Abstract To examine emergency communication on Twitter across different phases of Hurricane Irma, we developed a Python script to access the Twitter Premium application programming interface (API). We applied a stratified sampling method to build a representative sample of 1,168,484 Irma-related tweets posted from August 30, 2017 to September 28, 2017. Using natural language processing (NLP) algorithms, we categorized tweet topics and traced the evolution of communication patterns in affected Florida communities. Our analysis validated established temporal markers for hurricane phases, such as the issuance of watches and the timing of landfall. We further compared emergency communication patterns between counties that were more directly impacted by Hurricane Irma and those outside its path, highlighting significant geographic variations. The results underscore the importance of phase- and location-specific messaging and suggest that emergency management agencies should adopt a more proactive communication approach during the pre-event stage and immediately following landfall.
Abstract Satellite altimetry is a key method for monitoring water level, analyzing variations from intraseasonal to interannual scales, and aiding drought monitoring in watersheds. Radio detection and ranging (RADAR) altimetry has been employed to determine water surface elevations, yet assessing the accuracy of satellite RADAR altimetry in specific regions remains important. This research focuses on validating water level time series derived from satellite altimetry and on exploring their usefulness for drought detection. Afterward, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) were used to analyze the relationships between satellite altimetry-measured water level changes and drought occurrence. Water level time series from environmental satellite (Envisat), satellite with ARgos and ALtiKa (SARAL), Sentinel-3A, Sentinel-3B, The Joint Altimetry Satellite Oceanography Network (JASON) series were extracted and compared with observed daily water levels for eight water bodies in Iran between 2000 and 2022. Results showed lower root mean square error (RMSE) values (0.04 and 0.07 m) for SARAL and Sentinel-3A, respectively, and higher R-squared ( R 2 ) values (0.98 and 0.97) for Sentinel-3A and SARAL, respectively. Subsequently, the SPI and the SPEI were used to analyze relationships between satellite altimetry-measured water-level changes and drought occurrence, SPI and SPEI trends, and the capability of SPI and SPEI in monitoring drought events. Results showed that the Envisat altimetry mission at Zarivar Lake performed optimally, with an average probable drought detection of 73% in detecting mild drought conditions. Overall, this research has demonstrated that satellite altimetry data can monitor water-level changes for drought assessment.
Abstract This study examines whether companion animal guardianship characteristics have an association with disaster risk perception, impact perception, and evacuation willingness among Atlantic Canadian guardians. While previous research often treats guardianship as a binary measure, this study considers the count, type, health status, and role of companion animals. Based on 936 valid survey responses, proportional odds logistic regression models show each additional companion animal reduced the odds of higher risk perception (OR = 0.872, p < 0.01 ), impact perception (OR = 0.910, p = 0.016 ), and evacuation willingness (OR = 0.876, p < 0.01 ). Animal type was associated with odds ranges of 0.70–1.52 for risk perception, 0.68–1.71 for impact perception, and 1.00–1.92 for evacuation willingness. Guardianship of animals with chronic health conditions or emotional-support or service animals was associated with higher perceived impact but lower evacuation willingness. Findings suggest preparedness should address multianimal households and species-specific evacuation needs.
Abstract Drought disasters are the most severe natural disasters affecting China’s grain yields, characterized by prolonged duration and extensive impact. Under the integrity of the ecosystem and the interaction among factors, drought not only directly affects grain yields in the affected areas, but also further influences grain yields in neighboring regions by changing agricultural production conditions. Based on the panel data of 31 provinces in China from 1989 to 2023, this paper employs Moran’s I to analyze the spatial autocorrelation and agglomeration characteristics of grain yields. The spatial Durbin model is further utilized to examine the spatial spillover effects of drought disasters on grain yields and their regional heterogeneity. The empirical results reveal significant spatial autocorrelation in grain yields, with local characteristics of high–high and low–low agglomeration. Drought disasters exert a significant inhibitory effect on grain yields and generate negative spillover effects that transcend administrative boundaries, impacting surrounding regions. These effects exhibit pronounced regional heterogeneity: from the perspective of economic development, significant spillover effects are observed in the eastern and northeastern regions, but not in the central region. In terms of industrial attributes, the intensity of spillover effects is greater in major grain-producing areas compared to nonmajor grain-producing areas. The findings of this paper provide insights for integrating disaster response into the national food security industrial belt planning, enabling a closed-loop management framework that encompasses “disaster impact, spatial transmission, and policy response.”
