Soil Moisture Retention Curve Research Articles

A technical evaluation on the mathematical attitudes and fitting accuracy of soil moisture retention curve models

Numerous mathematical equations have been formulated in the literature of different researchers for describing soil moisture retention curve (SMRC), which can be applied to simulate and solve soil hydraulic modeling problems. The primary concern lies in selecting an efficient model to simulate accurately the S-shaped curve or sigmoid-type of the SMRC for soils with different textures. Therefore, the objective of this study was a comprehensive and technical evaluation of 50 developed models of the SMRC based on the influence of parameters on the behavior of the curve and the ability of their fitting accuracy on 728 soil samples of the UNSODA dataset, that has not been investigated so far. Statistical criteria including corrected Akaike’s information criterion (AICc), root mean square error (RMSE) and coefficient of determination (R2) together with Duncan's multiple range test and cluster analysis were employed to assess the fitting accuracy of SMRC models to measured data. Results from fitting accuracy on the UNSODA dataset indicated that Brutsaert model provided the best fit to the measured data compared to other models in 14.6% of the soil samples with RMSE = 0.0125 and AICc = -94.18. This model was classified in the same cluster with Groenevelt and Grant (GG1, GG2 and GG3), Dexter, Mualem, and Fredlund and Xing models and did not have a significant difference in terms of RMSE. Also, Brutsaert model had the highest fitting accuracy in 67% of different soil textural classes compared to other models. Finally, the technical evaluation in terms of accuracy, flexibility and simplicity of the fitting process showed that the Brutsaert, Mualem, Dexter and GG3 models can be selected for better simulation of the SMRC in water and soil research.

Hydro- physical, biological and economic study on simply, an environment- friendly and valuable rice straw-based hydrogel production

In a recent study, we synthesized hydrogel from various sources, where we carried out their identification and characterization, and then rice straw-based hydrogel (SH) was used to cultivate beans and maize in both sandy and calcareous soils, comparing it to acrylamide hydrogel (AH). In this work, we focused only on the biological effects and effectiveness of the hydrogel made from rice straw on sandy and calcareous soils, as well as the economic analysis of hydrogel synthesized from rice straw. Biological effects and efficiency of two different ratios, 0.2% & 0.4%, from SH and AH were investigated using an incubation experiment that presents at the National Research Centre. The results generally showed that the two ratios of the hydrogels (AH & SH) improved the hydro-physical (moisture content, bulk density, particle density, total soil porosity, soil moisture retention curve, and pores diameters), physiochemical (organic matter, organic carbon), and biological (total count of bacteria, dehydrogenase and nitrogenase in the rhizosphere) properties on the two types of soil textures used. On the other hand, calculating the cost of producing one tonne of SH and comparing it to AH's price and attributes allowed for an economic appraisal. According to the data, one tonne of AH costs $2727 to import, compared to $1106 for one tonne of SH.

Evaluation of Seasonal Effects of Tillage and Drainage Management Practices on Soil Physical Properties and Infiltration Characteristics in a Silt-Loam Soil

