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Tropical wetland ecosystems are threatened by climate change but also play a key role in its mitigation and adaptation through management of land use and other drivers. Local-level assessments are needed to support evidence-based wetland management in the face of climate change. This study aims to examine the local communities’ knowledge and perception of climate change in Yala wetland, Kenya, and compare them with observed data on climate trends. Such comparisons are useful to inform context-specific climate change adaptation actions.

The study used a mixed methods approach that combined analysis of climate data with perceptions from the local community. Gridded data on temperature and rainfall for the period from 1981 to 2018 were compared with data on climate change perceptions from semi-structured questionnaires with 286 key informants and community members.

Majority of the respondents had observed changes in climate parameters – severe drought (88.5%), increased frequency of floods (86.0%) and irregular onset and termination of rains (90.9%) in the past 20 years. The perceptions corresponded with climate trends that showed a significant increasing trend in the short rains and the average maximum temperature, high incidence of very wet years and variability in onset and termination of rainfall between 1981 and 2018. Gender, age and education had little influence on knowledge and awareness of climate change, except for frequency of floods and self-reported understanding of climate change. The community perceived the wetland to be important for climate change adaptation, particularly the provision of resources such as grazing grounds during drought.

The study faced challenges of low sample size, use of gridded climate data and reproducibility in other contexts. The results of this study apply to local communities in a tropical wetland in Western Kenya, which has a bi-modal pattern of rainfall. The sample of the study was regional and may therefore not be representative of the whole of Kenya, which has diverse socioeconomic and ecological contexts. Potential problems have been identified with the use of gridded data (for example, regional biases in models), although their usefulness in data scarce contexts is well established. Moreover, the sample size has been found to be a less important factor in research of highly complex socio-ecological systems where there is an attempt to bridge natural and social sciences.

This study addresses the paucity of studies on climate change trends in papyrus wetlands of sub-Saharan Africa and the role of local knowledge and perceptions in influencing the management of such wetlands. Perceptions largely influence local stakeholders’ decisions, and a study that compares perceptions vs “reality” provides evidence for engagement with the stakeholders in managing these highly vulnerable ecosystems. The study showed that the local community’s perceptions corresponded with the climate record and that adaptation measures are already ongoing in the area.

This study presents a case for the understanding of community perceptions and knowledge of climate change in a tropical wetland under threat from climate change and land use change, to inform management under a changing climate.

The purpose of this study is to analyze the seasonal spatiotemporal climate variability in the Borena zone of Ethiopia and its effects on agriculture and livestock production. By examining these climate variables in relation to global sea surface temperatures (SST) and atmospheric pressure systems, the study seeks to understand the underlying mechanisms driving local climate variability. Furthermore, it assesses how these climate variations impact crop yields, particularly wheat and livestock production, providing valuable insights for developing effective adaptation strategies and policies to enhance food security and economic stability in the region.

The design and methodology of this study involve a multifaceted approach to analyzing seasonal spatiotemporal climate variability in the Borena zone of Ethiopia. The research uses advanced statistical techniques, including rotated empirical orthogonal function (EOF) and rotated principal component analysis (RPCA), to identify and quantify significant patterns in seasonal rainfall, temperature and drought indices over the period from 1981 to 2022. These methods are used to reveal the spatiotemporal variations and trends in climate variables. To understand the causal mechanisms behind these variations, the study correlates seasonal rainfall data with global SST and examines atmospheric pressure systems and wind vectors. In addition, the impact of climate variability on agricultural and livestock production is assessed by linking observed climate patterns with changes in crop yields, particularly wheat and livestock productivity. This comprehensive approach integrates statistical analysis with environmental and agricultural data to provide a detailed understanding of climate dynamics and their practical implications.

The findings of this study reveal significant seasonal spatiotemporal climate variability in the Borena zone of Ethiopia, characterized by notable patterns and trends in rainfall, temperature and drought indices from 1981 to 2022. The analysis identified that over 84% of the annual rainfall occurs during the March to May (MAM) and September to November (SON) seasons, with MAM contributing approximately 53% and SON over 31%, highlighting these as the primary rainfall periods. Significant spatiotemporal variations were observed, with northwestern (35.4%), southern (34.9%) and northeastern (19.3%) are dominant variability parts of the zone during MAM season, similarly southeastern (48.7%), and northcentral (37.8%) are dominant variability parts of the zone during SON season. Trends indicating that certain subregions experience more pronounced changes in climate variables in both seasons. Correlation with global SST and an examination of atmospheric pressure systems elucidated the mechanisms driving these variations, with significant correlation with the southern and central part of Indian Ocean. This study also found that fluctuations in climate variables significantly impact crop production, particularly wheat and livestock productivity in the region, underscoring the need for adaptive strategies to mitigate adverse effects on agriculture and food security.

The implications of this study highlight the need for robust adaptation strategies to mitigate the effects of climate variability. Detailed research on seasonal climate patterns and the specific behaviors of livestock and crops is essential. Gaining a thorough understanding of these dynamics is critical for developing resilient adaptation strategies tailored to the unique ecological and economic context of the Borana zone. Future research should focus on seasonal climate variations and their implications to guide sustainable development and livelihood adjustments in the region.

This study offers significant originality and value by providing a detailed analysis of seasonal spatiotemporal climate variability in the Borena zone of Ethiopia, using advanced statistical techniques such as rotated EOF and RPCA. By integrating these methods with global SST data and atmospheric pressure systems, the research delivers a nuanced understanding of how global climatic factors influence local weather patterns. The study’s novel approach not only identifies key trends and patterns in climate variables over an extensive historical period but also links these findings to practical outcomes in crop and livestock production. This connection is crucial for developing targeted adaptation strategies and policies, thereby offering actionable insights for enhancing agricultural practices and food security in the region. The originality of this work lies in its comprehensive analysis and practical relevance, making it a valuable contribution to both climate science and regional agricultural planning.

Using two main research questions, the purpose of this paper is to examine well-being and preparedness among Cambodian and Laotian immigrants living near the Gulf Coast of the USA, and the ways in which indicators such as sense of community and risk perception are related to these constructs.

This study employed a cross-sectional prospective design to examine disaster preparedness and well-being among Laotian and Cambodian immigrant communities. Quantitative survey data using purposive snowball sampling were collected throughout several months in Alabama, Mississippi, Florida and Louisiana.

Results from two multiple regressions revealed that sense of community and age contributed to well-being and were significant in the model, but with a negative relationship between age and well-being. Risk perception, confidence in government, confidence in engaging household preparedness and ability to cope with a financial crisis were significant predictors and positively related to disaster preparedness.

Well-being and disaster preparedness can be bolstered through community-based planning that seeks to address urgent needs of the people residing in vulnerable coastal locations. Specifically, immigrants who speak English as a second language, elder individuals and households in the lowest income brackets should be supported in disaster planning and outreach.

Cambodian and Laotian American immigrants rely upon the Gulf Coast’s waters for fishing, crab and shrimp income. Despite on-going hazard and disasters, few studies address preparedness among immigrant populations in the USA. This study fills a gap in preparedness research as well as factors associated with well-being, an important aspect of long-term resilience.