气候变化领域集成服务门户(CCPortal): An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China

CCPortal

DOI	10.1007/s11069-020-04159-6
	An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China
	Zhang Y.; Huang K.; Yu Y.; Wu L.
发表日期	2020
ISSN	0921030X
起始页码	91
结束页码	110
卷号	104 期号:1
英文摘要	Agricultural water sustainability in a basin environment experiencing climate change has become a critical issue in the past few decades. This study used the DPSIR (Driver–Pressure–State–Impact–Response) framework as a conceptual basis to explore the relationship between water footprint (WF) trends and climate change and agricultural-economic variation. With the aim of mitigating water crisis and ensuring robust responses to the uncertainty of the future, an uncertainty-based multivariate statistical approach was proposed for WF prediction by using various scenarios combined with multiple linear regression and Monte Carlo simulation. Lake Dianchi in China was used as the case study area. The results indicate that (1) the total WF had an increasing trend of 394.39 m3 ton−1 year−1; the WFgreen (the precipitation used in the crop production process) had a decreasing trend, while the WFblue (the irrigation water withdrawn from the ground or surface water) and WFgrey (the water used to dilute the load of pollutants, based on existing ambient water quality standards) exhibited an increasing trend; (2) the total WF showed a distinct increasing trend under climate change and agricultural-economic variation due to the increase of the WFgrey during 1981–2011; and (3) the impact of agricultural-economic factors on WF trends, especially on the WFblue and WFgrey, far outweighed the impact of climatic factors under the alternative scenarios. Our results suggest that adaptive management of anthropogenic activities should be prioritized to mitigate water stress under climate change. © 2020, Springer Nature B.V.
关键词	Climate change DPSIR framework Lake Dianchi Basin Monte Carlo simulation Water footprint
英文关键词	agricultural application; agricultural economics; climate change; climate effect; computer simulation; crop production; hydrological response; Monte Carlo analysis; multivariate analysis; nature-society relations; numerical model; statistical analysis; uncertainty analysis; water footprint; water management; water use; weather forecasting; China; Dianchi Basin; Yunnan
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206044
作者单位	College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, China; Beijing Municipal Research Institute of Environmental Protection, Beijing, 100037, China; Beijing Key Laboratory of Environmental Science and Engineering, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China
推荐引用方式 GB/T 7714	Zhang Y.,Huang K.,Yu Y.,et al. An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China[J],2020,104(1).
APA	Zhang Y.,Huang K.,Yu Y.,&Wu L..(2020).An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China.Natural Hazards,104(1).
MLA	Zhang Y.,et al."An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China".Natural Hazards 104.1(2020).