The impacts of tillage and drainage managements on soil infiltration characteristics need to be scrutinized for a better understanding and prediction of soil hydrological processes, such as runoff, evapotranspration and soil water storage. The objectives of this study were i) to evaluate the effects of tillage and drainage practices on soil physical properties and water infiltration, and ii) to compare prediction accuracy of estimated and optimized infiltration model characteristics for the observed and predicted quantities of infiltration process in the soil. The research site contains the Crosby-Kokomo soil series (fine, mixed, mesic, Aeric Ochraqualf and fine, mixed, mesic Typic Argiaquoll, respectively). The experiment was a two factorial completely randomized block design with two levels of tillage (chisel plow (CH) and no-till (NT)) and two levels of drainage (drained (D) and undrained (UD)) with three replicates. Soil bulk density (ρb), saturated hydraulic conductivity (Ksat), soil moisture retention curves (SMRC), soil infiltration capacity and piezometric water head in each treatment were also measured. Soil drainage flows at each drain lateral and outlet discharges were measured. The results showed that The UD treatments were always higher for Ksat values than the D treatments regardless of the tillage practices for both depths and the CH treatments always had greater Ksat values than those in the NT at both the depths regardless of drainage practices. The D treatments reduced the soil bulk density by 4.2 and 0.8 % in the surface soil and 4.61 and 6.7% for the subsurface soil in respect to no-till-UD and chisel-UD treatments. The UD treatments had higher bulk density at both of the depths than those of the D treatments regardless of tillage practices except the CH-UD treatments. The NT had higher bulk density at both depths of the soil than those of the CH treatments regardless of drainage practices. Drainage increased pore size distribution significantly higher than the UD treatments (p<0.05). The D treatments had significantly higher storage pores and effective pores (9.37%) (pores retaining water at -10kPa pressure head) than the ones in the UD treatments (8.96%) (p<0.05). The NT treatments yielded higher infiltration rates than the CH and the D treatments produced higher apparent infiltration rates and cumulative infiltration values than the UDs. The changes in soil physical properties were found to be strongly and significantly dependent on season, soil depth, and rainfall (p<0.05). The optimized infiltration models predicted larger range of infiltration rate values for each treatment than the estimated infiltration models, indicating that the optimization produced higher accuracy and validity of the predicted models in the field. To conclude, soil dry bulk density, soil saturated hydraulic conductivity, and increased macropore volumes can significantly impact soil hydrological responses to soil water infiltration, soil water storage and drainage flow under conservation tillage and drainage management practices on a seasonal basis. This impact enhances greater potential to capture water in soil for future crop use in the study site.

Influence of Tillage Intensity and Weed Control Practices on Mesopore Hysteresis Loop of Soil Moisture Retention Curves in Southwest Nigeria

Influence of Tillage Intensity and Weed Control Practices on Mesopore Hysteresis Loop of Soil Moisture Retention Curves in Southwest Nigeria

English

The hydraulic properties of soil are very important to determine the soil relationship with water. The Soil Hydraulic Properties (SHP) are used to determine the soil water relationship as it is very crucial for estimation of water holding capacity of soil. In real field conditions, most widely used model for soil moisture retention curves is van Genuchten model. The soil samples were taken randomly from five different depths with three replications of each depth. Moreover, two samples at the depth of 5-35cm and 35-155cm were selected for evaluation of integrated approach to find soil characteristic curves. Soil moisture characteristic curves calculated by integral approach were compared with, soil moisture curves measured by pressure plate apparatus, and Soil Plant Air Water (SPAW) hydrology a computer based Model. The accuracy of results were evaluated by determination of Root Mean Square Error (RMSE), Normalize Root Mean Square Error (NRMSE) and Willmott’s index of agreement (£). A good agreement was observed between integrated approach, high-pressure technique and SPAW model results. In case of integrated method versus high-pressure technique the RMSE was 9.51×10-3 for 5-30cm depth and 8.32×10- 3 for 30-155cm depth. The NRMSEs was 6.52 ×10-2 for 5-30cm depth and 6.1×10-2 for 30-155cm depth, similarly £ was 0.881 and 0.893 for both depths 5-30cm and 30-155cm respectively. Regression coefficient (R2 ) for 5-35cm depth for sorptivity (s) and wetting distance (d) were 0.991 and 0.988 respectively and at depth of 35-155cm, R2 for (s) was 0.990 and for wetting distance (d) was 0.994. In case of integrated method versus SPAW hydrology the RMSEs was 1.3×10-2 for 5-30cm depth and 1.15×10-2 for 30-155cm depth. The NRMSEs for was 9.33 ×10-2 for 5-30cm depth and 7.15×10-2 for 30-155cm depth and similarly £ was 0.852 and 0.863 for both depths respectively. Results indicated a good relationship between high-pressure technique, SPAW hydrology results and integral method. However, integrated approach is best approach to determine the soil moisture characteristics curves of undisturbed and unsaturated soils

