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Trinity. Vol. 4, The search for Steve Trevor
\"Batman and Superman help Wonder Woman rediscover Themyscira, but finding Diana's lost homeland proves more difficult than these three heroes expected. While on their journey, a distress call from a nearby ship turns out to be more than meets the eye, and the Trinity find themselves marooned in the dread island of Skartaris. To make their way back home, our heroes will have to overcome Deimos, who will stop at nothing to seal off Skartaris from the rest of world forever!\"-- Provided by publisher.
Book
Evaluating the economic impact of water scarcity in a changing world
Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.
The impacts of water scarcity depend on physical basin characteristics and global economic dynamics. Here, the authors show scenario assumptions can yield either highly positive or negative economic impacts due to water scarcity, and the drivers of these impacts are basin-specific and cannot be determined a priori.
Journal Article
Invisible mafia
\"The devastating repercussions from the Man of Steel graphic novel still reverberate as Metropolis enters a new age! The Daily Planet teeters on the brink of disaster! A new criminal element has made its way onto the streets of Superman's hometown!\"-- Provided by publisher.
Book
Exploiting Multi‐Objective Reinforcement Learning and Explainable Artificial Intelligence to Navigate Robust Regional Water Supply Investment Pathways
Urban water utilities are adopting more advanced dynamic and adaptive infrastructure investment frameworks in the face of hydrologic extremes, accelerating demand, and financial constraints. Evolutionary multi‐objective reinforcement learning has enhanced the identification of high‐performing infrastructure investment pathways that balance conflicting objectives and remain robust amid these challenges. However, current evaluations of robustness are based on highly aggregated regional metrics that potentially conceal emerging individual robustness conflicts between cooperating utilities, largely failing to effectively demonstrate the path‐dependent, state‐aware nature of these adaptive investment pathways. This study addresses this nontrivial challenge by contributing the Deeply Uncertain (DU) Pathways Time‐varying Regional Assessment of Infrastructure Pathways for the Long‐ and Short‐term (TRAILS) framework. We apply the TRAILS framework on the North Carolina Research Triangle, a challenging six‐utility cooperative regional system confronting $1 billion in investments to support the maintenance and expansion of its water infrastructure by 2060. Our results reveal that individual robustness preferences can fundamentally change the dynamics and deeply uncertain drivers of the system. We discover critical periods of robustness conflicts between cooperating actors' infrastructure pathways. Furthermore, we apply explainable artificial intelligence methods to reveal that delayed infrastructure construction and rapid demand growth drive robustness during these critical conflict periods. We utilize Information Theoretic sensitivity analysis to clarify consequential state information‐action feedbacks between demand, capacity, and storage on individual utilities' decisions. Overall, the analytics facilitated by the DU Pathways TRAILS framework elucidate how financially significant long‐term investments and short‐term operational actions shape individual and regional robustness over time.
Journal Article
Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response
Managing ecosystems with deeply uncertain threshold responses and multiple decision makers poses nontrivial decision analytical challenges. The problem is imbued with deep uncertainties because decision makers do not know or cannot converge on a single probability density function for each key parameter, a perfect model structure, or a single adequate objective. The existing literature on managing multistate ecosystems has generally followed a normative decision-making approach based on expected utility maximization (MEU). This approach has simple and intuitive axiomatic foundations, but faces at least two limitations. First, a prespecified utility function is often unable to capture the preferences of diverse decision makers. Second, decision makers’ preferences depart from MEU in the presence of deep uncertainty. Here, we introduce a framework that allows decision makers to pose multiple objectives, explore the trade-offs between potentially conflicting preferences of diverse decision makers, and to identify strategies that are robust to deep uncertainties. The framework, referred to as many-objective robust decision making (MORDM), employs multiobjective evolutionary search to identify trade-offs between strategies, re-evaluates their performance under deep uncertainty, and uses interactive visual analytics to support the selection of robust management strategies. We demonstrate MORDM on a stylized decision problem posed by the management of a lake in which surpassing a pollution threshold causes eutrophication. Our results illustrate how framing the lake problem in terms of MEU can fail to represent key trade-offs between phosphorus levels in the lake and expected economic benefits. Moreover, the MEU strategy deteriorates severely in performance for all objectives under deep uncertainties. Alternatively, the MORDM framework enables the discovery of strategies that balance multiple preferences and perform well under deep uncertainty. This decision analytic framework allows the decision makers to select strategies with a better understanding of their expected trade-offs (traditional uncertainty) as well as their robustness (deep uncertainty).
