Detection and attribution of nitrogen runoff trend in China's croplands

Hou, Xikang; Zhan, Xiaoying; Zhou, Feng; Yan, Xiaoyuan; Gu, Baojing; Reis, Stefan ORCID: https://orcid.org/0000-0003-2428-8320; Wu, Yali; Liu, Hongbin; Piao, Shilong; Tang, Yanhong. 2018 Detection and attribution of nitrogen runoff trend in China's croplands. Environmental Pollution, 234. 270-278. https://doi.org/10.1016/j.envpol.2017.11.052

Before downloading, please read NORA policies.

Preview

Text N519610PP.pdf - Accepted Version Download (832kB) | Preview

Abstract/Summary

Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990–2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.envpol.2017.11.052
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	0269-7491
Additional Keywords:	nitrogen runoff, temporal trend, spatial pattern, attribution analysis, Bayesian inference
NORA Subject Terms:	Agriculture and Soil Science
Date made live:	22 Mar 2018 11:54 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/519610

Actions (login required)

View Item

Document Downloads

More statistics for this item...