Estimation Of Soil Hydraulic Properties Research Articles

Impact of urea fertilization rates on nitrogen losses, productivity and profitability in East African sugarcane plantations

AbstractFertilizer‐intensive sugarcane plantations are expanding in sub‐Saharan Africa (SSA) amidst increased groundwater pollution and carbon footprint concerns. Yet, the impact of nitrogen (N) levels on N losses, productivity and profitability in these plantations remains unclear. To address this gap, we conducted a completely randomized design experiment in a Ugandan sugarcane plantation using three N fertilization rates (low, standard and high) as treatments. N leaching under the different treatments was determined using the average drainage fluxes across a 1‐m‐layered profile which we estimated with a suite of pedotransfer functions (PTFs) and leachate N concentrations from suction cup lysimeters. Soil nitrous oxide fluxes were determined using static vented chamber bases and gas chromatography. Partial factor crop productivity was estimated from the average field fresh weight under each treatment and the amount of N fertilizer applied, while the return on investment was determined from the factory price of the field fresh weight and the market price of fertilizers. Our findings indicate that three out of five PTFs effectively estimated soil hydraulic properties at our test site, based on the close match between measured and predicted soil matric potential values. Notably, N leaching at low and standard N rates were comparable but significantly lower than at higher‐than‐standard N rates. Additionally, we measured comparable soil nitrous oxide emissions and field fresh weight but partial factor productivity and return on investment declined along the fertilizer intensification gradient. In conclusion, the study demonstrates the promising application of certain PTFs in N‐leaching modelling in the data‐scarce SSA. Furthermore, obtaining comparable field fresh weight with minimal N losses at lower‐than‐standard N rates presents an opportunity to mitigate groundwater pollution and greenhouse gas emissions. However, the potential impact of the switch from standard to low N rates on soil organic carbon stocks and sugarcane yields warrants further investigation.

Estimating soil hydraulic properties from oven-dry to full saturation using shortwave infrared imaging and inverse modeling

To minimize uncertainty related to soil processes in extreme events, we need accurate soil hydraulic properties across the entire range of soil water content. However, conventional methods are time-consuming and limited to specific ranges. To estimate soil hydraulic properties throughout the entire range, we conducted inverse modeling using upward infiltration experiments, where a shortwave infrared imaging camera was used to obtain high-resolution soil moisture data in space and time. Because the commonly used van Genuchten–Mualemmodel is unsuitable for describing soil hydraulic properties for dry conditions, we tested an alternative model, the Peters-Durner-Iden model, which considers both capillary and film water. The inverse modeling successfully estimated soil hydraulic properties for sandy loam and loam soils, and we demonstrated that the Peters-Durner-Iden model captured soil moisture dynamics better than the van Genuchten–Mualemmodel for dry conditions. However, both models could not adequately describe the soil moisture data for the other soils. The direct observation of the water flow via shortwave infrared images clarified that the reduced success was because of violating the one-dimensional flow assumption for coarse-textured soils and the micro-heterogeneity in soil hydraulic properties for soils with fine silt and clay materials.

Development and functional evaluation of pedotransfer functions for estimating soil hydraulic properties in Portuguese soils: Implications for soil water dynamics

Accurate knowledge of soil hydraulic properties is essential for effectively simulating soil water dynamics in field plots. However, determining these properties directly can be challenging, especially when a large amount of data is required. Therefore, the primary objective of this study was to develop four hierarchical pedotransfer functions (PTFs) to estimate the soil hydraulic parameters of the Mualem-van Genuchten model. The PTFs that were developed ranged from utilizing a limited set of variables to those comprising a more comprehensive set of variables. These included particle size distribution, soil bulk density, soil depth, and soil water contents at matric potentials of −33 and − 1580 kPa. The functional performance of these PTFs was also assessed in simulations of the soil water balance in ten agricultural fields over two growing seasons using the HYDRUS-1D model. The simpler PTFs exhibited moderate accuracy in estimating soil water contents at different matric potentials, while the more complex PTFs with a greater number of variables yielded increasing accuracy. However, estimation errors were more noticeable in the drier range of the soil water retention curve. The inclusion of bulk density as a predictor only affected the wetter range of the same curve. Estimation of log Ks improved from the simpler to the more complex PTFs, but the statistical indicators were consistently modest. The functional performance of the PTFs showed increased errors in simulating soil water contents in the ten study sites compared to reference simulations. The more complex PTFs with the greatest number of variables provided the best predictions in most cases. The PTFs primarily affected flow conditions in the soil profile, resulting in increased soil water storage and impacting predictions of deep percolation the most. Water availability in the soil profile then influenced actual transpiration, alleviating stress in some cases but causing waterlogging in one situation. The study emphasizes the cautious use of the PTFs and the need for additional information on clay-rich soils and hydraulic conductivity data to enhance their performance.

