Uncertainty analysis and ensemble bias-correction method for predicting nitrate leaching in tea garden soils

Abstract

Pedotransfer functions were often applied to predict the soil water contents at field capacity (FC) and permanent wilting point (PWP), which are the key parameters used in the soil nitrogen (N) biogeochemical models for simulating the nitrate (NO3--N) leaching. However, the PTF prediction uncertainty was often ignored. In addition, the uncertainty of the N model structure (soil N cycling is described with a set of equations) can also be substantial. Based on the 12 classic pedotransfer functions (PTFs) (namely Baumer, Brakensiek/Rawls, British Soil Survey Topsoil, British Soil Survey Subsoil, EPIC, Hutson, Manrique, Rawls, Campbell, Mayr/Jarvis, Rawls/Brakensiek, and Vereecken) and 2 biogeochemical models (DayCent and DeNitrification-DeComposition (DNDC)), this study evaluated the PTF prediction and model structural uncertainty in soil NO3−-N leaching modelling on a tea garden hillslope in Taihu Lake Basin, China. The ensemble mean was then applied to combine the 12 outputs of each model and the 24 outputs of both models. Finally, the linear bias-correction combined with the ensemble mean, i.e., the ensemble bias-correction (EBC), was also applied for the prediction of the leachate NO3−-N concentrations. Data on basic soil properties were used to derive the FC and PWP by using the 12 PTFs. Results showed that both the PTF prediction and model structural uncertainty were equally important in soil NO3--N leaching modelling at four slope positions. The coefficients of variation of the NO3--N concentration forecasts obtained by different PTFs, representing the PTF prediction uncertainty, were positively related to the climate factors, especially when PTFs were used in DayCent. Ensemble mean was found to produce a very large bias in the prediction of the leachate NO3--N concentrations, which is due to the prediction bias of PTFs. The EBC can substantially improve the prediction of the soil NO3--N leaching, especially when the 24 outputs of both models were combined.