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Creators/Authors contains: "Nateghi, Roshanak"

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  1. Abstract

    As the climate crisis intensifies, it is becoming increasingly important to conduct research aimed at fully understanding the climate change impacts on various infrastructure systems. In particular, the water-electricity demand nexus is a growing area of focus. However, research on the water-electricity demand nexus requires the use of demand data, which can be difficult to obtain, especially across large spatial extents. Here, we present a dataset containing over a decade (2007–2018) of monthly water and electricity consumption data for 46 major US cities (2018 population >250,000). Additionally, we include pre-processed climate data from the North American Regional Reanalysis (NARR) to supplement studies on the relationship between the water-electricity demand nexus and the local climate. This data can be used for a number of studies that require water and/or electricity demand data across long time frames and large spatial extents. The data can also be used to evaluate the possible impacts of climate change on the water-electricity demand nexus by leveraging the relationship between the observed values.

     
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  2. Abstract

    Building community resilience in the face of climate disasters is critical to achieving a sustainable future. Operational approaches to resilience favor systems’ agile return to the status quo following a disruption. Here, we show that an overemphasis on recovery without accounting for transformation entrenches ‘resilience traps’–risk factors within a community that are predictive of recovery, but inhibit transformation. By quantifying resilience including both recovery and transformation, we identify risk factors which catalyze or inhibit transformation in a case study of community resilience in Florida during Hurricane Michael in 2018. We find that risk factors such as housing tenure, income inequality, and internet access have the capability to trigger transformation. Additionally, we find that 55% of key predictors of recovery are potential resilience traps, including factors related to poverty, ethnicity and mobility. Finally, we discuss maladaptation which could occur as a result of disaster policies which emphasize resilience traps.

     
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  3. Abstract

    Access to accurate, generalizable and scalable solar irradiance prediction is critical for smooth solar-grid integration, especially in the light of the accelerated global adoption of solar energy production. Both physical and statistical prediction models of solar irradiance have been proposed in the literature. Physical models require meteorological forecasts—generated by computationally expensive models—to predict solar irradiance, with limited accuracy in sub-daily predictions. Statistical models leveragein-situmeasurements which require expensive equipment and do not account for meso-scale atmospheric dynamics. We address these fundamental gaps by developing a convolutional global horizontal irradiance prediction model, using convolutional neural networks and publicly accessible satellite cloud images. Our proposed model predicts solar irradiance in 12 different locations in the US for various prediction time horizons. Our model yields up to 24% improvement in an hour-ahead predictions and 26% in a day-ahead predictions compared to a persistence forecast. Moreover, using saliency maps and target-location-focused cropping, we demonstrate the benefits of incorporating meso-scale atmospheric dynamics for prediction performance. Our results are critical for energy systems planners, utility managers and electricity market participants to ensure efficient harvesting of the solar energy and reliable operation of the grid.

     
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  4. Abstract

    Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

     
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