使用机器学习方法将降水极端和作物生产联系起来,并使用CMIP6数据预测未来的极端情况
Firdos Khan1,2,3, Gunter Spöck4, Yuei-An Liou5
1School of Natural Sciences (SNS), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan. fkyousafzai@gmail.com.
Environmental science and pollution research international
|September 1, 2024
概括
极端降水对巴基斯坦有很大的影响.
科学领域:
- 气候科学 气候科学
- 农业科学 农业科学
- 环境科学 环境科学
背景情况:
- 包括连续干旱日 (CDD) 和极端降水事件 (R99p) 在内的极端降水事件在全球范围内正在增加.
- 巴基斯坦的农业部门非常容易受到气候变化的影响,需要对极端降水和作物生产进行研究.
- 作为一个重要的农业地区的旁遮普省,需要对气候极端对粮食生产的影响进行集中分析.
研究的目的:
- 评估巴基斯坦极端降水量与作物生产之间的关联,重点关注旁遮普省.
- 在不同的气候场景下使用机器学习模型预测极端降水的未来趋势.
- 了解这些趋势对农业和基础设施的影响.
主要方法:
- 分析了历史数据 (1980-2014) 以确定降水极端 (CDDs,R99p,PRCPTOT,SDII) 和关键作物产量之间的相关性.
- 采用了四个机器学习算法,根据其性能选择了渐变增强机 (GBM).
- 利用SSP2-4.5和SSP5-8.5情景下的CMIP6数据来预测未来的极端降水 (1985-2014).
主要成果:
- 确立了特定的极端降水量与小麦,大米,大,枣,moong和masoor的生产之间的显著相关性.
- 连续最多湿天 (CWDs) 的预计上升趋势和连续干天 (CDDs) 的下降趋势.
- 在大多数车站观察到大雨天数 (R10mm,R25mm) 的总体上升趋势.
结论:
- 极端的降水对巴基斯坦的农业和基础设施构成风险,包括突然洪水.
- 未来的预测表明降水模式发生了重大变化,极端事件的增加和减少.
- 适应和减缓战略对于最大限度地减少未来极端气候带来的潜在损失和损害至关重要.
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