Guidance for parameterizing post‐fire hydrologic models with in situ infiltration measurements

AbstractWildfire can alter soil‐hydraulic properties, often resulting in an increased prevalence of infiltration‐excess overland flow and greater potential for debris‐flow hazards. Mini disk tension infiltrometers (MDIs) can be used to estimate soil hydraulic properties, such as field‐saturated hydraulic conductivity (Kfs) and wetting front potential (Hf), and their spatial variability following wildfire. However, the small (point‐scale) footprint of MDI measurements makes it challenging to use these data to parameterize hydrologic models at the hillslope and watershed scales where hydrologic hazards, such as debris flows, initiate. Here, we designed numerical experiments to estimate spatially constant or watershed‐scale effective hydrologic parameters (EHPs) that approximate the response of spatially variable hydrologic parameters with distributions derived from MDI measurements at five sites in the southwestern United States. We found that it is possible to define EHPs for both Kfs and Hf based on the MDI measurements that lead to reasonable approximations of run‐off hydrographs at the outlets of small watersheds (<1 km2). We found that watershed EHPs are functions of rainfall characteristics, although they are most sensitive to rainfall intensity and relatively less sensitive to the temporal distribution of rainfall. EHPs are lower than the arithmetic mean of the MDI measurements and are better approximated by the median or geometric mean of the MDI measurements, particularly for storms with recurrence intervals of approximately 1 year or less that commonly initiate post‐fire debris flows. This work demonstrated that using the proposed upscaling method to estimate watershed‐scale EHPs, as opposed to approximating EHPs based on the arithmetic mean of the MDI measurements, improved the ability of a hydrologic model to identify storms that are likely to produce debris flows. Results improved our ability to link point‐scale MDI measurements and watershed‐scale EHPs in post‐fire settings and helped guide our ability to use MDI data to parameterize post‐fire hydrologic models.

Examination of Measured to Predicted Hydraulic Properties for Low Impact Development Substrates

To counter the impacts of climate change and urbanization, engineers have developed ingenious solutions to reduce flooding and capture stormwater contaminants through the use of Low Impact Developments (LIDs). The soil is generally considered to be completely saturated when designing for the LIDs. However, this may not always be an accurate or realistic approach, as the soil could be variably unsaturated leading to inaccurate designs. To analyse the flow under variably unsaturated conditions, Richards’ equation can be used. To solve the Richards’ equation, two nonlinear hydraulic properties, namely soil water characteristic curve (SWCC) and the unsaturated hydraulic conductivity function are required. Laboratory and field measurements of unsaturated hydraulic properties are cumbersome, expensive and time- consuming. Pedotransfer functions (PTFs) estimate soil hydraulic properties using routinely measured soil properties. This paper presents a comparison between the direct measurement obtained through experimental procedures and the use of PTFs to estimate soil hydraulic properties for two green roof and three bioretention soil medias. Comparison between the measured and estimated soil hydraulic properties was accomplished using two different approaches. Statistical analyses and visual comparisons were used to compare the measured and estimated soil hydraulic properties. Additionally, numerical modelling to predict the water balance at the ground surface was conducted using the measured and estimated soil hydraulic properties. In some instances, the use of predicted hydraulic properties resulted in overestimation of the cumulative net infiltration of as much as 60 % for the green roof substrate, but was considered negligible for the bioretention substrate. Design performance criteria for green roof and bioretention facilities were examined using the measured and estimated soil hydraulic properties under extreme precipitation analysis. Results indicate that there is a high level of uncertainty when using PTFs for LID materials. A percent difference between the measured and predicted properties for the green roof peak time delay under a 2-year storm can be as much as 300%. For the bioretention design criteria of a 25-year storm, the surface runoff was overestimated by 14.7 cm and by 100% for the ponding time percent difference.

Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: A case study from Morocco

Soil hydraulic properties are an important factor to optimize and adapt water management for a given crop. Pedotransfer functions (PTFs) present a solution to predict soil variables such as hydraulic properties, using fundamental soil properties. In this research, we compared two sources of soil information: iSDAsoil data and field data, in four regions in Morocco. We then used this data to evaluate published data and developed new PTFs using soil information to estimate soil gravimetric moisture content at saturation (w0), field capacity (w330) and permanent wilting point (w15000). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya and Tadla. The data was divided into calibration and validation datasets. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest approaches. PTFs developed by Dijkerman (Geoderma, 1988, 42, 29–49) presented the best performance, showing lower RMSE, Bias and MAE compared to other PTFs. Using multiple linear regression to develop PTFs, models based on clay, silt and soil organic matter as input variables showed the best performance after calibration (R2 of 0.590, 0.785, 0.786 for w0, w330, and w15000, respectively). Regarding the techniques based on machine learning, random forest showed the best performance after calibration compared with other algorithms (R2 of 0.930, 0.955, 0.954 for w0, w330, and w15000, respectively). PTFs represent a low cost and easy technique to estimate soil hydraulic properties, to improve water management efficiency for the farmers.

Multi-model based soil moisture simulation approach under contrasting weather conditions

We developed and tested a multi-model based soil moisture simulation approach for improving parameterization scheme and its transferability under contrasting weather conditions. Three popular hydrologic models in combination with three optimization schemes were adapted to consider uncertainties in physical and optimization model structures for estimating near-surface soil moisture dynamics. In order to improve parameterization and its transferability under contrasting weather conditions, a multiple set of weights based on different land surface wetness conditions (wet, normal, and dry) were used using a Multiple Weighting Algorithm (MWA). A Differential Split Sample Testing (DSST) scheme was used to test the transferability of parameterizations in the optimization and physical model domains. Data from three experimental sites (Little Washita, LW13 in Oklahoma, Everglades in Florida and Bondville/Olney sites in Illinois) were used to verify our proposed approach. Our findings indicated that the multi-model outputs were highly influenced both by the physical and optimization model structures. Overall, our approach performed well in the test of transferability under different weather conditions. However, we confirmed that the overfitted parameters (to the in-situ measurements during the calibration period) due to the optimization model structures can undermine the transferability of multi-model approach. Also, our findings indicate that the estimated soil hydraulic properties during the dry years can be limited in representing the soil wetness conditions for the wet years.

Review of Ground Penetrating Radar Applications for Water Dynamics Studies in Unsaturated Zone

For water dynamics investigation in unsaturated (vadose) zones, ground penetrating radar is a popular hydro-geophysical method because it is non-invasive for soil, has high resolution and the results have a direct link with water content. Soil water content and soil hydraulic properties are two key factors for describing the water dynamics in vadose zones. There has been tremendous progress in soil water content and soil hydraulic properties estimation with ground penetrating radar. The purpose of this paper is to provide an overview of the application of ground penetrating radar for soil water dynamics studies. This paper first summarizes various methods for the determination of soil water content. including traditional methods in the surveys of surface ground penetrating radar, borehole ground penetrating radar, and off-ground ground penetrating radar, as well as relatively new methods, such as full waveform inversion, the average envelope amplitude method, and the frequency shift method. This paper further provides a review for estimating soil hydraulic properties with GPR according to the types of ground penetrating radar data. We hope that this review can provide a reference for the application of ground penetrating radar in soil water dynamics studies in the future.

