Search Results (7)

In 2021, Winter Storm Uri severely affected several Texan agricultural sectors, including dairy production. To understand how dairy producers experienced this extreme weather event, this qualitative phenomenological case study explored perceptions of preparedness, coping strategies, and information needs and preferences for dealing with extreme weather events among dairy producers in Texas, conducting individual semi-structured interviews. The findings indicated that farmers felt unprepared to deal with extreme weather events and suffered significant economic losses due to this lack of preparedness. In response to winter storm Uri, dairy farmers modified traditional operations and management practices to mitigate negative impacts on farm labor, infrastructure, and herds. Our results, along with the existing literature on communication for extreme weather event management, highlighted that dairy farmers do not receive adequate information to effectively prevent and cope with similar occurrences in the future. Consequently, this study recommends exploring effective strategies to help agricultural producers develop plans to manage the effects of extreme weather events. Additionally, it integrates place-based, pluralistic, and demand-driven approaches to identify the best communication practices, enhance timely information dissemination on extreme weather, and strengthen the technical capacities of public and private entities, including Cooperative Extension Systems, as trusted resources for agricultural producers. Full article

Estuarine phytoplankton communities are acclimated to environmental parameters that change seasonally. With climate change, they are having to respond to extreme weather events that create dramatic alterations to ecosystem function(s) on the scale of days. Herein, we examined the short term (<1 month) shifts in phytoplankton communities associated with four pulse disturbances (Tax Day Flood in 2016, Hurricane Harvey in 2017, Tropical Storm Imelda in 2019, and Winter Storm Uri in 2021) that occurred in Galveston Bay (TX, USA). Water samples collected daily were processed using an Imaging FlowCytobot (IFCB), along with concurrent measurements of temperature, salinity, and chlorophyll-a. Stronger storm events with localized heavy precipitation and flooding had greater impacts on community composition, increasing diversity (Shannon–Weiner and Simpson Indices) while a cold wave event lowered it. Diatoms and dinoflagellates accounted for the largest fraction of the community, cyanobacteria and chlorophytes varied mostly with salinity, while euglenoids, cryptophytes, and raphidophytes, albeit at lower densities, fluctuated greatly. The unconstrained variance of the redundancy analysis models pointed to additional environmental processes than those measured being responsible for the changes observed. These findings provide insights into the impact of pulse disturbances of different magnitudes, durations, and timings on phytoplankton communities. Full article

Winter storms occur in the Gulf of Mexico (GoM) every few years, but there are not many studies on oceanic responses to severe winter storms. Although usually considered less destructive than hurricanes, they can result in cumulative damages. Winter Storm Outbreak of February 2021 (WSO21), the most intense winter storm to impact Texas and the GoM in 30 years, passed over the western GoM and brought severe cold to the GoM coastal regions, which caused a sudden cooling of the ocean surface, resulting in an extensive loss of marine life. In this study, we analyze multiple datasets from both in situ and satellite observations to examine the oceanic changes due to WSO21 in order to improve our understanding of oceanic responses to winter storms. Although the pre-storm sea surface temperature (SST) was 1–2 °C warmer than normal, severe coastal cold spells caused a significant cooling of the order of −3 °C to −5 °C during WSO21 and a −1 °C average cooling in the mixed layer (ML) over the western GoM. Net surface heat loss played a primary role in the upper ocean cooling during WSO21 and explained more than 50% of the cooling that occurred. Convective mixing due to surface cooling and turbulent mixing induced by enhanced wind speeds significantly increase the surface ML in the western GoM. Apart from rapid changes in SST and heat fluxes due to air-sea interactions, persistent upwelling brings nutrients to the surface and can produce coastal “winter” blooms along the Texas and Mexico coast. Prominent salinity increases along the coastal regions during and after WSO21 were another indicator of wind-induced coastal upwelling. Our study demonstrates the utility of publicly-available datasets for studying the impact of winter storms on the ocean surface. Full article

Texas was hit by a record-setting cold snap from the 14–17 February 2021 after three decades that resulted in power outages, disruption of the public water systems, and other cascading effects. This study investigates the unprecedented impact of winter storm Uri on power outages, air quality, and water systems in Texas, USA. Analysis of the Parameter Regression of Independent Slopes Model (PRISM) gridded climate data showed that the average daily freezing temperature range was 0–−19 °C on 14 February 2021, with severe levels (−17–−19 °C) occurring in the Texas High Plains. Our results showed that the extreme freezing temperature persisted from 14–17 February 2021, significantly affecting power operation and reliability, and creating power outages across Texas. Uri impacted the public water systems and air quality on time scales ranging from a few minutes to several days, resulting in 322 boiling notices. The air quality index level exceeded the standard limit by 51.7%, 61.7%, 50.8%, and 60% in Dallas–Fort Worth, Houston–Galveston, Austin, and Lubbock regions. The level of the pollutants exceeded the EPA NAAQS standard allowable limits during winter storm Uri. In general, this study gives information on the government’s future preparedness, policies, communication, and response to storm impacts on vulnerable regions and communities. Full article

Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method. Full article

The winter storm Uri that occurred in February 2021 affected many regions in Canada, the United States, and Mexico. The State of Texas was severely impacted due to the failure in the electricity supply infrastructure compounded by its limited connectivity to other grid systems in the United States. The georeferenced estimation of the storm’s impact is crucial for response and recovery. However, such information was not available until several months afterward, mainly due to the time-consuming and costly assessment processes. The latency to provide timely information particularly impacted people in the economically disadvantaged communities, who lack resources to ameliorate the impact of the storm. This work explores the potential for disaster impact estimation based on the analysis of instant social media content, which can provide actionable information to assist first responders, volunteers, governments, and the general public. In our prototype, a deep neural network (DNN) uses geolocated social media content (texts, images, and videos) to provide monetary assessments of the damage at zip code level caused by Uri, achieving up to 70% accuracy. In addition, the performance analysis across geographical regions shows that the fully trained model is able to estimate the damage for economically disadvantaged regions, such as West Texas. Our methods have the potential to promote social equity by guiding the deployment or recovery resources to the regions where it is needed based on damage assessment. Full article

Although evidence suggests that successive climate disasters are on the rise, few studies have documented the disproportionate impacts on communities of color. Through the unique lens of successive disaster events (Hurricane Harvey and Winter Storm Uri) coupled with the COVID-19 pandemic, we assessed disaster exposure in minority communities in Harris County, Texas. A mixed methods approach employing qualitative and quantitative designs was used to examine the relationships between successive disasters (and the role of climate change), population geography, race, and health disparities-related outcomes. This study identified four communities in the greater Houston area with predominantly non-Hispanic African American residents. We used data chronicling the local community and environment to build base maps and conducted spatial analyses using Geographic Information System (GIS) mapping. We complemented these data with focus groups to assess participants’ experiences in disaster planning and recovery, as well as community resilience. Thematic analysis was used to identify key patterns. Across all four communities, we observed significant Hurricane Harvey flooding and significantly greater exposure to 10 of the 11 COVID-19 risk factors examined, compared to the rest of the county. Spatial analyses reveal higher disease burden, greater social vulnerability, and significantly higher community-level risk factors for both pandemics and disaster events in the four communities, compared to all other communities in Harris County. Two themes emerged from thematic data analysis: (1) Prior disaster exposure prepared minority populations in Harris County to better handle subsequent disaster suggesting enhanced disaster resilience, and (2) social connectedness was key to disaster resiliency. Long-standing disparities make people of color at greater risk for social vulnerability. Addressing climate change offers the potential to alleviate these health disparities. Full article