Variability of Precipitation-Stable Isotopes and Moisture Sources of Two Typical Landforms in the Eastern Loess Plateau, China
Journal of Hydrology Regional Studies(2023)
Shanxi Normal Univ
Abstract
Study region: Two typical landforms in the eastern Loess Plateau, China Study focus: Hydrogen and oxygen stable isotopes in precipitation are highly efficient tracers for quantitatively identifying the regional water cycle. Based on 237 precipitation event data points from seven precipitation stable isotope monitoring stations in the eastern Loess Plateau (ECLP), this study first presented the spatial-temporal variation of precipitation isotopes. It explored the mainly controlling factor and source of water vapor for regional precipitation. New hydrological insights for the region: This research found a consistent temporal trend was observed in the ECLP except for the Jiexiu station, characterized by enrichment from December to May and depletion from June to October. Signals of the summer monsoon reaching the ECLP were recorded using precipitation delta 18O (delta 18Op), especially in July. Relatively smaller slope of the local meteoric water line (LMWL) of summer precipitation reflects that sub-cloud evaporation strongly influences regional precipitation process. delta 18Op values did not exhibited significant relationships with air temperature and an apparent "precipitation amount effect" was observed in precipitation samples with precipitation amount less than 10 mm. An apparent "anti-altitude" effect appeared in precipitation samples with the elevation between 400 and 750 m of the ECLP. The sub-cloud evaporation had stronger influence on the precipitation process of the the Fenhe river basin valley (FRV) especially in the Jiexiu station. The water vapor of precipitation in the ECLP mainly originated from the Bohai Sea and the East China Sea in the near-surface and southeast path directions, especially in summer. These studies are essential in regional water resources alloca-tion, especially under the influence of climate change.
MoreTranslated text
Key words
Precipitation,Hydrogen and oxygen isotopes,Spatial distribution mode(EOF),Controlling factor,Water vapor source
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined