The Correlation Between Water–Sediment Index and Floodplain Transverse Slope Based on Wavelet Analysis
WATER(2024)
Yellow River Conservancy Commiss
Abstract
The floodplain transverse slope is a significant parameter reflecting the degree of development of a secondary suspended river, as well as a crucial index of the flood risk in the river channel. Clarifying the factors that influence the evolution of the floodplain transverse slope has always been a hot and difficult topic for researchers working on the Yellow River management. We took the severe section of the secondary suspended river from Dongbatou to Gaocun in the lower Yellow River as the research object, selecting the annual runoff, annual sediment load, annual sediment coefficient, and the intensity of flood-season flow scouring at the Huayuankou station in the downstream as the water–sediment indexes. The correlation between different water–sediment indexes and the floodplain transverse slope under three modes: interannual, flood season, and flood-season overbank was studied through methods such as cross-wavelet transform and wavelet coherence analysis. The results showed that under the three modes, the annual sediment load and annual sediment coefficient had a high correlation with the evolution cycle of the transverse slope, followed by the intensity of flood-season flow scouring, and the annual runoff had the lowest correlation. Meanwhile, the change in the transverse slope had a good correlation with the flood-season overbank mode, indicating there was a high similarity between the water–sediment characteristics of floodplain flooding and the evolution cycle of the transverse slope; that is, the change in the transverse slope is greatly influenced by floodplain flooding events.
MoreTranslated text
Key words
floodplain transverse slope,water–sediment indexes,cross-wavelet transform,wavelet coherence analysis,wavelet phase angle,secondary suspended river
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