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Extreme weather-related events are showing how infrastructure disruptions in hinterlands can affect cities. This paper explores the risks to city infrastructure services including transportation, electricity, communication, fuel supply, water distribution, stormwater drainage, and food supply from hinterland hazards of fire, precipitation, post-fire debris flow, smoke, and flooding. There is a large and growing body of research that describes the vulnerabilities of infrastructures to climate hazards, yet this work has not systematically acknowledged the relationships and cross-governance challenges of protecting cities from remote disruptions. An evidence base is developed through a structured literature review that identifies city infrastructure vulnerabilities to hinterland hazards. Findings highlight diverse pathways from the initial hazard to the final impact on an infrastructure, demonstrating that impacts to hinterland infrastructure assets from hazards can cascade to city infrastructure. Beyond the value of describing the impact of hinterland hazards on urban infrastructure, the identified pathways can assist in informing cross-governance mitigation strategies. It may be the case that to protect cities, local governments invest in mitigating hazards in their hinterlands and supply chains.

Disruption of legacy infrastructure systems by novel digital and connected technologies represents not simply the rise of cyberphysical systems as hybrid physical and digital assets but, ultimately, the integration of legacy systems into a new cognitive ecosystem. This cognitive ecosystem, an ecology of massive data flows, artificial intelligence, institutional and intellectual structures, and connected technologies, is poised to alter how humans and artificial intelligence understand and control our world. Infrastructure managers need to be ready for this paradigm shift, recognizing their systems are increasingly being absorbed into an emerging suite of data, analytical tools, and decisionmaking technologies that will fundamentally restructure how legacy systems behave and are controlled, how decisions are made, and most importantly how workers interact with the systems. Infrastructure managers must restructure their organizations and engage in cross-organizational sensemaking if they are to be capable of navigating the complexity of the cognitive ecosystem. The cognitive ecosystem is fundamentally poised to change what infrastructures are, necessitating the need for managers to take a close look at the functions and actions of their own systems. The continuing evolution of the Anthropocene and the cognitive ecosystem has profound implications for infrastructure education. A sustained commitment to change is necessary that restructures and reorients infrastructure organizations within the cognitive ecosystem, where knowledge is generated, and control of services is wielded by myriad stakeholders.

Detection and attribution studies generally examine individual climate variables such as temperature and precipitation. Thus, we lack a strong understanding of climate change impacts on correlated climate extremes and compound events, which have become more common in recent years. Here we present a monthly‐scale compound warm and dry attribution study, examining CMIP6 climate models with and without the influence of anthropogenic forcing. We show that most regions have experienced large increases in concurrent warm and dry months in historical simulations with human emissions, while no coherent change has occurred in historical natural‐only simulations without human emissions. At the global scale, the likelihood of compound warm‐dry months has increased 2.7 times due to anthropogenic emissions. With this multivariate perspective, we highlight that anthropogenic emissions have not only impacted individual extremes but also compound extremes. Due to amplified risks from multivariate extremes, our results can provide important insights on the risks of associated climate impacts.

The estimation of exceedance probabilities for extreme climatic events is critical for infrastructure design and risk assessment. Climatic events occur over a greater space than they are measured with point‐scale in situ gauges. In extreme value theory, the block maxima approach for spatial analysis of extremes depends on properly modeling the spatially varying Generalized Extreme Value marginal parameters (i.e., trend surfaces). Fitting these trend surfaces can be challenging since there are numerous spatial and temporal covariates that are potentially relevant for any given event type and region. Traditionally, covariate selection is based on assumptions regarding the topmost relevant drivers of the event. This work demonstrates the benefit of utilizing elastic‐net regression to support automatic selection from a relatively large set of physically relevant covariates during trend surface estimation. The trend surfaces presented are based on 24‐hr annual maximum precipitation for northeastern Colorado and the Texas‐Louisiana Gulf Coast.

Synoptic sampling of streams is an inexpensive way to gain insight into the spatial distribution of dissolved constituents in the subsurface critical zone. Few spatial synoptics have focused on urban watersheds although this approach is useful in urban areas where monitoring wells are uncommon. Baseflow stream sampling was used to quantify spatial variability of water chemistry in a highly developed Piedmont watershed in suburban Baltimore, MD having no permitted point discharges. Six synoptic surveys were conducted from 2014 to 2016 after an average of 10 days of no rain, when stream discharge was composed of baseflow from groundwater. Samples collected every 50 m over 5 km were analyzed for nitrate, sulfate, chloride, fluoride, and water stable isotopes. Longitudinal spatial patterns differed across constituents for each survey, but the pattern for each constituent varied little across synoptics. Results suggest a spatially heterogeneous, three‐dimensional pattern of localized groundwater contaminant zones steadily contributing solutes to the stream network, where high concentrations result from current and legacy land use practices. By contrast, observations from 35 point piezometers indicate that sparse groundwater measurements are not a good predictor of baseflow stream chemistry in this geologic setting. Cross‐covariance analysis of stream solute concentrations with groundwater model/backward particle tracking results suggest that spatial changes in base‐flow solute concentrations are associated with urban features such as impervious surface area, fill, and leaking potable water and sanitary sewer pipes. Predicted subsurface residence times suggest that legacy solute sources drive baseflow stream chemistry in the urban critical zone.

Infrastructure systems must change to match the growing complexity of the environments they operate in. Yet the models of governance and the core technologies they rely on are structured around models of relative long-term stability that appear increasingly insufficient and even problematic. As the environments in which infrastructure function become more complex, infrastructure systems must adapt to develop a repertoire of responses sufficient to respond to the increasing variety of conditions and challenges. Whereas in the past infrastructure leadership and system design has emphasized organization strategies that primarily focus on exploitation (e.g., efficiency and production, amenable to conditions of stability), in the future they must create space for exploration, the innovation of what the organization is and does. They will need to create the abilities to maintain themselves in the face of growing complexity by creating the knowledge, processes, and technologies necessary to engage environment complexity. We refer to this capacity asinfrastructure autopoiesis. In doing so infrastructure organizations should focus on four key tenets. First, a shift to sustained adaptation—perpetual change in the face of destabilizing conditions often marked by uncertainty—and away from rigid processes and technologies is necessary. Second, infrastructure organizations should pursue restructuring their bureaucracies to distribute more resources and decisionmaking capacity horizontally, across the organization’s hierarchy. Third, they should build capacity for horizon scanning, the process of systematically searching the environment for opportunities and threats. Fourth, they should emphasize loose fit design, the flexibility of assets to pivot function as the environment changes. The inability to engage with complexity can be expected to result in a decoupling between what our infrastructure systems can do and what we need them to do, and autopoietic capabilities may help close this gap by creating the conditions for a sufficient repertoire to emerge.

Merging multiple data streams together can improve the overall length of record and achieve the number of observations required for robust statistical analysis. We merge complementary information from different data streams with a regression-based approach to estimate the 1 April snow water equivalent (SWE) volume over Sierra Nevada, USA. We more than double the length of available data-driven SWE volume records by leveragingin-situsnow depth observations from longer-length snow course records and SWE volumes from a shorter-length snow reanalysis. With the resulting data-driven merged time series (1940–2018), we conduct frequency analysis to estimate return periods and associated uncertainty, which can inform decisions about the water supply, drought response, and flood control. We show that the shorter (~30-year) reanalysis results in an underestimation of the 100-year return period by ~25 years (relative to the ~80-year merged dataset). Drought and flood risk and water resources planning can be substantially affected if return periods of SWE, which are closely related to potential flooding in spring and water availability in summer, are misrepresented.