通过使用深度学习,在整个生物群体中诊断出对缺水的蒸发转化反应
Francesco Giardina1,2, Pierre Gentine3,4, Alexandra G Konings5
1Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH Zürich, Zürich, CH-8092, Switzerland.
The New phytologist
|August 25, 2023
概括
植物生长 植物生长
科学领域:
- 环境科学环境科学
- 生态生态学 生态生态学
- 水文学的水文学
背景情况:
- 了解植被对干旱的反应对于预测生态系统变化至关重要.
- 量化水对蒸发转化 (ET) 的限制是复杂的,因为不同的植被和气候.
- 现有的模型难以捕捉水压对ET在不同环境中的不同影响.
研究的目的:
- 开发一种方法来分离和量化由水限制引起的ET减少.
- 研究不同的植被类型和环境对逐渐干旱的反应.
- 为了确定控制植被对水应激敏感性的因素.
主要方法:
- 训练有素的深度神经网络使用的共变量流量测量来分析ET.
- 开发了一种水应激因子 (fET),以将ET减少与其他驱动因素分开.
- 与累积缺水相对应的回归fET,以评估湿度可用性的影响.
主要成果:
- 在不同生态系统中观察到ET对水应激反应的显著变化.
- 草原和草原地表现出快速的ET下降,而大多数森林表现出较轻微的减少.
- 对干旱最敏感的ET反应发生在干旱和温暖的地方.
结论:
- 植物表现出各种策略,包括口腔/液压调节和深水获取,以应对干旱.
- 这些多样化的反应与特定地点的条件,如干旱和温度有关.
- 当前的陆地表面模型可能过于简化了地下水的动态,限制了它们在预测干旱影响方面的准确性.
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