverse pipeline and soil types typologies and nature (i.e. empirical, analytical); and then we compare the seismic damage results. The aim is to have a palpable overview of the variability of damage results while using different selected buried pipelines fragilities, various ground motion parameters and different hazard scenarios. To achieve this comparative study, the buried pipeline wastewater network of Byblos, Lebanon, is used as a case study. The methodology used to assess damage to the waste-water network encompasses hazard assessment, wastewater network inventory, wastewater damage functions, and model development. The Ergo platform was especially updated by implementing the list of selected fragilities to achieve the damage to wastewater comparison in this article.Resultsand recommendations are offered; first for a more adequate usage of those fragilities; second, for improvement of existing and future developed buried pipelines fragility functions; and third, for the Byblos wastewater pipelines network, allowing the establishment of a resilient earthquake preparedness strategy and recovery plan for infrastructure network in Byblos.

The emerging transdiscipline of Computational Archival Science (CAS) links frameworks such as Brown Dog and repository software such as Digital Repository At Scale To Invite Computation (DRAS-TIC) to yield an understanding of working with digital collections at scale for cultural data. The DRAS-TIC and Brown Dog projects here serve as the basis for an expandable distributed storage/service architecture with on-demand, horizontally scalable integrated digital preservation and analysis services.

Land use planners, landscape architects, and water resource managers are using Green Infrastructure (GI) designs in urban environments to promote ecosystem services including mitigation of storm water flooding and water quality degradation. An expanded set of urban sustainability goals also includes increasing carbon sequestration, songbird habitat, reducing urban heat island effects, and improvement of landscape aesthetics. GI is conceptualized to improve water and ecosystem quality by reducing storm water runoff at the source, but when properly designed, may also benefit these expanded goals. With the increasing use of GI in urban contexts, there is an emerging need to facilitate participatory design and scenario evaluation to enable better communication between GI designers and groups impacted by these designs. Major barriers to this type of public participation is the complexity of both parameterizing, operating, visualizing and interpreting results of complex ecohydrological models at various watershed scales that are sufficient to address diverse ecosystem service goals. This paper demonstrates a set of workflows to facilitate rapid and repeatable creation of GI landscape designs which are incorporated into complex models using web applications and services. For this project, we use the RHESSys (Regional Hydro-Ecologic Simulation System) ecohydrologic model to evaluate participatory GI landscape designs generated by stakeholders and decision makers, but note that the workflow could be adapted to a set of other watershed models.

We present an open source extensible web framework for the analysis of different farm practices and programs and easy dissemination of their results to the users. Currently, this framework is being applied to two use cases --- a web-based decision support system for cover crop management and a web-based farm program analysis tool to assist farmers, academics, and policymakers to understand programs and policies surrounding the Farm Bill. Through the first use case, we address the problem of bridging the gap between the scientific research that happens in labs and experimental plots and the day to day practices and decisions taken by the farmers in the fields. Specifically, this use case focuses on the practice of cover crops, their management, and the impact on reducing nutrient runoff into water bodies. Through the second use case, we address the problem of predicting the expected payment amounts and measured risk or probability of payment for different government insurance programs authorized by the 2018 Farm Bill, namely the Agriculture Risk Coverage (ARC) and Price Loss Coverage (PLC). This helps the farmers compare these programs based on forecasted crop yields and prices. In this paper, we describe the overall architecture of the framework and its major components, the use cases that are currently benefiting from using this framework and share screenshots of the web applications developed using this framework for those use cases. We also share our plans for future work and conclusions about applying this framework to the two use cases.

