气候变化领域集成服务门户(CCPortal): A review of modeling the effects of vegetation on large wood recruitment processes in mountain catchments

CCPortal

DOI	10.1016/j.earscirev.2019.04.013
	A review of modeling the effects of vegetation on large wood recruitment processes in mountain catchments
	Gasser E.; Schwarz M.; Simon A.; Perona P.; Phillips C.; Hübl J.; Dorren L.
发表日期	2019
ISSN	00128252
起始页码	350
结束页码	373
卷号	194
英文摘要	Large wood (LW) is an important element in riparian and mountain ecosystems and influences geomorphic controls, particularly in torrents and mountain rivers. Despite several advantages, LW can also exacerbate flood damage near infrastructure due to logjams or backwater rise and is often treated as a potential hazard. In an attempt to reduce such problems, channel slopes and banks are often clear cut in practice. This leads to a debate between the opinions of different scientists as well as stream and forest managers, where it is difficult to find a compromise between optimizing risk mitigation and eco-morphological functionality. Regarding risk mitigation due to LW, situations where the positive effects of vegetation succumb to negative effects should be identified to distinguish if shrubs or small trees (DBH < 10 cm) are more suitable to maintain streambank and hillslope stability. However, drastically removing vegetation can severely influence the ecosystem which should be avoided. In the case where large trees (DBH > 10 cm) have the potential to reduce the magnitude and frequency of recruitment processes, practices for forest management need to be optimized. Based on literature research, this article summarizes state of the art knowledge of vegetation effects on LW recruitment processes. In doing so, it focuses on three main recruitment processes: hydraulic bank erosion, geotechnical bank erosion and hillslope failure. Hydraulic bank erosion is responsible for delivering high volumes of sediment as well as LW in mountain catchments by removing streambank material through excess shear stress. The positive effects of vegetation can be quantified and implemented in modeling approaches through the adjustment of discharge-specific resistance coefficients or by using stochastic approaches. Geotechnical bank erosion and hillslope failure contribute to LW recruitment through failure. Root reinforcement is predominately the most important effect on how vegetation stabilizes streambanks and hillslopes. Based on the information mentioned above, a flow chart was formulated that uses specific criteria to define conditions in which forest management should be performed to mitigate potential LW recruitment without drastically removing all trees. © 2019 Elsevier B.V.
关键词	Large wood (LW) recruitment processes Modeling of LW recruitment processes Modeling the effects of vegetation on streambank and hillslope processes
英文关键词	bank erosion; catchment; forest management; hillslope; mountain stream; recruitment (population dynamics); slope failure; vegetation structure
语种	英语
来源期刊	Earth Science Reviews
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/203432
作者单位	Bern University of Applied Sciences, Laenggasse 85, Zollikofen, 3052, Switzerland; University of Natural Resources and Life Sciences Vienna (BOKU), Peter-Jordan-Strasse 82, Vienna, 1190, Austria; Cardno, P.O. Box 1236, Oxford, MS 38655, United States; School of Engineering, The University of Edinburgh, Mayfield Road, Edinburgh, Scotland EH9 3JL, United Kingdom; Manaaki Whenua Landcare Research, P.O. Box 69040, Lincoln, 7640, New Zealand
推荐引用方式 GB/T 7714	Gasser E.,Schwarz M.,Simon A.,et al. A review of modeling the effects of vegetation on large wood recruitment processes in mountain catchments[J],2019,194.
APA	Gasser E..,Schwarz M..,Simon A..,Perona P..,Phillips C..,...&Dorren L..(2019).A review of modeling the effects of vegetation on large wood recruitment processes in mountain catchments.Earth Science Reviews,194.
MLA	Gasser E.,et al."A review of modeling the effects of vegetation on large wood recruitment processes in mountain catchments".Earth Science Reviews 194(2019).