A Bayesian approach of high impaired river reaches identification and total nitrogen load estimation in a sparsely monitored basin

In this study, a modeling framework based on the theory of SPAtially Referenced Regression On Watershed attributes (SPARROW) model was developed to identify impaired river reaches with respect to total nitrogen (TN) and estimate the TN sources in the Xin'anjiang River basin, which had limited monitoring sites. A Bayesian approach was applied to estimate the mean values and uncertainties of parameters, including land use export coefficients and in-stream attention rates. Based on the parameters, the midranges (25-75 %) of annual TN concentrations were assessed by the model and 4.5 % of river reaches in the basin were found to be with higher impaired probabilities (namely [TN] > 1.5 mg/l) than other reaches. The amount and yields of TN discharged from diffuse sources were estimated for each county in the basin. The results suggested that Tunxi City had the highest TN yields from farm land and population, while the highest TN yields in Huangshan City were from tea plantations. The outcomes of this study will guide the implementation of practical management measures to reduce TN loads.