DOI: 10.1007/s11069-020-04159-6
论文题名: An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China
作者: Zhang Y. ; Huang K. ; Yu Y. ; Wu L.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 104, 期: 1 起始页码: 91
结束页码: 110
语种: 英语
中文关键词: 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
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(the precipitation used in the crop production process) had a decreasing trend, while the WFblue (the irrigation water withdrawn from the ground or surface water) and WFgrey (the water used to dilute the load of pollutants, based on existing ambient water quality standards) exhibited an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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168843
Appears in Collections: 气候变化与战略
There are no files associated with this item.
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
Recommended Citation:
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]. Natural Hazards,2020-01-01,104(1)