Browsing by Author "Doss-Gollin, James"
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Item Compound Climate Risk: Diagnosing Clustered Regional Flooding at Inter-Annual and Longer Time Scales(MDPI, 2023) Amonkar, Yash; Doss-Gollin, James; Lall, UpmanuThe potential for extreme climate events to cluster in space and time has driven increased interest in understanding and predicting compound climate risks. Through a case study on floods in the Ohio River Basin, we demonstrated that low-frequency climate variability could drive spatial and temporal clustering of the risk of regional climate extremes. Long records of annual maximum streamflow from 24 USGS gauges were used to explore the regional spatiotemporal patterns of flooding and their associated large-scale climate modes. We found that the dominant time scales of flood risk in this basin were in the interannual (6–7 years), decadal (11–13 years), and secular bands and that different sub-regions within the Ohio River Basin responded differently to large-scale forcing. We showed that the leading modes of streamflow variability were associated with ENSO and secular trends. The low-frequency climate modes translated into epochs of increased and decreased flood risk with multiple extreme floods or the absence of extreme floods, thus informing the nature of compound climate-induced flood risk. A notable finding is that the secular trend was associated with an east-to-west shift in the flood incidence and the associated storm track. This is consistent with some expectations of climate change projections.Item Differential effects of climate change on average and peak demand for heating and cooling across the contiguous USA(Springer Nature, 2023) Amonkar, Yash; Doss-Gollin, James; Farnham, David J.; Modi, Vijay; Lall, UpmanuWhile most electricity systems are designed to handle peak demand during summer months, long-term energy pathways consistent with deep decarbonization generally electrify building heating, thus increasing electricity demand during winter. A key question is how climate variability and change will affect peak heating and cooling demand in an electrified future. We conduct a spatially explicit analysis of trends in temperature-based proxies of electricity demand over the past 70 years. Average annual demand for heating (cooling) decreases (increases) over most of the contiguous US. However, while climate change drives robust increases in peak cooling demand, trends in peak heating demand are generally smaller and less robust. Because the distribution of temperature exhibits a long left tail, severe cold snaps dominate the extremes of thermal demand. As building heating electrifies, system operators must account for these events to ensure reliability.Item Enhanced solar and wind potential during widespread temperature extremes across the U.S. interconnected energy grids(IOP Publishing, 2024) Singh, Deepti; Bekris, Yianna S.; Rogers, Cassandra D. W.; Doss-Gollin, James; Coffel, Ethan D.; Kalashnikov, Dmitri A.Several recent widespread temperature extremes across the United States (U.S.) have been associated with power outages, disrupting access to electricity at times that are critical for the health and well-being of communities. Building resilience to such extremes in our energy infrastructure needs a comprehensive understanding of their spatial and temporal characteristics. In this study, we systematically quantify the frequency, extent, duration, and intensity of widespread temperature extremes and their associated energy demand in the six North American Electric Reliability Corporation regions using ERA5 reanalysis data. We show that every region has experienced hot or cold extremes that affected nearly their entire extent and such events were associated with substantially higher energy demand, resulting in simultaneous stress across the entire electric gird. The western U.S. experienced significant increases in the frequency (123%), extent (32%), duration (55%) and intensity (29%) of hot extremes and Texas experienced significant increases in the frequency (132%) of hot extremes. The frequency of cold extremes has decreased across most regions without substantial changes in other characteristics. Using power outage data, we show that recent widespread extremes in nearly every region have coincided with power outages, and such outages account for between 12%–52% of all weather-related outages in the past decade depending on the region. Importantly, we find that solar potential is significantly higher during widespread hot extremes in all six regions and during widespread cold extremes in five of the six regions. Further, wind potential is significantly higher during widespread hot or cold extremes in at least three regions. Our findings indicate that increased solar and wind capacity could be leveraged to meet the higher demand for energy during such widespread extremes, improving the resilience and reliability of our energy systems in addition to limiting carbon emissions.Item How unprecedented was the February 2021 Texas cold snap?(IOP Publishing, 2021) Doss-Gollin, James; Farnham, David J.; Lall, Upmanu; Modi, VijayWinter storm Uri brought severe cold to the southern United States in February 2021, causing a cascading failure of interdependent systems in Texas where infrastructure was not adequately prepared for such cold. In particular, the failure of interconnected energy systems restricted electricity supply just as demand for heating spiked, leaving millions of Texans without heat or electricity, many for several days. This motivates the question: did historical storms suggest that such temperatures were known to occur, and if so with what frequency? We compute a temperature-based proxy for heating demand and use this metric to answer the question ‘what would the aggregate demand for heating have been had historic cold snaps occurred with today’s population?’. We find that local temperatures and the inferred demand for heating per capita across the region served by the Texas Interconnection were more severe during a storm in December 1989 than during February 2021, and that cold snaps in 1951 and 1983 were nearly as severe. Given anticipated population growth, future storms may lead to even greater infrastructure failures if adaptive investments are not made. Further, electricity system managers should prepare for trends in electrification of heating to drive peak annual loads on the Texas Interconnection during severe winter storms.Item Mesoscale Modeling of Distributed Water Systems Enables Policy Search(Wiley, 2023) Zhou, Xiangnan; Duenas-Osorio, Leonardo; Doss-Gollin, James; Liu, Lu; Stadler, Lauren; Li, Qilin; Nanosystems Engineering Research Center for Nanotechnology-Enabled Water TreatmentIt is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, it is unclear which are beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a mesoscale representation model that approximates DWSs with reduced backbone networks to enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multiobjective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston's water and wastewater infrastructure. The backbone DWS with greater than 92% link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case-specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The mesoscale modeling approach and the multiobjective optimization model developed in this study can serve as practical decision-support tools for stakeholders to search for alternative DWS options in urban settings.Item Water Supply Vulnerability Testing and Robust Planning Analysis with Exploratory Modeling under Deep Uncertainty(2022-04-19) Graham, Alyssa A; Bedient, Phil; Doss-Gollin, JamesHouston faces growing population and severe droughts, suggesting a need for robust water supply planning. Current literature proposes an exploratory modeling approach; however, Texas regions typically project water needs as point predictions based on historical droughts and an uncertainty factor. This thesis employs exploratory modeling to identify under which drought and demand conditions Houston’s water will be insufficient. The model includes a reservoir mass balance, Monte Carlo simulation to generate scenarios, and scenario discovery of shortages regardless of likelihood. Scenarios are compared to projections for context. Generally, scenarios with higher demand and 2011 drought demonstrated moderate supply shortage; scenarios with worse drought demonstrated extreme supply shortage. Houston’s proposed strategies mitigated higher demand, but had negligible mitigation for worse drought conditions. Given that climate change is expected to exacerbate droughts in Texas, this analysis underscores a need for water resource management in Houston to explore many possible futures.