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  1. 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.

  2. 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.

  3. Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to conventional approaches that are generally not conducive to integrating smart-meter data. However, CBAs are computationally costly and suffer from the curse of dimensionality, especially under scenarios involving smart-meter data from millions of customers. In this work, we propose an efficient reduced model approach (RMA) that leverages a novel hierarchical dimension reduction algorithm to enable the integration of fine-resolution high-dimensional smart-meter data for millions of customers in load prediction. We demonstrate the applicability of our proposed approach by using data from a utility company, based in Illinois, United States, with more than 3.7 million customers and present model performance in-terms of forecast accuracy. The proposed hierarchical dimension reduction approach enables utilizing the high-resolution data from smart- meters in a scalable manner that is not exploitable otherwise. The results shows significant improvements inmore »forecast accuracy compared to the available approaches that either do not harness fine-resolution data or are not scalable to large-scale smart-meter big data.« less
  4. Abstract Tropical cyclones cause significant inland hazards, including wind damage and freshwater flooding, which depend strongly on how storm intensity evolves after landfall. Existing theoretical predictions for storm intensification and equilibrium storm intensity have been tested over the open ocean but have not yet been applied to storms after landfall. Recent work examined the transient response of the tropical cyclone low-level wind field to instantaneous surface roughening or drying in idealized axisymmetric f -plane simulations. Here, experiments testing combined surface roughening and drying with varying magnitudes of each are used to test theoretical predictions for the intensity response. The transient response to combined surface forcings can be reproduced by the product of their individual responses, in line with traditional potential intensity theory. Existing intensification theory is generalized to weakening and found capable of reproducing the time-dependent inland intensity decay. The initial (0–10 min) rapid decay of near-surface wind caused by surface roughening is not captured by existing theory but can be reproduced by a simple frictional spindown model, where the decay rate is a function of surface drag coefficient. Finally, the theory is shown to compare well with the prevailing empirical decay model for real-world storms. Overall, results indicate themore »potential for existing theory to predict how tropical cyclone intensity evolves after landfall.« less
  5. Abstract Inland tropical cyclone (TC) impacts due to high winds and rainfall-induced flooding depend strongly on the evolution of the wind field and precipitation distribution after landfall. However, research has yet to test the detailed response of a mature TC and its hazards to changes in surface forcing in idealized settings. This work tests the transient responses of an idealized hurricane to instantaneous transitions in two key surface properties associated with landfall: roughening and drying. Simplified axisymmetric numerical modeling experiments are performed in which the surface drag coefficient and evaporative fraction are each systematically modified beneath a mature hurricane. Surface drying stabilizes the eyewall and consequently weakens the overturning circulation, thereby reducing inward angular momentum transport that slowly decays the wind field only within the inner core. In contrast, surface roughening initially (~12 h) rapidly weakens the entire low-level wind field and enhances the overturning circulation dynamically despite the concurrent thermodynamic stabilization of the eyewall; thereafter the storm gradually decays, similar to drying. As a result, total precipitation temporarily increases with roughening but uniformly decreases with drying. Storm size decreases monotonically and rapidly with surface roughening, whereas the radius of maximum wind can increase with moderate surface drying. Overall, thismore »work provides a mechanistic foundation for understanding the inland evolution of real storms in nature.« less