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..

数据说明：McInerney等人（2018）用于土壤与水评估工具（Soil and Water Assessment Tool, SWAT）模型率定的三种不同灌溉调度模型的输入数据，分别为：空间均一连续型（Spatially uniform, continuous, SU_C）、空间均一事件驱动型（Spatially uniform, event-based, SU_EB）以及空间变异事件驱动型（Spatially variable, event-based, SV_EB）。

McInerney等人（2018）研究摘要
灌溉对众多农业产流流域的水量平衡与环境状态具有显著贡献。本研究聚焦于灌溉调度模型中灌溉深度的时空变异性表征。灌溉变异性源于农户灌溉实践的差异，但当前学界对其在支撑管理决策的分布式水文预报中产生的影响仍知之甚少。本研究以澳大利亚墨累-达令盆地的巴尔溪流域为案例，系统对比了四种灌溉调度模型，涵盖空间均一与空间变异两类模式，以及连续时间与事件驱动两类表征方式。研究分别在水文响应单元与流域尺度上对模拟灌溉过程开展评估，并阐明了灌溉调度对利用SWAT得到的径流、蒸散发及潜在补给量模拟结果的影响。本研究开发了一种新型空间变异事件驱动型灌溉调度模型。将该模型用于为SWAT提供灌溉输入时，可实现两项改进：其一，可降低空间均一连续时间灌溉假设下实际蒸散发的高估问题（偏差从约40%降至约2%）；其二，相较于空间均一事件驱动型灌溉假设，该模型可更好地复现降雨事件下的快速径流响应（经季节校正的纳什-萨克利夫效率（Nash-Sutcliffe Efficiency）从0.15提升至0.56）。该新型模型的随机特性可表征灌溉调度的不确定性，进而优化流域模拟径流的不确定性表征效果，且可用于不确定性分解。总体而言，本研究凸显了分布式水文模型输入数据的时空变异性的重要性，以及利用多变量响应数据测试并优化环境模型的必要性。

参考文献
McInerney, D., Thyer, M., Kavetski, D., Githui, F., Thayalakumaran, T., Liu, M. & Kuczera, G. (2018), 灌溉输入的时空变异性对灌溉流域水文模拟的重要性, Water Resour. Res.