IDW Interpolation of Soil Moisture Retention Curve Utilizing GIS

This search aim to analyse the Soil Moisture Retention Curve (SMRC) by spatial interpolation mapping using Inverse Distance Weighted scheme (IDW). The SMRC is a Graphical Representation the mathematical relationship between the metric suction of soil (it represents the difference between the pore of air pressure (ua), and the pore of water pressure (uw) and either soil water contents (volumetric and gravimetric) or S (degree of saturation). The soil samples were obtained from three locations of Baghdad University by drilling pits of depth 0.5 to 1 m under the ground level. An extensive Laboratory Testing Program was performed on the studied samples. Rosetta program was utilized to estimate Unsaturated Hydraulic properties from the data of surrogate soil such as the data of soil texture and bulk density. After obtaining the properties for the three soil samples applying IDW spatial interpolation techniques by utilizing the Arc Map-GIS program on a satellite image of the Landsat-8 satellite with a high recognition accuracy for studding properties for the areas surrounding calculated points.

Parameter estimation of soil hydraulic characteristics by inverse modeling of the analytical equation for unsaturated subsurface water flow

AbstractIn drip-irrigated systems, the understanding of the soil wetting pattern is essential in defining the area effectively irrigated, the spacing between the emitters and their installation depth, and the irrigation rate. Thus, this study aims to estimate soil hydraulic characteristics through inverse modeling of an analytical equation used in wetting bulb simulation based on soil moisture measurements obtained in the field. The parameters of the Gardner model, which describes the unsaturated soil hydraulic conductivity, and the van Genuchten model that describes the soil moisture retention curve, were estimated by inverse modeling techniques. The following options were considered: (A) estimating parameter β while considering the other parameters, Ko, θr, θs, α, n, and m, as known and obtained experimentally; (B) estimating parameters Ko and β while considering the experimental retention curve as known; (C) estimating parameters Ko, β, α, n, and m while considering the values of the θr and θs volumetric moisture as known; (D) estimating all the parameters of Gardner and van Genuchten models (Ko, θr, θs, α, n, and m). The results indicate that option D showed better concordance between the estimated and observed moisture values. Thus, the inverse modeling of the analytical equation is an important tool for irrigation design and management.

Determining and Comparing Deep Drainage between Two Years – Dry (2012) and Wet (2013) – using Darcy’s Law

Understanding of the downward flux of water below the plant root zone, known as deep drainage (DD), is significant in agriculture and soil water conservation. It plays a key role to determine the amount of water that travels below the plant root zone and can potentially cause groundwater recharge. The DD in soil varies with location, soil texture, and topography. Thus, the objectives of this study were to determine the unsaturated hydraulic conductivity, soil water storage, and DD for the years 2012 (dry year) and 2013 (wet year) at the University of Guelph’s Arboretum. The depths to the water table data were collected using a Mini Water Level Meter. CS616 sensors were used to determine the soil volumetric water content. The soil temperature was extracted with the use of T107 Temperature Probes. The slug test, based on the Hvorslev method, was performed to determine the field saturated hydraulic conductivity. The soil moisture retention curve was produced based on the data collected in the lab with the use of pressure plate systems, using van Genuchten’s equation. The unsaturated hydraulic conductivity was also determined using van Genuchten’s equation. Darcy’s law was used to determine the specific discharge, which was then converted to the total DD. In general, the soil water storage was 38.5 mm higher in 2013 relative to 2012. The unsaturated hydraulic conductivity was approximately 2 times higher in 2013 than 2012. The average DD was approximately 25 mm higher in 2013. This study provides information needed to better understand the movement and amount of water flux and DD in larger details.

Effect of free swelling index on improving estimation of the soil moisture retention curve by different methods

Several variables have been used to estimate the soil moisture retention curve (SMRC) so far, but one of the main points in using pedotransfer functions (PTFs) is the selection of readily available soil properties as inputs. In this study, 147 disturbed and undisturbed soil samples were taken from five provinces of Iran. Some soil properties such as particle size distribution (PSD), bulk density (BD), free swelling index (FSI) and SMRC were measured. A double exponential model was fitted to the SMRC data, and PTFs were developed to predict the model parameters, by multivariate linear regression and artificial neural network (ANN) methods. Using FSI with the soil basic properties (silt to sand ratio, clay and BD) as estimators improved the estimation of SMRC significantly for the full data set by ANN method (by 10.9 and 9.0% in the training and testing steps, respectively), and in the textural groups of “clay loam, silty clay loam”, “clay”, “loam”, “sandy loam” and “sand”, by the linear regression method in the range of 1.0 to 14.9%. Grouping the soil samples significantly improved the accuracy of the estimates, especially in the sandy soil textural group. In estimating SMRC using total data, ANN method had a greater accuracy compared with the linear regression method. In general, FSI significantly improved the accuracy of parametric estimation of the SMRC and grouping of data had a positive impact on improving estimates in some textural groups.

