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. Nutrient data from catchments discharging to receiving waters are monitored for catchmentmanagement. However, nutrient data are often sparse in time and space and have non-linearresponses to environmental factors, making it difficult to systematically analyse long- andshort-term trends and undertake nutrient budgets. To address these challenges, we developed ahybrid machine learning (ML) framework that first separated baseflow and quickflow from totalflow, generated data for missing nutrient species, and then utilised the pre-generated nutrientdata as additional variables in a final simulation of tributary water quality. Hybrid randomforest (RF) and gradient boosting machine (GBM) models were employed and their performancecompared with a linear model, a multivariate weighted regression model, and stand-alone RF and GBMmodels that did not pre-generate nutrient data. The six models were used to predict six differentnutrients discharged from two study sites in Western Australia: Ellen Brook (small and ephemeral)and the Murray River (large and perennial). Our results showed that the hybrid RF and GBM modelshad significantly higher accuracy and lower prediction uncertainty for almost all nutrient speciesacross the two sites. The pre-generated nutrient and hydrological data were highlighted as themost important components of the hybrid model. The model results also indicated differenthydrological transport pathways for total nitrogen (TN) export from two tributary catchments. We demonstrated thatthe hybrid model provides a flexible method to combine data of varied resolution and quality andis accurate for the prediction of responses of surface water nutrient concentrations to hydrologicvariability.

ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients