Comparison of transit time models for exploring seasonal variation of preferential flow in a Moso bamboo watershed

•The two-reservoir model incorporated with SAS functions performs best in simulating streamflowδ18O.•The model reproduces temporal variations ofδ18O in soil moisture and groundwater.•The SAS modeling results confirm occurrences of preferential flow in the Moso bamboo watershed. Understanding seasonal variations of the hydrological fluxes and preferential flow is crucial in exploring contaminant fate and transport processes in a watershed of interest. Here, performance of different StorAge Selection (SAS)-based rainfall-runoff models was compared for a Moso bamboo watershed under the East Asian monsoon climate. Most of these models are composed of a two-parallel-reservoir flow module and a companioned SAS module. The results show that the temporal variation in streamflow is well captured by a two-parallel-reservoir flow module. The tracer simulations from different models show that two-uniform-reservoir (SSU) model with no SAS function has the lowest NSE value (0.17), the model including an SAS in one of the two reservoir outflows (SSD(U) and SSD(L)) improves performance (NSE greater than 0.5). The performance in simulating streamflow δ18O is further improved when SAS functions are applied for outflows from both reservoirs (SSD (U, L), NSE = 0.76). Additional inclusion an SAS function for ET fluxes in the model (SSD (U, ET, L)) results in a minor improvement from SSD (U, L). The results of SSD (U, ET, L) have achieved the best transport performance (NSE = 0.77), with implication for preferential flow in the watershed. The preferential flow for deep drainage and lower reservoir outflow shows seasonal variation, with more young water in the East Asian summer monsoon period than that in the East Asian winter monsoon period. The upper reservoir storage and deep drainage rates are the main factors determining the temporal variation of preferential flow for deep drainage and lower reservoir outflow, respectively.