In situ estimation of soil hydraulic and hydrodispersive properties by inversion of electromagnetic induction measurements and soil hydrological modeling

Abstract. Soil hydraulic and hydrodispersive properties are necessary for modeling water and solute fluxes in agricultural and environmental systems. Despite the major efforts in developing methods (e.g., laboratory-based, pedotransfer functions), their characterization at applicative scales remains an imperative requirement. Accordingly, this paper proposes a noninvasive in situ method integrating electromagnetic induction (EMI) and hydrological modeling to estimate soil hydraulic and transport properties at the plot scale. To this end, we carried out two sequential water infiltration and solute transport experiments and conducted time-lapse EMI surveys using a CMD Mini-Explorer to examine how well this methodology can be used to (i) monitor water content dynamic after irrigation and to estimate the soil hydraulic van Genuchten–Mualem parameters from the water infiltration experiment as well as (ii) to monitor solute concentration and to estimate solute dispersivity from the solute transport experiment. We then compared the results with those estimated by direct time domain reflectometry (TDR) and tensiometer probe measurements. The EMI significantly underestimated the water content distribution observed by TDR, but the water content evolved similarly over time. This introduced two main effects on soil hydraulic properties obtained by the two methods: (i) similar water retention curve shapes, but underestimated saturated water content from the EMI method, resulting in a scaled water retention curve when compared with the TDR method; the EMI-based water retention curve can be scaled by measuring the actual saturated water content at the end of the experiment with TDR probes or by weighing soil samples; (ii) almost overlapping hydraulic conductivity curves, as expected when considering that the shape of the hydraulic conductivity curve primarily reflects changes in water content over time. Nevertheless, EMI-based estimations of soil hydraulic properties and transport properties were found to be fairly accurate in comparison with those obtained from direct TDR measurements and tensiometer probe measurements.

Inverse Modelling to Estimate Soil Hydraulic Properties at the Field Scale

Field-scale estimation of soil hydraulic parameters is important for simulating water movement, particularly in the vadose zone. The accuracy of soil hydraulic parameters significantly affects the output of models using these parameters as input variables. A new approach for an inverse method, often referred to as inverse modelling, was presented to calibrate soil hydraulic parameters. The new method is based on the use of uniform design theory to generate uncertainty estimates of the soil hydraulic parameters. The uncalibrated and calibrated soil hydraulic parameters were evaluated by comparing the predicted and measured soil water contents using the HYDRUS-1D model. The results show that the calibrated soil hydraulic parameters that were inversely estimated by uniform design theory were accurate and reasonable. In addition, the results provided recommendations for improving the estimation of soil hydraulic parameters when there are no available observation data for calibrating and optimizing soil hydraulic parameters.

An approximate solution to one-dimensional upward infiltration in soils for a rapid estimation of soil hydraulic properties

• A concise soil water content profile expression was obtained by an analytical method. • An approximate solution was derived for one-dimensional upward infiltration. • A method with a standard ring was proposed to determine BC model parameters. Soil hydraulic properties quantify soil pore structure and are essential input parameters for simulating soil hydrological processes in the vadose zone. Although methods have been proposed to estimate soil hydraulic properties, efficient and applicable ones are still scarce. In this study, we derived an approximate solution for a one-dimensional upward infiltration process with initially uniform soil water content distribution. Founded on our solution, a nonlinearly constrained optimization method was developed to simultaneously estimate the parameters K s and n , h d of the Brooks-Corey model by measuring the upward infiltration process in a 5-cm high soil column. Numerical experiments simulated with Hydrus-1D were performed on six theoretical soils to evaluate the accuracy of the new method. The results indicate that the approximate solution can accurately simulate the upward infiltration process at the low initial soil water content range, with a relative error ( Re ) of 2.85% to 5.47% for soil water content distribution in space and time. For the cumulative infiltration curve, the Re ranges from 0.2% to 1.01%. The estimated parameters K s and n , h d of the Brooks-Corey model are close to the actual parameter values, with a Re of 0.59% to 4.68% for n , 0.00% to 7.91% for h d , and 0.50% to 8.83% for K s , respectively. It is concluded that the new method provides a simple and valuable tool for the accurate estimation of soil hydraulic properties.

