Stable hydrogen isoscape in precipitation generated using data fusion for East China

IF 6 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Jiacheng Chen, Jie Chen, Xunchang John Zhang, Peiyi Peng
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引用次数: 0

Abstract

The stable hydrogen isotope in precipitation is an effective environmental tracer for climatic and hydrologic studies. However, accurate and high-precision precipitation hydrogen isoscapes are currently unavailable in China. In this study, a data fusion method based on Convolutional Neural Networks (CNN) is used to fuse the hydrogen isotopic composition (δ2Hp) of observations and isotope-equipped general circulation model (iGCM) simulations. A precipitation hydrogen isoscape with a temporal resolution of monthly and a spatial resolution of 50–60 km is established for East China for the 1969–2017 period. Prior to building the isoscape, the performance of three data fusion methods (DFMs) and two bias correction methods (BCMs) is compared. The results indicate that the CNN fusion method performs the best with a correlation coefficient larger than 0.90 and root mean square error smaller than 10.5‰when using observation as a benchmark. The fusion methods based on back propagation and long short-term memory neural network perform similarly, while slightly outperforming the bias correction methods. Thus, the CNN method is used to generate the hydrogen isoscape, and the temporal and spatial distribution characteristics of the hydrogen isotope in precipitation are analyzed based on this dataset. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ2Hp is consistent with the temperature effect in northern China, and consistent with the precipitation amount effect in southern China. The trend of the δ2Hp time series is consistent with that of observed precipitation and temperature. Overall, the generated isoscape effectively reproduces the observations, and has the characteristics of time continuity and relative spatial regularity, which can provide valuable data support for tracking atmospheric and hydrological processes.

利用数据融合生成的华东地区降水中稳定氢等值线图
降水中的稳定氢同位素是气候和水文研究的有效环境示踪剂。然而,中国目前还没有精确的高精度降水氢同位素图。本研究采用基于卷积神经网络(CNN)的数据融合方法,将观测数据和配备同位素的大气环流模式(iGCM)模拟数据的氢同位素组成(δ2Hp)进行融合。建立了华东地区1969-2017年降水氢等值线图,时间分辨率为月,空间分辨率为50-60千米。在建立等值线之前,比较了三种数据融合方法(DFM)和两种偏差校正方法(BCM)的性能。结果表明,以观测值为基准,CNN融合方法的相关系数大于0.90,均方根误差小于10.5‰,表现最佳。基于反向传播和长短期记忆神经网络的融合方法表现类似,但略微优于纠偏方法。因此,利用 CNN 方法生成了氢等值线图,并基于该数据集分析了降水中氢同位素的时空分布特征。生成的等值线图显示出与观测数据相似的时空分布特征。总体而言,δ2Hp 的分布模式与华北地区的温度效应一致,与华南地区的降水量效应一致。δ2Hp时间序列的变化趋势与观测到的降水和气温变化趋势一致。总之,生成的等值线图有效地再现了观测资料,具有时间连续性和空间相对规则性的特点,可为跟踪大气和水文过程提供宝贵的数据支持。
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来源期刊
Science China Earth Sciences
Science China Earth Sciences GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
9.60
自引率
5.30%
发文量
135
审稿时长
3-8 weeks
期刊介绍: Science China Earth Sciences, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research.
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