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Irrigation contributes substantially to the water balance and environmental condition of many agriculturally productive catchments. This study focuses on the representation of spatiotemporal variability of irrigation depths in irrigation schedule models. Irrigation variability arises due to differences in farmers' irrigation practices, yet its effects on distributed hydrological predictions used to inform management decisions are currently poorly understood. Using a case study of the Barr Creek catchment in the Murray Darling Basin, Australia, we systematically compare four irrigation schedule models, including uniform versus variable in space, and continuous-time versus event-based representations. We evaluate simulated irrigation at hydrological response unit and catchment scales, and demonstrate the impact of irrigation schedules on the simulations of streamflow, evapotranspiration, and potential recharge obtained using the Soil and Water Assessment Tool (SWAT). A new spatially variable event-based irrigation schedule model is developed. When used to provide irrigation inputs to SWAT, this new model: (i) reduces the over-estimation of actual evapotranspiration that occurs with spatially uniform continuous-time irrigation assumptions (biases reduced from similar to 40% to similar to 2%) and (ii) better reproduces the fast streamflow response to rainfall events compared to spatially uniform event-based irrigation assumptions (seasonally adjusted Nash-Sutcliffe Efficiency improves from 0.15 to 0.56). The stochastic nature of the new model allows representing irrigation schedule uncertainty, which improves the characterization of uncertainty in simulated catchment streamflow and can be used for uncertainty decomposition. More generally, this study highlights the importance of spatiotemporal variability of inputs to distributed hydrological models and the importance of using multivariate response data to test and refine environmental models.

Plain Language Summary In many regions irrigation is the primary water input for agriculture, and affects key productivity aspects such as the yield and quality of crops. From the hydrological perspective, irrigation affects important responses such as surface runoff, potential recharge, and actual evapotranspiration. Understanding and predicting these responses at relevant spatial and temporal scales is critical for efficient management of irrigated landscapes, in particular to improve agricultural productivity and ecosystem sustainability. This study evaluates irrigation schedule models that are used to provide inputs into spatially distributed hydrological models. We compare four of these models in terms of several criteria, including consistency with typical irrigation practices and ability to produce reliable hydrological simulations when fed as inputs into spatially distributed hydrological models. Our results emphasize the importance of representing spatiotemporal variability in irrigation inputs, with the best performance obtained using a new spatially variable event-based model. The findings provide guidance on the use of irrigation schedule models for hydrological modelling of irrigated catchments, as well as on the broader scientific question of representing variability in environmental processes.