An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case stud
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An uncertainty‑based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China Yue Zhang1,2 · Kai Huang1 · Yajuan Yu3 · Linxiu Wu1 Received: 11 November 2019 / Accepted: 3 July 2020 © Springer Nature B.V. 2020
Abstract 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(the precipitation used in the crop production process) had a decreasing trend, while the WFblue (the irrigation water withdrawn from the ground or surface water) and WFgrey (the water used to dilute the load of pollutants, based on existing ambient water quality standards) exhibited an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 Fgrey, far outweighed the impact of climatic factors trends, especially on the W Fblue and W under the alternative scenarios. Our results suggest that adaptive management of anthropogenic activities should be prioritized to mitigate water stress under climate change. Keywords Water footprint · Climate change · DPSIR framework · Monte Carlo simulation · Lake Dianchi Basin
* Kai Huang [email protected]; [email protected] 1
College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China
2
Beijing Municipal Research Institute of Environmental Protection, Beijing 100037, China
3
Beijing Key Laboratory of Environmental Science and Engineering, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
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Natural Hazards
1 Introduction The sustainability of agricultural water use under climate change has become a critical issue over the past few decades (Sun et al. 2012; Bocchiola et al. 2013). Climate change, which leads to temperature and precipitation changes, has considerably affected the aquatic environment in China (Zhuo et al. 2016; Wang et al. 2018). Moreover, increases in the frequency of natural hazards and the impacts of human activities can also degrade water quality in agricultural production processes (Bell et al. 2010). The water footprint (WF) concept was proposed to describe water resou
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