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

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: NH-N, nitrate-N: NO-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 NH-N, NO-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 NO-N simulation, which contributed to 75.48% of predicted TN uncertainty. It is concluded that reducing the parameter uncertainty of NO-N loading simulation during the rice-growing season is the key factor to improving stream water N modeling precision in rice agricultural watersheds.

The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis

“…We use analysis of variance (ANOVA) to partition the variability in simulated flood peak frequency into various contributors, which are shown in Table . ANOVA is useful for estimating the variation of response due to different factors (Bosshard et al, ; Ma et al, ) including continuous, discrete, and mixed variables (Cardinal & Aitken, ; Yip et al, ). Yip et al () use ANOVA to partition sources of variability in global climate model simulations, while Meresa and Romanowicz () and Bosshard et al () apply it to hydrologic extremes.…”

Model-Based Evaluation of Land Management Strategies with Regard to Multiple Ecosystem Services

“…For example, van Griensven et al [42] found that the influence of input uncertainty (i.e., climate and pollution data) is minor in comparison to SWAT parameterization uncertainty. Similarly, Ma et al [43] found that parameters uncertainty is the most significant factor in uncertainty analysis in comparison with precipitation input uncertainty. Our results indicate that the uncertainty in management setup a minor role in the overall uncertainty.…”

Impacts of temporal/spatial rainfall heterogeneities on peak runoff distribution and intensities for an urban river basin of south China

“…Analysis of variance (ANOVA) is useful for estimating the variation of response due to different factors (Bosshard et al, 2013;Ma et al, 2018) including continuous, discrete, and mixed variables (Cardinal & Aitken, 2013). Yip et al (2011) used ANOVA to partition sources of variability in global climate model simulations, while (Meresa & Romanowicz, 2017) apply it to hydrologic extremes.…”