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Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China

文献类型：期刊论文

作者Ma, Qiumei1; Xiong, Lihua1; Li, Yong2,3; Li, Siyue4; Xu, Chong-Yu1,5
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2018-03-15
卷号618页码:1298-1313
关键词Stream water nitrogen loading Multi-source uncertainties Bayesian Rice agricultural watershed
ISSN号0048-9697
DOI10.1016/j.scitotenv.2017.09.235
英文摘要Uncertainty is recognized as a critical consideration for accurately predicting stream water nitrogen (N) loading, but identifying the relative contribution of individual uncertainty sources within the total uncertainty remains unclear. In this study, a powerful method, referred to as the Bayesian inference combined with analysis of variance (BayeANOVA) was adopted to detect the timing and magnitude of multiple uncertainty sources and their relative contributions to total uncertainty in simulating daily loadings of three stream water N species (ammonium-N: NH4+-N, nitrate-N: NO3--N and total N: TN) in a rice agricultural watershed (the Tuojia watershed) as influenced by non-point source N pollution. Five sources of uncertainty have been analyzed in this study, which arise from model structure, parameters, inputs, interaction effects between parameters and inputs, and internal variability (induced by random errors of model or environment). The results show that uncertainty in parameters relating to the process f both N and hydrologic cycles contributed the largest fractions of total uncertainty in N loading simulations (58.83%, 63.48% and 61.64% for NH4+-N, NO3--N and TN loading, respectively). Additionally, three of the largest uncertainties (i.e. parameters, inputs and interaction effects) in all three simulated N loadings were on average significantly greater in the rice-growing season relative to the fallow season, primarily due to the excess fertilization application during the rice-growing season. The predicted TN uncertainty was mainly attributed to the inaccuracy of NO3--N simulation, which contributed to 75.48% of predicted TN uncertainty. It is concluded that reducing the parameter uncertainty of NO3--N loading simulation during the rice-growing season is the key factor to improving stream water N modeling precision in rice agricultural watersheds. (C) 2017 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China[51525902] ; National Natural Science Foundation of China[51479139] ; "Hundred-Talent Program" of the Chinese Academy of Sciences[R53A362Z10]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000424130500130
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.138/handle/2HOD01W0/4494]  
专题生态水文研究中心
通讯作者Xiong, Lihua
作者单位1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Hubei, Peoples R China
2.Chinese Acad Sci, Changsha Res Stn Agr & Environm Monitoring, Changsha 410125, Hunan, Peoples R China
3.Chinese Acad Sci, Inst Subtrop Agr, Key Lab Agroecol Proc Subtrop Reg, Changsha 410125, Hunan, Peoples R China
4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
5.Univ Oslo, Dept Geosci, POB 1022 Blindern, N-0315 Oslo, Norway
推荐引用方式
GB/T 7714		 Ma, Qiumei,Xiong, Lihua,Li, Yong,et al. Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2018,618:1298-1313.
APA Ma, Qiumei,Xiong, Lihua,Li, Yong,Li, Siyue,&Xu, Chong-Yu.(2018).Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China.SCIENCE OF THE TOTAL ENVIRONMENT,618,1298-1313.
MLA Ma, Qiumei,et al."Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China".SCIENCE OF THE TOTAL ENVIRONMENT 618(2018):1298-1313.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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