The importance of spatiotemporal variability in irrigation inputs for hydrological modelling of irrigated catchments

Abstract

Irrigation contributes substantially to the water balance and environmental condition of many agriculturally productive catchments. This study focuses on the representation of spatio‐temporal 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 vs variable in space, and continuous‐time vs 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 ∼40% 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 spatio‐temporal variability of inputs to distributed hydrological models and the importance of using multi‐variate response data to test and refine environmental models.