Institute of Hydrology and Water Resources Management, Leibniz University Hannover, Germany.
Institute of Hydrology and Water Resources Management, Leibniz University Hannover, Germany.
Sci Total Environ. 2019 Feb 1;649:846-865. doi: 10.1016/j.scitotenv.2018.08.248. Epub 2018 Aug 22.
Irrigation water is one of the most substantial water uses worldwide. Thus, global simulation studies about water availability and demand typically include irrigation. Nowadays, regional scale is of major interest for water resources management but irrigation lacks attention in many catchment modelling studies. This study evaluated the performance of the agro-hydrological model SWAT (Soil and Water Assessment Tool) for simulating streamflow, evapotranspiration and irrigation in four catchments of different agro-climatic zones at meso-scale (Baitarani/India: Subtropical monsoon; Ilmenau/Germany: Humid; Itata/Chile: Mediterranean; Thubon/Vietnam: Tropical). The models were calibrated well with Kling-Gupta Efficiency (KGE) varying from 0.74-0.89 and percentage bias (PBIAS) from 5.66-6.43%. The simulated irrigation is higher when irrigation is triggered by soil-water deficit compared to plant-water stress. The simulated irrigation scheduling scenarios showed that a significant amount of water can be saved by applying deficit irrigation (25-48%) with a small reduction in annual average crop yield (0-3.3%) in all climatic zones. Many catchments with a high share of irrigated agriculture are located in developing countries with a low availability of input data. For that reason, the application of uncorrected and bias-corrected National Centers for Environmental Prediction (NCEP) and ERA-interim (ERA) reanalysis data was evaluated for all model scenarios. The simulated streamflow under bias-corrected climate variables is close to the observed streamflow with ERA performing better than NCEP. However, the deviation in simulated irrigation between observed and reanalysis climate varies from -25.5-45.3%, whereas the relative irrigation water savings by deficit irrigation could be shown by all climate input data. The overall variability in simulated irrigation requirement depends mainly on the climate input data. Studies about irrigation requirement in data scarce areas must address this in particular when using reanalysis data.
灌溉用水是全球用水量最大的用途之一。因此，关于水资源可利用性和需求的全球模拟研究通常包括灌溉。如今，水资源管理主要关注区域尺度，但许多流域建模研究都忽略了灌溉。本研究评估了农业水文模型 SWAT（土壤和水评估工具）在模拟不同农业气候带中尺度（印度巴特纳尼：亚热带季风；德国伊尔梅瑙：湿润；智利伊塔塔：地中海；越南图邦：热带）四个流域的径流量、蒸散量和灌溉的性能。模型校准效果良好，Kling-Gupta 效率（KGE）在 0.74-0.89 之间，百分比偏差（PBIAS）在 5.66-6.43%之间。与植物水分胁迫相比，当灌溉由土壤水分亏缺触发时，模拟的灌溉量更高。模拟的灌溉计划情景表明，在所有气候带中，通过应用亏缺灌溉（25-48%）可以节省大量水，而对作物年平均产量（0-3.3%）的影响较小。许多灌溉农业比例较高的流域位于输入数据可用性低的发展中国家。出于这个原因，评估了所有模型情景下未校正和偏差校正的美国国家环境预报中心（NCEP）和欧洲中期天气预报中心再分析（ERA）数据的应用。在偏差校正的气候变量下模拟的径流量接近观测到的径流量，ERA 的表现优于 NCEP。然而，观测气候与再分析气候之间模拟灌溉的偏差在-25.5-45.3%之间变化，而通过亏缺灌溉节省的相对灌溉水量可以由所有气候输入数据显示。模拟灌溉需求的总体变化主要取决于气候输入数据。在数据稀缺地区进行灌溉需求研究时，必须特别注意这一点，尤其是在使用再分析数据时。
