水保所知识产出(1956---)知识库

Although concern for ephemeral gully (EG) erosion has been growing within the research community, knowledge
gaps still need to be identified for a state-of-the-art review. This review is based on an extensive search of
published literature in the Web of Science database that specifically addressed EG erosion. This review included
173 papers published in 56 scientific journals over the last 30 years. The results indicated that the main EG
erosion topics were related to gully erosion processes, factors, methodology, models, and the detrimental effects
of EGs. The definition of EG was identical worldwide. There is an urgency to define critical morphological values
under different conditions for identifying EG channel locations in fields in order to map and model their
development quickly and accurately. Topographic factors have been much more studied than other contributing
factors, including climate, land management, soil, and hydraulics. Fifteen different methodological approaches
or technologies, which were categorized into four kinds of methods, including field monitoring, modelling,
simulation experiments, and review, have been employed to assess and quantify EG erosion. The average soil
erosion rate of EGs in 36 publications increased and decreased with increase of slope and catchment areas,
respectively, and the number of EGs linearly increased with catchment area. The complexity of the development
of EG channels and the driving force mechanisms should also be studied intentionally, e.g., the quantification of
soil detachment and sediment transport mechanisms for both surface and subsurface flow during different sub-
processes, including gully head advance, sidewall collapse and bed incision. It was concluded that the interactive
effects of different contributing factors on EG erosion across various spatio-temporal scales should be further
researched. The comprehensive utilization or application of new methods and technologies, e.g., automatic
monitoring technologies, artificial intelligence, machine learning algorithms, would be helpful to improve study
efficiency and accuracy. The scientific basis of prediction models still needs further development and refinement.
This review provides recommendations and suggestions to improve knowledge, prediction, and control of EG
erosion.