摘要
Due to the complex dynamic of aeolian and fluvial interacted processes behind the landform development,most of previous works started from classifying the types of landscape characterized by various aeolian and fluvial features.Such classifications are usually generalized based on large geomorphic data set abstracted from satellite images without field verification and dynamic field data.In this study,we identified river banks in deserts as a unique geographical unit dominated by aeolian-fluvial processes.Three distinct locations have been identified as representative study cases,which are in the Keriya River Basin in the west,the Mu Bulag River Basin in the middle and the Xar Moron River Basin in the east of the northern China.The aeolian-fluvial interaction types were quantified based on site observation and measurement,topographic mapping and remote-sensing image analysis.Dimensional morphological relationship between river channel and adjacent sand dunes areas were explored.We concluded that different channels are often associated with different distributions of riparian dunes.The quantitative data enabled us to distinguish statistically four different types of landscape in aeolian-fluvial dominant environment,namely riverside dunes-straight channel,symmetrical interleaving dunes-meandering channel,river-island dunes-braiding channel,and grid-like dunes-anastomosing channel,aiming to provide compensational information to current aeolian-fluvial interaction studies.The angle of interaction between aeolian and fluvial systems,the windward and leeward sites of the bank,vegetation coverage and underlying landform determines the distribution,morphology,scale and direction of extension of the riparian dunes.The results of the work study can provide a reference for study of aeolian-fluvial interactions at different spatial scales in arid region.
基金
Under the auspices of the National Natural Science Foundation of China(No.41801004,41871010)
the Fundamental Research Funds for the Central Universities(No.GK202001003,GK202003067)
China Postdoctoral Science Foundation(No.2020M673334)
Natural Science Foundation of Shaanxi Province(No.2021JQ-313)。