Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity

The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.

Research on Influence of Soil Moisture Content of Farmland on Infiltration Model Parameters

In order to study the influence of the initial moisture content on the parameters of the infiltration model using an indoor soil column test method, and the relationship between the initial moisture content and each model parameter was analyzed by using the Green-Ampt model, the Kostiakov model, and the Horton model. The results show that there is a certain relationship between the initial water content and the parameters of the infiltration model. Based on comprehensive considerations, the Kostiakov model is the best surface irrigation infiltration model, and the Kostiakov model has the best effect when the observation time is not less than 80 minutes to simulate the soil infiltration process.

Assessment of Empirical and Semi-Empirical Models for Estimating a Soil Infiltration Function

HighlightsThe hydraulic performance computed with the KE or GA model is nearly the same, as long the models are calibrated using the same observation data.The GA model with the soil hydraulic parameters obtained from the pedotransfer functions adequately represented the soil infiltration function.The particle size distribution or the soil texture are recommended to estimate soil hydraulic parameters with the VG-ROSETTA model. Abstract. Field-scale estimation of a soil infiltration function is important for the design, simulation, and/or evaluation of surface irrigation systems. Semi-empirical and empirical infiltration models are used to estimate the infiltration function. Semi-empirical infiltration models have substantial computational and parameterization complexities, e.g., soil hydraulic parameters are needed to estimate the infiltration function. In contrast, empirical infiltration models are generally not considered to have specific initial and boundary conditions. The objectives of this study were to compare a semi-empirical infiltration model and an empirical infiltration model. The Green-Ampt model (GA) and the Kostiakov model (KE) were used as semi-empirical and empirical infiltration models, respectively. The soil hydraulic parameters for the GA model were estimated using various pedotransfer functions (PTFs), and in an additional assessment, the measured water content data were used to calibrate and validate the soil hydraulic parameters using the HYDRUS-1D model. The results show that the hydraulic performance computed with the KE or GA model is nearly the same, as long as they are calibrated using the same observation data. Additionally, the results indicate that the GA model with the soil hydraulic parameters obtained from the PTFs adequately represented the soil infiltration function in the borders. Keywords: Empirical infiltration model, Green-Ampt model, Infiltration model, Kostiakov model, Semi-empirical infiltration model, Soil hydraulic properties.

Application of Artificial Intelligence Techniques for the Prediction of Infiltration Process

Infiltration process was analysed using predictive models of Multi-Linear regression (MLR), Random Forest regression (RF), artificial neural network (ANN), M5P tree and their performances were compared with empirical model: Kostiakov model. These models were assessed using field dataset containing 340 observations. Field experimental data was implemented for training and testing the above models and their outcomes were assessed with the help of suitable performance assessment parameters. The RF based models performs batter than other models with Nash-Sutcliffe model efficiency (NSE) equal to 0.9963 and 0.9904 for the training and testing stages, correspondingly. ANN, MLR and M5P model also gives a good prediction performance. Sensitivity investigation suggests that the parameters, cumulative time and moisture content in the soil are the most effective parameters for the assessment of cumulative infiltration of soil..