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首页> 外文期刊>International journal of soil science >Comparison of Different Models for Estimating Cumulative Infiltration
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Comparison of Different Models for Estimating Cumulative Infiltration

机译：估算累积入渗量的不同模型的比较

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Infiltration process is one of the most important components of the hydrologic cycle. The ability to quantify infiltration is of great importance in watershed management. Prediction of flooding, erosion and pollutant transport all depend on the rate of runoff which is directly affected, by the rate of infiltration. Quantification of infiltration is also necessary to determine the availability of water for crop growth and to estimate the amount of additional water needed for irrigation. Thus, an accurate model is required to estimate infiltration process. In this study, the ability of seven different infiltration models (i.e., Philip (PH), Soil Conservation Service (SCS), Kostiakov (KO), Horton (HO), Swartzendruber (SW), Modified Kostiakov (MK) and Revised Modified Kostiakov (RMK) models) to fit infiltration data were evaluated. For this purpose, 95 sets of infiltration data with four-texture classes were utilized. Comparison criteria including Coefficient of Determination (R2), Mean Root Mean Square Error (MRMSE), Root Mean Square Error (RMSE) were used to determine the optimum model. The greatest amounts of R2 values were obtained with RMK, MK and Swartzendruber models. The SCS model with two parameters yielded to the lowest R2. According to the results obtained from mean of RMSE (MRMSE) values, the MK model provided the lowest values, indicating that infiltration was well described by this model. The results of ranking models according to two criteria: RMSE, and MRMSE, indicated that based on RMSE the goodness of cumulative infiltration can be estimated by the RMK, MK, Kostiakov, Swartzendruber, Horton, Philip, SCS models, respectively. But according to the MRMSE statistics cumulative infiltration can be estimated by the MK, RMK, Swartzendruber, Philip, Kostiakov, SCS and Horton models, respectively. Based on the results of ranking model the CSC model obtained the lowest ranking between the all of the models.
机译：入渗过程是水文循环最重要的组成部分之一。量化入渗的能力在流域管理中非常重要。洪水，水土流失和污染物迁移的预测都取决于径流率，径流率直接受到渗透率的影响。为了确定作物生长所需的水量并估算灌溉所需的额外水量，还需要对渗透进行量化。因此，需要一个精确的模型来估计渗透过程。在这项研究中，七个不同渗透模型（即Philip（PH），水土保持服务（SCS），Kostiakov（KO），Horton（HO），Swartzendruber（SW），Modified Kostiakov（MK）和修正Modified Kostiakov）的能力（RMK）模型）以评估渗透数据。为此，使用了95组具有四个纹理类别的渗透数据。以测定系数（R 2 ），均方根均方误差（MRMSE），均方根均方根误差（RMSE）为比较标准，确定了最佳模型。 RMK，MK和Swartzendruber模型获得最大的R 2 值。具有两个参数的SCS模型产生最低的R 2 。根据从RMSE（MRMSE）值的平均值获得的结果，MK模型提供了最低的值，表明该模型很好地描述了渗透。根据两个标准（RMSE和MRMSE）对模型进行排名的结果表明，基于RMSE，可以分别通过RMK，MK，Kostiakov，Swartzendruber，Horton，Philip，SCS模型来估计累积渗透的优势。但是根据MRMSE统计，累积渗透率可以分别由MK，RMK，Swartzendruber，Philip，Kostiakov，SCS和Horton模型估算。基于排名模型的结果，CSC模型在所有模型之间获得了最低的排名。

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