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可解释深度学习方法量化极端温度对中国植被生产力的影响
Dewei Xie1, Zhaopei Zheng2, Xin Ding1
1College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
Scientific reports
|August 20, 2025
概括
极端温度事件显著影响中国的初级净产量 (NPP),这是植物碳捕获的衡量标准. 深度学习模型揭示了复杂的,非线性关系和驱动这些变化的值效应.
科学领域:
- 生态与环境科学 生态与环境科学
- 气候变化研究 气候变化研究
- 遥感和地理空间分析
背景情况:
- 净初级生产 (NPP) 是一个关键的生态参数,反映了植物的光合作用效率和碳捕获.
- 气候变暖正在增加极端温度事件的频率和强度,深刻影响核电站.
- 之前对核电站驱动器的研究主要采用线性模型,可能缺少复杂的相互作用.
研究的目的:
- 量化确定极端温度事件与中国各地核电站动态之间的因果关系.
- 研究核电站的时空变化和2001年至2020年的极端温度事件.
- 将深度神经网络 (DNN) 的性能与模拟核电站的传统模型进行比较.
主要方法:
- 整合全国每日气象数据 (2001-2020年) 和MODIS年度核电站产品.
- 应用深度神经网络 (DNN) 结合SHAP (夏普利增量解释) 方法进行因果推理.
- 对核电站趋势,极端温度事件频率及其区域影响的空间显式分析.
主要成果:
- 在中国各地,极端高温事件显著增加,极端寒冷事件减少,区域差异较大.
- 在全国范围内,核电厂的总体上升趋势,但在一些南部山区和丘陵地区略有下降.
- 对比区域核电站的反应:与南部和西藏高原的极端热量有积极的关联,而中度寒冷有利于北部的植被.
结论:
- 与其他模型相比,DNN-SHAP框架提供了一种优越的方法 (R2≈0.89),用于模拟核电站的时空分布.
- 日和年降水被确定为核电站变化的主要驱动因素,表现出依赖值的效应.
- 了解这些复杂的非线性关系对于预测核电站对未来气候变化的反应至关重要.
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