干旱区生态环境知识资源中心(Arid): Using artificial neural networks to predict future dryland responses to human and climate disturbances

Arid

DOI	10.1038/s41598-019-40429-5
	Using artificial neural networks to predict future dryland responses to human and climate disturbances
	Buckland, C. E.1; Bailey, R. M.1; Thomas, D. S. G.1,2
通讯作者	Buckland, C. E.
来源期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
出版年	2019
卷号	9
英文摘要	Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropogenic pressures, identifying the role of external disturbances in driving geomorphic response is vitally important. We developed a novel approach, using artificial neural networks (ANNs) applied to time series of historical reactivation-deposition events from the Nebraska Sandhills, to determine the relationship between historic periods of sand deposition in semi-arid grasslands and external climatic conditions, land use pressures and wildfire occurrence. We show that both vegetation growth and sediment re-deposition episodes can be accurately estimated. Sensitivity testing of individual factors shows that localised forcings (overgrazing and wildfire) have a statistically significant impact when the climate is held at present-day conditions. However, the dominant effect is climate-induced drought. Our approach has great potential for estimating future landscape sensitivity to climate and land use scenarios across a wide range of potentially fragile dryland environments.
类型	Article
语种	英语
国家	England ; South Africa
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000460508600085
WOS关键词	NEBRASKA SAND HILLS ; HOLOCENE DUNE ACTIVITY ; GREAT-PLAINS ; VEGETATION DYNAMICS ; LAND DEGRADATION ; SOUTHERN AFRICA ; VARIABILITY ; BLOWOUTS ; DROUGHT ; REACTIVATION
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
来源机构	University of Oxford
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218760
作者单位	1.Univ Oxford, Sch Geog & Environm, Oxford OX1 3QY, England; 2.Univ Witwatersrand, Geog Archaeol & Environm Studies, Johannesburg, South Africa
推荐引用方式 GB/T 7714	Buckland, C. E.,Bailey, R. M.,Thomas, D. S. G.. Using artificial neural networks to predict future dryland responses to human and climate disturbances[J]. University of Oxford,2019,9.
APA	Buckland, C. E.,Bailey, R. M.,&Thomas, D. S. G..(2019).Using artificial neural networks to predict future dryland responses to human and climate disturbances.SCIENTIFIC REPORTS,9.
MLA	Buckland, C. E.,et al."Using artificial neural networks to predict future dryland responses to human and climate disturbances".SCIENTIFIC REPORTS 9(2019).