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In soil science, machine learning algorithms are preferred for pedotransfer functions due to their rapid data acquisition and high prediction accuracy. The current study aims to evaluate the prediction of soil quality in agricultural lands dominated by the humid Black Sea climate using various algorithms. Both classification and regression-based algorithms (Random Forest-RF, Light Gradient Boosting-LGB, Extreme Gradient Boosting-XGBoost, k-nearest neighbors-kNN, Logistic Regression, multilayer perceptron-MLP, Linear Regression-LR and Bayesian Ridge- BR) were used in the method. The comparison of soil maps is also included. Furthermore, the present study evaluates the Grid Search optimization method with K-Fold Cross Validation (K = 5) for both classification and regression-based algorithms. The prediction of soil quality was performed using class-based and regression-based algorithms. As a result of the study, the RF and XGBoost algorithms achieved an approximate accuracy rate of 92 % in the class-based prediction. In regression-based predictions, the most successful algorithms were BR and LR, with an R2 Score of 0.84. The Grid Search optimization method was used to improve the R2 Score, resulting in an increase to 0.90 and 0.88 for BR and LR, respectively. The optimized hyperparameters showed improved performance in predicting the soil quality index. The present study found that Gaussian and Spherical models had the lowest prediction errors in spatial distribution maps. Tree-based algorithms were found to be suitable for class-based prediction of soil quality, while the linear regression method was appropriate for regression predictions. This study is characterized by a rainy climate resulting in acidic soils with high organic matter content. Planning of new studies in different climates and soil properties is recommended. [ABSTRACT FROM AUTHOR]

Understanding the effects of salinity on soil structure and hydraulic properties is critical for addressing environmental challenges in coastal saline and sodic areas. In this study, soil samples were collected from a coastal region in eastern China to investigate how salinity affected the soil structure and hydraulic properties based on lab experiments. A comprehensive soil dataset was also compiled from the experimental results to develop a salinity-based pedotransfer function (PTF-S) tailored to the coastal environment. The results showed that salinity significantly altered the soil aggregate size distribution and hydraulic properties. Higher salinity promoted the formation of larger aggregates (0.25–2 mm), particularly in silty clay soil. Salinity positively correlated with the saturated hydraulic conductivity (Ks) in sandy loam soil, regardless of the cation type (Na⁺ or Ca2⁺). By comparison, Na+ increased the Ks of silty clay soil up to a certain threshold, while Ca2+ enhanced the Ks regardless of the soil texture. Increased salinity also reduced the soil water retention of sandy loam soil; however, Na+ increased the soil water retention of silty clay soil and Ca2+ had different effects depending on the suction levels. The newly developed PTF-S model, which included the electrical conductivity (EC) and cation exchange capacity (CEC), showed better predictions for the volumetric water content (R = 0.886 and RMSE = 0.057 cm3/cm3) and log Ks (R = 0.991 and RMSE = 0.073 mm/h) than the traditional model that excludes the salinity variables EC and CEC (PTF-N) (R = 0.839 and RMSE = 0.066 cm3/cm3 for the volumetric water content, and R = 0.966 and RMSE = 0.140 mm/h for the log Ks). This study highlights the importance of developing salinity-based PTFs for addressing soil salinization challenges. [ABSTRACT FROM AUTHOR]

Accurate soil organic carbon storage (SOCS) estimation is crucial for sustaining ecosystem health and mitigating climate change impacts. This study investigated the accuracy and variability of SOCS predictions, focusing on the role of pedotransfer functions (PTFs) in estimating soil bulk density (BD). Utilizing a comprehensive dataset from the Korean Rural Development Administration (RDA database), which includes 516 soil horizons, we evaluated 36 widely-used BD PTFs, well-established formulas that estimate BD by considering soil properties, including soil organic carbon (SOC), soil organic matter (OM), sand, gravel, silt, and clay. These PTFs demonstrated varying levels of precision, with root mean squared errors (RMSE) ranging from 0.177 to 0.377 Mg m−3 and coefficients of determination (R2) from 0.176 to 0.658; hence, the PTFs have been classified into excellent, moderate, and poor-performing groups for predicting BD. Further, a novel PTF based on an exponential function of SOC was developed, showing superior predictive power (R2 = 0.73) compared to existing PTFs, using an independent validation dataset. Our findings reveal significant differences in SOCS predictions and observations among the PTFs, with a p-value

Abstract Key message The hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction. Context To overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic. Aims This study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails. Methods Four hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails. Results The compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(K sat)) and the soil water content at saturation (θ sat). For the skid trails, θ sat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(K sat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils. Conclusion Estimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm).

Novel soil datasets and the application of pedotransfer functions provide soil hydraulic input data for modelling hydrological processes at different scales. We aimed to evaluate the reliability of soil hydraulic parameters derived by indirect methods in simulation of soil moisture time series and water budgets at profile level of three sites (Forest, Orchard and Grassland) from a Central European catchment (Lake Balaton, Hungary). Five soil-vegetation-atmosphere model variants were set up with the Hydrus-1D model for each site, differing only in the parametrization of input soil data: i) a calibrated reference, ii) measured values, iii) values predicted from measured basic soil properties, iv) values predicted from national soil map information, v) values derived from the 3D soil hydraulic dataset of Europe. Calibrated soil parameters led to Nash-Sutcliffe efficiency 0.50, 0.54 and 0.71 for the Forest, Orchard and Grassland Site respectively. The outcomes for model efficiency of soil moisture underline the superiority of local databases over regional ones and the need for more detailed vertical discretization during modelling. The model performance according to soil moisture and water budget accuracy led to different rank order of model variants. Water budget comparisons indicated moderate differences between the hydrologic fluxes simulated by the different model variants, emphasizing the uncertainties associated with soil hydraulic parametrization either at local or at watershed scale.