Surface soil moisture monitoring with near-ground sensors: Performance assessment of a matric potential-based method

This study develops an indirect method for the estimation of surface soil moisture (SM) by means of monitoring in situ soil water potential. Field work was carried out in ten sampling units selected according to coverage, soil, and topographic factors. Relative humidity and soil temperature were hourly measured to assess the soil–water potential through Kelvin's Law for each sampling unit. Water potential was used as input in the soil moisture retention curve for field conditions to assess SM by fitting the Fredlund-Xing model. To verify the performance of our approach, we measured the surface volumetric SM by the gravimetric method for 2,211 soil samples collected in the same survey period. Results show a non-linear relationship between gravimetric SM and the indirect estimation displaying a determination coefficient of 68.4%. The proposed approach is a suitable exploratory alternative to support continuous SM monitoring studies.

Plant Available Water Capacity (PAWC) for Deep-Rooted Crops in Cracking Clay Soils (Vertisols) of Semi-Arid Central India

Smectite is the dominant clay mineral in the cracking clay soils (mainly Vertisols) in the semi-arid tropical (SAT) region (Vidarbha andMarathwada).Amongst the crystalline clay minerals, smectite has the highest soil moisture retention capacity depicted as the available water between field capacity and wilting point, which increases with soil depth. But in SAT Vertisols of Vidarbha and Marathwada regions, the release of this soil water is unavailable to deep-rooted crops, causing loss of crop yield is frequently observed. Thus, the water holding between33 and 1500 kPa tension is not in reality available soil moisture in these soils. The release of soil moisture is, however, seriously constrained by the dispersion of smectitic clay colloids due to the presence of both Mg and Na ions on soil exchange complex. In reality, in calcareous Vertisols of non-sodic nature, soil moisture is held at 100 kPa while in calcareous Vertisols of sodic nature it is held at 300 kPa. Based on this field observation the concept of plant available water content, which relates to profile water content, was adopted earlier to calculate the plant available water capacity (PAWC) considering the soil moisture held between 100-1500 kPa for non-sodic and 300-1500 kPa for sodic soils. This pragmatic PA WC method showed a significant positive correlation between the PA WC and the yield of cotton (lint + seed) of 32 Vertisols of the Vidarbha region, however, it did not address the status of soil moisture release at higher tensions. It is apprehended that at higher tensions, the release of soil moisture is not enough during the dry period of the season that can prevent the extension of vertical cracks in the slickensided horizons at deeper depth of 100 cm and beyond. Less amount of soil water in the subsoils is due to the decrease of saturated hydraulic conductivity(sHC) with depth preventing the free flow of rainwater in the subsoils. This makes subsoils as more water constraint horizons but enriches subsoils with pH > 8.0, more amount of Mg2+ and Na+ ions on soil exchange complex. Such Vertisols do not support the second crop during the winter months. In view of this predicament in agricultural land use plan of SAT Vertisols, the present study was undertaken to understand the cause and effect relationship between soil moisture and relevant soil parameters that prevent the release of soil moisture at higher tensions. In order to find an insight into this queer issue, soil moisture retention and release curves at 33, 100, 300, 500, 800, 1000 and 1500 kPa, were obtained for each genetic horizon of 33 representative Vertisols ofVidarbha andMarathwada region. In Vidarbha, Vertisols are Aridic/Typic/Sodic Haplusterts and Calciusterts, and in Marathwada, they are Typic/Sodic Haplusterts. Moisture tension curves indicate that the release of water beyond 800 kPa is negligible. Failure in the release of soil water beyond 800 kPa is due to the dispersion of fine clay size smectite (< 200 nm) consisting primarily of nano-size smectite (<100 nm), caused by the Mg2+ and Na+ ions present on the soil exchange complex. The dispersed nano-size smectite blocks the macropores and simultaneously increase the micropores, where soil moisture is held very tightly. In the present study, PA WC calculation was done considering the soil water held between 100-800 kPa for soils of non-sodic nature, and for sodic soils, it was between 300-800 kPa. The revised PAWC data showed a better significant positive correlation with yields of cotton on farmers ’field, in comparison to correlation obtained earlier. Thus, it stands as a robust index parameter of soil abiotic stress in SAT environment. This unique parameter having a legacy to major pedogenetic processes in SAT Vertisols. In Indian states under SAT, PAWC emerges as a formidable biophysical parameter for evaluation of rain-fed Vertisols for growing deep-rooted crops.

Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models

Soil water retention curve (SWRC) is of fundamental importance in analyzing both flow and contaminant transport in the vadose zone. Field and/or laboratory-based measurements of soil moisture and soil suction – the two main variables that are used to develop SWRC – is often time consuming and sometimes impossible. In this study, plausibility of various machine learning techniques to simulate SWRC of loamy sand are evaluated. Specifically, the machine learning techniques that are investigated include: three support vector regression (SVR) models (i.e. radial basis function (RBF), linear and polynomial kernels), single-layer artificial neural network (ANN), and deep neural network (DNN). The soil moisture and soil suction were measured using time-domain reflectometer (TDR) and tensiometer, respectively. The data were collected under both monotonic wetting and drying of a disturbed sample of loamy sand soil. These datasets were used to train and test the machine learning techniques. Results show that the RBF-based SVR outperforms all the other machine learning techniques in simulating SWRC for loamy sand subjected to either monotonic wetting or drying. The ANN and DNN models simulated soil water content with a RMSE of 0.004–0.009 cm3/cm3 for monotonic wetting in the training phase; and 0.002–0.003 cm3/cm3 for monotonic drying in training phase. In the testing phase, ANN and DNN models simulated soil water content with RMSE of 0.02–0.121 cm3/cm3 and 0.003. The RBF-based SVR model – the best performing machine learning model – simulated soil water content with RMSE of 0.006 and 0.002 cm3/cm3 for soil subjected monotonic wetting and drying, respectively. In the testing phase, the RBF-based SVRmodel simulated soil water content with RMSE of 0.02–0.033 cm3/cm3 and 0.003–0.006 cm3/cm3 for soil under monotonic wetting and drying, respectively. These machine models, therefore, provided plausible SWRC simulations, and the models do not require knowledge of physical soil parameters.

Soil physical and chemical properties in response to long-term cattle grazing on sloped rough fescue grassland in the foothills of the Rocky Mountains, Alberta

Soil physical and chemical properties are critical indicators for soil health assessment. It is important to understand how these qualities respond to different grazing intensities at different landscape slope positions. This study evaluated how soil physical and chemical properties respond to cattle grazing intensity and slope position on a rough fescue grassland. Cattle have been grazing at four stocking rates, 0, 1.2, 2.4 and 4.8 animal-unit-months (AUM) ha−1 since 1949 to simulate Control (CK), Light (L), Heavy (H), and Very Heavy (VH) grazing intensities. Surface soil penetration resistance was measured using a portable penetrometer and soil samples were taken from the top and bottom slope positions for each grazing paddock (but only from the top position for CK) in September 2016. Soil texture, aggregate stability, pH, available phosphorus (AP), water-soluble ions concentration and soil cation exchange capacity (CEC) were determined. Bulk density, porosity and hydraulic properties were measured using a HYPROP system. The S-index, the slope at the inflection point on the soil moisture retention curve, was calculated. Soil clay and silt contents were lower in VH than L treatments; total porosity, air capacity, and saturated water content followed the same pattern. However, the opposite was true for sand content, penetration resistance, and bulk density, with differences between the two grazing treatments greater at the top than the bottom slope position. Grazing changed the shape of soil moisture retention curve in both slope positions with the curve steeper in L than H and VH treatments. The S-index and saturated hydraulic conductivity were reduced by grazing but their responses to slope position and its interaction with grazing intensity were not significant. Soil pH, AP, and water-soluble Na+, K+, Mg2+, Ca2+ and SO42− concentration increased with the animal stocking rate while Cl− and CEC were not affected. Similarly, greater increases in soil pH with increased animal stocking rates were observed in the top than bottom position, while AP, Na+, K+, and SO42− increased with animal stocking rate only in the bottom position. Our results indicate that soil physical properties may be more sensitive to grazing at the top than bottom of a slope. Thus, slope should be considered when developing rangeland soil health assessment indices and grazing management strategies.

