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Models that consider the interconnectivity between urban systems, including water and electricity, are becoming more common, both in research and in practice. However, there are still too few that consider the impact of climate change, and fewer still that look beyond the baseline climate data (i.e., precipitation and temperature). Here, a data-driven, regional model that considers a wider array of climate variables is built and tested to evaluate the impact of climate change on the coupled water and electricity demand nexus in the Midwestern USA. The model, which is based on a state-of-the-art statistical learning algorithm, is first used to compare model runs comprised of different climatic variables. The model runs included a baseline model that considers only precipitation and temperature, as well as a selected feature model that considered a wider array of climatic variables, including relative humidity and wind speed. Following this comparison, the model is used to make future projections of the coupled water and electricity demand as a function of future climate change scenarios. The results indicate that (1) the inclusion of additional climate variables beyond the baseline provides a significant improvement in predictive accuracy, and (2) the climate-sensitive portions of summer electricity and water use are expected to increase in the region by 19% and 7%, respectively. Finally, the regional-scale model is leveraged to make city-level projections, indicating a 10–20% (2–5%) increase in electricity (water) use across the analyzed cities due to a warming climate.

The importance of sustainable development has risen in recent years due to the significant number of people affected by lack of access to essential resources as well as the need to prepare for and adapt to intensifying climate change and rapid urbanization. Modeling frameworks capable of effectively assessing and tracking sustainability lie at the heart of creating effective policies to address these issues. Conventional frameworks, such as the Environmental Performance Index (EPI), that support such policies often involve ranking countries based on a weighted sum of a number of relevant environmental metrics. However, the selection and weighing processes are often biased. Moreover, the ranking process fails to provide policymakers with possible avenues to improve their country&rsquo, s environmental sustainability. This study aimed to address these gaps by proposing a novel data-driven framework to assess the environmental sustainability of countries objectively by leveraging unsupervised learning theory. Specifically, this framework harnesses a clustering technique known as Self-Organized Maps to group countries based on their characteristic environmental performance metrics and track progression in terms of shifts within clusters over time. The results support the hypothesis that the inconsistencies in the EPI calculation can lead to misrepresentations of the relative sustainability of countries over time. The proposed framework, which does not rely on ranking or data transformations, enables countries to make more informed decisions by identifying effective and specific pathways towards improving their environmental sustainability.

In recent years, smart cities have grown in popularity, both in research and in practice. The focus of smart city studies and policy initiatives has historically been on technology, particularly how information and communication technology can be leveraged to improve city functions. This focus has begun to shift towards sustainability, with many researchers calling for the development of smart, sustainable cities, which can aid efforts of climate change mitigation and adaptation. The connection between smart cities and climate change, however, is not clear. In this review article, we aim to synthesize the recent literature surrounding smart cities and climate change, and to discuss the benefits (or costs) of smart cities with regard to climate change mitigation and adaptation efforts. In particular, we focus on five key aspects of urban resilience to climate change: infrastructure, public health and well-being, accessibility and equity, sustainable systems, and governance. The literature reveals a higher level of emphasis on infrastructure resilience to climate change than the other categories. Moreover, the research areas differ in the level of connection between smart city initiatives and climate change adaptation and mitigation efforts. For example, within the critical infrastructure research area, studies on smart energy systems focus on climate change mitigation, particularly reducing emissions, while studies on smart water systems emphasize adaptation to future floods and droughts. Going beyond the aforementioned research areas, we discuss the role of big data in smart cities, including the benefits and challenges associated with collecting large amounts of data from smart technology, as well as the techniques needed to analyze such data. Finally, we highlight future directions that we believe the research on smart cities needs to focus on, based on the results from our literature review. These include infrastructure and disaster resilience, public health and social equity, and sustainability.

often recognized too late, typically when changing the adopted technologies and behavioral norms is difficult. A similar story may unfold if society continues to blindly transition to an unregulated and environmentally unaudited digital world, a transition path that has been facilitated by the fourth industrial revolution and is now accelerated by the global COVID-19 crisis. The newly developed digital lifestyle has major environmental benefits, including the reduction of travel-related CO2 emissions. Yet, increased Internet use has some hidden environmental impacts that must be uncovered (Fig. 1a) to make the transition to a lowcarbon and green economy successful. The data centers’ electricity consumption accounts for 1% of the global energy demand (Masanet et al., 2020), more than the national energy consumption of many countries. Depending on the energy supply mix and use efficiency, Internet traffic contributes differently to negative environmental impacts and climate change. As the number of Internet users increases, the number of online services and applications they use grow. This trend exacerbates the environmental footprint of the Internet, despite the many successful and significant efforts to improve the efficiency of data centers (Masanet et al., 2020) and reduce their reliance on fossil energy. In order to build a sustainable digital world, it is imperative to carefully assess the environmental footprints of the Internet and identify the individual and collective actions that most affect its growth.

