Unveiling heatwave events in Bangladesh: Insights from observational records and ERA5 reanalysis data
Notice bibliographique
Résumé
Heatwaves (HWs) are escalating in frequency and intensity, posing serious risks to human health, agriculture, and infrastructure worldwide. However, the lack of a universally accepted definition of HWs complicates consistent characterization across regions. In Bangladesh, a subtropical country increasingly vulnerable to extreme heat, the dynamics of HWs remain insufficiently understood. This study aims to bridge that knowledge gap by analyzing three decades of observational data to characterize HWs in Bangladesh, using ambient and apparent temperature metrics. Five HW indices were employed to assess 24-hour (EHF), daytime (CTX90pct, TX90), and nocturnal (CTN90pct, TN90) HW patterns, with humidity effects incorporated through apparent temperature-based indices. HWs were defined as events lasting at least three consecutive days, reflecting the heightened health risks of prolonged exposure. HWs were evaluated in terms of frequency, duration, intensity, and early onset patterns. Station-based observations were compared against corresponding estimates derived from ERA5 reanalysis data. The 90 th percentile of daily temperature emerged as a robust operational threshold for HW characterization in Bangladesh. Declines in temperature variability during HW events were linked to reduced intensities for indices sensitive to short-term variability or independent of seasonality. Humidity exerted a stronger influence on nocturnal HWs than on daytime events, while seasonal variations in temperature and humidity during the pre- and post-monsoon periods significantly shaped HW characteristics. These findings provide new insights into the spatiotemporal dynamics of HWs in Bangladesh, offering an evidence base to inform adaptation strategies in other subtropical regions facing similar climate threats. This study provides critical insights into the growing challenges of HWs in Bangladesh, highlighting their increasing frequency, duration, intensity, and earlier onset. The findings underscore the importance of adopting the 90 th percentile of daily temperature as a reliable threshold for HW characterization, tailored to Bangladesh’s subtropical climate. The study reveals distinct regional and seasonal patterns, with coastal areas experiencing prolonged HWs and humidity-driven nocturnal events, which significantly disrupt nighttime recovery and productivity. Policymakers can leverage these insights to develop localized mitigation strategies, such as early warning systems, urban heat management plans, and infrastructure adaptations to reduce HW impacts. The results emphasize the role of humidity in intensifying heat stress, calling for integrated approaches that consider both ambient temperature and apparent temperature metrics in HW assessments. Furthermore, the methodology used in this study is transferable to other similar climatic contexts, making the results valuable for informing policy in regions beyond Bangladesh that face comparable challenges. By addressing gaps in observational data and incorporating indoor heat stress and continuous surface data in future research, the findings offer a pathway to designing more robust climate resilience frameworks. These measures are essential for safeguarding vulnerable populations, ensuring public health, and minimizing socio-economic losses from extreme heat events both locally and globally.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».