Brown Dog is a data transformation service for auto-curation of long-tail data. In this digital age, we have more data available for analysis than ever and this trend will only increase. According to most estimates, 70--80% of this data is unstructured, and together with unsupported data formats and inaccessible software tools, in essence, this data is not either easily accessible or usable to its owners in a meaningful way. Brown Dog aims at making this data more accessible and usable by auto-curation and indexing, leveraging existing and novel data transformation tools. In this paper, we discuss the recent major component improvements to Brown Dog including transformation tools called extractors and converters; desktop, web and terminal-based clients which perform data transformations; libraries written in multiple programming languages which integrate with existing software and extend their data curation capabilities; an online tool store for users to contribute, manage and share data transformation tools and receive credit for developing them; cyberinfrastructure for deploying the system on diverse computing platforms leveraging scalability via Docker swarm; workflow management service for creatively integrating existing transformations to generate custom, reproducible workflows which meet research needs, and its data management capabilities. This paper also discusses data transformation tools developed to support some scientific and allied use cases, thereby benefiting researchers in diverse domains. Finally, we briefly discuss our future directions with regard to production deployments as well as how users can access Brown Dog to manage their un-curated unstructured data.

This study presents the contents of the development of a risk assessment for complex urban disasters, mainly focusing on super high-rise buildings and complex facilities. It is expected that the developed contents will provide an integrated CPS for responding to high-rise and mixed-use building disasters with important analysis information for complex disaster analysis and behavior prediction. This study presents the contents of the development of a Korean-style prediction system for risk damage assessment using Ergo, which is open-source software, and also describes the necessary technologies and basic conditions for achieving this goal. The results of this study will contribute to the development of disaster analysis and damage prediction technology, guidelines for the construction of the integrated disaster response CPS, and the construction of an integrated information system for complex disasters.

The old potable water network in Byblos city is provided mainly from Ibrahim River nearby. Located in a seismic region, the aging network needs to tolerate seismic threats; thus, damage to the potable water network needs to be assessed. Therefore, first, enhancing infrastructure resilience is briefly discussed, noting briefly the need to bridge specifically between heritage risk management and engineering. Second, Byblos potable water network, seismicity, and geology are detailed. Third, the potable water network damage assessment methodology is presented. It encompasses hazard assessment, network inventory, damage functions, and model development. Data and maps are prepared using the Geographic Information System and then modeled in Ergo platform to obtain the damage to buried pipelines in the event of likely earthquake scenarios. Ergo is updated to consider recommended ground motion prediction equations (GMPEs) for the Middle East region, to consider amplification of the peak ground velocity in hazard maps due to different soil types, and to consider adequate fragility functions. Moreover, different Byblos geotechnical maps, landslide hazard, and liquefaction are investigated and embedded. Damage results to pipelines are dependent on the hazard maps obtained using different GMPEs and geotechnical maps. Asbestos cement pipelines will be most damaged, followed by polyethylene and then by ductile iron. Finally, recommendations are offered to consider an improved sustainable rehabilitation solution. The study provides a better understanding of Byblos potable water network and allows the establishment of a sustainable and resilience-to-earthquake preparedness strategy and recovery plan.

Resiliency of communities prone to natural hazards can be enhanced through the use of risk-informed decision-making tools. These tools can provide community decision makers key information, thereby providing them the ability to consider an array of mitigation and/or recovery strategies. The Center for Risk-Based Community Resilience Planning, headquartered at Colorado State University in Fort Collins, Colorado, developed an Interdependent Networked Community Resilience (IN-CORE) computational environment. The purpose of developing this computational environment is to build a decision-support system, for professional risk planners and emergency responders, but even more focused on allowing researchers to explore community resilience science. The eventual goal was being to integrate a broad range of scientific, engineering and observational data to produce a detailed assessment of the potential impact of natural and man-made hazards for risk mitigation, planning and recovery purposes. The developing computational environment will be capable of simulating the effects from different natural hazards on the physical and socioeconomic sectors of a community, accounting for interdependencies between the sectors. However, in order to validate this computational tool, hindcasting of a real event was deemed necessary. Therefore, in this study, the community of Joplin, Missouri in the USA, which was hit by an EF-5 tornado on May 22, 2011, is modeled in the IN-CORE v1.0 computational environment. An explanation of the algorithm used within IN-CORE is also provided. This tornado was the costliest and deadliest single tornado in the USA in the last half century. Using IN-CORE, by uploading a detailed topological dataset of the community and the estimated tornado path combined with recently developed physics-based tornado fragilities, the damage caused by the tornado to all buildings in the city of Joplin was estimated. The results were compared with the damage reported from field studies following the event. This damage assessment was done using three hypothetical idealized tornado scenarios, and results show very good correlation with observed damage which will provide useful information to decision makers for community resilience planning.