The effects of land subsidence and rehabilitation on soil hydraulic properties in a mining area in the Loess Plateau of China

Land subsidence caused by underground coalmining gives rise to severe surface deformation and results in a number of soil cracks, which markedly affect soil hydraulic properties; moreover, land rehabilitation is an effective measure to restore the ecological function of impacted lands. To analyze the effects of land subsidence and rehabilitation on soil hydraulic properties, an underground coalmine in the Loess Plateau of China was selected to conduct a field plot experiment. Four plots were designed, including one unmined plot (UMP), two subsided plots (SPI and SPII) and one rehabilitated plot (RHP), and 16 sampling points were located in each plot. The bulk density (BD), soil moisture retention curve (SMRC), field capacity (FC), saturated hydraulic conductivity (Ks) and soil disintegration rate (SDR) at the depths of 0–20cm, 20–40cm, 40–60cm and 60–80cm at each sampling point were measured, and soil pore size distribution (PSD) based on SMRC was analyzed. The correlation analysis among soil hydraulic properties and the path analysis of effects of subsided cracks on the hydraulic properties were carried out in this study. Land subsidence increased the variability of soil hydraulic properties; whereas, they became relatively uniform after land rehabilitation. Land subsidence significantly altered soil hydraulic properties, increasing BD, Ks, SDR and soil micropores and decreasing FC; however, land rehabilitation can improve soil hydraulic properties and increase the use efficiency of soil water, decreasing BD, Ks, SDR and increasing FC and soil macropores. The cracks related to subsidence and vegetation had significant effects on soil hydraulic properties, especially BD; the crack width and vegetation coverage had a marked effect on BD.

Numerical Tests of the Lookup Table Method in Solving Richards’ Equation for Infiltration and Drainage in Heterogeneous Soils

The lookup table option, as an alternative to analytical calculation for evaluating the nonlinear heterogeneous soil characteristics, is introduced and compared for both the Picard and Newton iterative schemes in the numerical solution of Richards’ equation. The lookup table method can be a cost-effective alternative to analytical evaluation in the case of heterogeneous soils, but it has not been examined in detail in the hydrological modeling literature. Three layered soil test problems are considered, and the robustness and accuracy of the lookup table approach are assessed for uniform and non-uniform distributions of lookup points in the soil moisture retention curves. Results from the three one-dimensional test simulations show that the uniform distributed option gives improved convergence and robustness for the drainage problem compared to the non-uniform strategy. On the other hand, the non-uniform technique can be chosen for test problems involving flow into initially dry layered soils.

Saturated hydraulic conductivity model computed from bimodal water retention curves for a range of New Zealand soils

Abstract. Descriptions of soil hydraulic properties, such as the soil moisture retention curve, θ(h), and saturated hydraulic conductivities, Ks, are a prerequisite for hydrological models. Since the measurement of Ks is expensive, it is frequently derived from statistical pedotransfer functions (PTFs). Because it is usually more difficult to describe Ks than θ(h) from pedotransfer functions, Pollacco et al. (2013) developed a physical unimodal model to compute Ks solely from hydraulic parameters derived from the Kosugi θ(h). This unimodal Ks model, which is based on a unimodal Kosugi soil pore-size distribution, was developed by combining the approach of Hagen–Poiseuille with Darcy's law and by introducing three tortuosity parameters. We report here on (1) the suitability of the Pollacco unimodal Ks model to predict Ks for a range of New Zealand soils from the New Zealand soil database (S-map) and (2) further adaptations to this model to adapt it to dual-porosity structured soils by computing the soil water flux through a continuous function of an improved bimodal pore-size distribution. The improved bimodal Ks model was tested with a New Zealand data set derived from historical measurements of Ks and θ(h) for a range of soils derived from sandstone and siltstone. The Ks data were collected using a small core size of 10 cm diameter, causing large uncertainty in replicate measurements. Predictions of Ks were further improved by distinguishing topsoils from subsoil. Nevertheless, as expected, stratifying the data with soil texture only slightly improved the predictions of the physical Ks models because the Ks model is based on pore-size distribution and the calibrated parameters were obtained within the physically feasible range. The improvements made to the unimodal Ks model by using the new bimodal Ks model are modest when compared to the unimodal model, which is explained by the poor accuracy of measured total porosity. Nevertheless, the new bimodal model provides an acceptable fit to the observed data. The study highlights the importance of improving Ks measurements with larger cores.

