The border region of South Korea has undergone dramatic social and environmental changes since the late 1990s with shifts in governmental regimes. Under the proliberal government (1997–2007) that enhanced economic ties between North and South Korea, the border region was open for introducing new people and industries. With a new conservative governmental regime in the past decade (2007–2017), social and environmental challenges emerged in the border region. Such challenges were not uniformly present throughout different areas, however. We examined the spatial transformation of the border region using sociodemography, economy, landscape fragmentation, and water quality data with a focus on two gateway regions (Paju and Goseong) as representative cases. Although these two regions are similar in size and served as central nodes of flow between the two Koreas, they experienced different trajectories under disparate national and regional policies. In Paju, a closer region to Seoul, the capital of South Korea, the landscape became more fragmented as a result of urban expansion, but different subcenters were formed to accommodate the growing population and industries that were less dependent on external shocks, contributing to the economic and environmental resilience of the region. In contrast, with continuous declining aging population, Goseong’s landscape became less fragmented with one remaining main urban center, but its economy, society, and environment became fragile after the closure of the Kumgangsan tour. These different patterns of regional resilience can be fully understood by considering various social, environmental, and institutional factors acting on multiple scales that helped shape the region’s stability. Key Words: border region, landscape fragmentation, resilience, scale, water quality.

This study explores the spatial distribution of green stormwaterinfrastructure (GSI) relative to sociodemographic and landscape characteristics in Portland, OR, and Baltimore, MD, USA at census block group (CBG) and census tract scales. GSI density is clustered in Portland, while it is randomly distributed over space in Baltimore. Variables that exhibit relationships with GSI density are varied over space, as well as between cities. In Baltimore, GSI density is significantly associated with presence of green space (+), impervious surface coverage (+), and population density (−) at the CBG scale; though these relationships vary over space. At the census tract scale in Baltimore, a different combination of indicators explains GSI density, including elevation (+), population characteristics, and building characteristics. Spatial regression analysis in Portland indicates that GSI density at the CBG scale is associated with residents identifying as White (−) and well-draining hydrologic soil groups A and B (−). At both census tract and CBG scales, GSI density is associated with median income (−) and sewer pipe density (−). Hierarchical modelling of GSI density presents significant spatial dependence as well as group dependence implicit to Portland at the census tract scale. Significant results of this model retain income and sewer pipe density as explanatory variables, while introducing the relationship between GSI density and impervious surface coverage. Overall, this research offers decision-relevant information for urban resilience in multiple environments and could serve as a reminder for cities to consider who is inherently exposed to GSI benefits.

Recent evidence suggests that many densely populated areas of the world will be uninhabitable in the coming century due to the depletion of resources, climate change, and increasing urbanization. This poses serious questions regarding the actions that require immediate attention, and opportunities to stave off massive losses of infrastructure, populations, and financial investments. The present study utilizes microclimate modeling to examine the role of landscape features as they affect ambient temperatures in one of the fastest growing regions of the world: Doha, Qatar. By modeling three study sites around Doha—one highly urbanized, one newly urbanizing, and one coastal low-density urbanized—the research indicates that at the neighborhood scale, the most effective scenario was that of adding mature trees along the sides of roads. In the coastal study area, the model results estimated a maximum hourly air temperature reduction of 1.35 °C, and in the highly urbanized inland site, surface temperature reductions were up to 15 °C at 12:00. While other scenarios were effective at reducing air and surface temperatures, the mean radiant temperature was also increased or nearly neutral for most of the other scenarios. This result highlights the need to develop improved shading measures for pedestrian pathways and outdoor recreational areas, especially for highly urbanized inland areas in Doha and cities with similar climatic conditions.

This study explores the spatial distribution of green stormwater infrastructure (GSI) relative to sociodemographic and landscape characteristics in Portland, OR, and Baltimore, MD, USA at Census Block Group (CBG) and census tract scales. GSI density is clustered in Portland, while it is randomly distributed over space in Baltimore. Variables that exhibit relationships with GSI density are varied over space, as well as between cities. In Baltimore, GSI density is significantly associated with presence of green space (+), impervious surface coverage (+), and population density (−) at the CBG scale; though these relationships vary over space. At the census tract scale in Baltimore, a different combination of indicators explains GSI density, including elevation (+), population characteristics, and building characteristics. Spatial regression analysis in Portland indicates that GSI density at the CBG scale is associated with residents identifying as White (−) and well-draining hydrologic soil groups A and B (−). At both census tract and CBG scales, GSI density is associated with median income (−) and sewer pipe density (−). Hierarchical modelling of GSI density presents significant spatial dependence as well as group dependence inherent in Portland at the census tract scale. Significant results of this model retain income and sewer pipe density as explanatory variables, while introducing the relationship between GSI density and impervious surface coverage. Overall, this research offers decision-relevant information for urban resilience in multiple environments and could serve as a reminder for cities to consider who is inherently exposed to GSI benefits.

The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

Traditional infrastructure adaptation to extreme weather events (and now climate change) has typically been techno‐centric and heavily grounded in robustness – the capacity to prevent or minimize disruptions via a risk‐based approach that emphasizes control, armoring, and strengthening (e.g., raising the height of levees). However, climate and non‐climate challenges facing infrastructure are not purely technological. Ecological and social systems also warrant consideration to manage issues of overconfidence, inflexibility, interdependence, and resource utilization ‐ among others. As a result, techno‐centric adaptation strategies can result in unwanted tradeoffs, unintended consequences, and under‐addressed vulnerabilities. Techno‐centric strategies that “lock‐in” today's infrastructure systems to vulnerable future design, management, and regulatory practices may be particularly problematic by exacerbating these ecological and social issues rather than ameliorating them. Given these challenges, we develop a conceptual model and infrastructure adaptation case studies to argue the following: 1) infrastructure systems are not simply technological, and should be understood as complex and interconnected social, ecological, and technological systems (SETS); 2) infrastructure challenges, like lock‐in, stem from SETS interactions that are often overlooked and underappreciated; 3) framing infrastructure with a “SETS lens” can help identify and prevent maladaptive issues like lock‐in; and 4) a SETS lens can also highlight effective infrastructure adaptation strategies that may not traditionally be considered. Ultimately, we find that treating infrastructure as SETS shows promise for increasing the adaptive capacity of infrastructure systems by highlighting how lock‐in and vulnerabilities evolve, and how multidisciplinary strategies can be deployed to address these challenges by broadening the options for adaptation.

Rooted networks provide a conceptual framework that embeds network thinking in nature-society geography in order to investigate socio-ecological relations, while emphasizing the place-specific materiality of these relations. This progress report examines how geographers have put the framework into scholarly practice. The conceptual approach has enabled researchers to: 1) articulate the territoriality and materiality of networks as assemblages, which may be simultaneously rooted and mobile; 2) discern diverse types of power that flow through network connections; and 3) conduct analyses that unearth multiply-situated knowledges within networks. Challenges emerge as we seek to integrate the approach more fully with disciplinary traditions, including organizing complex relationships into bounded scholarly formats; choosing which aspects of the network are most salient to analyze; and illustrating networks for effective communication. We describe the ways in which rooted networks can be used as a tool for action, as a pedagogical guide, and to strengthen collective capacity to imagine and negotiate alternative futures based on ‘seeing multiple.’ Finally, we call for geographers and other scholars, researchers and activists to build upon a rooted networks framework as a tool for design, analysis, understanding and communication in the search for more socially just and ecologically viable futures.

The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.