Evaluating the Sensitivity of HeatWave Definitions among North Carolina Physiographic Regions

Exposure to extreme heat is a known risk factor that is associated with increased heat-related illness (HRI) outcomes. The relevance of heat wave definitions (HWDs) could change across health conditions and geographies due to the heterogenous climate profile. This study compared the sensitivity of 28 HWDs associated with HRI emergency department visits over five summer seasons (2011–2016), stratified by two physiographic regions (Coastal and Piedmont) in North Carolina. The HRI rate ratios associated with heat waves were estimated using the generalized linear regression framework assuming a negative binomial distribution. We compared the Akaike Information Criterion (AIC) values across the HWDs to identify an optimal HWD. In the Coastal region, HWDs based on daily maximum temperature with a threshold \u3e 90th percentile for two or more consecutive days had the optimal model fit. In the Piedmont region, HWD based on the daily minimum temperature with a threshold value \u3e 90th percentile for two or more consecutive days was optimal. The HWDs with optimal model performance included in this study captured moderate and frequent heat episodes compared to the National Weather Service (NWS) heat products. This study compared the HRI morbidity risk associated with epidemiologic-based HWDs and with NWS heat products. Our findings could be used for public health education and suggest recalibrating NWS heat products

Mapping Heat Vulnerability Index Based on Different Urbanization Levels in Nebraska, USA

Heatwaves cause excess mortality and physiological impacts on humans throughout the world, and climate change will intensify and increase the frequency of heat events. Many adaptation and mitigation studies use spatial distribution of highly vulnerable local populations to inform heat reduction and response plans. However, most available heat vulnerability studies focus on urban areas with high heat intensification by Urban Heat Islands (UHIs). Rural areas encompass different environmental and socioeconomic issues that require alternate analyses of vulnerability. We categorized Nebraska census tracts into four urbanization levels, then conducted factor analyses on each group and captured different patterns of socioeconomic vulnerabilities among resultant Heat Vulnerability Indices (HVIs). While disability is the major component of HVI in two urbanized classes, lower education, and races other than white have higher contributions in HVI for the two rural classes. To account for environmental vulnerability of HVI, we considered different land type combinations for each urban class based on their percentage areas and their differences in heat intensifications. Our results demonstrate different combinations of initial variables in heat vulnerability among urban classes of Nebraska and clustering of high and low heat vulnerable areas within the highest urbanized sections. Less urbanized areas show no spatial clustering of HVI. More studies with separation on urbanization level of residence can give insights into different socioeconomic vulnerability patterns in rural and urban areas, while also identifying changes in environmental variables that better capture heat intensification in rural settings

Evaluating 21st Century Climate Change for Bolivia: A Comprehensive Dynamical Downscaling Strategy Using the WRF Regional Climate Model

Bolivia is a low-latitude, developing country at grave risk for the effects of human-induced climate changes. This means evaluating the consequences of projected future climate changes is of significant importance. Unfortunately, the complex topography and high elevation of much of the country pose particular challenges, as these effects cannot be suitably resolved at the approximately 100 km spatial resolution of current global climate models (GCM). Therefore, a comprehensive suite of high-resolution climate change simulations was made focused on Bolivia are run using three different GCMs with three different emission scenarios for each to drive the WRF regional climate model. Beyond the results specific to Bolivia, this study is a demonstration of a robust yet viable approach to providing high-resolution, practical, and useable climate change information for any region regardless of global location. GCM performances in Bolivia show three CMIP5 GCMs of MPI-ESM-LR, MIROC5 and CCSM4 are among the models that can successfully regenerate the large-scale atmospheric circulation over South America and more specifically over Bolivia. Initializing the WRF model by the above mentioned GCMs and the NCEP/NCAR reanalysis data then provides us with finer resolution climatic data at 36, 12 and 4 km that are later used for the climate change assessment over Bolivia. The results for the WRF model evaluation confirm the added value of the regional climate model in capturing the effects of topography and local features, on simulating more realistic weather and climate especially on the mountainous regions. Finally, the outcomes of the climate change assessment confirm that the climate mean and extreme patterns are changing in Bolivia as the precipitation is predicted to increase over the Amazon, particularly in the flood-prone region to the west, and decrease in the drier Altiplano. The temperature is predicted to increase across the country with more pronounced warming on the higher elevations where water availability is already a challenge. As one of the costliest hazards in the country, drought patterns are projected to change in the lowlands by having shorter lengths with greater severity while in the highlands conditions are worsening where drought events are predicted to last longer with enhanced severity

Evaluating 21st Century Climate Change for Bolivia: A Comprehensive Dynamical Downscaling Strategy Using the WRF Regional Climate Model

