Variability of Furrow Infiltration and Estimated Infiltration Parameters in a Macroporous Soil

Abstract

Understanding the spatial and temporal variations of infiltration in furrows is essential for the design and management of furrow irrigation systems. A key difficulty in quantifying the process is that infiltration depends on the depth of flow, which varies along a furrow and with time. An additional difficulty is that under many field conditions, a large fraction of the infiltrated water flows through cracks and/or macropores. This study examines the spatial and temporal variability of a furrow-irrigated field and evaluates a proposed semiphysical furrow infiltration model that accounts for flow-depth and macroporosity effects. Parameter estimation techniques were used to determine two parameters of the infiltration model, the hydraulic conductivity and the macroporosity term, in addition to the Manning roughness coefficient. The methodology was tested using published data from 30 furrow irrigation data sets collected in six furrows over five irrigation events. The evaluation revealed substantial variations in the final infiltrated volume among furrows and from one irrigation event to the next. Variability patterns differed markedly for infiltration measured during the advance phase in comparison with infiltration measured during the storage phase of the irrigations. Advance-phase infiltration varied systematically between irrigations for all furrows. Interfurrow inflow rate variability contributed to the variability of the infiltration during the postadvance phase, but not during the advance phase. Thus, cracks and/or macropores were an important contributor to the variability of infiltration during the advance phase. The analysis produced reasonable estimates of hydraulic conductivity relative to values reported in the literature. Hydraulic conductivity and post-advance infiltration volumes exhibited similar patterns of temporal variability. Hydraulic conductivity estimates were statistically correlated to the applied inflow rate. Although the reasons for this correlation are not clear, a possible explanation is that they are the result of systematic differences in the applied inflow rate among furrows. As with the advance-phase infiltration volumes, the estimated macroporosity term exhibited greater variation among irrigation events than among furrows during an event. The estimation procedure produced smaller differences between volume balance computed infiltration volumes and predicted values when using the semiphysical infiltration model than when using an empirical infiltration equation. Overall, the results show that the proposed furrow infiltration model represents the infiltration process adequately and that, at least for the studied data sets, the proposed estimation procedure yields a coherent set of infiltration and hydraulic resistance parameter values.

Soil and water conservation effects of different tillage measures on phaeozems sloping farmland in northeast China

“…Soil water storage can effectively characterize the soil water characteristics. Reasonable tillage measures can reduce the production of surface runoff and increase the utilization rate of rainfall, promote the infiltration rate of soil moisture, improve the permeability of soil, and increase the soil water storage (Daniela et al, 2019; Nie et al, 2018). In this study, the use of contour tillage treatments (TP, TP‐R, and TP‐S) resulted in higher soil water storage throughout the entire growth period.…”

Experimental and Modeling Evaluation of Impacts of Different Tillage Practices on Fitting Parameters of Kostiakov’s Cumulative Infiltration Empirical Equation

“…The constant "b" is just an infiltration curve fitting coefficient. For the empirical infiltration models, it is commonly implicit that, while infiltration is defined as a function of time, the residual parameters are calibration constants signifying the situations under which the test was directed, including the initial soil moisture content and any other factors that may induce differences in infiltration [80]. Recently, there have been improved research efforts offering alternate approaches for precisely predicting the quantity of soil water infiltration using altered types of models based on data mining models.…”

“…In this case, the K* outcomes were the best for the present dataset, and it is important to note that it is tested here as a regression algorithm and its parameters were left as default in Weka 3.8 software. Furthermore, the KStar algorithm is better than the other prediction regression algorithms, namely, Linear Regression, RBFNetwork, Decision Stump Simple, and Linear Regression in Weka for disorganized data [80]. For prediction of organic matter quantity and clay content in a soil, the Lazy KStar algorithm was shown to have more satisfactory performance when mining the data, with higher determination coefficients and smaller errors [81].…”