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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identification of surface urban heat versus cool islands for arid cities depends on the choice of urban and rural definitions

The urban heat island (UHI) effect in arid cities can be small or even negative, the latter known as the urban cool island (UCI) effect. Differences in defining urban and rural areas can introduce uncertainties in detecting UHI or UCI, especially when the UHI signal is small. Here, we compared the surface UHI intensity (SUHII) estimated by a dozen different methods (with multiple urban and/or rural definitions) across 104 arid cities globally, providing a comprehensive evaluation of the uncertainty in SUHII estimates. Results show that the absolute difference in annual average SUHII (ΔSUHII) among methods exceeded 1°C in about half of the arid cities during both daytime and nighttime. Further, the overall annual mean ΔSUHII for all arid cities was 1.35°C during daytime and 1.03 °C at night. The uncertainty arising from simultaneous variations in urban and rural definitions was generally higher than that resulting from their individual changes. It was observed that, with varying definitions of urban and rural areas, nearly 50% of arid cities experienced a sign reversal in daytime SUHII estimates, while approximately 15% exhibited a sign reversal in nighttime SUHII. Variations in urban-rural differences in surface properties, such as vegetation index and albedo, due to differing urban and rural definitions, contributed strongly to the observed SUHII uncertainties. Overall, our results offer new insights into the ongoing debate on heat and cold islands in arid cities, emphasizing a critical need to standardize SUHII estimation frameworks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparison of potential drinking water source contamination across one hundred U.S. cities

Abstract Drinking water supplies of cities are exposed to potential contamination arising from land use and other anthropogenic activities in local and distal source watersheds. Because water quality sampling surveys are often piecemeal, regionally inconsistent, and incomplete with respect to unregulated contaminants, the United States lacks a detailed comparison of potential source water contamination across all of its large cities. Here we combine national-scale geospatial datasets with hydrologic simulations to compute two metrics representing potential contamination of water supplies from point and nonpoint sources for over a hundred U.S. cities. We reveal enormous diversity in anthropogenic activities across watersheds with corresponding disparities in the potential contamination of drinking water supplies to cities. Approximately 5% of large cities rely on water that is composed primarily of runoff from non-pristine lands (e.g., agriculture, residential, industrial), while four-fifths of all large cities that withdraw surface water are exposed to treated wastewater in their supplies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Strategic Energy Plan: City of Key West, Florida

This Strategic Energy Plan for the city of Key West, Florida—developed through the U.S. Department of Energy’s Energy Technology Innovation Partnership Project (ETIPP)—outlines a comprehensive strategy to advance the city’s energy vision: to improve energy efficiency and independence using local energy resources to foster long-term resilience. To guide this effort, the plan is structured around four focus areas: • Energy efficiency: Reduce overall energy consumption and utility costs across municipal, residential, and commercial buildings. • Local energy generation: Increase the share of energy produced from local sources to enhance energy independence. • Resilience: Strengthen critical infrastructure and community preparedness for flooding, hurricanes, and other natural weather hazards. • Electric transportation: Support the addition of new electric vehicles (EVs) and develop reliable charging infrastructure to reduce reliance on imported fuels. ETIPP provides strategic energy planning, technical assistance, and direct funding to U.S. coastal, remote, and island communities to improve energy resilience. Key West was part of ETIPP’s fourth cohort. As a low-lying island community vulnerable to infrastructure damage due to natural weather hazards, Key West seeks to reduce its dependence on external energy and build long-term sustainability. The Strategic Energy Plan identifies specific challenges, sets clear goals, and proposes actionable opportunities with implementation timelines and key stakeholders. A baseline assessment reveals significant energy consumption in both city-owned and residential/commercial buildings, limited local energy generation, and an early-stage EV market with vulnerable charging infrastructure. The proposed solutions emphasize a multifaceted approach, leveraging both established and innovative technologies, while addressing financial, technical, and community engagement challenges. The baseline assessment provided a snapshot of current energy conditions in Key West, evidencing key challenges and opportunities across the city’s energy priorities. Building on the baseline assessment and extensive input from city staff, local stakeholders, and community partners, a set of targeted opportunities was identified to address Key West’s energy goals. Each was evaluated in terms of its potential benefits, key implementation steps, associated challenges and mitigation strategies, and the city departments and stakeholders best positioned to lead or support progress.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Improved Access and Analysis of Data Provides New Opportunities for Smart Cities

Making smart, informed, data-driven energy decisions for cities requires large amounts of high-value data and analysis. Cities’ energy data has traditionally been difficult to access and costly to store and manage for city administrators. When data is available, many cities lack the expertise to properly analyze and model large data sets. NREL’s transparency and knowledge about data availability, integration, and interpretation is critical to decision-making for cities.

cities' data↗
DOE OSTI2022

Exploring the Spatial Relationship Between Demographic Indicators and the Built Environment of a City

In addition to global and regional drivers of urbanization, neighborhood development in urban areas across the United States has been shown to be influenced by various local socio-economic factors. These factors, despite varying across socio-economic groups, have large implications regarding a population’s vulnerability to extreme climate events, including heat waves resulting in adverse health impacts. Additionally, the demographics of an urban area can shape its infrastructural characteristics, causing different populations groups to face varying levels of risks and benefits. As a result, the urban morphology and socio-economic characteristics of a city are deeply intertwined; however, their interactions on a finer scale are not yet fully understood. This research aims to better understand the relationships between various socio-economic factors and the built environment of a city, considering variability in building types, and temperature patterns. This research focuses on the city of Las Vegas, NV, and uses spatial data analysis to understand the correlation between of socio-economic characteristics, building morphology, building characteristics, and temperature data to understand the correlation between these various factors. Results of these research shows there is a distinct pattern of clustering of socio-economic characteristics with the city and there is a distinct correlation between age and cost, socio-economic characteristics, and locations of high heat distribution within the city.