Key message: The hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction. Context: To overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic. Aims: This study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails. Methods: Four hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails. Results: The compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(Ksat)) and the soil water content at saturation (θsat). For the skid trails, θsat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(Ksat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils. Conclusion: Estimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm). [ABSTRACT FROM AUTHOR]

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions. [ABSTRACT FROM AUTHOR]

This study focused on the hydrophysical characteristics of an abandoned limestone quarry in Czechia. Six sites were examined; two sites were undergoing natural succession (the Quarry Wall and Reed Canary Grass plots, which had undeveloped arboreal layers) and four sites were undergoing managed forest reclamation. Of the four forest reclamation sites, three were classified as prospering (the Prospering Lime, Prospering Maple and Prospering Lime + Oatgrass plots) and one was in decline (the Declining Larch + Lime plot). The arboreal layer included small-leaved lime (Tilia cordata Mill.), sycamore maple (Acer pseudoplatanus L.), and European larch (Larix decidua Mill.). Our results showed that Lime + Oatgrass plot retained more water than other plots. Field soil moisture measurements indicated that throughout the 1096-day monitoring period, only the soils at the successional sites reached the wilting point (Quarry Wall plot: 159 days; Reed Canary Grass plot: 43 days). Soil heterogeneity in the reclaimed areas was due to variation in the soil profile depth, disturbance from mining activities, reclamation efforts, and the availability of quality soil material. Soil conditions and the dynamics at the quarry created less than ideal conditions for tree regeneration. This primarily relates to limiting and significantly heterogeneous successional plots.

The capacity of soil to retain water and make it available to plants is an essential soil functions for the sustainability of terrestrial ecosystems. A lot of progress has been made in estimating water retention and availability as a function of soil texture. On the other hand, a lower effort has been dedicated to seeking correlations between plant-available water (AW) and soil compaction. In this study, we compiled a dataset with 2479 records from experiments conducted in Brazilian soils to evaluate the relationship between AW and bulk density (BD). The dataset was split into sub-datasets defined by soil textural classes to reduce the effect of texture on AW–BD relationships. In each sub-dataset, AW–BD relationships were described by linear regression. In general, there was a weak association between AW and BD. The strongest correlations were found in the Silty Loam (R2 = 0.26) and Loam (R2 = 0.13) classes. However, the partitioning of the overall dataset by textural classes was not effective to eliminate the effect of texture on AW–BD relationships. Still, the data showed that soil compaction may increase or reduce AW. Nevertheless, more experimental research is needed to bring a better understanding of how AW is affected by changes in BD. [ABSTRACT FROM AUTHOR]

This study focused on the hydrophysical characteristics of an abandoned limestone quarry in Czechia. Six sites were examined; two sites were undergoing natural succession (the Quarry Wall and Reed Canary Grass plots, which had undeveloped arboreal layers) and four sites were undergoing managed forest reclamation. Of the four forest reclamation sites, three were classified as prospering (the Prospering Lime, Prospering Maple and Prospering Lime + Oatgrass plots) and one was in decline (the Declining Larch + Lime plot). The arboreal layer included small-leaved lime (Tilia cordata Mill.), sycamore maple (Acer pseudoplatanus L.), and European larch (Larix decidua Mill.). Our results showed that Lime + Oatgrass plot retained more water than other plots. Field soil moisture measurements indicated that throughout the 1096-day monitoring period, only the soils at the successional sites reached the wilting point (Quarry Wall plot: 159 days; Reed Canary Grass plot: 43 days). Soil heterogeneity in the reclaimed areas was due to variation in the soil profile depth, disturbance from mining activities, reclamation efforts, and the availability of quality soil material. Soil conditions and the dynamics at the quarry created less than ideal conditions for tree regeneration. This primarily relates to limiting and significantly heterogeneous successional plots. [ABSTRACT FROM AUTHOR]

Understanding the effects of salinity on soil structure and hydraulic properties is critical for addressing environmental challenges in coastal saline and sodic areas. In this study, soil samples were collected from a coastal region in eastern China to investigate how salinity affected the soil structure and hydraulic properties based on lab experiments. A comprehensive soil dataset was also compiled from the experimental results to develop a salinity-based pedotransfer function (PTF-S) tailored to the coastal environment. The results showed that salinity significantly altered the soil aggregate size distribution and hydraulic properties. Higher salinity promoted the formation of larger aggregates (0.25–2 mm), particularly in silty clay soil. Salinity positively correlated with the saturated hydraulic conductivity (Ks) in sandy loam soil, regardless of the cation type (Na⁺ or Ca2⁺). By comparison, Na+ increased the Ks of silty clay soil up to a certain threshold, while Ca2+ enhanced the Ks regardless of the soil texture. Increased salinity also reduced the soil water retention of sandy loam soil; however, Na+ increased the soil water retention of silty clay soil and Ca2+ had different effects depending on the suction levels. The newly developed PTF-S model, which included the electrical conductivity (EC) and cation exchange capacity (CEC), showed better predictions for the volumetric water content (R = 0.886 and RMSE = 0.057 cm3/cm3) and log Ks (R = 0.991 and RMSE = 0.073 mm/h) than the traditional model that excludes the salinity variables EC and CEC (PTF-N) (R = 0.839 and RMSE = 0.066 cm3/cm3 for the volumetric water content, and R = 0.966 and RMSE = 0.140 mm/h for the log Ks). This study highlights the importance of developing salinity-based PTFs for addressing soil salinization challenges.