Bolivia is a low-latitude, developing country at grave risk for the effects of human-induced climate changes. This means evaluating the consequences of projected future climate changes is of significant importance. Unfortunately, the complex topography and high elevation of much of the country pose particular challenges, as these effects cannot be suitably resolved at the approximately 100 km spatial resolution of current global climate models (GCM). Therefore, a comprehensive suite of high-resolution climate change simulations was made focused on Bolivia are run using three different GCMs with three different emission scenarios for each to drive the WRF regional climate model. Beyond the results specific to Bolivia, this study is a demonstration of a robust yet viable approach to providing high-resolution, practical, and useable climate change information for any region regardless of global location. GCM performances in Bolivia show three CMIP5 GCMs of MPI-ESM-LR, MIROC5 and CCSM4 are among the models that can successfully regenerate the large-scale atmospheric circulation over South America and more specifically over Bolivia. Initializing the WRF model by the above mentioned GCMs and the NCEP/NCAR reanalysis data then provides us with finer resolution climatic data at 36, 12 and 4 km that are later used for the climate change assessment over Bolivia. The results for the WRF model evaluation confirm the added value of the regional climate model in capturing the effects of topography and local features, on simulating more realistic weather and climate especially on the mountainous regions. Finally, the outcomes of the climate change assessment confirm that the climate mean and extreme patterns are changing in Bolivia as the precipitation is predicted to increase over the Amazon, particularly in the flood-prone region to the west, and decrease in the drier Altiplano. The temperature is predicted to increase across the country with more pronounced warming on the higher elevations where water availability is already a challenge. As one of the costliest hazards in the country, drought patterns are projected to change in the lowlands by having shorter lengths with greater severity while in the highlands conditions are worsening where drought events are predicted to last longer with enhanced severity

Evaluating the Sensitivity of Heat Wave Definitions among North Carolina Physiographic Regions

Exposure to extreme heat is a known risk factor that is associated with increased heat-related illness (HRI) outcomes. The relevance of heat wave definitions (HWDs) could change across health conditions and geographies due to the heterogenous climate profile. This study compared the sensitivity of 28 HWDs associated with HRI emergency department visits over five summer seasons (2011–2016), stratified by two physiographic regions (Coastal and Piedmont) in North Carolina. The HRI rate ratios associated with heat waves were estimated using the generalized linear regression framework assuming a negative binomial distribution. We compared the Akaike Information Criterion (AIC) values across the HWDs to identify an optimal HWD. In the Coastal region, HWDs based on daily maximum temperature with a threshold > 90th percentile for two or more consecutive days had the optimal model fit. In the Piedmont region, HWD based on the daily minimum temperature with a threshold value > 90th percentile for two or more consecutive days was optimal. The HWDs with optimal model performance included in this study captured moderate and frequent heat episodes compared to the National Weather Service (NWS) heat products. This study compared the HRI morbidity risk associated with epidemiologic-based HWDs and with NWS heat products. Our findings could be used for public health education and suggest recalibrating NWS heat products

Evaluating the Sensitivity of Heat Wave Definitions among North Carolina Physiographic Regions.

Exposure to extreme heat is a known risk factor that is associated with increased heat-related illness (HRI) outcomes. The relevance of heat wave definitions (HWDs) could change across health conditions and geographies due to the heterogenous climate profile. This study compared the sensitivity of 28 HWDs associated with HRI emergency department visits over five summer seasons (2011-2016), stratified by two physiographic regions (Coastal and Piedmont) in North Carolina. The HRI rate ratios associated with heat waves were estimated using the generalized linear regression framework assuming a negative binomial distribution. We compared the Akaike Information Criterion (AIC) values across the HWDs to identify an optimal HWD. In the Coastal region, HWDs based on daily maximum temperature with a threshold > 90th percentile for two or more consecutive days had the optimal model fit. In the Piedmont region, HWD based on the daily minimum temperature with a threshold value > 90th percentile for two or more consecutive days was optimal. The HWDs with optimal model performance included in this study captured moderate and frequent heat episodes compared to the National Weather Service (NWS) heat products. This study compared the HRI morbidity risk associated with epidemiologic-based HWDs and with NWS heat products. Our findings could be used for public health education and suggest recalibrating NWS heat products

Estimating the Burden of Heat‐Related Illness Morbidity Attributable to Anthropogenic Climate Change in North Carolina

Abstract Climate change is known to increase the frequency and intensity of hot days (daily maximum temperature ≥30°C), both globally and locally. Exposure to extreme heat is associated with numerous adverse human health outcomes. This study estimated the burden of heat‐related illness (HRI) attributable to anthropogenic climate change in North Carolina physiographic divisions (Coastal and Piedmont) during the summer months from 2011 to 2016. Additionally, assuming intermediate and high greenhouse gas emission scenarios, future HRI morbidity burden attributable to climate change was estimated. The association between daily maximum temperature and the rate of HRI was evaluated using the Generalized Additive Model. The rate of HRI assuming natural simulations (i.e., absence of greenhouse gas emissions) and future greenhouse gas emission scenarios were predicted to estimate the HRI attributable to climate change. Over 4 years (2011, 2012, 2014, and 2015), we observed a significant decrease in the rate of HRI assuming natural simulations compared to the observed. About 3 out of 20 HRI visits are attributable to anthropogenic climate change in Coastal (13.40% [IQR: −34.90,95.52]) and Piedmont (16.39% [IQR: −35.18,148.26]) regions. During the future periods, the median rate of HRI was significantly higher (78.65%: Coastal and 65.85%: Piedmont), assuming a higher emission scenario than the intermediate emission scenario. We observed significant associations between anthropogenic climate change and adverse human health outcomes. Our findings indicate the need for evidence‐based public health interventions to protect human health from climate‐related exposures, like extreme heat, while minimizing greenhouse gas emissions