Clowder is an open source data management system to support data curation of long tail data and metadata across multiple research domains and diverse data types. Institutions and labs can install and customize their own instance of the framework on local hardware or on remote cloud computing resources to provide a shared service to distributed communities of researchers. Data can be ingested directly from instruments or manually uploaded by users and then shared with remote collaborators using a web front end. We discuss some of the challenges encountered in designing and developing a system that can be easily adapted to different scientific areas including digital preservation, geoscience, material science, medicine, social science, cultural heritage and the arts. Some of these challenges include support for large amounts of data, horizontal scaling of domain specific preprocessing algorithms, ability to provide new data visualizations in the web browser, a comprehensive Web service API for automatic data ingestion and curation, a suite of social annotation and metadata management features to support data annotation by communities of users and algorithms, and a web based front-end to interact with code running on heterogeneous clusters, including HPC resources.

Brown Dog is an extensible data cyberinfrastructure, that provides a set of extensible and distributed data conversion and metadata extraction services to enable access and search within unstructured, un-curated and inaccessible research data across different domains of sciences and social science, which ultimately aids in supporting reproducibility of results. We envision that Brown Dog, as a data cyberinfrastructure, is an essential service in a comprehensive cyberinfrastructure which includes data services, high performance computing services and more that would enable scholarly research in a variety of disciplines that today is not yet possible. Brown Dog focuses on four initial use cases, specifically, addressing the conversion and extraction needs in the research areas of ecology, civil and environmental engineering, library and information science, and use by the general public. In this paper, we describe an architecture that supports contribution of data transformation tools from users, and automatic deployment of the tools as Brown Dog services in diverse infrastructures such as cloud or high performance computing (HPC) based on user demands and load on the system. We also present results validating the performance of the initial implementation of Brown Dog.

Byblos, one of the oldest continuously inhabited cities in the world and a UNESCO World Heritage Site, is indeed a resilient city that has thrived for more than 7000 years while mitigating shocks and stresses. Byblos, at the threshold of the 21st century, is still adapting, growing and changing. In this paper, aiming to bridge between international heritage resilience and disaster risk management: first, the resilience of international heritage is discussed in general and resilience qualities of Byblos in particular; second earthquakes, one of the main threats faced by Byblos is identified; and third the earthquake damage to Byblos buildings is assessed by mean of different likely earthquake scenarios. For that purpose, data for Byblos building inventory have been gathered through a ground survey. The earthquake hazard for the region has been defined, hazard maps have been digitized, and structural vulnerability functions were assigned. After preparation of the needed files using the Geographic Information System, the Ergo platform was used to model the earthquake-induced building damage. It was obtained that the unreinforced masonry structure type is the most vulnerable to earthquakes, the reinforced masonry structures type is the second most vulnerable, followed by reinforced concrete frame structures, and finally by reinforced concrete frames with shear walls structures. It was recommended to upgrade, whenever possible, the unreinforced masonry buildings to reinforced masonry buildings while preserving their historical aspect, and to strengthen the frame concrete buildings by adding shear walls whenever possible. All new buildings to be constructed in the future are recommended to strictly follow the codes. This study helps to gain a better understanding of the extent of potential damage; it allows establishing an earthquake preparedness strategy and recovery plan to enhance the resilience of the city.