Journal Article
Skill (or lack thereof) of data-model fusion techniques to provide an early warning signal for an approaching tipping point
Many coupled human-natural systems have the potential to exhibit a highly nonlinear threshold response to external forcings resulting in fast transitions to undesirable states (such as eutrophication in a lake). Often, there are considerable uncertainties that make identifying the threshold challenging. Thus, rapid learning is critical for guiding management actions to avoid abrupt transitions. Here, we adopt the shallow lake problem as a test case to compare the performance of four common data assimilation schemes to predict an approaching transition. In order to demonstrate the complex interactions between management strategies and the ability of the data assimilation schemes to predict eutrophication, we also analyze our results across two different management strategies governing phosphorus emissions into the shallow lake. The compared data assimilation schemes are: ensemble Kalman filtering (EnKF), particle filtering (PF), pre-calibration (PC), and Markov Chain Monte Carlo (MCMC) estimation. While differing in their core assumptions, each data assimilation scheme is based on Bayes' theorem and updates prior beliefs about a system based on new information. For large computational investments, EnKF, PF and MCMC show similar skill in capturing the observed phosphorus in the lake (measured as expected root mean squared prediction error). EnKF, followed by PF, displays the highest learning rates at low computational cost, thus providing a more reliable signal of an impending transition. MCMC approaches the true probability of eutrophication only after a strong signal of an impending transition emerges from the observations. Overall, we find that learning rates are greatest near regions of abrupt transitions, posing a challenge to early learning and preemptive management of systems with such abrupt transitions.
Journal Article
Low cost satellite constellations for nearly continuous global coverage
Satellite services are fundamental to the global economy, and their design reflects a tradeoff between coverage and cost. Here, we report the discovery of two alternative 4-satellite constellations with 24- and 48-hour periods, both of which attain nearly continuous global coverage. The 4-satellite constellations harness energy from nonlinear orbital perturbation forces (e.g., Earth’s geopotential, gravitational effects of the sun and moon, and solar radiation pressure) to reduce their propellant and maintenance costs. Our findings demonstrate that small sacrifices in global coverage at user-specified longitudes allow operationally viable constellations with significantly reduced mass-to-orbit costs and increased design life. The 24-hour period constellation reduces the overall required vehicle mass budget for propellant by approximately 60% compared to a geostationary Earth orbit constellation with similar coverage over typical satellite lifetimes. Mass savings of this magnitude permit the use of less expensive launch vehicles, installation of additional instruments, and substantially improved mission life.
Telecommunication, navigation and remote sensing services are highly dependent on how well satellites provide global coverage. Here the authors show a pair of four-satellite low-cost longer-life constellations that provide nearly continuous global coverage to support Earth observation.
Journal Article
A multi-objective paleo-informed reconstruction of western US weather regimes over the past 600 years
Extreme weather variability has long posed difficulties for food, energy, and water systems in the Western US and is only projected to become more variable in a changing climate. The key to understanding weather in the West lies in understanding variability of large-scale weather regimes that dictate regional weather patterns. In this study, we propose a novel, multi-objective optimization and regression-based framework that reconstructs the annual frequency of regional weather regimes based on a gridded, tree-ring based reconstruction of cold season precipitation. The approach optimally smooths and selects the tree-ring based information used in the weather regime reconstruction to enhance out of sample performance and minimize model complexity. Multiple objectives are considered within the optimization to balance the preservation of low and high frequency modes of variability in the multivariate weather regime dynamics. We reconstruct weather regime frequencies back to 1400 CE and show that the reconstructed weather regimes are consistent with previously identified megadroughts and pluvials. Further, the reconstructed weather regimes exhibit significant variability in the 3–15-year frequency band and extend far beyond the bounds of the instrumental period. Overall, the weather regime reconstructions developed here provide important insight into the extent of natural atmospheric variability that can influence Western US weather.
Journal Article
Unintended consequences of climate change mitigation for African river basins
Emerging climate change mitigation policies focus on the implementation of global measures relying on carbon prices to attain rapid emissions reductions, with limited consideration for the impacts of global policies at local scales. Here, we use the Zambezi Watercourse in southern Africa to demonstrate how local dynamics across interconnected water–energy–food systems are impacted by mitigation policies. Our results indicate that climate change mitigation policies related to land-use change emissions can have negative side effects on local water demands, generating increased risks for failures across all the components of the water–energy–food systems in the Zambezi Watercourse. Analogous vulnerabilities could impact many river basins in southern and western Africa. It is critical to connect global climate change mitigation policies to local dynamics for a better exploration of the full range of possible future scenarios while supporting policy makers in prioritizing sustainable mitigation and adaptation solutions.Global climate change mitigation policies aim to reduce emissions, but can have unintended local consequences. Mitigation in the land sector could impact local water resources, along with food and energy in the Zambezi Watercourse and similar river basins.
Journal Article
Multi-Objective Design of a Lunar GNSS
The success of future lunar missions depends on quality positioning, navigation, and timing (PNT) information. Earthbound GNSS signals can be received at lunar distances but suffer from poor geometric dilution of precision (GDOP) and provide no coverage of the lunar far side. This article explores the design space of a dedicated GNSS system in lunar orbit by using a multi-objective evolutionary algorithm framework to optimize GDOP, availability, space segment cost, station-keeping ΔV, and robustness to single-satellite failure. Results show that Pareto approximate solutions that achieve a global GDOP availability (GDOP ≤ 6.0) greater than 98% contain a minimum of 24 satellites in near-circular polar orbits at an altitude of ~2 lunar radii. The impact of single-satellite failure on GDOP outage is analyzed and a no-maneuver scenario is considered. Design rules characterizing optimal solutions are identified and trade-offs between station-keeping maneuver frequency, performance, and design lifetime are discussed.
Journal Article