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In water-stressed regions, droughts pose a critical challenge to urban water security for low-income households. Droughts reduce water availability, forcing water providers to invest in additional supplies or enact expensive, short-term emergency measures. These costs are frequently passed on to households through increased rates and surcharges, driving up water bills for low-income households and altering patterns of water consumption. Here we have developed a socio-hydrological modelling approach that integrates hydrology, water infrastructure, utility decision-making and household behaviour to understand the impacts of droughts on household water affordability. We present here an application based on Santa Cruz, California and show that many drought resilience strategies raise water bills for low-income households and lower them for high-income households. We also found that low-income households are most vulnerable to both changing drought characteristics and infrastructure lock-in.Unaffordable water prices threaten human health and well-being in the United States and beyond 1-5 . In California alone, one million people lack access to affordable clean drinking water 6 . Low-income households are disproportionately impacted by unaffordable water bills 4 , with one-third of water systems in low-income areas of the United States charging unaffordable rates 7 . Unaffordable water threatens health by limiting both domestic water use 8 and other essential household expenditures. Indeed, 14% of the US population reports that a US$12 monthly increase in water bills would reduce access to groceries and basic medical care 9 . Water bills are currently increasing faster than inflation 3 and the strain of climate change on water supplies threatens to accelerate cost increases.Droughts exacerbate affordable water access in many waterstressed regions by reducing water availability and increasing the cost of supplying water. Water providers must use expensive short-term mitigation measures such as curtailment or invest in additional water supplies 10,11 to provide reliability, but these measures may increase water rates 12 . Counterintuitively, these measures can reduce household water security by making water unaffordable for more people 13,14 . This impact is supported by empirical evidence from recent droughts that showed that households in urban areas of California paid higher water bills during drought periods 15 .Household water-use behaviour compounds the affordability challenges created by droughts and disconnects household and utility water security. During droughts, households change their water use as a result of drought-related water restrictions 13,16 , responses to price increases 17,18 , and changing water use based on socio-economic and demographic factors [19][20][21] . Household changes in water use in response to exogenous factors alter utility revenue and potentially necessitate additional rate increases. Accordingly, quantifying water affordability requires explicitly incorporating household-level water...

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.

Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a ''resilience fingerprint'' and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that major disasters such as hurricanes and earthquakes have a unique resilience fingerprint which is consistent between different events of the same type. Specifically, hurricanes have a distinct fingerprint which differentiates them from other major events. We analyze the components underlying the similarity among hurricanes and find that ecological, infrastructure and economic components of community resilience are the primary drivers of the difference between the community resilience of hurricanes and other major events.INDEX TERMS Data analysis, human computer interaction, resilience, Twitter.

Current projections of the climate-sensitive portion of residential electricity demand are based on estimating the temperature response of the mean of the demand distribution. In this work, we show that there is significant asymmetry in the summertime temperature response of electricity demand in the state of California, with high-intensity demand demonstrating a greater sensitivity to temperature increases. The greater climate sensitivity of high-intensity demand is found not only in the observed data, but also in the projections in the near future (2021-2040) and far future periods (2081-2099), and across all (three) utility service regions in California. We illustrate that disregarding the asymmetrical climate sensitivity of demand can lead to underestimating high-intensity demand in a given period by 37-43%. Moreover, the discrepancy in the projected increase in the climate-sensitive portion of demand based on the 50th versus 90th quantile estimates could range from 18 to 40% over the next 20 years. Electricity demand is influenced by many factors, including socio-demographic characteristics 4 , technology 5 , markets 6 , and climate 7-9. Here, we focus on understanding the climate sensitivity of residential electricity demand, which is a critical factor in ensuring the resilient operation of the grid under climate change 1-3. Recent work has isolated the effect of climate variability and change on both peak load (i.e., the highest load in a given time period) and total electricity consumption, indicating climate change will lead to greater electricity use, particularly in the residential sector 2,10-16. This has significant implications as unanticipated increases in cooling demand in the residential sector during heat waves (i.e., periods with sustained positive temperature anomalies) can lead to unexpected supply shortages 17 , distorted electricity market prices 18,19 , as well as increased morbidity and mortality 20 , particularly in vulnerable populations and disadvantaged communities 21. To minimize the economic and social costs of interrupted electricity service, researchers forecast the climatesensitive portion of residential electricity demand during extreme temperatures by harnessing methodologies from various fields, including econometrics 4,22,23 , engineering 23,24 , statistics and machine learning 13,15. However, the existing body of literature has primarily focused on modeling the temperature response of the central tendency (i.e., mean/median) of the demand distribution, as opposed to considering its entire distribution 2,25-27. We hypothesize that projections solely based on the mean/median values of the load distribution underestimate the climate sensitivity of high-intensity demand. Our central hypothesis is that while projections of the climate-demand nexus based on the mean/median values of demand distributions help to characterize the general trends in electricity use over time, they are likely inadequate in characterizing the climate sensitivity of the upper extremes of demand which are...

Credibly assessing the resilience of energy infrastructure in the face of natural disasters is a salient concern facing researchers, government officials, and community members. Here, we explore the influence of the spatial distribution of disruptions due to hurricanes and other natural hazards on the resilience of power distribution systems. We find that incorporating information about the spatial distribution of disaster impacts has significant implications for estimating infrastructure resilience. Specifically, the uncertainty associated with estimated infrastructure resilience metrics to spatially distributed disaster-induced disruptions is much higher than determined by previous methods. We present a case study of an electric power distribution grid impacted by a major landfalling hurricane. We show that improved characterizations of disaster disruption drastically change the way in which the grid recovers, including changes in emergent system properties such as antifragility. Our work demonstrates that previous methods for estimating critical infrastructure resilience may be overstating the confidence associated with estimated network recoveries due to the lack of consideration of the spatial structure of disruptions.

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