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深度学习表明,由于气候变化,德国的地下水位将在 2100 年之前下降。

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change.

机构信息

Karlsruhe Institute of Technology, Karlsruhe, Germany.

Federal Institute for Geosciences and Natural Resources, Berlin, Germany.

出版信息

Nat Commun. 2022 Mar 9;13(1):1221. doi: 10.1038/s41467-022-28770-2.

DOI:10.1038/s41467-022-28770-2
PMID:35264569
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8907324/
Abstract

In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21 century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under different RCP scenarios (2.6, 4.5, 8.5). We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. While less pronounced and fewer significant trends can be found under RCP2.6 and RCP4.5, we detect significantly declining trends of groundwater levels for most of the sites under RCP8.5, revealing a spatial pattern of stronger decreases, especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

摘要

在这项研究中,我们调查了在 21 世纪,气候变化将如何直接影响德国的地下水资源。我们应用了一种基于卷积神经网络的机器学习地下水水位预测方法,该方法使用了德国各地分布广泛的 118 个站点,以评估在不同 RCP 情景(2.6、4.5、8.5)下地下水水位的发展情况。我们只考虑直接的气象输入,而排除了地下水开采等高度不确定的人为因素。虽然在 RCP2.6 和 RCP4.5 下,趋势不太明显,也没有那么多显著的趋势,但我们发现,在 RCP8.5 下,大多数站点的地下水位呈显著下降趋势,揭示了一个更强烈下降的空间模式,尤其是在德国的北部和东部地区,这突显了这些地区已经存在的下降趋势。我们还可以表明,在本世纪末,年循环期间地下水水位的可变性增加,低水位持续时间延长。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/962045dda7f0/41467_2022_28770_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/a059109180ad/41467_2022_28770_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/eb287b2a320c/41467_2022_28770_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/4c86ff436ba3/41467_2022_28770_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/8dc6c360207b/41467_2022_28770_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/857dcc8da9ca/41467_2022_28770_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/00008788eec3/41467_2022_28770_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/4e279d5642a2/41467_2022_28770_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/962045dda7f0/41467_2022_28770_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/a059109180ad/41467_2022_28770_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/eb287b2a320c/41467_2022_28770_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/4c86ff436ba3/41467_2022_28770_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/8dc6c360207b/41467_2022_28770_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/857dcc8da9ca/41467_2022_28770_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/00008788eec3/41467_2022_28770_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/4e279d5642a2/41467_2022_28770_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/323c/8907324/962045dda7f0/41467_2022_28770_Fig8_HTML.jpg

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