Extreme events are of interest worldwide given their potential for substantial impacts on social, ecological, and technical systems. Many climate‐related extreme events are increasing in frequency and/or magnitude due to anthropogenic climate change, and there is increased potential for impacts due to the location of urbanization and the expansion of urban centers and infrastructures. Many disciplines are engaged in research and management of these events. However, a lack of coherence exists in what constitutes and defines an extreme event across these fields, which impedes our ability to holistically understand and manage these events. Here, we review 10 years of academic literature and use text analysis to elucidate how six major disciplines—climatology, earth sciences, ecology, engineering, hydrology, and social sciences—define and communicate extreme events. Our results highlight critical disciplinary differences in the language used to communicate extreme events. Additionally, we found a wide range in definitions and thresholds, with more than half of examined papers not providing an explicit definition, and disagreement over whether impacts are included in the definition. We urge distinction between extreme events and their impacts, so that we can better assess when responses to extreme events have actually enhanced resilience. Additionally, we suggest that all researchers and managers of extreme events be more explicit in their definition of such events as well as be more cognizant of how they are communicating extreme events. We believe clearer and more consistent definitions and communication can support transdisciplinary understanding and management of extreme events.

We test the hypothesis that climate and environmental conditions are becoming favorable for dengue transmission in San Juan, Puerto Rico. Sea Level Pressure (SLP), Mean Sea Level (MSL), Wind, Sea Surface Temperature (SST), Air Surface Temperature (AST), Rainfall, and confirmed dengue cases were analyzed. We evaluated the dengue incidence and environmental data with Principal Component Analysis, Pearson correlation coefficient, Mann-Kendall trend test and logistic regressions. Results indicated that dry days are increasing and wet days are decreasing. MSL is increasing, posing higher risk of dengue as the perimeter of the San Juan Bay estuary expands and shorelines move inland. Warming is evident with both SST and AST. Maximum and minimum air surface temperature extremes have increased. Between 1992 and 2011, dengue transmission increased by a factor of 3.4 (95% CI: 1.9–6.1) for each 1 °C increase in SST. For the period 2007–2011 alone, dengue incidence reached a factor of 5.2 (95% CI: 1.9–13.9) for each 1 °C increase in SST. Teenagers are consistently the age group that suffers the most infections in San Juan. Results help understand possible impacts of different climate change scenarios in planning for social adaptation and public health interventions.

Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

Extreme heat episodes are becoming more common worldwide, including in tropical areas of Australia, India, and Puerto Rico. Higher frequency, duration, and intensity of extreme heat episodes are triggering public health issues in most mid-latitude and continental cities. With urbanization, land use and land cover have affected local climate directly and indirectly encouraging the Urban Heat Island effect with potential impacts on heat-related morbidity and mortality among urban populations. However, this association is not completely understood in tropical islands such as Puerto Rico. The present study examines the effects of heat in two municipalities (San Juan and Bayamón) within the San Juan metropolitan area on overall and cause-specific mortality among the population between 2009 and 2013. The number of daily deaths attributed to selected causes (cardiovascular disease, hypertension, diabetes, stroke, chronic lower respiratory disease, pneumonia, and kidney disease) coded and classified according to the Tenth Revision of the International Classification of Diseases was analyzed. The relations between elevated air surface temperatures on cause-specific mortality were modeled. Separate Poisson regression models were fitted to explain the total number of deaths as a function of daily maximum and minimum temperatures, while adjusting for seasonal patterns. Results show a significant increase in the effect of high temperatures on mortality, during the summers of 2012 and 2013. Stroke (relative risk = 16.80, 95% CI 6.81-41.4) and cardiovascular diseases (relative risk = 16.63, 95% CI 10.47-26.42) were the primary causes of death most associated with elevated summer temperatures. Better understanding of how these heat events affect the health of the population will provide a useful tool for decision makers to address and mitigate the effects of the increasing temperatures on public health. The enhanced temperature forecast may be a crucial component in decision making during the National Weather Service Heat Watches, Advisories, and Warning process.

Increased frequency and length of high heat episodes are leading to more cardiovascular issues and asthmatic responses among the population of San Juan, the capital of the island of Puerto Rico, USA. An urban heat island effect, which leads to foci of higher temperatures in some urban areas, can raise heat-related mortality. The objective of this research is to map the risk of high temperature in particular locations by creating heat maps of the city of San Juan. The heat vulnerability index (HVI) maps were developed using images collected by satellite-based remote sensing combined with census data. Land surface temperature was assessed using images from the Thermal Infrared Sensor flown on Landsat 8. Social determinants (e.g., age, unemployment, education and social isolation, and health insurance coverage) were analyzed by census tract. The data were examined in the context of land cover maps generated using products from the Puerto Rico Terrestrial Gap Analysis Project (USDA Forest Service). All variables were set in order to transform the indicators expressed in different units into indices between 0 and 1, and the HVI was calculated as sum of score. The tract with highest index was considered to be the most vulnerable and the lowest to be the least vulnerable. Five vulnerability classes were mapped (very high, high, moderate, low, and very low). The hottest and the most vulnerable tracts corresponded to highly built areas, including the Luis Munoz International Airport, seaports, parking lots, and high-density residential areas. Several variables contributed to increased vulnerability, including higher rates of the population living alone, disabilities, advanced age, and lack of health insurance coverage. Coolest areas corresponded to vegetated landscapes and urban water bodies. The urban HVI map will be useful to health officers, emergency preparedness personnel, the National Weather Service, and San Juan residents, as it helps to prepare for and to mitigate the potential effects of heat-related illnesses.