Heatstroke Risk Projection in Japan under Current and Near Future Climates

Nakamura, Shingo; Kusaka, Hiroyuki; Sato, Ryogo; Sato, Toshiki

doi:10.2151/jmsj.2022-030

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Moreover, heat adaptation will reduce the heatstroke risk (Nakamura et al 2022, Oka et al 2023c. Although heatstroke risk may change over a long period due to heat adaptation by physiological and non-physiological acclimatization (e.g.…”

Section: Discussionmentioning

confidence: 99%

Prediction of ambulance transport system collapse under extremely high temperatures induced by climate change

Oka,

Honda,

Hijioka

2024

Environ. Res.: Health

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The heat-related health impacts of extremely high temperatures have become increasingly severe worldwide. Quantitative information on these impacts is necessary to plan and implement adaptation measures. The present study aimed to predict the number of heatstroke ambulance calls (HAC) and ambulance occupancy rate of patients with heatstroke (AORPH) under extremely high temperatures in Tokyo, Japan. Extremely high temperatures were defined as the annual highest value of daily maximum temperature that occurs once every 30 y (30y temp) or 50 y (50y temp). Combinations of three greenhouse gas (GHG) emission scenarios and five climate models were adopted to predict the impacts during the base years (1985–2014), mid-21st century (2021–2050), and late 21st century (2071–2100). Furthermore, two types of populations were adopted: fixed and changing. In the case of 50y temp, the HAC during the peak hour in the base years ranged from 52 to 130 persons across the climate models. In the case of a fixed population, in the late 21st century it ranged from 123 to 2618 persons across the climate models and GHG scenarios. In the case of a changing population, the corresponding values in late 21st century ranged from 128 to 3078 persons. The AORPH during the peak hour ranged from 32 % to 80 % during the base years for 50y temp, assuming that all ambulances were dispatched for heatstroke only. In the late 21st century, it ranged from 75 % to 1605 % in the fixed population case. With population change, the corresponding values in late 21st century ranged from 83 % to 1781 %. In many scenarios, not all patients with heatstroke could be transported (AORPH ≥ 100 %) even if all ambulances have been dispatched. Therefore, adaptation measures that mitigate such an ambulance transport system collapse must be planned and implemented.

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Section: Discussionmentioning

confidence: 99%

Prediction of ambulance transport system collapse under extremely high temperatures induced by climate change

Oka,

Honda,

Hijioka

2024

Environ. Res.: Health

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“…Our numerical experiments focused on the use of this method to forecast the daily maximum temperature at a finite number of locations. We are motivated to tackle the problem of forecasting the daily maximum temperature since it is strongly related to heatstroke damage mitigation, especially amid the current global climate change issue, where this heatstroke problem is becoming more severe [12,13]. The idea is to use domain adaptation to increase the forecasting power of the model in a region for which only a small amount of data is available.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Temperature Forecasting Using Deep-Neural-Network-Based Domain Adaptation

Tran,

Septier,

Murakami

et al. 2024

Atmosphere

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Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with domain adaptation across different geographical regions. We evaluated the effectiveness of DNN-based domain adaptation for daily maximum temperature forecasting in experimental low-resource settings. We used an attention-based transformer deep learning architecture as the core forecasting framework and used kernel mean matching (KMM) for domain adaptation. Domain adaptation significantly improved forecasting accuracy in most experimental settings, thereby mitigating domain differences between source and target regions. Specifically, we observed that domain adaptation is more effective than exclusively training on a small amount of target-domain training data. This study reinforces the potential of using DNNs for temperature forecasting and underscores the benefits of domain adaptation using KMM. It also highlights the need for caution when using small amounts of target-domain data to avoid overfitting. Future research includes investigating strategies to minimize overfitting and to further probe the effect of various factors on model performance.

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Heat-Related Health Impacts of Climate Change and Adaptation Strategies in Japan

Oka

2023

Global Perspectives on Health Geography

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Heatstroke Risk Projection in Japan under Current and Near Future Climates

Cited by 6 publications

References 49 publications

Prediction of ambulance transport system collapse under extremely high temperatures induced by climate change

Prediction of ambulance transport system collapse under extremely high temperatures induced by climate change

Spatial–Temporal Temperature Forecasting Using Deep-Neural-Network-Based Domain Adaptation

Heat-Related Health Impacts of Climate Change and Adaptation Strategies in Japan

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