Featured Application: Landowners, regional and national governments, and researchers can use predictions of the soil hydrological properties created in this work, such as wilting point, field capacity, macroporosity, and total available water content, to characterize soils for soil management decisions in New Zealand, e.g., in terms of irrigation requirements, or for policy, e.g., nutrient budgets and regulations. This paper describes a new pedotransfer function (PTF) for the soil water content of New Zealand soils at seven specific tensions (0, −5, −10, −20, −40, −100, −1500 kPa) using explanatory variables derived from the S-map soil mapping system. The model produces unbiased and physically plausible estimates of the response at each tension, as well as unbiased and physically plausible estimates of the response differences that define derived properties (e.g., macroporosity and total available water content). The PTF is a development of an earlier model using approximately double the number of sites compared with the earlier study, a change in fitting methodology to a semi-parametric GAM Beta response, and the inclusion of sample depth. The results show that the new model has resulted in significant improvements for the soil water content estimates and derived quantities using standard goodness-of-fit measures, based on validation data. A comparison with an international PTF using explanatory variables compatible with variables available from S-map (EUPTF2) suggests that the model is better for prediction of soil water content using the limited information available from the S-map system. [ABSTRACT FROM AUTHOR]

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Soil temperature regime (STR) is important for soil classification and land use. Generally, STR is delineated by estimating the mean annual soil temperature at a depth of 50 cm (MAST50) according to the Chinese Soil Taxonomy (CST). However, delineating the STR of China remains a challenge due to the difficulties in accurately estimating MAST50. The objectives of this study were to explore environmental factors that influence the spatial variation of MAST50 and generate an STR map for China. Soil temperature measurements at 40 and 80 cm depth were collected from 386 National Meteorological Stations in China during 1971–2000. The MAST50 was calculated as the average mean annual soil temperature (MAST) from 1971–2000 between 40 and 80 cm depths. In addition, 2 048 mean annual air temperature (MAAT) measurements from 1971 to 2000 were collected from the National Meteorological Stations across China. A zonal pedotransfer function (PTF) was developed based on the ensemble linear regression kriging model to predict the MAST50 in three topographic steps of China. The results showed that MAAT was the most important variable related to the variation of MAST50. The zonal PTF was evaluated with a 10% validation dataset with a mean absolute error (MAE) of 0.66°C and root mean square error (RMSE) of 0.78°C, which were smaller than the unified model with MAE of 0.83°C and RMSE of 0.96°C, respectively. This study demonstrated that the zonal PTF helped improve the accuracy of the predicted MAST50 map. Based on the prediction results, an STR map across China was generated to provide a consistent scientific base for the improvement and application of CST and land use support.

Liming is an effective measure to increase the soil pH and to counterbalance soil acidification. Therefore, the liming recommendations (LRs) for agricultural practice consider two aspects: changing the initial pH to the desired pH and compensating for all pH decreases taking place within the liming interval. The separation of these aspects is essential to optimize LRs and to minimize lime losses to the environment. Therefore, we developed a pedotransfer function (PTF) to calculate the lime demand to change the initial pH to the desired pH and compared the results with the LRs for agricultural practice. Applying this PTF to a set of 126 soil samples that were analyzed for base neutralization capacity could explain approximately 78% of the variability in the pH changes after the addition of different amounts of Ca(OH)2. Consequently, the lime demand to change the initial pH to the desired pH increased by approximately one-sixth compared to the lime demand proposed by the liming recommendation scheme, which is commonly used in Germany. From the numerical difference between the lime demand according to the LRs and the PTF, we calculated the annual acidification rates based on the soil texture, organic matter content and initial pH. Decoupling the abovementioned two aspects of LRs might be helpful to optimize the LRs by adapting to different regions, diverse management strategies and a changing climate. [ABSTRACT FROM AUTHOR]

Accurate saturated hydraulic conductivity (Ksat) predictions are critical for precise water flow estimations. Pedotransfer functions (PTFs) have been used to estimate Ksat based on soil structural and textural properties. However, PTF accuracy must be validated with observed Ksat values to improve confidence in model predictions. A study was conducted in the seasonal wetlands of a representative mixed land-use watershed in West Virginia (WV), USA. The observed data included soil characteristics and observed piezometric Ksat using slug tests. Soil texture was predominantly sandy, and the observed average Ksat ranged from 35.90 to 169.64 m/d. The average bulk dry density (bdry) increased, while porosity and volumetric water content decreased significantly with a depth to 45 cm (p < 0.05). The degree of saturation varied significantly between monitoring sites (p < 0.05). A Pearson correlation matrix and Principal Component Analysis (PCA) revealed that Ksat was more connected to soil textural properties, specifically clay. Single parameter PTFs that estimated Ksat as a function of clay content performed better (ME = −90.19 m/d, RMSE = 102.87 m/d) than the PTFs that used silt or sand percentages (ME= −96.86 m/d, RMSE = 108.77). However, all five PTFs predicted Ksat with low accuracy (RMSE > 100 m/d), emphasizing the need to calibrate existing PTFs with observed data or develop site-specific PTFs. These results provide valuable insights into Ksat estimation in riparian wetlands of mixed land-use watersheds and are a helpful reference for land managers and future work. [ABSTRACT FROM AUTHOR]

Modelling the lime requirement (LR) is a fast and efficient way to determine the amount of lime required to obtain a pH that can overcome the adverse effects caused by soil acidification. This study aimed to model the LR based on the properties of soil and lime. A total of 17 acidic soils and 39 lime samples underwent soil–lime incubation in the laboratory. The predictive equations for the LR (t ha−1) were modelled using ∆pH (the difference between the target pH and initial pH), the neutralizing value (NV, mmol kg−1) of lime, soil pH, soil clay content (%), soil bulk density (BD, g cm−3), and the depth of soil (h, cm) as the factors in an exponential equation. The generic predictive equation, L R = ∆ p H × e − 3.88 − 0.069 × N V + 0.51 × p H + 0.025 × C l a y × B D × h , was validated as the most reliable model under field conditions. Simplified predictive equations for different soil textures when limed with quicklime and limestone are also provided. Furthermore, the LR proportions provided by hydrated lime, quicklime, limestone, and dolomite in commercially available lime can be expressed as 0.58:0.64:0.97:1.00. This study provides a novel and robust model for predicting the amount of lime product containing components with different neutralizing abilities that are required to neutralize soils with a wide range of properties. It is of great significance to agronomic activities and soil remediation projects. [ABSTRACT FROM AUTHOR]

This paper describes a new pedotransfer function (PTF) for the soil water content of New Zealand soils at seven specific tensions (0, −5, −10, −20, −40, −100, −1500 kPa) using explanatory variables derived from the S-map soil mapping system. The model produces unbiased and physically plausible estimates of the response at each tension, as well as unbiased and physically plausible estimates of the response differences that define derived properties (e.g., macroporosity and total available water content). The PTF is a development of an earlier model using approximately double the number of sites compared with the earlier study, a change in fitting methodology to a semi-parametric GAM Beta response, and the inclusion of sample depth. The results show that the new model has resulted in significant improvements for the soil water content estimates and derived quantities using standard goodness-of-fit measures, based on validation data. A comparison with an international PTF using explanatory variables compatible with variables available from S-map (EUPTF2) suggests that the model is better for prediction of soil water content using the limited information available from the S-map system.

Soil erodibility (K) reflects the sensitivity of soil to detachment and transport and is a key factor for estimating the loss of soil. Most of the models for estimating K are complex and experiential, simple and local estimation model in the hilly and mountainous southeastern China is rare. This study aims to build local pedotransfer functions (PTFs) for soil erodibility estimation and evaluate the performance of the built PTFs, i.e. multiple linear regression (MLR), MLR with deformed forms of variables (MLR-DFV) and artificial neutral network with deformed forms of variables (ANN-DFV) to estimating K in southeastern China. The local true K values were obtained by a comprehensive method that considering the optimization prediction model and runoff-plot monitoring data. The best predictive variables were determined using correlation analysis, principal component analysis, importance evaluation and minimum variable-set determination. Mean K in the study area was 0.043 t ha h ha−1 MJ−1 mm−1, ranging from 0.019 to 0.060 t ha h ha−1 MJ−1 mm−1, showed a moderate spatial variability. Soil organic-matter content (SOM) was the most important factor influencing K and accounted for 17.5 % of the total importance. Soil sand content, geometric mean diameter of aggregates, SOM and synthetic curvature were identified as the best predictive variables representing soil physical properties, aggregate characteristics, nutrient and topographical conditions, respectively. The accuracies of MLR-DFV and ANN-DFV were high and similar but higher than the accuracy of MLR. K estimated using ANN-DFV was more similar in magnitude, distribution, and spatial variability to the true K data than K estimated using MLR-DFV. We developed the first local PTFs for estimating K in the hilly and mountainous southeastern China, which could provide empirical basis and method support for studying K in similar regions.

Soil organic matter (SOM) plays a key role in ecosystems. Reduction of its content due to land-use changes has a negative impact on the soil, but also on the wider environment. Accordingly, SOM content is routinely analyzed in the laboratory. As these are expensive and/or time-consuming, indirect ones are also tested. The aim of this study was to examine the possibility of predicting SOM content by linear regression using soil color as the predictor, at three locations in Zagreb (Croatia), with different soil types (eutric cambisol anthropogenic, humofluvisol, pseudogley) and different land uses (plough land, meadow, forest, respectively). At each location, 5 samples of the surface soil layer were taken. Soil color was determined using the Munsell system, and the hue was 2.5Y and 10YR in dry and moist soil, respectively. Laboratory analyzes showed that the soils are very acid to neutral silt loams. In line with the land-use, they differed significantly in SOM content and were poorly humic (plough land), moderately to highly humic (meadow), and highly humic (forest). Correlation between soil color dimensions and SOM content was significant only for the dry samples, between chroma and SOM and between value/chroma ratio and SOM. Regression analysis showed high coefficients of determination for these two relationships (R2 = 0.88 for chroma-SOM, R2 = 0.76 for value/chroma-SOM). The results suggest that visual soil color determination can be used to estimate SOM content, but only in dry soil. The model calibrated in this paper needs to be validated using samples of other (different) soils.

【Background】 Accurate prediction of soil water dynamics requires soil hydraulic parameters and the PedoTransfer Function (PTF) is an indirect method estimating soil hydraulic parameters based on easy-to-measure soil properties. 【Objective】 The purpose of this paper is to compare different machine learning methods as a pedotransfer function to estimate soil hydraulic parameters. 【Method】 We used the UNSODA soil hydraulic property database and compared three machine learning methods: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The hydraulic parameters were described by the van Genuchten formula, and the relationship between its parameters with fractions of sand, silt and clay, as well as soil bulk density was analyzed using the three methods. The accuracy of each method was evaluated using measured water retention curves and saturated hydraulic conductivity from soils with different textures. 【Result】 The SVM model was most accurate and KNN the least to predict soil hydraulic parameters using these easy-to-measure soil properties. Evaluation of operating efficiency of all three methods revealed that the ANN model was least efficient and the KNN the most in model training. In contrast, the ANN model was most efficient while KNN the least in predicting soil hydraulic parameters. Comparison of the three models against the Rosetta model - a commonly used neural-network pedotransfer function with a single hidden layer - found that neural-network models with multiple hidden layers, as used in this paper, were more accurate. We also found that for all three models, increasing the number of input data improved their estimation accuracy. 【Conclusion】 Of the three models, SVM is most accurate for predicting soil hydraulic parameters using fractions of clay, sand and silt, and bulk density, followed by ANN, when the database was not large enough. With the size of the database increasing, the ANN model becomes increasingly more efficient. Since ANN can use the mini-batch method to train the model without increasing computational costs, our results suggest that selecting a suitable method to calculate soil hydraulic parameters should consider computational cost and estimation accuracy when the size of the database increases.

In this study, different methods were compared in order to determine the soil hydraulic conductivity at the saturation (Ks) of two heavily anthropized soils in northern Italy: an irrigated field and a landfill cover. In situ, laboratory measurements (falling head and evaporation method) and pedotransfer functions (ROSETTA and HYPRES) were used for the Ks estimation. In accordance with scientific literature, the results have shown that Ks is largely dependent on the type of technique used in taking the measurements. The ROSETTA and HYPRES pedotransfer functions show quite similar performances, while their easiness and convenient use make them potential alternative techniques for the Ks estimation in comparison with the in situ and laboratory measurements. The Ks estimate is sensitive to the selected method and this sensitivity affects the hydrological model simulations. Therefore, none of the tested methods can be considered as a benchmark, but the results found in this study confirm that the applied method for the determination of Ks, may provide a first estimate of Ks to be subsequently optimised after the simulations.

