Biochar is known as a well-developed porous carbonaceous material with multifunctional abilities that can enhance the physical properties of soils. However, the lack of certainty about the consequences of biochar application to soils has limited its acceptability. Application of biochar can lead to a series of changes in the physical functions of soil, which are crucial in both agricultural and environmental management. The type of feedstock, pyrolysis conditions, size of particles, and rate of amendments are responsible for biochar effectiveness. Concurrently, the physical characteristics of soil, such as particle-size distribution, can intensify the impacts. Beside the physical attributes, the chemical components and interactions between biochar and the soil interface may play an important role. The chemical properties, such as the value of electrical conductivity, pH and zeta potential, are the remarkable parameters in the hydrophysical behavior. The summary proposes that biochar has a great contribution In enhancing the definite range of aggregation formation, reduction of compaction and shear strength frequency and/or intensity, improvement of microorganisms activity, and abundance. Simultaneously, biochar plays a devastating role by filling the pores, blocking the water flow pathways, and inhibiting macro fauna growth. Particle size of biochar as a major factor, and surface functional groups as a minor factor, affect the performance of biochar in improving the hydrophysical properties of amended soils. The increment in the dosage of biochar application is not promising to enhance the physical properties of soils. Therefore, it is necessary to find a balance between the consumption of biochar and promotion of the soil-water dynamic. This review provides an overview of fastidious perspectives on how to achieve an efficient and sustainable use of biochar in hydrophysical properties.

This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5_cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST_5cm and ST _20cm of Tabriz station and ST_10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.

Soil water retention curves (SWRCs) are fundamental inputs for simulating vadose-zone processes, yet their direct measurement is labor-intensive and often impractical across large spatial domains. Pedotransfer functions (PTFs), therefore, provide an essential alternative for estimating SWRCs from readily measured soil properties. This study developed machine learning-based PTFs to estimate SWRCs using the UNSODA 2.0 database. An extreme gradient boosting (XGB) model was implemented and optimized using two Bayesian hyperparameter tuning frameworks, Hyperopt and Optuna, across eleven input scenarios incorporating combinations of textural, structural, and compositional soil attributes. Model performance was assessed using RMSE, R², and Kling–Gupta efficiency (KGE). To prevent data leakage from the hierarchical structure of the UNSODA 2.0 database, a nested grouped cross-validation framework was employed, ensuring an unbiased assessment of model generalization performance across independent soil samples. The Optuna-tuned XGB model trained on the full feature set achieved the highest accuracy, with a test RMSE of 0.0183, R² of 0.9815, and KGE of 0.9825, outperforming both the baseline and Hyperopt-optimized models. Feature importance and SHAP analyses indicated that soil texture dominated the estimations, while porosity, bulk density, and organic matter provided complementary improvements and particle density contributed marginally. These findings demonstrate that advanced hyperparameter optimization enhances the accuracy and interpretability of XGB-based PTFs, offering a robust framework for improved estimation of SWRCs in hydrological and soil-management applications.