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With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios.

Wind energy often plays a major role in meeting renewable energy policy objectives; however, increased deployment can raise concerns regarding the impacts of wind plants on certain wildlife. Particularly, estimates suggest hundreds of thousands of bat fatalities occur annually at wind plants across North America, with potential implications for the viability of several bat species. One approach to reducing bat fatalities is shutting down (or curtailing) turbines when bats are most at risk, such as at night during relatively low wind speed periods throughout summer and early autumn. While curtailment has consistently been shown to reduce bat fatalities, the lost power production reduces revenues for wind plants. This study conducted simulations with a range of curtailment scenarios across the contiguous United States to examine sensitivities of annual energy production (AEP) loss and potential impacts on economic metrics for future wind energy deployment. We found that AEP reduction can vary across the country from less than 1% to more than 10% for different curtailment scenarios. From an estimated 2891 gigawatts (GW) of simulated economically viable wind capacity (measured by a positive net present value), we found the mid curtailment scenario (6.0 m/s wind speed cut‐in from July 1 through October 31) reduced the quantity of economic wind capacity by 274 GW or 9.5%. Our results indicate that high levels of curtailment could substantially reduce the future footprint of financially viable wind energy. In this context, future work that illuminates cost‐effective strategies to minimize curtailment while reducing bat fatalities would be of value.

Scholarly understanding of human migration’s environmental dimensions has greatly advanced in the past several years, motivated in large part by public and policy dialogue around “climate migrants”. The research presented here advances current demographic scholarship both through its substantive interpretations and conclusions, as well as its methodological approach. We examine temporary rural South African outmigration as related to household-level availability of proximate natural resources. Such “natural capital” is central to livelihoods in the region, both for sustenance and as materials for market-bound products. The results demonstrate that the association between local environmental resource availability and outmigration is, in general, positive: households with higher levels of proximate natural capital are more likely to engage in temporary migration. In this way, the general findings support the “environmental surplus” hypothesis that resource security provides a foundation from which households can invest in migration as a livelihood strategy. Such insight stands in contrast to popular dialogue, which tends to view migration as a last resort undertaken only by the most vulnerable households. As another important insight, our findings demonstrate important spatial variation, complicating attempts to generalize migration-environment findings across spatial scales. In our rural South African study site, the positive association between migration and proximate resources is actually highly localized, varying from strongly positive in some villages to strongly negative in others. We explore the socio-demographic factors underlying this “operational scale sensitivity”. The cross-scale methodologies applied here offer nuance unavailable within more commonly used global regression models, although also introducing complexity that complicates story-telling and inhibits generalizability.

Scholarly understanding of human migration’s environmental dimensions has greatly advanced in the past several years, motivated in large part by public and policy dialogue around “climate migrants”. The research presented here advances current demographic scholarship both through its substantive interpretations and conclusions, as well as its methodological approach. We examine temporary rural South African outmigration as related to household-level availability of proximate natural resources. Such “natural capital” is central to livelihoods in the region, both for sustenance and as materials for market-bound products. The results demonstrate that the association between local environmental resource availability and outmigration is, in general, positive: households with higher levels of proximate natural capital are more likely to engage in temporary migration. In this way, the general findings support the “environmental surplus” hypothesis that resource security provides a foundation from which households can invest in migration as a livelihood strategy. Such insight stands in contrast to popular dialogue, which tends to view migration as a last resort undertaken only by the most vulnerable households. As another important insight, our findings demonstrate important spatial variation, complicating attempts to generalize migration-environment findings across spatial scales. In our rural South African study site, the positive association between migration and proximate resources is actually highly localized, varying from strongly positive in some villages to strongly negative in others. We explore the socio-demographic factors underlying this “operational scale sensitivity”. The cross-scale methodologies applied here offer nuance unavailable within more commonly used global regression models, although also introducing complexity that complicates story-telling and inhibits generalizability.

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s-1. Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s-1, while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s-1. The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s-1 for SAR and less than 0.2 m s-1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

This paper details the validation of a methodology which spatially allocates Census microdata to census tracts, based on known, aggregate tract population distributions. To protect confidentiality, public-use microdata contain no spatial identifiers other than the code indicating the Public Use Microdata Area in which the individual or household is located. This study demonstrates and evaluates such an approach, using historical census data for which a 100% count of the full population is available at a fine spatial resolution. The approach described allows for testing of the behavior of a maximum entropy imputation and spatial allocation model under different specifications. The results indicate that the validation procedure provides useful statistics, allowing an in-depth evaluation of the household allocation and identifying optimal configurations for model parameterization. This provides important insights as to how to design a validation procedure at a CRDC for spatial allocations using contemporary census data.

OBJECTIVE This paper details the validation of a methodology which spatially allocates Census microdata to census tracts, based on known, aggregate tract population distributions. To protect confidentiality, public-use microdata contain no spatial identifiers other than the code indicating the Public Use Microdata Area (PUMA) in which the individual or household is located. Confirmatory information including the location of microdata households can only be obtained in a Census Research Data Center (CRDC). Due to restrictions in place at CRDCs, a systematic procedure for validating the spatial allocation methodology needs to be implemented prior to accessing CRDC data. METHODS This study demonstrates and evaluates such an approach, using historical census data for which a 100Š count of the full population is available at a fine spatial resolution. The approach described allows for testing of the behavior of a maximum entropy imputation and spatial allocation model under different specifications. The imputation and allocation is performed using a microdata sample of records drawn from the full 1880 Census enumeration and synthetic summary files created from the same source. The results of the allocation are then validated against the actual values from the 100Š count of 1880. RESULTS The results indicate that the validation procedure provides useful statistics, allowing an in-depth evaluation of the household allocation and identifying optimal configurations for model parameterization. This provides important insights as to how to design a validation procedure at a CRDC for spatial allocations using contemporary census data.

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

Land cover datasets are generally produced from satellite imagery using state-of-the-art model-based classification methods while integrating large amounts of ancillary data to help improve accuracy levels. The knowledge base encapsulated in this process is a resource that could be used to produce new data of similar quality, more efficiently. Specifically, the question addressed in this dissertation is whether this richness of information could potentially be extracted from the underlying remote sensing imagery to then classify an image for a different geographic extent or a different point in time. This research developed a machine learning framework to replicate the U.S. National Land Cover Database (NLCD) from Landsat 5 TM imagery in the spatial and temporal domains. Information characterizing individual land cover classes was extracted using a maximum entropy classifier on a Landsat image to create a generalizable model for land cover data replication. This framework was then demonstrated for spatial extrapolation and temporal extension of the NLCD by applying the model to Landsat imagery for a different geographic extent and for a different point in time. The experimental setup of this dissertation used three study areas in the U.S. featuring different landscape compositions to test the stability and generalizability of this framework. Results for the spatial and temporal replication of the NLCD showed that the objective of reproducing similar levels of overall and within class accuracies could be met and demonstrated that the knowledge base encapsulated in the NLCD can effectively be extracted for replication. The algorithm proved to be generalizable to the range of landscapes represented by the three study sites and showed stability in both spatial and temporal replication. This dissertation demonstrates how such a framework could potentially extend the NLCD into Canada or Mexico, for example, and how it could be implemented to produce annual land cover data. Effective replication of the NLCD provides a valuable case study since similar land cover datasets exist in many countries and an automated method for spatial extrapolation or temporal extension of such data would benefit the scientific community and advance similar areas of research.