ABSTRACT The saturated hydraulic conductivity (Ks) is an essential property for modeling water and contaminants movement into aquifers. However, Ks is extremely variable, even when considering nearby locations, which poses a challenge for modeling at catchment scales. Field measurements of Ks are most of the time expensive, time-consuming and labor-intensive. This study aimed to obtain, for modeling purposes, and using pedotransfer functions (PTFs), a composite value of Ks at a catchment scale, in a recharge area of the Guarani Aquifer System. Soil samples were taken across the study area, and the Ks for each sampling point were determined by several PTF methods. At the same locations, Ks field measurements were taken using a Guelph permeameter. Average values of Ks for all the sampling points calculated by PTFs were similar to the average value obtained by field measurements. The use of PTFs proved to be a faster and simpler method to efficiently determine the Ks value for the watershed and to capture the stochastic variation in terms of soil pore combination at the watershed scale.

Abstract Agriculture needs methodologies that assist in the determination of soil attributes and variability mapping attributes with greater levels of detail. Therefore, the objective of this research was to evaluate magnetic susceptibility as auxiliary variable for estimating soil attributes in areas of Indian Black Earths in the south of Amazonas State. Three Indian Black Earth areas are located in the municipalities of Apuí and Manicoré - Amazonas, under uses with coffee, cocoa and pasture. The soils were collected at the crossing points in the depth of 0.00 - 0.20 m, making a total of 88 sampling points/area, and totaling 264 samples. The points were georeferenced for geostatistical modeling. After that, physical and chemical analyzes were performed to obtain the values of soil and magnetic susceptibility attributes. Descriptive statistics, Pearson correlation, linear regression and geostatistical analyzes were applied for Pedotransfer Function modeling and the spatial variability of the analyzed attributes. Magnetic susceptibility showed a high degree of spatial dependence in the study areas, high range values, correlating with most of the assessed attributes, mainly physical, indicating potential in the prediction of the attributes in these environments. Pedotransfer functions vary among IBE’s sites in attribute prediction, ensuring moderate estimates for predicting soil attributes in IBE’s areas.

A key parameter for the design of soil drainage and irrigation facilities and for the modelling of surface runoff and erosion phenomena in land-formed areas is the saturated hydraulic conductivity (Ks). There are many methods for determining its value. In situ and laboratory measurements are commonly regarded as the most accurate and direct methods; however, they are costly and time-consuming. Alternatives can be found in the increasingly popular models of pedotransfer functions (PTFs), which can be used for rapid determination of soil hydrophysical parameters. This study presents an analysis of the Ks values obtained from in situ measurements conducted using a double-ring infiltrometer (DRI). The measurements were conducted using a laboratory permeability meter (LPM) and were estimated using five PTFs in the Rosetta program, based on easily accessible input data, i.e., the soil type, content of various grain sizes in %, density, and water content at 2.5 and 4.2 pF, respectively. The degrees of matching between the results from the PTF models and the values obtained from the in situ and laboratory measurements were investigated based on the root-mean-square deviation (RMSD), Nash-Sutcliffe efficiency (NSE), and determination coefficient (R2). The statistical relationships between the tested variables tested were confirmed using Spearman's rank correlation coefficient (rho). Data analysis showed that in situ measurements of Ks were only significantly correlated with the laboratory tests conducted on intact samples; the values obtained in situ were much higher. The high sensitivity of Ks to biotic and abiotic factors, especially in the upper soil horizons, did not allow for a satisfactory match between the values from the in situ measurements and those obtained from the PTFs. In contrast, the laboratory measurements, showed a significant correlation with the Ks values, as estimated by the models PTF-2 to PTF-5; the best match was found for PTF-2. [ABSTRACT FROM AUTHOR]

Unlike many other countries, tropical regions such as Indonesia still lack publications on pedotransfer functions (PTFs), particularly ones dedicated to the predicting of soil bulk density. Soil bulk density affects soil density, porosity, water holding capacity, drainage, and the stock and flux of nutrients in the soil. However, obtaining access to a laboratory is difficult, time-consuming, and costly. Therefore, it is necessary to utilise PTFs to estimate soil bulk density. This study aims to define soil properties related to soil bulk density, develop new PTFs using multiple linear regression (MLR), and evaluate the performance and accuracy of PTFs (new and existing). Seven existing PTFs were applied in this study. For the purposes of evaluation, Pearson’s correlation (r), mean error (ME), root mean square error (RMSE), and modelling efficiency (EF) were used. The study was conducted in five soil types on Bintan Island, Indonesia. Soil depth and organic carbon (SOC) are soil properties potentially relevant for soil bulk density prediction. The ME, RMSE, and EF values were lower for the newly developed PTFs than for existing PTFs. In summary, we concluded that the newly developed PTFs have higher accuracy than existing PTFs derived from literature. The prediction of soil bulk density will be more accurate if PTFs are applied directly in the area that is to be studied. [ABSTRACT FROM AUTHOR]

Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [ABSTRACT FROM AUTHOR]

This work describes the effect of different agricultural land use on potential soil erodibility (K) at cultivated farming areas in Cameron Highlands. Ordinarily, soils are assigned with K factors depending on geological properties only which can result into erroneous calculation of soil erosion. This study explores roles of different agricultural land use on the spatial variability of soil erodibility on hilly farms at Cameron Highlands. Soil samples, slopes and spatial locations were collected based on crop types being cultivated. Meanwhile, the land use and type of equipment for each crop are recorded and ranked depending on the degree of soil disturbances. The results showed that, K values are ranged from 0.0084 to 0.0161. Shallow-root crops, such as vegetables and flowers have higher K values due to shallow soil rootzone and frequency of surface operations. However, tea cultivated areas and forests have low K values, indicating comparably higher ability to resist erosion. Furthermore, the erodibility factor for tea farms shows increasing patterns along the developmental stages while the reverse was found in vegetable farms. Spatial variability of the K is influenced by various farming operations at different growing stages and the peculiarity of each crop. This work demonstrated that, the soil erodibility factor can be determined considering the crops and stages of development, in addition to geological attributes.

