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Modelling and predicting nitrate (NO3-N) concentrations at the catchment scale remain challenging as they are controlled by available sources, hydrological connectivity and biogeochemical transformations along the dominant flow paths, which are often spatially heterogenous and highly interacted. To unravel the controlling factors of catchment NO3-N cycling, a grid-based model, mHM-Nitrate, was applied to a 68 km2 mixed land use catchment (Demnitzer Millcreek) near Berlin. Results showed that landscape characteristics dictated the spatial distribution of NO3-N while hydroclimatic variability dominated its temporal dynamics. Restoration of riparian wetlands also mediated the NO3-N concentrations, leading to a modest reduction on NO3-N export (~10% reduction during 2001-2019). Further, the influence of three factors was validated in a spatially distributed sensitivity analysis (SSA) applied on key hydrological and nitrate parameters with a one-year moving window. The SSA results showed that the spatial pattern of parameter sensitivity was determined by NO3-N inputs and hydrological transport capacity, while its temporal dynamics were regulated by annual wetness conditions. Restoration management also contributed to the increase in sensitivity of denitrification parameters. Moreover, SSA identified the influential zones and time periods affecting simulation of NO3-N mobilisation and transport, which provides an evidence base for future model development and optimising of monitoring schemes.