Data uncertainties in anthropogenic phosphorus flow analysis of lake watershed

Abstract The data uncertainty is a crucial limitation for substance flow analysis (SFA) studies. Monte Carlo (MC) simulation is used to assess the data uncertainty of the anthropogenic phosphorous (P) flow analysis in Chaohu Watershed. The study selects the key data in crop farming, large-scale breeding, and rural consumption subsystems, which are the biggest contributors to P emissions. The results show that in the crop farming subsystem, the P-containing rate of crop, soil deposition rate, harvest of crop, proportion of large-scale livestock excrement to field, and the amount of applied chemical fertilizer are the greatest contributors to the output uncertainty. While the amount of feed consumed per large-scale livestock, amount of large-scale livestock, P-containing rate of feed consumed by large-scale livestock, and proportion of large-scale livestock excrement to field have the greatest uncertainties in the large-scale breeding subsystem. Moreover, in the rural consumption subsystem, both of the P-containing rate of crop and the amount of crop consumed per rural people have the greatest uncertainties. By analyzing the reasons leading to the data uncertainties, the suggestions for minimizing the uncertainty are also proposed. The study also shows that the MC methodology is an efficient tool to solve the data uncertainty in SFA study.

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