Study region: Upper region of the Greater Chao Phraya River (GCPR) basin in Thailand. Study focus: This study presents a (∼1 km resolution) distributed hydrological model, wflow_sbm, with global spatial data and parameterization for estimating daily streamflow in the upper GCPR basin, with the aim to overcome in situ data scarcity often occurring in Southeast Asia. We forced the model with the MSWEP V2 precipitation and eartH2Observe potential evapotranspiration datasets. Seamless distributed parameter maps based on pedotransfer functions (PTFs) and literature review were applied to bypass calibration. Only the KsatHorFrac parameter determining the lateral subsurface flow was calibrated. A target storage-and-release-based reservoir operation module (ROM) was implemented to simulate reservoir releases. We compared the simulated daily streamflows obtained from different PTFs and evaluated the model performance in the period 1989–2014. New hydrological insights for the region: The global-data-driven wflow_sbm model can reconstruct daily streamflow in the upper GCPR basin, especially for natural catchments (KGE = 0.78). The ROM can capture the seasonal variability of reservoir releases, but not very accurately at the daily timescale (KGE = 0.43) since the actual reservoir operations are too complex. Different PTFs and KsatHorFrac values only introduce little uncertainty in the streamflow results. Therefore, the proposed model provides an opportunity for streamflow estimation in other ungauged or data-scarce basins in Southeast Asia. Nonetheless, the difficulty in the reservoir system modeling reflects the necessity of better understanding of human intervention on daily streamflow.

Simultaneous scaling of soil water retention and hydraulic conductivity functions provides an effective means to characterize the heterogeneity and spatial variability of soil hydraulic properties in a given study area. The statistical significance of this approach largely depends on the number of soil samples collected. Unfortunately, direct measurement of the soil hydraulic functions is tedious, expensive and time consuming. Here we present a simple and cost-effective hybrid scaling approach that combines the use of ancillary information (e.g., particle-size distribution and soil bulk density) with direct measurements of saturated soil water content and saturated hydraulic conductivity. Our results demonstrate that the presented approach requires far fewer laboratory measurements than conventional scaling methods to adequately capture the spatial variability of soil hydraulic properties. © 2013. American Geophysical Union. All Rights Reserved.

A key parameter for the design of soil drainage and irrigation facilities and for the modelling of surface runoff and erosion phenomena in land-formed areas is the saturated hydraulic conductivity (Ks). There are many methods for determining its value. In situ and laboratory measurements are commonly regarded as the most accurate and direct methods; however, they are costly and time-consuming. Alternatives can be found in the increasingly popular models of pedotransfer functions (PTFs), which can be used for rapid determination of soil hydrophysical parameters. This study presents an analysis of the Ks values obtained from in situ measurements conducted using a double-ring infiltrometer (DRI). The measurements were conducted using a laboratory permeability meter (LPM) and were estimated using five PTFs in the Rosetta program, based on easily accessible input data, i.e., the soil type, content of various grain sizes in %, density, and water content at 2.5 and 4.2 pF, respectively. The degrees of matching between the results from the PTF models and the values obtained from the in situ and laboratory measurements were investigated based on the root-mean-square deviation (RMSD), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2). The statistical relationships between the tested variables tested were confirmed using Spearman’s rank correlation coefficient (rho). Data analysis showed that in situ measurements of Ks were only significantly correlated with the laboratory tests conducted on intact samples; the values obtained in situ were much higher. The high sensitivity of Ks to biotic and abiotic factors, especially in the upper soil horizons, did not allow for a satisfactory match between the values from the in situ measurements and those obtained from the PTFs. In contrast, the laboratory measurements, showed a significant correlation with the Ks values, as estimated by the models PTF-2 to PTF-5; the best match was found for PTF-2.

Literature overview. The parameterization of hydrological models requires knowledge of the soil filtration properties. Generally, soil profiles are characterized by properties such as sand, silt and clay content, bulk density, organic carbon fraction or humus content, and no data on filtration properties are available. Ukrainian soil database, created in Geoecophysics of soil laboratory of National Scientific Center “Institute for Soil Science and Agrochemistry Researched named after O.N. Sokolovsky” (Laktionova et al., 2012), among other properties has extensive data on texture and bulk density for more than 2000 profiles, less on organic carbon content, and almost no data on saturated hydraulic conductivity (Ksat). The most probable ranges of Ksat for most types of Ukrainian soils are given in the Atlas of natural conditions and natural resources of the Ukrainian SSR (“Pochvenno-meliorativnoye rayonirovaniye. Masshtab 1:4000000,” 1978), however, the data doesn’t present Ksat for different textures inside one soil type. To fill this gap, the best solution is the applying of pedotransfer function (PTF). The purpose of this work is to synthesize the most realistic Ksat of the main soil groups of Ukraine, corresponding to a scale map of 1:2 500 000 (Krupskiy, 1977), as well as their genetic horizons, on the basis of calculated and experimental values available in the literature. Material and methods. Ten PTFs used in the study are based on regression equations (Cosby et al., 1984; Saxton & Rawls, 2006; Weynants et al., 2009; Wösten et al., 1999), decision tree (Tóth et al., 2015), or neural network (Zhang & Schaap, 2017). Ksat was estimated for 942 horizons of 171 profiles which represented all 40 soil groups (corresponding to the legend of 1:2 500 000 map) of Ukraine according to Dokuchaev classification. Results. Wösten and Rosetta3 PTFs are determined as the most relevant by comparing the calculated Ksat values with the available data of the bottom (horizons A2, B, C) and top (A0, A1) soil layers of Ukraine. In particular, they are relevant for widespread soils such as Soddy podzolic soils (WRB – Eutric podzoluvisols), dark gray podzolized soils (Phaeozems Albic), chernozems podzolized (Chernozems Chernic), chernozems southern (Chernozems Calcic), meadow-chernozemic soils (Phaeozems Haplic), dark chestnut and chestnut soils (Kastanozems Haplic and Kastanozems Luvic), meadow soils (Umbrisols Gleic, Fluvisols Dystrict, Fluvisols Eutryc, Leptosols Umbric), mountain soils (Cambisols), and top layer of Chernozems ordinary (Chernozems Chernic). Unfortunately, all ten PTFs underestimate 2-4 times Ksat of bottom layer of ordinary and typical chernozems (Chernozems Chernic) and overestimate 2-5 times for relatively impermeable horizons (< 2 mm/h). Conclusions. Based on the calculated and experimental values, the map of Ksat of the top and bottom soil layers was obtained. Sandy soils, common in Polissia, have the highest filtration rate. Ksat of loam and clay soils of forest-steppe and steppe can differ between different types by an order. The highest Ksat have soils with high structural properties (Chernozems Luvic, Chernozems Chernic). The lowest Ksat (0.2-3 mm/h) have Phaeozems Sodic, Solonetz, Solonchaks, Planosols Albic, and bottom layer of soddy manly gley (Arenosols Protic/ Haplic) and loamy soddy podzolic soils (Albeluvisols Umbric). The estimated values should be considered as the most probable because Ksat depends on landscape location of soil profile, tillage operations, and soil temperate. The results are acceptable to use in hydrological calculations and modeling. [ABSTRACT FROM AUTHOR]

