Pedotransfer functions (PTFs) are widely used to estimate soil hydraulic properties (SHPs) from easily measurable characteristics. However, most existing PTFs rely on unimodal hydraulic models, which fail to accurately represent the bimodal SHPs caused by soil structure common in field conditions. In this study, we developed new PTFs using two bimodal soil hydraulic models and introduced soil physics-informed neural networks (SPINN) to embed the models into PTF training. The results showed that the new PTFs effectively captured bimodality in hydraulic conductivity curves, achieving an RMSE of 0.578 in the test set, compared to 0.709 for unimodal models. The PTFs also improved soil water retention curve (SWRC) predictions but struggled with bimodal SWRCs for some samples, likely due to the limited number of bimodal SWRCs in the dataset. An independent dataset evaluation revealed that the RMSE for hydraulic conductivity predicted by the new PTFs was approximately one-third of that of classic PTFs. This underscores the significant role of soil structure in SHPs, which classic PTFs fail to capture. Additionally, PTFs developed using the SPINN method outperformed those optimized fitted hydraulic parameters via machine learning, a common approach in the literature. We also found that separate versus simultaneous optimization of water retention and hydraulic conductivity greatly affects PTF performance. Finally, we provided global 1 km-resolution maps of soil hydraulic parameters for the bimodal model.

Soil hydraulic properties (SHPs) are impacted by various mechanisms such as soil structure, capillarity, and adsorption forces, often showing a bimodal shape. Developing soil hydraulic models that describe SHPs over the entire saturation range often involves balancing the representation of multiple processes while minimizing the number of free-fitted parameters. Existing soil hydraulic models rarely capture bimodal soil hydraulic properties across the full moisture range or introduce too many free-fitted parameters. In this study, we propose a novel framework to describe SHPs over the entire moisture range, accounting for the effects of soil structure, capillarity, adsorption forces, and vapor diffusion. In its four free-fitted parameters form, the proposed models can capture unimodal soil water retention curves (SWRC) and bimodal hydraulic conductivity curves (HCC). With one additional free-fitted parameter, the proposed models can capture both bimodal SWRC and HCC. Testing with 107 and 52 soil samples from two public datasets demonstrated that the proposed models performed exceptionally well in describing SHPs across the entire moisture range. The reported lowest root-mean-square error values were 0.005 and 0.009 cm3 cm−3 for fitting SWRCs, and 0.465 and 0.666 for predicting HCCs, respectively. Due to the minimal introduction of free-fitted parameters, the proposed framework showed significant application potential.

Hydraulic conductivity curves (HCCs) are important parameters in land surface modeling. The general way for predicting HCC from soil water retention curve (SWRC) requires an additional input of the saturated hydraulic conductivity. The time-consuming in measurement and more importantly, the macro-effect near saturation, however, often result in difficulty and poor performance in predicting the conductivity. In this study, we provided a physically based method for predicting the HCC fully from SWRC requiring no additional parameters. This is achieved by applying an estimated conductivity (from SWRC) in the dry range as new matching point, in together with modifying the HCC model developed by Wang et al. (2018) that accounts for both capillarity and adsorption forces. Testing with a total of 159 soil samples yielded that the new model significantly improved the predictions of HCC, with R2 being 0.74 and root mean value being 0.84 cm d−1, nearly double and half of the value predicted with the input of the saturated hydraulic conductivity, respectively. The abrupt drop near saturation of the HCC model that provided by Wang et al. (2018) for soils with small n values close to 1, a parameter in shaping the SWRC, was also overcome by introducing a non-zero air-entry value.

The significant impact of soil structure on soil hydraulic properties and then on the associated water and solute transport is well recognized. However, existing soil hydraulic models that account for the effect of soil structure are often at the cost of overparameterization, hindering further application on large scales. In this study, we developed a new model that considers the effect of soil structure in hydraulic conductivity prediction when introducing no new free parameters. Testing with 152 soil samples that include different soil types shows that the new model considerably improves the prediction of conductivity, with an average root-mean-square-value (RMSE) of 0.65 cm d−1. When applying the new model in fitting observations, the model showed excellent performance, with an RMSE of only 0.30 cm d−1 remaining. This new soil hydraulic model provides a simple and practical way to incorporate the influence of soil structure in water and solute transport simulations.

The commonly applied pedotransfer functions (PTFs), which predict soil hydraulic properties (SHPs) from easily measured soil properties such as texture information, often account only for capillary forces. Recent advances in soil hydraulic modeling suggest that, to improve the prediction of SHPs under dry conditions, the impact of adsorption forces has to be taken into account. However, the lack of observations in particularly dry conditions, due to the difficult and time-consuming measurement, hinders the development of PTFs that predict SHPs from saturation to oven dryness. In this paper, we first present a simple method for predicting complete SHPs with limited measurements that cover only a relatively high matric potential range. With this method, we extended a public dataset to cover dry conditions, and then applied it to develop PTFs that can predict SHPs from saturation to oven dryness. This was achieved by applying the complete soil hydraulic model proposed by Wang et al. (2021), which accounts for both capillary and adsorptions forces and overcomes the unrealistic decrease near saturation for fine-textured soils. The impact of vapor diffusion was also considered. We further applied this method in extending an existing capillary-based PTF to dry conditions. The results showed that: 1) the proposed method performs very well in describing SHPs over the entire moisture range; 2) the PTFs developed with the extended observations and the complete model show a superior prediction performance, especially for the hydraulic conductivity; and 3) the extended capillary-based PTF improves the performance in describing SHPs under dry conditions.