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

Qiumei Ma; Lihua Xiong; Yong Li; Siyue Li; Chong-Yu Xu

doi:10.1016/j.scitotenv.2017.09.235

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

Sci Total Environ. 2018 Mar 15:618:1298-1313. doi: 10.1016/j.scitotenv.2017.09.235. Epub 2017 Oct 20.

Authors

Qiumei Ma¹, Lihua Xiong², Yong Li³, Siyue Li⁴, Chong-Yu Xu⁵

Affiliations

¹ State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China.
² State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China. Electronic address: xionglh@whu.edu.cn.
³ Changsha Research Station for Agricultural & Environmental Monitoring and Key Laboratory of Agro-ecological Processes in Subtropical Regions, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Hunan 410125, China.
⁴ Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
⁵ State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China; Department of Geosciences, University of Oslo, P.O. Box 1022 Blindern, N-0315 Oslo, Norway.

PMID: 29056387
DOI: 10.1016/j.scitotenv.2017.09.235

Abstract

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 processes of 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.

Keywords: Bayesian; Multi-source uncertainties; Rice agricultural watershed; Stream water nitrogen loading.