There are global aspirations to harmonize soil particle- size distribution data measured by the laser diffraction method and by traditional sedimentation techniques, e.g. sieve-pipette methods. The need has arisen therefore to build up a database, containing particle-size distribution values measured by the sieving and pipette method according to the Hungarian standard (sieve-pipette methods-MSZ) and the laser diffraction method according to a widespread and widely used procedure. In our current publication, 155 soil samples measured with sieve-pipette methods-MSZ and laser diffraction method (Malvern Mastersizer 2000, HydroG dispersion unit) were compared. Through the application of the usual size limits at the laser diffraction method, the clay fraction was under- and the silt fraction was overestimated compared to the sieve-pipette methods-MSZ results, and subsequently the soil texture classes were determined according to the results of both methods also differed significantly from each other. Based on our previous experience, the extension of the upper size limit of the clay fraction from 2 to 7 µm increases the comparability of sievepipette methods-MSZ and laser diffraction method, in this way the texture classes derived from the particle-size distributions were also more in accordance with each other. The difference between the results of the two kinds of particle-size distribution measurement methods could be further reduced with the pedotransfer functions presented. [ABSTRACT FROM AUTHOR]

Modeling of water flow through vadose zone under unsaturated conditions necessitates the knowledge of soil hydraulic properties, which are water retention curve and water field capacity of soil. Indirect prediction of these characteristics based on readily available soil properties in the form of pedotransfer functions (PTFs) as a fast and low-cost solution has been widely practiced and very useful in irrigation and drainage. This study aimed to present the proper PTFs using mathematical modelling for estimating soil moisture at point of field capacity for Khuzestan province soils under laboratory and field conditions. The buried probes of the time domain reflectometry device (TDR) were inserted at various depths in order to monitor soil moisture conditions in both the physical model and experimental field under a surface-point source drip irrigation with discharge rates of 4 lph. Then, physical soil properties and soil water contents at their specific matric potentials were measured to calculate the hydraulic parameters of Van Genuchten-Mualem model with the RETC program. The results of this research to evaluate the performance of several well-known Point-PTFs showed that quasi-empirical models based on physical principles that have been tested in the field can be a good alternative to traditional methods for estimating water field capacity of soil. So that, the PTF of Twarakavi et al. could carefully predict that water field capacity of soil with indices of normalized root mean square error (3.1 percent) and standard error (0.5 percent) and more accurate than Rosetta artificial neural network approach or Dexter equation. Accordingly, another two PTFs were proposed to improve the accuracy of the water field capacity prediction in the form of regression equations on the basis of the parameters of Van Genuchten model and readily available soil properties for the semi-arid region of Khuzestan province. Results of obtained PTFs showed clearly negative effects of soil compaction and the amount of sand on the water field capacity of soil. On the contrary, the amount of clay and silt had positive and increasing effects on the water field capacity of soil, significantly.

Aimed at reducing simulation uncertainty of hydrological models in data-sparse basins where soil hydraulic data are unavailable, a method of estimating soil water parameters of soil and water assessment tool (SWAT) from readily available soil information using pedotransfer functions was introduced. The method was evaluated through a case study of Jinjiang Basin, China and was performed based on comparison between two model calibrations: (1) soil parameters estimated from pedotransfer functions and other parameters obtained from calibration; and (2) all parameters derived from calibration. The generalized likelihood uncertainty estimation (GLUE) was used as a model calibration and uncertainty analysis tool. The results show that information contained in streamflow data is insufficient to derive physically reasonable soil parameter values via calibration. The proposed method can reduce simulation uncertainty, resulting from greater average performance of behavioral parameter sets identified by GLUE. Exploring the parameter space reveals that the means of estimating soil parameters has little influence on other parameters. These facts indicate the decrease in uncertainty most likely results from a more realistic description of soil water characteristics than calibration. Thus, the proposed method is superior to calibration for estimating soil parameters of SWAT model when basin data are sparse.

