Processes, Feedbacks, and Morphodynamic Evolution of Tidal Flat–marsh Systems: Progress and Challenges
Water Science and Engineering(2022)
Hohai Univ
Abstract
Tidal flats and saltmarshes have been a long-standing research focus because of their high socio-economic and ecological values. The evolution of tidal flat–marsh systems is highly complex due to the intertwined processes operating over a variety of spatial and temporal scales. As a traditional research highlight, the role of regular hydrodynamic processes such as tides, waves, and river flows have been explored comprehensively with fruitful outcomes. Over past decades, the changing environment (e.g., sea level rise, increasing anthropogenic activities, and extreme weather conditions) has attracted more attention with many reported insightful results. More recent advances indicate that biological activities play a critical role in tidal flat–marsh morphodynamics but are still poorly understood. The field of research that connects the biological and physical processes is commonly described as “biogeomorphology” and requires the joint efforts by scientists from multiple disciplines ranging from hydraulics, ecology, and geography to sociology. This review aims to provide a synthesis of the current research status of tidal flat–marsh morphodynamics, with a particular emphasis on the understanding of various processes and feedbacks underlying the development of morphodynamic models. Some future research needs and challenges are identified to facilitate a more sustainable management strategy for tidal flats and saltmarshes under climate change.
MoreTranslated text
Key words
Tidal flats,Coastal wetlands,Morphodynamics,Saltmarshes,Tidal channels,Morphodynamic modeling
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