An improved pore-solid fractal model for predicting coastal saline soil hydraulic properties based on changepoints determined by genetic algorithm-support vector regression

Coastal soil is an important reserve land resource. The accurate prediction of soil hydraulic properties plays an important role in understanding the improvement of soil properties after reclamation in coastal areas. With the development of modern mathematical theory, the pore-solid fractal (PSF) model has become an important basis for simulating soil hydraulic properties. The accuracy of PSF model depends on the accurate acquisition of changepoints and fractal dimensions. Previous studies have found that micro-CT scanning combined with image processing could accurately obtain fractal dimensions. However, we still need soil water retention data to determine changepoints, which greatly limits the application of fractal models for estimating soil hydraulic properties. In this case, we determined the relationship between changepoints and soil physical and chemical properties and established a genetic algorithm-support vector regression (GA-SVR) model to predict changepoints. Then an improved PSF model was adopted to predict soil water retention curve in coastal areas of Jiangsu Province based on image processing. The results showed that soil physical and chemical properties changed the soil water movement and changepoints by affecting soil pore distribution. In coastal saline soil, four parameters (BD, silt, EC, SOM) were selected to predict changepoints. The mass fractal dimension (Dm) was mainly influenced by the porosity and the heterogeneity of pore space. The porosity, pore diameter and specific surface area were the determining factors of DS value. Reclamation activities in coastal reclamation area changed Dm value but had no clear effect on DS value. By combining predicted changepoints, measured water content at the suction of changepoints and fractal dimensions, the accuracy of the PSF model had been greatly improved.

Coupled full-waveform inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic parameters: A synthetic study

Coupled inversion is a promising technique for determining soil hydraulic properties from time-lapse geophysical measurements. In this inversion approach, a hydrological model is coupled with ground penetrating radar (GPR) forward modelling to avoid interpretation errors from data processing. In this study, a workflow for coupled GPR full-waveform inversion (CFWI) is proposed that combines the benefits of coupled inversion and full-waveform inversion (FWI) to estimate soil hydraulic properties from time-lapse horizontal borehole GPR measurements. In particular, a synthetic modelling study is presented that compares the performance of coupled inversion of full waveforms and first arrival time data from zero-offset profile (ZOP) borehole measurements obtained during an infiltration event for estimating hydraulic parameters and the thickness of the first layer for a 2-layer soil profile. It was found that the thickness of the first layer could be more accurately estimated by CFWI because of the additional information contained in reflected and refracted waves from the air–soil layer boundary. Furthermore, the hydraulic parameters estimated by the coupled inversion of GPR travel times slightly deviated from true values and showed a relatively large uncertainty, whereas the results of CFWI precisely matched the known water retention and hydraulic conductivity functions. Despite these promising results, several challenges should be solved before CFWI can be applied to experimental GPR data. First, conducting CFWI needs accurate conceptual setups for the hydro(geo-)logical model. Furthermore, a robust approach should be developed to accurately estimate an effective source wavelet from the experimental ZOP data, and a more sophisticated hydrological modelling approach has to be considered to better estimate the distribution of soil electrical conductivity during the infiltration event. Finally, the accuracy and efficiency of current GPR modelling methods may need to be improved.

Development of Pedotransfer Functions to Predict Soil Physical Properties in Southern Quebec (Canada)

Pedotransfer functions (PTFs) are empirical fits to soil property data and have been used as an alternative tool to in situ measurements for estimating soil hydraulic properties for the last few decades. PTFs of Saxton and Rawls, 2006 (PTFs’S&R.2006) are some of the most widely used because of their global aspect. However, empirical functions yield more accurate results when trained locally. This study proposes a set of agricultural PTFs developed for southern Quebec, Canada for three horizons (A, B, and C). Four response variables (bulk density (ρb), saturated hydraulic conductivity (Ksat), volumetric water content at field capacity (θ33), and permanent wilting point (θ1500)) and four predictors (clay, silt, organic carbon, and coarse fragment percentages) were used in this modeling process. The new PTFs were trained using the stepwise forward regression (SFR) and canonical correlation analysis (CCA) algorithms. The CCA- and SFR-PTFs were in most cases more accurate. Θ1500 and at θ33 estimates were improved with the SFR. The ρb in the A horizon was moderately estimated by the PTFs’S&R.2006, while the CCA- and SFR-PTFs performed equally well for the B and C horizons, yet qualified weak. However, for all PTFs for all horizons, Ksat estimates were unacceptable. Estimation of ρb and Ksat could be improved by considering other morphological predictors (soil structure, drainage information, etc.).

