基于WinSRFR模拟灌溉农田土壤入渗参数年变化规律

土壤入渗参数是确定地面灌溉灌水技术参数的主要依据之一,而农田土壤入渗特性随着灌水与耕作存在着周期性变化。为了探究这种周期性变化规律,在冬小麦-夏玉米轮作体系下,基于泾惠渠灌区2012-2015年不同灌水时期的灌水资料,利用Win SRFR4.1软件与拟合度检验相结合的方法对考斯加科夫土壤入渗模型参数值进行模拟反推得出最佳优化结果,结果表明模拟与实测的水流推进及水流消退过程的均方根误差分别为0.15~2.1和2.5~7.8 min,决定系数均在0.7以上(P0.05)。在此基础上,根据影响土壤水分入渗的主导因素土壤容重及土壤质量含水率,建立考斯加科夫土壤水分入渗模型2参数值与影响因素间的定量关系,分析土壤入渗参数在年内的变化规律,结果表明:在冬小麦-夏玉米轮作体系下,不同灌水时期的土壤入渗系数和入渗指数变化明显,变化范围分别在95.0~210.0 mm/h和0.42~0.67之间,土壤水分入渗模型两参数值与土壤含水率及土壤表层容重之间存在较好地复合对数关系,决定系数分别为0.846和0.741(P0.05)。研究结果可为年内不同灌水时期确定农田地面灌水技术参数提供依据。

Yearly variation of soil infiltration parameters in irrigated field based on WinSRFR4.1

Abstract: Soil infiltration parameters, which contain infiltration coefficient and infiltration index, determine the conversion velocity and distribution from irrigation water to soil water. Thus, they affect the irrigation effect and quality of ground irrigation and are of characteristic of time variations in summer maize-winter wheat rotation system, which may lead to different irrigation quality in different irrigation times. In order to reveal changes of soil infiltration parameters with time, this study obtained a series of infiltration coefficients and infiltration indexes of soil Kostiakov infiltration equation in different irrigation times based on field experimental data. The border irrigation experiment was conducted in 2012-2015 under summer maize-winter wheat rotation system at the Jinghui Canal irrigation area of Guanzhong Plain in Shaanxi Province. WinSRFR4.1, an integrated software for analyzing surface irrigation system, was used to estimate a field-averaged infiltration function from the field measured geometry in order to optimize the soil Kostiakov infiltration parameters. Manning function was to estimate the field synthetic roughness coefficient in different irrigation times, and then the Merriam-Keller post-irrigation volume balance analysis of WinSRFR4.1 model based on the advance-recession data was applied to simulate the process of field irrigation. Goodness of fit between simulated and measured values was evaluated by the root-mean-square error (RMSE) of advance-recession time and determination coefficient R2. The results showed that the root mean square error of the simulated water flow and the water flow regression processes were between 0.15-2.1 and 2.5-7.8 min, respectively. The coefficients of determination were more than 0.7. There were a wide variety of factors affecting soil infiltration parameters, such as soil bulk density, soil water content, organic matter, soil texture. Soil surface bulk density and water content changed with tillage, irrigation, and raining, which would affect soil infiltration parameters. Based on that, we took the soil surface bulk density, and soil water content as the main factors. According to the dominant factors affecting soil infiltration with the optimal soil infiltration parameters values, we had established the quantitative relationships between the infiltration parameters of Kostiakov infiltration equations and main factors, analyzed the yearly variations of soil infiltration parameters. The results indicated that the soil infiltration parameters changed significantly in the different irrigation periods with 95.0-210.0 mm/h and 0.42-0.67, respectively. And the relationship among the two infiltration parameters and soil moisture content, and soil surface bulk density conformed to logarithm function law, which adjusted R2 was 0.846 and 0.741, respectively. According to these, we had built up the experimental regression equations to estimate the soil infiltration parameters of different irrigation period with soil surface bulk density and water content. These results have theoretical value for ascertaining irrigation technique parameters and have practical value for water management with irrigation.