| 干旱区引水灌区灌溉退水计算方法 |
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| 引用本文： | 介飞龙,费良军,李山,朱红艳,郝琨,刘利华.干旱区引水灌区灌溉退水计算方法[J].农业工程学报,2021,37(13):66-73. |
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| 作者姓名： | 介飞龙 费良军 李山 朱红艳 郝琨 刘利华 |
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| 作者单位： | 西安理工大学西北旱区生态水利国家重点实验室,西安 710048 |
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| 基金项目： | 国家自然科学基金资助项目（52079105,51779205）;甘肃省景泰川电力提灌灌区退（回归）水监测与利用研究项目（ZKGK-2016-023）;陕西省教育厅自然科学专项（16JK1539）;陕西省自然科学基础研究计划（2017JM5107）;西安理工大学博士学位论文创新基金（310-252072019） |
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| 摘 要： | 准确计算干旱地区灌区退(回归)水,对水资源高效利用具有重要意义。针对中国西北干旱地区大量引水灌区的特点,该研究结合退水单位线和"水桶模型"(根系层的水均衡模型)建立了灌溉退水计算模型,并将该模型应用于甘肃省景电灌区(黄河流域部分)的退水计算,结果表明:灌区退水量计算值与监测值拟合良好,模型率定期和验证期决定系数分别为0.82和0.71,模型可靠。2000年至2019年深层渗漏量和退水量的分析结果表明:年深层渗漏量与净灌溉和有效降雨的总量呈显著正相关,相关系数r=0.718(P0.01);月深层渗漏量受灌区作物生长期的影响显著,69.6%的深层渗漏在冬灌(10-11月)期间产生;年深层渗漏系数与年深层渗漏量呈显著正相关(r=0.944,P0.01);月深层渗漏系数在作物主要生长期(4-9月)小于0.4,非生长期(11月至次年2月)大于0.8;年退水量与年深层渗漏量呈显著正相关(r=0.716,P0.01);月退水量与月深层渗漏量相关性较差,原因是灌溉退水存在明显的滞后性;研究区退水单位线表明灌溉退水滞后峰值在2个月左右,但深层渗漏对退水的影响可达24个月左右。退水单位线的参数具有明确的物理意义且易于确定,针对灌溉退水具有明显滞后性的干旱地区,该方法能够有效计算灌区灌溉退水量,可为灌区水资源管理和决策提供科学支撑。
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| 关 键 词： | 地下水 入渗 灌溉 引水 退水单位线 干旱区 |
| 收稿时间： | 2021/5/10 0:00:00 |
| 修稿时间： | 2021/6/10 0:00:00 |
Calculation method for irrigation return flow in a water diversion irrigation district of arid areas |
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| Jie Feilong,Fei Liangjun,Li Shan,Zhu Hongyan,Hao Kun,Liu Lihua.Calculation method for irrigation return flow in a water diversion irrigation district of arid areas[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(13):66-73. |
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| Authors: | Jie Feilong Fei Liangjun Li Shan Zhu Hongyan Hao Kun Liu Lihua |
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| Institution: | State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi''an University of Technology, Xi''an 710048, China |
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| Abstract: | Many water diversion irrigation projects were launched in the arid areas of northwest China in recent years. Intense human activities have changed the water cycle of "diversion-irrigation-return" in the irrigation areas. In this study, the "Unit Return-Flow-Graph" was defined as the curve of irrigation return flow weight formed by deep percolation with uniform temporal and spatial distribution in a given watershed within a unit period. The Unit Return-Flow-Graph was then combined with the "Bucket model" (water balance model for crop root zone), thereby establishing the calculation model for the irrigation return flow. Deep percolation was also evaluated under the water balance, and then the Unit Return-Flow-Graph was combined to calculate the irrigation return flow. The study area was set as the Jingdian Irrigation District (part of the Yellow River Basin) in Gansu Province, China. The results showed that the calculated value of irrigation return flow in the study area fitted well with the monitored. The determination coefficient R2 in the model calibration and validation period were 0.82 and 0.71, respectively, indicating the reliable performance of the models. The validated model was used to calculate the deep percolation in the study area from 2000 to 2019 under the irrigation return flow from 2002 to 2019. The calculation results showed that the main influencing factor of yearly deep percolation was the sum amount of net irrigation and effective rainfall. The correlation coefficient between the sum amount of net irrigation and effective rainfall was 0.718 (P<0.01), indicating a significantly positive correlation. The main influencing factor of monthly deep percolation was the crop growth period in the irrigation area, where 69.6% of deep percolation occurred during winter irrigation (October to November). A large amount of irrigation water and rainfall were consumed by crops in the form of evapotranspiration during the crop non-growth period, with less deep percolation. The coefficient of yearly deep percolation was significantly positively correlated with the yearly deep percolation (R=0.944, P<0.01). The monthly coefficient of deep percolation was significantly dependent on the crop growth. It was less than 0.4 in the crop growing period, but greater than 0.8 in the non-growing period. The reason was that the crop consumed more water during the growing period, but less for deep percolation. Low water consumption but more deep percolation occurred in the crop non-growing period. The main influencing factor was the yearly deep percolation, where the correlation coefficient between the two was 0.716 (P<0.01), showing a significant positive correlation. The monthly irrigation return flow was correlated with the monthly deep percolation. The reason was that there was a significant lag time in the process of irrigation return flow. Since the curve was fitted to the Unit Return-Flow-Graph in the study area. The lagging peak of irrigation return flow was about 2 months, but the impact of deep percolation on the irrigation return flow reached more than 20 months. The parameters of Unit Return-Flow-Graph presented clear physical meanings, relatively easy to determine the parameters using measured data. The Unit Return-Flow-Graph was effectively utilized to calculate the amount of irrigation return flow in water diversion irrigation areas, particularly on the water resources management in irrigation areas. In addition, the yearly and monthly deep percolation and irrigation return flow changed significantly, which affected the irrigation effect in Jingdian Irrigation District. The findings can provide a sound potential reference for water diversion in the irrigation districts of arid areas. |
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| Keywords: | groundwater infiltration irrigation water diversion Unit Return-Flow-Graph arid areas |
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