Effects of passive storage on modelling hydrological function and isotope dynamics in a karst flow system in southwest China

Abstract. Representing passive storage in coupled flow-isotope models can facilitate simulation of mixing and retardation effects on tracer transport in many natural systems, such as catchments or rivers. However, the effectiveness of incorporating passive storages in models of complex karst flow systems remains poorly understood. In this study, we developed a coupled flow-isotope model that conceptually represents both “fast” and “slow” flow processes in heterogeneous aquifers to represent hydrological connections between hillslopes and low-lying depression units in cockpit karst landscapes. As this model originally included a varying number of passive storages at different positions of the flow system (e.g. fast/slow flow reservoirs combined with different hillslope/depression units), the model structure and relevant parameters were optimized using a multi-objective optimization algorithm. This was used to match detailed observational data of hydrological processes and isotope concentration in the Chenqi catchment in southwest China. Results show that the optimal structure for a coupled flow-isotope model incorporated only two passive storages in fast flow and slow flow paths of the hillslope unit. Using fewer or greater numbers of passive stores in the model could lead to under- or over-mixing of isotope signatures. This optimized model structure could effectively improve simulation accuracies for outlet discharge and isotope signatures, with > 0.65 of the modified Kling-Gupta efficiency. Additionally, the optimal tracer-aided model yields reasonable parameter values and estimations of hydrological components (e.g. more than 80 % of fast flow in the total discharge). Furthermore, results imply that the solute transport is primarily controlled by advection and hydrodynamic dispersion in steep hillslope unit, which is a remarkable phenomenon in the karst flow system. The study resulted in new insights, more realistic catchment conceptualizations and improved model formulation.