A Method for Determining the Discharge of Closed-End Furrow Irrigation Based on the Representative Value of Manning’s Roughness and Field Mean Infiltration Parameters Estimated Using the PTF at Regional Scale

Abstract

Infiltration parameters and Manning's roughness are essential input parameters for surface irrigation simulation models. Multiple points of infiltration experiments require time-consuming, costly data collection and problematic calculations of field mean infiltration parameters. This study estimated the field mean infiltration parameters and Manning's roughness values on a regional scale, on the basis of closed-end furrow irrigation experiments conducted at 45 experimental sites on the Guanzhong plain, and evaluated the influence of Manning's roughness on advance trajectory and performance indicators of closed-end furrow irrigation. Then, we present a functional normalization of the Kostiakov equation and pedotransfer function (PTF) to estimate the normalization factors. The proposed method can be used to estimate the field mean infiltration parameters using PTF and the represented Manning's roughness to determine the optimal discharge of closed-end furrow irrigation. The results revealed that the advance trajectory and performance of closed-end furrow irrigation was not sensitive to variations of Manning's roughness, which can be adopted as a representative value (i.e., 0.075) of the maize field. The normalization method was proven to be feasible for the Kostiakov equation, and the PTF reliably estimated the normalization factors. Last, the optimized values of the inflow discharge were determined using the proposed method, with various combinations of furrow lengths and bottom slopes in the study area, and also compared with the inflow discharge determined based on infiltration parameters and Manning's roughness, which were inversed by SIPAR_ID. The results indicated that the inflow discharge values obtained through using the two methods were approximately equal, proving that the proposed method was reliable and easy to use in practice.

Determining a reasonable distance of collecting irrigation data for real‐time management of furrow irrigation

“…One day before and after irrigation, five to eight measuring points were set along the length of the furrow, and each point was divided into five layers (i.e., depths of 10, 20, 40, 60, and 80 cm) for soil water content collection. The details of the closed-end furrow irrigation experiments are listed in Table 1, and more details of the experiments can be found in Nie, Li, Zhang, et al (2018).…”

“…Furthermore, the estimated infiltration parameters and Manning roughness at 0.5 L, 0.6 L, 0.7 L, and 1.0 L were input into WinSRFR, and then the furrow irrigation simulation results were outputted. Irrigation performance indicators comprise the application efficiency E a , distribution uniformity D u , and storage efficiency E s (Bautista, Clemmens, Strelkoff, & Niblack, 2009;Nie, Li, Zhang, et al, 2018). The mathematical expressions for these indicators are given as follows:…”

“…Knowledge of infiltration parameters and Manning roughness prior to or during an irrigation event could improve the effectiveness of existing surface irrigation simulation models in designing and managing irrigation. Because of the variability of soil infiltration characteristics, measuring infiltration parameters with the single-ring or double-ring methods prior to an irrigation event is time-consuming and labour-intensive (Bauti &sta Schlegel, 2017; Koech, Smith, & Gillies, 2014a, 2014bNie, Li, Zhang, et al, 2018;Raghuwanshi et al, 2011;Valiantzas, Aggelides, & Sassalou, 2001). In addition, numerous factors affect the Manning roughness, which cannot be directly measured and therefore is difficult to determine (Anwar et al, 2016;Seyedzadeh et al, 2019;Strelkoff, Clemmens, & Bautista, 2009a, 2009b.…”

“…Alternatively, infiltration parameters and Manning roughness can be estimated using surface irrigation data. Numerous researchers have proposed various methods for estimating infiltration parameters and Manning roughness by using irrigation data containing information such as advance trajectory (Elliott & Walker, 1982;Khatri & Smith, 2006;Koech, Smith, & Gillies, 2014a, 2014bLangat, Smith, & Raine, 2008;Nie, Fei, & Ma, 2012;Raghuwanshi et al, 2011;Valiantzas, Aggelides, & Sassalou, 2001), advance trajectory and water depth (Nie, Fei, & Ma, 2014;Nie, Li, Zhang, et al, 2018;Wang et al, 2007;Wu, 2015), advance and recession trajectories (Salahou, Jiao, & Lü, 2018;Xu et al, 2019), and advance trajectory and runoff (Gillies & Smith, 2005;Scaloppi, Merkley, & Willardson, 1995). However, the real-time management of surface irrigation needs to optimize the discharge and cut-off time before the water advances to the end of the field.…”

Improving border irrigation performance with predesigned varied-discharge

“…DU and RE reflect different aspects of irrigation performance. To better explain the irrigation performance, a comprehensive evaluation indicator Y was used for final evaluation [38]. Y has the maximum value of 100%, and the larger Y is, the better the irrigation performance is.…”