Constraining pesticide degradation in conceptual distributed catchment models with compound-specific isotope analysis (CSIA)

Abstract. The prediction of pesticide dissipation on the catchment scale through hydrological models often encounters challenges due to the limited availability of field data capable of distinguishing between degradative and non-degradative processes. This limitation complicates the calibration of pesticide dissipation and frequently results in equifinality, impeding the reliable forecast of pesticide persistence in soil and its transportation from agricultural plots to the catchment outlet. This study examines the benefits of integrating pesticide Compound-Specific Isotope Analysis (CSIA) data to improve the predictive accuracy of models assessing pesticide persistence in soil and off-site transport at the catchment scale. The research was conducted in a 47-ha crop catchment, focusing on the widely used pre-emergence herbicide S-metolachlor. A novel conceptual model, named PIBEACH, was developed to predict daily pesticide dissipation in soil and its transport to rivers, incorporating changes of the carbon isotopic signatures (δ¹³C) of the targeted pesticide during degradation. Parameter and model uncertainties were estimated using the Generalized Likelihood Uncertainty Estimation (GLUE) method. The inclusion of field data on S-metolachlor concentrations in the topsoil and their associated δ¹³C values in the model resulted in a more than two-fold reduction in uncertainties related to S-metolachlor degradation half-life and six metrics of pesticide persistence and off-site transport. Moreover, the study indicates that a moderate yet targeted sampling effort can effectively identify hot-spots and hot-moments of pesticide degradation in agricultural soil when isotope fractionation is integrated into the model. In summary, the incorporation of CSIA data into conceptual distributed hydrological models holds the potential to alleviate parameter equifinality, therewith significantly improving our ability to predict the dynamics of pesticide degradation on the catchment scale.