Singh, Ridhima↗
DOE OSTI2026

Integrating three plan evaluation approaches for coordinated heat resilience in cities across the Arizona urban corridor

Increasing heat poses a growing threat to cities worldwide due to both climate change and the urban heat island effect. While heat planning and governance are still emergent, research suggests that silos and conflicts within cities' networks of plans often impede heat resilience. Integrated heat resilience planning, therefore, requires a systematic and comprehensive analysis of the silos and conflicts relevant to heat resilience within networks of plans. This study is the first to combine three complementary plan evaluation methods to assess how cities' networks of plans address heat resilience. We applied 1) plan cross-referencing, 2) Plan Quality Evaluation for Heat Resilience, and 3) Plan Integration for Resilience Scorecard™ (PIRS™) for Heat to 19 plans from seven Arizona cities. We find similarities and differences in how these cities' networks of plans address heat hazards. The plans have consistently high-quality participation and coordination principles but lack details on vulnerability and climate change uncertainty, suggesting a need to move beyond immediate heat risks. We also identify opportunities to diversify policy mechanisms, spatially target high heat risk areas, and enhance the connection between planning efforts. These results validate that plan elements are interlinked and the importance of integrative plan development processes to improve heat resilience.

Extreme heat↗
DOE OSTI2022

Heterogeneous climate change impacts on electricity demand in world cities circa mid-century

Rising ambient temperatures due to climate change will increase urban populations’ exposures to extreme heat. During hot hours, a key protective adaptation is increased air conditioning and associated consumption of electricity for cooling. But during cold hours, milder temperatures have the offsetting effect of reducing consumption of electricity and other fuels for heating. We elucidate the net consequences of these opposing effects in 36 cities in different world regions. We couple reduced-form statistical models of cities’ hourly responses of electric load to temperature with temporally downscaled projections of temperatures simulated by 21 global climate models (GCMs), projecting the effects of warming on the demand for electricity circa 2050. Cities' responses, temperature exposures and impacts are heterogeneous, with changes in total annual consumption ranging from –2.7 to 5.7%, and peak power demand increasing by as much as 9.5% at the multi-GCM median. The largest increases are concentrated in more economically developed mid-latitude cities, with less developed urban areas in the tropics exhibiting relatively small changes. The results highlight the important role of the structure of electricity demand: large temperature increases in tropical cities are offset by their inelastic responses, which can be attributed to lower air-conditioning penetration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Surface warming in global cities is substantially more rapid than in rural background areas

Warming trends in cities are influenced both by large-scale climate processes and by local-scale urbanization. However, little is known about how surface warming trends of global cities differ from those characterized by weather observations in the rural background. Here, through statistical analyses of satellite land surface temperatures (2002 to 2021), we find that the mean surface warming trend is 0.50 ± 0.20 K·decade –1 (mean ± one S.D.) in the urban core of 2000-plus city clusters worldwide, and is 29% greater than the trend for the rural background. On average, background climate change is the largest contributor explaining 0.30 ± 0.11 K·decade –1 of the urban surface warming. In city clusters in China and India, however, more than 0.23 K·decade –1 of the mean trend is attributed to urban expansion. We also find evidence of urban greening in European cities, which offsets 0.13 ± 0.034 K·decade –1 of background surface warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensitivity and vulnerability to summer heat extremes in major cities of the United States

Many cities are experiencing increases in extreme heat because of global temperature rise combined with the urban heat island effect. The heterogeneity of urban morphology also leads to fine-scale variability in potential for heat exposure. Yet, how this rise in temperature and local variability together impacts urban residents differently at exposure-relevant scales is still not clear. Here we map the Universal Thermal Climate Index, a more complete indicator of human heat stress at an unprecedentedly fine spatial resolution (1 m), for 14 major cities in the United States using urban microclimate modeling. We examined the different heat exposure levels across different socioeconomic and racial/ethnic groups in these cities, finding that income level is most consistently associated with heat stress. We further conducted scenario simulations for a hypothetical 1 °C increase of air temperature in all cities. Results show that a 1 °C increase would have a substantial impact on human heat stress, with impacts that differ across cities. The results of this study can help us better evaluate the impact of extreme heat on urban residents at decision-relevant scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

VDiSC: An Open Source Framework for Distributed Smart City Vision and Biometric Surveillance Networks

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose the Distributed Smart City framework for Vision, or VDiSC. VDiSC serves as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. VDiSC additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. VDiSC ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), VDiSC also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.

Brogan, Joel↗
DOE OSTI2020

ARIC 2019 workshop report: The 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities: Chicago, IL, USA November 5, 2019

The advancements in sensor technology and ubiquity of connected devices has enabled the generation of large volume of disparate, dynamic and geographically distributed data both by scientific communities and citizens. With astonishing technological innovations and convergence, there have been major changes in peoples daily activities and social interaction. The socio-technological innovations motivate the concept of smart and connected cities. A smart city, however, is subjected to the same challenges as a conventional city, such as environmental damages, hazard impacts, access to services and resources, due to continuous population and economic growth. Therefore, it is imperative to improve our understanding of Resilient and Intelligent Cities in order to leverage technologies and artificial intelligence to tackle the challenges cities face, which range from climate change, public health, traffic congestion, economic growth, to digital divide, social equity, political movements, and cultural conflicts, among others.

Kar, Bandana↗
DOE OSTI2025

Cost Benefit Analysis of Solar PV for the City of Oldsmar, FL

The City of Oldsmar is in the early stages of exploring renewable energy opportunities as a part of their initiative to reduce energy costs, improve backup power capacity, and begin the transition to more sustainable operations. The city is particularly interested in deploying a floating or ground-mounted solar PV system at its wastewater treatment plant. Currently, there are no solar PV installations in Oldsmar and there has been hesitancy to implement such a project due to the possible capital costs and limited technical capacity. Despite this the initiative has gained internal buy-in among staff working for the city. To assist with gaining more buy-in and making future decisions the city sought technical assistance (TA) from the Energy to Communities (E2C) Expert Match Program. Through the Expert Match Program, the city received TA provided by the Pacific Northwest National Lab (PNNL) to evaluate the cost effectiveness of several solar options. This report details the analysis and results of the work performed for TA.

14 SOLAR ENERGY↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
DOE OSTI2022

Case Study of the City of Reno: Resilience Valuation and Planning for Solar and Storage on Critical Infrastructure

This case study report documents the City of Reno's efforts to quantify the value of resilience and the entrepreneurial ways through which the City approached the questions of what role solar and storage could play in their resilience efforts and how to value and pay for solar-plus-storage systems. It documents how and why the City of Reno came to the specific conclusion to integrate solar and storage into their Public Safety Center retrofit project and apply for Federal Emergency Management Agency (FEMA) Building Resilient Infrastructure and Communities (BRIC) funding. This report provides an overview for other local governments to draw inspiration from when considering how to prioritize community values and pursue multi-stakeholder dialogue around resilient energy solutions. The City of Reno's experience can serve as a starting point for other communities interested in valuing the resilience benefits of solar and storage.

14 SOLAR ENERGY↗
DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗
DOE OSTI2021

Urban Forests as Main Regulator of the Evaporative Cooling Effect in Cities

Abstract Higher temperatures in urban areas expose a large fraction of the human population to potentially dangerous heat stress. Green spaces are promoted worldwide as local and city‐scale cooling strategies but the amount, type, and functioning of vegetation in cities lack quantification and their interaction with urban climate in different settings remains a matter of debate. Here we use state‐of‐the‐art remote sensing data from 145 city clusters to disentangle the drivers of surface urban heat islands (SUHI) intensity and quantify urban‐rural differences in vegetation cover, species composition, and evaporative cooling. We show that nighttime SUHIs are affected mostly by abiotic factors, while daytime SUHIs are highly correlated with vegetation characteristics and the wetness of the background climate. Magnitude and seasonality of daytime SUHIs are controlled by urban‐rural differences in plant transpiration and leaf area, which explain the dependence of SUHIs on wetness conditions. Leaf area differences are caused primarily by changes in vegetation type and a loss of in‐city forested areas, highlighting the importance of maintaining “natural reserves” as a sustainable heat mitigation policy.

Paschalis, Athanasios↗
DOE OSTI2023

Groundwater Volume Loss in Mexico City Constrained by InSAR and GRACE Observations and Mechanical Models

Abstract Groundwater withdrawal can cause localized and rapid poroelastic subsidence, spatially broad elastic uplift of low amplitude, and changes in the gravity field. Constraining groundwater loss in Mexico City, we analyze data from the Gravity Recovery and Climate Experiment and its follow‐on mission (GRACE/FO) and Synthetic Aperture Radar (SAR) Sentinel‐1A/B images between 2014 and 2021. GRACE/FO observations yield a groundwater loss of 0.85–3.87 km 3 /yr for a region of ∼300 × 600 km surrounding Mexico City. Using the high‐resolution interferometric SAR data set, we measure >35 cm/yr subsidence within the city and up to 2 cm/yr of uplift in nearby areas. Attributing the long‐term subsidence to poroelastic aquifer compaction and the long‐term uplift to elastic unloading, we apply respective models informed by local geology, yielding groundwater loss of 0.86–12.57 km 3 /yr. Our results suggest Mexico City aquifers have been depleting at faster rates since 2015, exacerbating the socioeconomic and health impacts of long‐term groundwater overdrafts.

58 GEOSCIENCES↗