Soil water retention (SWR) function is an important model that provides an empirical relationship between soil moisture and capillary pressure. We present a simple Python tool for fitting different types of SWR functions to laboratory-measured soil moisture data. Three different optimization methods including the Levenberg-Marquardt (LM) method, Trust Region Reflective (TR) method, and Dog Box (DB) method are considered. We used all three methods to fit the van Genuchten (VG) and Brooks and Corey (BC) models to ten soil moisture datasets. Our results show that the TR method, which allows the user to search for optimal parameter values within a constrained region, is the best approach for fitting these models. We developed a new graphical procedure for evaluating the guesstimates and bounds for different SWR model parameters. Overall, the TR method available in Python, together with the proposed graphical procedure, is an excellent approach for fitting both VG and BC models to soil moisture data.

土壤保水 (SWR) 函数是一个重要的模型，它提供了土壤水分和毛细管压力之间的经验关系。我们提供了一个简单的 Python 工具，用于将不同类型的 SWR 函数拟合到实验室测量的土壤湿度数据。考虑了三种不同的优化方法，包括 Levenberg-Marquardt (LM) 方法、Trust Region Reflective (TR) 方法和 Dog Box (DB) 方法。我们使用所有三种方法将 van Genuchten (VG) 和 Brooks and Corey (BC) 模型拟合到十个土壤水分数据集。我们的结果表明，允许用户在受限区域内搜索最佳参数值的 TR 方法是拟合这些模型的最佳方法。我们开发了一种新的图形程序来评估不同 SWR 模型参数的估计值和界限。全面的，