中国农业气象 ›› 2021, Vol. 42 ›› Issue (01): 13-23.doi: 10.3969/j.issn.1000-6362.2021.01.002

• 农业生态环境栏目 • 上一篇    下一篇

基于实时含水率数据的土壤墒情动态建模及预测

王铁英,王仰仁,战国隆,牛少卿,姚丽   

  1. 1. 天津农学院水利工程学院,天津300392;2. 天津市大禹节水灌溉技术研究院,天津301712
  • 收稿日期:2020-08-25 出版日期:2021-01-20 发布日期:2021-01-17
  • 通讯作者: 王仰仁,教授,主要从事灌溉排水技术研究,E-mail:wyrf@163.com E-mail:wyrf@163.com
  • 作者简介:王铁英,E-mail:2696984426@qq.com
  • 基金资助:
    国家自然科学基金项目(51779174);天津市农业科技成果转化与推广项目(201701150);天津市科技支撑重点项目(18YFZCSF00650)

Dynamic Modeling and Prediction of Soil Moisture Based on Real-Time Water Content Data

WANG Tie-ying, WANG Yang-ren,ZHAN Guo-long, NIU Shao-qing, YAO Li   

  1. 1.School of Hydraulic Engineering, Tianjin Agricultural University, Tianjin 300392, China;2.Tianjin Dayu Water-Saving Irrigation Technology Research Institute, Tianjin 301712
  • Received:2020-08-25 Online:2021-01-20 Published:2021-01-17

摘要: 实时准确地预测墒情是进行灌溉预报,实现农田水分精准化管理,提高水分利用效率的重要措施。基于根区(0−60cm土层)水量平衡原理,利用泰勒级数对根区下界面水分通量和作物蒸腾量进行了线性化处理,并以实时根区平均土壤含水率为自变量构建了动态的土壤墒情预测模型。采用天津市武清区西吕村无线土壤墒情监测系统(包含3个监测点)实时监测数据(地表下30cm和60cm处的土壤含水率),分别选取5d、10d、15d和20d作为建模系列长度进行回归分析,确定模型参数,对10d和15d两种预见期进行了土壤墒情预测精度分析。结果表明:(1)实时预测模型拟合程度较好,三种建模系列长度条件下的确定性系数均达到0.80以上(样本数均大于550);(2)15d建模系列长度下相对误差最小;(3)15d建模系列长度、15d预见期、10%相对误差界限值条件下,3个监测点的墒情预测合格率分别达到98%、100%和89%。由此可见,研究提出的实时墒情预测模型预测精度较高,便于建模分析,为土壤墒情的预测提供了新方法。

关键词: 土壤墒情, 动态建模, 传感器, 水量平衡, 墒情预测

Abstract: Real-time and accurate prediction of moisture content is to carry out irrigation forecasts, and to achieve precise management of farmland water, which is an important measure to improve water efficiency. Based on the principle of water balance in the root zone (0−60cm soil layer), the crop transpiration and water flux at the lower interface of the root zone are linearized by using the Taylor series. On this basis, a dynamic soil moisture prediction model was constructed with the real-time average soil moisture content of the root zone as an independent variable. The real-time monitoring data (soil moisture content at 30cm and 60cm below the ground surface) of the wireless soil moisture monitoring system (including three monitoring points) in Xilv Village, Wuqing District, Tianjin City are used, and 5 days, 10 days, 15 days and 20 days are selected as the modeling series length respectively, and regression analysis is performed to determine the model parameters. The prediction accuracy of soil moisture was analyzed, using the two forecast periods of 10 days and 15 days. The results showed that: (1)the real-time prediction model fits well, and the deterministic coefficients under the condition of the three modeling series length can above 0.80 (the number of samples are all greater than 550).(2) The relative error of 15 days modeling series is the smallest.(3) Under the conditions of 15 days modeling series length, 15 days prediction period, and 10% relative error limit value, the moisture prediction pass rates of the three monitoring points reached 98%, 100% and 89%, respectively. It can be seen that the real-time moisture prediction model proposed by the research has high prediction accuracy, which is convenient for modeling and analysis, and provides a new method for soil moisture prediction.

Key words: 土壤墒情, 动态建模, 传感器, 水量平衡, 墒情预测