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Abstract Coastal communities are increasingly vulnerable to hurricanes, which cause billions of dollars in damage annually through wind, storm surge, and flooding. Mitigation efforts are essential to reduce these impacts but face significant challenges, including uncertainties in hazard prediction, damage estimation, and recovery costs. Resource constraints and the disproportionate burden borne by socioeconomically vulnerable groups further complicate retrofitting strategies. This study presents a probabilistic methodology to assess and mitigate hurricane risks by integrating hazard analysis, building fragility, and economic loss assessment. The methodology prioritizes retrofitting strategies using a risk‐informed, equity‐focused approach. Multi‐objective optimization balances cost‐effectiveness and risk reduction while promoting fair resource allocation among socioeconomic groups. The novelty of this study lies in its direct integration of equity as an objective in resource allocation through multi‐objective optimization, its comprehensive consideration of multi‐hazard risks, its inclusion of both direct and indirect losses in cost assessments, and its use of probabilistic hazard analysis to incorporate varying time horizons. A case study of the Galveston testbed demonstrates the methodology's potential to minimize damage and foster equitable resilience. Analysis of budget scenarios and trade‐offs between cost and equity underscores the importance of comprehensive loss assessments and equity considerations in mitigation and resilience planning. Key findings highlight the varied effectiveness of retrofitting strategies across different budgets and time horizons, the necessity of addressing both direct and indirect losses, and the importance of multi‐hazard considerations for accurate risk assessments. Multi‐objective optimization underscores that equitable solutions are achievable even under constrained budgets. Beyond a certain point, achieving equity does not necessarily increase expected losses, demonstrating that more equitable solutions can be implemented without compromising overall cost‐effectiveness. 

Abstract Integration of natural and cultural resource management is urgently needed to combat the effects of climate change. Scientists must contend with how human-induced climate change and rapid population expansion are fundamentally reworking densely inhabited coastal zones. We propose that a merger of archaeology, environmental science, and land management policy—different yet intertwined domains—is needed to address dramatic losses to biocultural resources that comprise coupled cultural-natural systems. We demonstrate the urgency of such approaches through analyses of coastal archaeological regions within the U.S. Atlantic and Gulf coasts where sea level rise is a primary threat, and we extend our findings globally through an assessment of primary risk factors and forecasts for archaeological sites in the Netherlands, Peru, and Oceania. Results show that across the U.S. Gulf Coast and in Oceania, where little hard infrastructure is in place to protect archaeological sites, hundreds of low-lying coastal sites will be lost under future climate scenarios. In other coasts, like that of the Rhine-Meuse Delta (the Netherlands), risks range from erosion caused by periods of flooding to the degradation of wetland sites caused by extreme droughts. In coastal Peru, population pressures pose the primary risk to archaeological sites through rapid agro-industrial growth, urban expansion, and El Niño climate variability. Across all risks, we propose that management strategies to mitigate losses to biocultural resources must be approached as a restoration process of linked sociocultural and physical environmental systems. 

Abstract Efficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end‐to‐end model that starts with satellite imagery as input and automates the generation of large‐scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large‐scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine‐tuning can enhance the robustness of the damage classification to generate highly accurate large‐scale damage maps. Those damage maps were validated against historical reports. 

Abstract As large areas of the Mississippi River Delta (MRD) of the USA disappear into the sea, present-day communities and cultural resources are lost. While the land loss may be readily quantified, describing the impact of cultural losses is less straightforward because cultural elements are frequently less tangible and difficult to map, identify, and categorize. The elision of cultural components of landscapes and ecosystems is evident in restoration practices and policies, although numerous scholars have identified the interlinked processes of culture and ecology as critical to rebuilding healthy and resilient environments. We define and measure cultural-ecosystem resilience (CER) in the Mississippi River Delta through analyses of Indigenous oral histories, mound-building practices and settlement patterns, and the persistence and reuse of archaeological sites. CER describes a system containing resilient properties embedded in human-natural settings including river deltas that may manifest in oral cultural traditions, architecture, and the selection of habitable environments. Our interdisciplinary approach demonstrates the role of human-modified landscapes in generating resilience for past and present coastal communities and highlights the importance of consulting records of historic and modern Indigenous traditions in shaping sustainable landscape-management strategies. Results show that archaeological earthen and shell mounds made by Native American Gulf Coast and MRD communities have been persistent features that endured for centuries and are sited in regions of high multicultural value within the dynamic delta. Yet, we document the rapid 20th-century loss of mounds due to coastal erosion, industry, and other human land-use practices. Present-day and future coastal land loss endangers what remains of these keystone features and thus lowers the resilience of modern Mississippi River Delta communities. 

Many coastal communities around the world are threatened by a near-field (or local) tsunami that could inundate the low-lying areas in a matter of minutes after generation. The universal consensus amongst emergency agencies and academic researchers is that a safe evacuation requires an effective response, which is typically assessed by the evacuation time estimate (ETE). ETE is an integral component of community emergency evacuation planning, especially areas prone to tsunamis. This paper aims to investigate the ETE for pedestrian evacuation during a tsunami through two different approaches: (1) the deterministic Least-Cost Distance (LCD) model; and (2) the dynamic Agent-Based Model (ABM). Then, the comparison of the two models in their intrinsic characteristics, strengths and weaknesses, and its applicability was discussed based on methodology behind of the LCD model and ABM. The LCD model was conducted to generate a spatially distributed ETE map, visualizing vulnerable areas where the evacuation time would be insufficient for individuals to reach safety. The ABM investigated uncertainty during tsunami evacuations, such as population distribution, walking speed, and milling time. This paper provides insights into the differences between the LCD model and ABM in terms of methodology and application. It assists the academic researchers and emergency managers, evacuation planners, and decision makers to choose an appropriate method for modeling pedestrian evacuation during tsunami.