Balancing exploitation and exploration: A novel hybrid global-local optimization strategy for hydrological model calibration

Optimization problems in hydrological modeling are frequently solved using local or global search strategies, which either maximize exploitation or exploration. Thus, the elevated performance of one strategy for one class of problems is often offset by poor performance for another class. To overcome this issue, we propose a hybrid strategy, G-CLPSO, that combines the global search characteristics of the Comprehensive Learning Particle Swarm Optimization (CLPSO) with the exploitation capability of the Marquardt-Levenberg (ML) method and implement it into the hydrological model, HYDRUS. Benchmarks involving optimizing non-separable unimodal and multimodal functions demonstrate that G-CLPSO outperforms CLPSO in terms of accuracy and convergence. Synthetic modeling scenarios involving the inverse estimation of soil hydraulic properties are used to compare the G-CLPSO against the original HYDRUS ML solver, the gradient-based algorithm PEST, and the stochastic SCE-UA strategy. Results demonstrate the superior performance of the G-CLPSO, suggesting a potential use in other environmental problems.

Prediction of soil hydraulic properties using VIS-NIR spectral data in semi- arid region of Northern Karnataka Plateau

Field capacity (FC) and permanent wilting point (PWP) are the important soil hydraulic properties which determine the availability of water for plant growth. Conventional estimation of soil hydraulic properties is tedious and expensive which are normally derived from pedo-transfer functions. Visible and near-infrared (Vis-NIR) spectroscopy is a low-cost, non-destructive alternative method for the rapid estimation of soil properties. The present study was carried out to predict soil hydraulic properties using Vis-NIR spectral data in the semi-arid region of the Northern Karnataka plateau. Spectral data of 558 soil samples were acquired using Field Spec ASD Spectroradiometer and three models (support vector machine, random forest and partial least square regression) were evaluated for prediction of FC and PWP. The models were calibrated using 2/3rd of total observations and validated using 1/3rd of observations. The validation results showed that RF and SVM provided slightly higher performance compared to PLSR model. Permanent wilting point was predicted well (R2 = 0.70–0.74, RMSE = 5.44–5.74%) compared to field capacity (R2 = 0.66–0.69 and RMSE = 7.25–7.51%). Among the soil orders, FC and PWP of Vertisols were poorly predicted by Vis-NIR spectra (R2 = 0.34&0.42) and moderately predicted for Alfisols (0.44&0.52) and Inceptisols (0.55&0.65) while excellent prediction was recorded for Entisols (0.83&0.76). The present results showed that Vis-NIR spectroscopy is helpful for fast estimation of hydraulic properties in semi-arid regions of the country with high accuracy.

Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data

Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially when it comes to the optimization of nitrogen fertilization. The present work aimed to exploit the ability of vegetation cover to identify the spatial variability of soil hydraulic properties through the expression of water stress. The assessment of the spatial distribution of saturated soil hydraulic conductivity (Ks) and field water capacity (FWC) was based on a combination of ground-based measurements and thermal and hyperspectral airborne imaging data. The crop water stress index (CWSI) was used as an indicator of crop water stress to assess the hydraulic properties of the soil. Supplementary vegetation indices were used. The support vector regression (SVR) method was used to estimate soil hydraulic properties from aerial data. Data analysis showed that the approach estimated Ks with good results (R2 = 0.77) for stands with developed crop water stress. The regression coefficient values for estimation of FWC for topsoil (0–0.3 m) ranged from R2 = 0.38 to R2 = 0.99. The differences within the study sites of the FWC estimations were higher for the subsoil layer (0.3–0.6 m). R2 values ranged from 0.12 to 0.99. Several factors affect the quality of the soil hydraulic features estimation, such as crop water stress development, condition of the crops, period and time of imaging, etc. The above approach is useful for practical applications for its relative simplicity, especially in precision agriculture.

Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia

Nitrate leaching through soil layers to groundwater may cause significant degradation of natural resources. The aims of this study were: (i) to estimate soil hydraulic properties (SHPs) of the similar soil type with same management on various locations; (ii) to determine annual water dynamics; and (iii) to estimate the impact of subsoil horizon properties on nitrate leaching. The final goal was to compare the influence of different SHPs and layering on water dynamics and nitrate leaching. The study was conducted in central Croatia (Zagreb), at four locations on Calcaric Phaeozem, Calcaric Regosol, and Calcaric Fluvic Phaeozem soil types. Soil hydraulic parameters were estimated using the HYPROP system and HYPROP-FIT software. Water dynamics and nitrate leaching were evaluated using HYDRUS 2D/3D during a period of 365 days. The amount of water in the soil under saturated conditions varied from 0.422 to 0.535 cm3 cm−3 while the hydraulic conductivity varied from 3 cm day−1 to 990.9 cm day−1. Even though all locations have the same land use and climatic conditions with similar physical properties, hydraulic parameters varied substantially. The amount and velocity of transported nitrate (HYDRUS 2D/3D) were affected by reduced hydraulic conductivity of the subsoil as nitrates are primarily transported via advective flux. Despite the large differences in SHPs of the topsoil layers, the deeper soil layers, having similar SHPs, imposed a buffering effect preventing faster nitrate downward transport. This contributed to a very similar distribution of nitrates through the soil profile at the end of simulation period. This case study indicated the importance of carefully selecting relevant parameters in multilayered soil systems when evaluating groundwater pollution risk.

Pedotransfer functions for estimating soil hydraulic properties from saturation to dryness

Current pedotransfer functions (PTFs) for estimating soil hydraulic curves are mostly developed to predict parameters of the Mualem-van Genuchten hydraulic functions. The Mualem-van Genuchten functions are recognised to be inadequate in representing soil water retention hydraulic conductivity curves at low pressure head ranges. This study presents neuroFX, a suite of PTFs, for estimating soil water retention and unsaturated hydraulic conductivity curves from saturation to complete dryness based on the Fredlund-Xing-Wang (FXW) model. The PTFs were calibrated using the neuro-m neural networks approach with three different sets of inputs: (1) SSCBD uses the sand, silt, and clay fractions, and bulk density; (2) SSC uses the sand, silt and clay fractions, and (3) soil textural class input. NeuroFX PTFs were trained using fitted parameters of the FXW model from selected data in the UNSODA database and validated using 5-fold cross-validation that was repeated 10 times. NeuroFX provides an uncertainty estimate of hydraulic parameters. The prediction quality of neuroFX was compared with two existing PTFs: ROSETTA and Brunswick-Weber (BW) PTFs. Based on multiple criteria, we found that neuroFX performed better than ROSETTA and BW PTFs in the same test sample. NeuroFX PTF with the SSCBD input yielded the best prediction of soil water retention and unsaturated hydraulic conductivity curves with RMSE in water content of 0.052 cm3 cm−3 and RMSE in log10(K) = 0.732 (in the magnitude of cm day−1), indicating the importance of including bulk density in the input of PTFs. NeuroFX was then used to map parameters of the FXW model for the whole of the continental USA over 6 depth intervals. The code of neuroFX in R software is available in Supplementary Material 1.

Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: II. The Soil Hydraulic Conductivity Curve

Direct measurement of unsaturated hydraulic parameters is costly and time-consuming. Pedotransfer functions (PTFs) are typically developed to estimate soil hydraulic properties from readily available soil attributes. For the first time, in this study, we developed PTFs to estimate the soil hydraulic conductivity (log(K)) directly from measured data. We adopted the pseudo continuous neural network PTF (PCNN-PTF) approach and assessed its accuracy and reliability using two independent data sets with hydraulic conductivity measured via the evaporation method. The primary data set contained 150 international soils (6963 measured data pairs), and the second dataset consisted of 79 repacked Turkish soil samples (1340 measured data pairs). Four models with different combinations of the input attributes, including soil texture (sand, silt, clay), bulk density (BD), and organic matter content (SOM), were developed. The best performing international (root mean square error, RMSE = 0.520) and local (RMSE = 0.317) PTFs only had soil texture information as inputs when developed and tested using the same data set to estimate log(K). However, adding BD and SOM as input parameters increased the reliability of the international PCNN-PTFs when the Turkish data set was used as the test data set. We observed an overall improvement in the performance of PTFs with the increasing number of data points per soil textural class. The PCNN-PTFs consistently performed high across tension ranges when developed and tested using the international data set. Incorporating the Turkish data set into PTF development substantially improved the accuracy of the PTFs (on average close to 60% reduction in RMSE). Consequently, we recommend integrating local HYPROPTM (Hydraulic Property Analyzer, Meter Group Inc., USA) data sets into the international data set used in this study and retraining the PCNN-PTFs to enhance their performance for that specific region.