The importance of Spatiotemporal Variability in irrigation inputs for hydrological modelling of irrigated catchments - Datasets

Data DescriptionInput data used by McInerney et al (2018) for SWAT model calibration for the three different irrigation schedule models:Spatially uniform, continuous (SU_C)Spatially uniform, event-based (SU_EB)Spatially variable, event-based (SV_EB)Abstract from McInerney et al (2018)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.ReferenceMcInerney, D. , Thyer, M. , Kavetski, D. , Githui, F. , Thayalakumaran, T. , Liu, M. and Kuczera, G. (2018), The Importance of Spatio‐Temporal Variability in Irrigation Inputs for Hydrological Modelling of Irrigated Catchments. Water Resour. Res..

数据集描述：麦金尼等（2018）用于SWAT模型校准的三种不同灌溉计划模型的输入数据：空间均匀、连续（SU_C）、空间均匀、基于事件的（SU_EB）以及空间变量、基于事件的（SV_EB）。摘要摘自麦金尼等（2018）：灌溉对于众多农业生产流域的水平衡和环境状况具有显著影响。本研究聚焦于灌溉计划模型中灌溉深度的时空变异性表征。灌溉变异源于农民灌溉实践的差异性，然而，其对用于指导管理决策的分布式水文预测的影响目前理解不足。以澳大利亚墨累-达令盆地巴里克溪流域为案例研究，我们系统地比较了四种灌溉计划模型，包括空间均匀与变量，以及连续时间与基于事件的表示。我们在水文响应单元和流域尺度上评估了模拟灌溉，并展示了灌溉计划对使用土壤与水资源评估工具（SWAT）进行的溪流流量、蒸散量和潜在补给模拟的影响。开发了一种新的基于事件的、空间变量的灌溉计划模型。当将此新模型用于向SWAT提供灌溉输入时，它：（i）降低了在空间均匀的连续时间灌溉假设下实际蒸散量的高估（偏差从约40%降至约2%）；（ii）与空间均匀的基于事件的灌溉假设相比，更好地再现了降雨事件对快速溪流响应的模拟（季节调整后的纳什-萨特克利夫效率从0.15提高至0.56）。新模型的不确定性特征使其能够表征灌溉计划的不确定性，从而改善对模拟流域溪流流量的不确定性描述，并可应用于不确定性分解。更普遍地，本研究突出了输入分布式水文模型时空变异性以及使用多元响应数据测试和改进环境模型的重要性。参考文献：麦金尼，D.，塞耶，M.，卡维茨基，D.，吉图伊，F.，塞亚拉库马拉纳，T.，刘，M.，库切拉，G.（2018），《灌溉输入时空变异性在灌溉流域水文建模中的重要性》。水资源研究。