Cation exchange capacity (CEC) is a good indicator of soil productivity and is useful for making recommendations of phosphorus, potassium, and magnesium for soils of different textures. Soil water holding capacity (SWHC) defines the ability of a soil to hold water at a particular time of the season. This research predicted CEC and SWHC of soils using pedotransfer models developed (using Minitab 17 statistical software) from basic soil properties (Sand(S), Clay(C), soil pH, soil organic carbon (SOC)) and verify the model by comparing the relationship between measured and estimated (obtained by PTFs) CEC and SWHC in the Forest Vegetative Zone of Nigeria. For this study, a total of 105 sampling points in 35 different locations were sampled in the study areas. Three sampling points were randomly selected per location and three undisturbed samples were collected at each sampling point. The results showed success in predicting CEC and SWHC from basic soil properties. In this study, five linear regression models for predicting soil CEC and seven linear regression models for predicting SWHC from some soil physical and chemical properties were suggested. Model 5 [CEC = -13.93+2.645 pH +0.0446 C (%)+2.267 SOC (%)] was best for predicting CEC while model 12 [SWHC (%)=36.0- 0.215 S (%)+0.113 C (%)+10.36 SOC (%)] is the most acceptable model for predicting SWHC.

Abstract The objective of this work was to evaluate the effect of soil carbon on landscape functioning of the Oxisols covering the plateaus of the Alto São Bartolomeu watershed, in the Cerrado (Brazilian savanna) region of Central Brazil. Soil organic carbon (SOC) concentration, carbon stocks, and some soil physical and chemical characteristics were determined at the 0-0.20-m depth on native and anthropogenic areas. Soils from cerrado stricto sensu patches were similar both physically and chemically, being affected by exchangeable Al3+ and by SOC concentrations, while anthropogenic matrices were affected by soil bulk density, pH, extractable P, and exchangeable Ca2+ and Mg2+. The estimate of spatial distribution of soil carbon better fitted had an adjusted R2 of 64.49% using soil C stock and 66.50% using SOC concentration from native and anthropic areas. Estimating SOC concentration from soil and landscape types, using geotechnologies to analyze vegetation indices, is a potential tool to evaluate the productivity of different agroecosystems, besides contributing to make management strategies more suitable on large scales.

The Green-Ampt (G-A) infiltration model (i.e., the G-A model) is often used to characterize the infiltration process in hydrology. The parameters of the G-A model are critical in applications for the prediction of infiltration and associated rainfall-runoff processes. Previous approaches to determining the G-A parameters have depended on pedotransfer functions (PTFs) or estimates from experimental results, usually without providing optimum values. In this study, rainfall simulators with soil moisture measurements were used to generate rainfall in various experimental plots. Observed runoff data and soil moisture dynamic data were jointly used to yield the infiltration processes, and an improved self-adaptive method was used to optimize the G-A parameters for various types of soil under different rainfall conditions. The two G-A parameters, i.e., the effective hydraulic conductivity and the effective capillary drive at the wetting front, were determined simultaneously to describe the relationships between rainfall, runoff, and infiltration processes. Through a designed experiment, the method for determining the G-A parameters was proved to be reliable in reflecting the effects of pedologic background in G-A type infiltration cases and deriving the optimum G-A parameters. Unlike PTF methods, this approach estimates the G-A parameters directly from infiltration curves obtained from rainfall simulation experiments so that it can be used to determine site-specific parameters. This study provides a self-adaptive method of optimizing the G-A parameters through designed field experiments. The parameters derived from field-measured rainfall-infiltration processes are more reliable and applicable to hydrological models.

The spatial pattern of soil bulk density in the grasslands of northern China largely remains undefined, which raised uncertainty in understanding and modeling various soil processes in large spatial scale. Based on the measured data of soil bulk density available from soil survey reports from the grasslands of northern China, we constructed a soil Stratified Pedotransfer function (SPTF) from the surface soil bulk density. Accordingly, the stratified bulk density data of soil vertical profile was reconstructed, and the estimation of soil bulk density data in horizontal space was performed. The results demonstrated that the soil bulk density of the grasslands of northern China was typically high in the central and northwestern regions and low in the eastern and mountainous regions. Mean soil bulk density of the grasslands was 1.52 g·cm−3. According to geographical divisions, the highest soil bulk density was observed in the Tarim basin, with mean soil bulk density of 1.91 g·cm−3. Conversely, the lowest soil bulk density was observed in the Tianshan Mountain area, with mean soil bulk density of 1.01 g·cm−3. Based on data obtained on various types of grasslands, the soil bulk density of alpine meadow was the lowest, with a mean soil bulk density of 0.75 g·cm−3, whereas that of temperate desert was the highest, with mean soil bulk density of 1.80 g·cm−3. Mean prediction error, root mean square deviation, relative error, and multiple correlation coefficient of soil bulk density data pertaining to surface layer (0–10 cm) in the grasslands of northern China were 0.018, 0.223, 16.2%, and 0.5386, respectively. The approach of employing multiple data sources via soil transfer function improved the estimation accuracy of soil bulk density from stratified soils data at the large scale. Our study would promote the accurate assessment of grassland carbon storage and fine land characteristics mapping.

Soil properties characterising pressure-saturation relationships (P-S), such as the fluid retention values or the fitting parameter of retention curves are basic input parameters for simulating the behaviour and transport of nonaqueous phase liquids (NAPLs) in subsurface. Recent investigations have shown the limited applicability of the commonly used estimation methods for predicting NAPL retention values in environmental practice. Alternatively, building pedotransfer functions (PTFs) based on the easily measurable properties of soils might give more accurate and reliable results for estimating hydraulic propertie s of soils and enable the utilisation of the wide range of data incorporated in Hungarian and international datasets. In spite of the availability of several well-established PTFs to predict the water retention of soils only a limited amount of research has been done concerning the NAPL retention of soils. Thus, in our study, data from our recent NAPL and water retention mea surements were collected into a dataset containing the basic soil properties as well. Relationships between basic soil propert ies and fluid retention of soils with water or an organic liquid (Dunasol 180/220) were investigated with principal component analysis. NAPL retention of soil samples were determined with PTFs, based on basic soil properties and their d erived values, and using a scaling method. Result of the statistical analysis (SPSS 13.1) revealed that using PTFs could be a promising alte rnative and could give more accurate results compared to the scaling method both for determining the NAPL saturation or the volumetric NAPL retention values of soils.