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1. Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics

   https://doi.org/10.1029/2022JD037617  

   Lockwood, Joseph W.; Lin, Ning; Oppenheimer, Michael; Lai, Ching‐Yao (December 2022, Journal of Geophysical Research: Atmospheres)
2. Sensitivity of dryland vegetation patterns to storm characteristics

   https://doi.org/doi.org/10.1002/eco.2269  

   Crompton, Octavia Thompson (January 2021, Ecohydrology)

   null (Ed.)

   Ecohydrological phenomena are o ften multiscale in nature, with behavioTur that emerges from the interaction of tightly coupled systems having characteristic timescales that differ by orders of magnitude. Models address these differences using timescale separation methods, where each system is held in psuedo‐steady state while the other evolves. When the computational demands of solving the ‘fast’ system are large, this strategy can become numerically intractable. Here, we use emulation modelling to accelerate the simulation of a computationally intensive ‘fast’ system: overland flow. We focus on dryland ecosystems in which storms generate overland flow, on timescales of 101 − 2 s. In these ecosystems, overland flow delivers crucial water inputs to vegetation, which grows and disperses ‘slowly’, on timescales of 107 − 9 s. Emulation allows for a physically realistic treatment of flow, advancing on phenomenological descriptions used in previous studies. Resolving the within‐storm processes reveals novel dynamics, including new transition pathways from patchy vegetation to desertification, that are specifically controlled by storm processes. 

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3. Predicting groundwater contamination to protect the storm-exposed vulnerable

   https://doi.org/10.1016/j.crm.2023.100499  

   Hochard, Jacob; Abashidze, Nino; Bawa, Ranjit; Carr, Grace; Kirkland, Bailey; Li, Yuanhao; Matlock, Kayla; Siu, Wai Yan (March 2023, Climate risk management)

   Domestic wells provide drinking water to 44 million people nationwide. Many of these wells, which remain federally unregulated and rarely tested for pollutants, serve rural populations clustered near surface-contaminated sites (e.g., hazardous waste sites, animal agriculture operations, coal ash ponds, etc.). The potential for natural disasters to deteriorate drinking water quality is well documented. Less understood is whether opportunistic post-disaster sampling might underrepresent vulnerable populations. When disaster strikes, well water sampling campaigns offer a glimpse into the quality of water for exposed residents. We examined over 8,000 well water samples from 2016 and 2017 to measure Hurricane Matthew’s impact on the presence of indicator bacteria. Bacteria presence was predicted at the household level following Hurricane Matthew’s landfall. The residential addresses associated with birth records as well as clinically estimated dates of conception and birth dates were used to predict the likelihood of indicator bacteria in drinking water sources that were unsampled but likely to have served pregnant women. We estimate that opportunistic well water sampling captures the average predicted contamination rates among households with pregnant women. Our approach documents a distribution of contamination risk where 2.7% of the vulnerable sample (670 unsampled households) have a 75% likelihood of total coliform presence. The predicted likelihood of indicator bacteria is elevated for a small share of households nearby swine lagoons that experienced the most torrential rainfall. However, the gap between sampled and unsampled households cannot otherwise be explained by the storm event or proximity to surface-contaminated sites. Findings suggest that sophisticated and holistic water quality prediction models may support post-disaster sampling campaigns by targeting individual households within vulnerable groups that are likely to experience higher risks from groundwater contamination. 

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4. Consumer Response to Boil Water Notifications During Winter Storm Uri

   https://doi.org/10.1002/awwa.1919  

   Day, Ashleigh M.; Islam, Khairul; O'Shay, Sydney; Taylor, Kristin; McElmurry, Shawn P.; Seeger, Matthew W. (June 2022, Journal AWWA)
5. Near‐Earth Reconnection Contributing to Recovery Phase of Geomagnetic Storm

   https://doi.org/10.1029/2024GL112730  

   Liu, Terry_Z; Angelopoulos, Vassilis; Nishimura, Yukitoshi; Shen, Yangyang; Shi, Xueling; Hartinger, Michael_D (December 2024, Geophysical Research Letters)

   Abstract Recent observations show very near‐Earth reconnection (∼8–13R_E) could efficiently power the ring current during the main phase of geomagnetic storms, but whether the recovery phase might be contributed remains unclear. During the recovery phase of the May 2024 major geomagnetic storm, intense auroral brightening and geomagnetic disturbances were observed at midnight, indicative of particle injections. Current wedges observed by mid‐latitude ground magnetometers around midnight suggest dipolarizing flux bundles (DFBs). The latitude of the auroral brightening was clearly lower than usual, suggesting near‐Earth reconnection (NERX) was closer to Earth than during substorms (∼20–30R_E). GOES‐18 at midnight detected magnetic field and plasma signatures consistent with DFBs, following an extremely thin current sheet likely compressed by strong upstream dynamic pressure. These results indicate NERX could have been close enough for resultant DFBs to penetrate geosynchronous orbit and contribute to the ring current during the recovery phase. This scenario deserves further examination in future. 

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