Classifying heatwaves: Developing health-based models to predict high-mortality versus moderate United States heatwaves.
Classifying heatwaves: Developing health-based models to predict high-mortality versus moderate United States heatwaves.
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DOI：
10.1007/s10584-016-1776-0
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发表时间：
2018-03
期刊：
影响因子：
4.8
通讯作者：
Peng RD
中科院分区：
文献类型：
--
作者：
Anderson GB;Oleson KW;Jones B;Peng RD
热浪分为中度，更常见的热浪和罕见的“高死亡率”热浪，这些热浪每天都会对健康产生极大的影响，我们将其定义为死亡风险增加20%或更高的热浪。更好地预测这些不同类型热浪的预期频率和暴露程度，可以帮助社区优化热缓解和应对计划，并衡量限制气候变化的潜在好处。热浪是高死亡率还是中等死亡率可能取决于多种热浪特征，包括强度，长度和时间。我们使用最近（1987-2005）来自82个大型美国城市社区的健康和天气数据创建的热浪训练数据集创建了热浪分类模型。我们建立了20个潜在的分类模型，并使用蒙特卡罗交叉验证来评估这些模型。我们最终确定了几个可以对高死亡率热浪进行充分分类的模型。这些模型可用于预测未来变化的不同情景下高死亡率热浪的未来趋势（例如，气候变化、人口变化）。此外，这些模型是新颖的方式，他们允许探索不同的情况下，适应热，因为它们包括，作为预测变量，热浪的特点，测量相对于一个社区的温度分布，允许不同的适应情况下，通过选择替代社区的温度分布进行探索。这三个选定的模型已被放置在GitHub上供其他研究人员使用，我们在一篇配套论文中使用它们来预测不同气候，人口和适应情景下高死亡率热浪的趋势。
Heatwaves are divided between moderate, more common heatwaves and rare “high-mortality” heatwaves that have extremely large health effects per day, which we define as heatwaves with a 20% or higher increase in mortality risk. Better projections of the expected frequency of and exposure to these separate types of heatwaves could help communities optimize heat mitigation and response plans and gauge the potential benefits of limiting climate change. Whether a heatwave is high-mortality or moderate could depend on multiple heatwave characteristics, including intensity, length, and timing. We created heatwave classification models using a heatwave training dataset created using recent (1987—2005) health and weather data from 82 large US urban communities. We built twenty potential classification models and used Monte Carlo cross-validations to evaluate these models. We ultimately identified several models that can adequately classify high-mortality heatwaves. These models can be used to project future trends in high-mortality heatwaves under different scenarios of a changing future (e.g., climate change, population change). Further, these models are novel in the way they allow exploration of different scenarios of adaptation to heat, as they include, as predictive variables, heatwave characteristics that are measured relative to a community’s temperature distribution, allowing different adaptation scenarios to be explored by selecting alternative community temperature distributions. The three selected models have been placed on GitHub for use by other researchers, and we use them in a companion paper to project trends in high-mortality heatwaves under different climate, population, and adaptation scenarios.