Projected climate change will significantly influence the shape of the end-use energy demand profiles for space conditioning—leading to a likely increase in cooling needs and a subsequent decrease in heating needs. This shift will put pressure on existing infrastructure and utility companies to meet a demand that was not accounted for in the initial design of the systems. Furthermore, the traditional linear models typically used to predict energy demand focus on isolating either the electricity or natural gas demand, even though the two demands are highly interconnected. This practice often leads to less accurate predictions for both demand profiles. Here, we propose a multivariate, multi-sector (i.e., residential, commercial, industrial) framework to model the climate sensitivity of the coupled electricity and natural gas demand simultaneously, leveraging advanced statistical learning algorithms. Our results indicate that the season-to-date heating and cooling degree-days, as well as the dew point temperature are the key predictors for both the electricity and natural gas demand. We also found that the energy sector is most sensitive to climate during the autumn and spring (intermediate) seasons, followed by the summer and winter seasons. Moreover, the proposed model outperforms a similar univariate model in terms of predictive accuracy, indicating the importance of accounting for the interdependence within the energy sectors. By providing accurate predictions of the electricity and natural gas demand, the proposed framework can help infrastructure planners and operators make informed decisions towards ensuring balanced energy delivery and minimizing supply inadequacy risks under future climate variability and change.

Volant organisms are adapted to atmospheric patterns and processes. Understanding the lives of animals that inhabit this aerial environment requires a detailed investigation of both the animal’s behavior and its environmental context—i.e., the environment that it encounters at a range of spatial and temporal scales. For aerofauna, it has been relatively difficult to observe the environment they encounter while they move. Large international efforts using satellite and weather model reanalysis now provide some of the environmental data on atmospheric environments throughout the globe. Track annotation—the approach of merging the environmental data with the movement track measured via telemetry—can be conducted automatically using online tools such as Movebank-Env-DATA or RNCEP. New parameterization approaches can use environmentally annotated tracks to approximate specific atmospheric conditions, such as uplift and tail wind, which are not typically observed at the exact locations of the movement, but are critical to movement. Reducing the complexity of movement to single-dimensional characteristic (such as flight speed, elevation, etc.) and defining the temporal scope of the movement phenomenon in the focus of the analysis (seasonal, daily, minutely, etc.) makes it possible to construct empirical models that explain the movement characteristic as driven by the environmental conditions during flight, despite the highly dynamic, complex, and scale-dependent structures of both the flight path and atmospheric variables. This chapter will provide several examples for such empirical movement models from different species of birds and using several resources for atmospheric data.

Accounting for the nexus between water and electricity demand is critical for ensuring efficiency and conservation measures are successful in lowering the net water and electricity use in a city. Considering the nexus is also critical for accurately estimating the price elasticity of demand and designing effective demand response programs. The importance of the water-electricity demand nexus is rapidly increasing as cities are stressed by factors such as global climatic and socioeconomic changes as well as unprecedented rates of urbanization and growth. Despite the extensive recent research efforts on electricity and water demand modeling, significant knowledge gaps remain that are primarily rooted in (i) the use of univariate approaches that cannot adequately account for the nexus and (ii) the lack of a comprehensive assessment of the role of climate drivers on the demand nexus. To address these gaps, we propose a multivariate (i.e., multi-response), algorithmic framework for assessing the climate-sensitivity of the coupled water-electricity demand nexus. To illustrate the applicability of the proposed framework, six Midwestern cities were selected as test cases. The results indicated that climate variability alone could account for 23–71% of variability in the water-electricity demand nexus with the seasonally adjusted dataset, and 47–87% of the variability on the non-adjusted dataset. The results also revealed that water use was more climate-sensitive than electricity use. Additionally, the importance of the variability in the global climate drivers such as the El Nino/Southern Oscillation cycle was demonstrated. The modeling results suggest that stronger El Ninos lead to an overall decrease in the climate-sensitive portion of the water and electricity use in the selected cities.

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation.

Approximately two thirds of migratory songbirds in eastern North America negotiate the Gulf of Mexico (GOM), where inclement weather coupled with no refueling or resting opportunities can be lethal. However, decisions made when navigating such features and their consequences remain largely unknown due to technological limitations of tracking small animals over large areas. We used automated radio telemetry to track three songbird species (Red-eyed Vireo, Swainson’s Thrush, Wood Thrush) from coastal Alabama to the northern Yucatan Peninsula (YP) during fall migration. Detecting songbirds after crossing ∼1,000 km of open water allowed us to examine intrinsic (age, wing length, fat) and extrinsic (weather, date) variables shaping departure decisions, arrival at the YP, and crossing times. Large fat reserves and low humidity, indicative of beneficial synoptic weather patterns, favored southward departure across the Gulf. Individuals detected in the YP departed with large fat reserves and later in the fall with profitable winds, and flight durations (mean = 22.4 h) were positively related to wind profit. Age was not related to departure behavior, arrival, or travel time. However, vireos negotiated the GOM differently than thrushes, including different departure decisions, lower probability of detection in the YP, and longer crossing times. Defense of winter territories by thrushes but not vireos and species-specific foraging habits may explain the divergent migratory behaviors. Fat reserves appear extremely important to departure decisions and arrival in the YP. As habitat along the GOM is degraded, birds may be limited in their ability to acquire fat to cross the Gulf.

Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US.