Soil water directly or indirectly affects almost all ecological processes. Soil available water capacity (AWC), the difference between field capacity, or drained upper limit (DUL), and wilting point, or lower limit (LL15), and saturated water content (SAT) are among the most important soil hydraulic properties controlling soil water dynamics. These properties vary across space and are expensive to measure directly. It is difficult to obtain reliable estimates of soil hydraulic properties at an appropriate scale for water and land management. Here we modelled LL15, DUL, SAT and AWC measurements from 1127 whole-soil profiles across Australian agricultural areas with the Random Forest machine learning model using 19 bioclimatic and 15 topographical covariates. The amount of variance explained by the model reached up to R² = 0.69 depending on the property and soil depth assessed. For all soil hydraulic properties, the bioclimatic variables alone contributed to more than 90% of the explained variance. Particularly, temperature of driest and wettest quarter, and precipitation of warmest month were the three most influential variables. Using the derived models, we also mapped the four hydraulic properties across Australian agricultural areas in six sequential depths down to 2 m at a spatial resolution of 90 m. Moreover, we combined our mapping of AWC with existing products via an ensemble model averaging approach which proved to be more accurate than each of the three contributing products. Our results uncover the significant role of bioclimatic variables in regulating soil hydraulic properties, providing a benchmark assessment of soil hydraulic properties in agricultural regions for efficient water-related land management.

土壤水直接或间接影响几乎所有生态过程。土壤有效水容量（AWC），田间容量或排水上限（DUL）与枯萎点或下限（LL15）和饱和含水量（SAT）之间的差是控制土壤的最重要的土壤水力学特性水动力学。这些属性随空间变化，直接测量非常昂贵。在水和土地管理的适当规模下，很难获得可靠的土壤水力特性估计值。在这里，我们使用19个生物气候和15个地形协变量的随机森林机器学习模型，对来自澳大利亚农业地区1127个全土壤剖面的LL15，DUL，SAT和AWC测量进行了建模。模型解释的方差量达到R ² = 0.69，取决于所评估的特性和土壤深度。对于所有土壤水力特性，仅生物气候变量就贡献了超过90％的解释方差。特别是，最干燥和最湿润的季度的温度以及最温暖月份的降水是影响最大的三个变量。使用导出的模型，我们还以90 m的空间分辨率绘制了澳大利亚农业区域内四个连续的深度（低至2 m）的四个水力特性图。此外，我们通过整体模型平均方法将AWC与现有产品的映射相结合，事实证明，该方法比三种贡献产品的准确性更高。我们的结果揭示了生物气候变量在调节土壤水力特性中的重要作用，