Quantifying groundwater recharge beneath furrow irrigated corn using lysimetry, an unsaturated zone water balance and numerical modeling.

摘要

Understanding the effects of new irrigation methods on groundwater recharge rates in semi-arid regions is becoming more important as the demand for water in these areas increases. Predicting groundwater recharge under furrow irrigated agricultural land can be a difficult task due to spatial variability of infiltration down a furrow, as well as the heterogeneity of hydraulic properties throughout the vadose zone. There are few methods currently being used to estimate the amount of recharge under these conditions. Each method has its own set of assumptions that create varying degrees of uncertainty in the results. The objective of this study is to quantify the amount of groundwater recharge beneath various irrigation methods and to evaluate the ability of a 2D unsaturated zone model to predict these results. The study will compare the results of two field water balance methods conducted at an experimental furrow irrigated agricultural site with those obtained using a 2D unsaturated zone model. For this experiment, a 15-acre corn field was sub-divided into three blocks with one block fully irrigated and two blocks under deficit irrigation. For each block, deep percolation (DP) was estimated at two to three locations using lysimetry and the unsaturated zone water balance (UZWB) method.;The HYDRUS (2D/3D) modeling software was used to create and calibrate a model that could effectively predict the quantity and timing of the drainage flux through the vadose zone. Two models were created for each site to test the effect of soil composition and layering on goodness-of-fit to the data collected during the two growing seasons. Model calibration was performed for the 2011 season and validated with the 2012 data. The layered model calibrated to the lysimeter data performed most consistently during the validation process, although the layered model calibrated to the UZWB data showed the least bias in results and the lowest average root mean squared error (14.63 cm). Overall, this study has shown that a layered model is needed to most accurately represent water flow in this scenario, and the results of UZWB method can be used to calibrate a predictive model.