Predicted and measured soil retention curve parameters in Lombardy region north of Italy

Water retention characteristics are fundamental input parameters in any modeling study on water flow and solute transport. These properties are difficult to measure and for that reason, we usually need to use direct and indirect methods to determine them. An extensive comparison between measured and estimated results is needed to determine their applicability for a range of different soils. However this study attempts to make a contribution specifically in this connection. These properties were determined in two representative sites located in Landriano field, in Lombardy region, northern Italy. In the laboratory we used the pressure plate apparatus and the tensiometric box. Field soil water retention was determined including measurements of soil water content with SENTEK probes and matric potential with tensiometers. The soil waer retention curves (SWRC) were also settled on with some recently developed pedo-transfert functions (PTFs). Field retention curves were compared against those obtained from PTFs estimations and laboratory measurements. The comparison showed that laboratory measurements were the most accurate. They had the highest ranking for the validation indices (RMSE ranging between 2.4% and 7.7% and bias between 0.1% and 6.4%). The second best technique was the PTF Rosetta (Schaap et al. 2001). They perform only slightly poorer than the laboratory measurements (RMSE ranging between 2.7% and 10% and bias between 0.3% and 7.7%). The lowest prediction accuracy is observed for the Rawls and Brakensiek (1985) PTF (RMSE ranging between 6.3% and 17% and bias between 5% and 10%) which is in contradiction with previous finding (Calzolari et al., 2001), showing that this function is well representing the retention characteristics of the area. Due to time and cost investments of laboratory and field measurements, we conclude that the Rosetta PTF developed by Schaap et al. (2001) appears to be the best to predict the soil moisture retention curve from easily available soil properties in the Lombardy area and further field investigations would be useful to support this finding.

Soil Water Retention Curve from Saturated Hydraulic Conductivity for Sandy Loam and Loamy Sand Textured Soils

Knowledge of the soil hydraulic properties is indispensable to solve many soil and water management problems related to agriculture, ecology, and environmental issues. Soil hydraulic properties are key factors that regulate the movement of groundwater and transport of solutes. The most important hydraulic properties of soils are the soil water retention curve (SWRC) and saturated hydraulic conductivity (ks). Determination of these hydraulic properties is needed for many studies and applications related to irrigation, drainage, water movement and solute transport in the soil. Although many advances are made for direct measurements of SWRC and ks, they are usually time consuming and costly. Some attempts have been made to indirectly predict these hydraulic properties. The objective of this study was to derive an empirical relationship to estimate soil water retention curve from saturated hydraulic conductivity. One hundred and one soil samples were collected from agricultural and forest sites at different depths, at different locations in the Pavanje river basin that lies in the southern coastal region of Karnataka, India. The saturated hydraulic conductivity was measured in the laboratory by variable falling head method using Permeameter and soil water retention curve data was obtained through pressure plate apparatus for all the agricultural and forest soils. Prediction accuracies were evaluated using coefficient of determination, root mean square error, mean error, Akaike Information Criteria, between measured and predicted values. The results obtained from the prediction of soil moisture retention curve from saturated hydraulic conductivity, shows that the developed relationship are reasonably useful to get the soil moisture retention curve for the soils of agricultural and forested hillslopes of the region under consideration.

Online appendix 2:Soil water retention curves for the major soil types of the Kruger National Park

Soil water potential is crucial to plant transpiration and thus to carbon cycling and biosphere–atmosphere interactions, yet it is difficult to measure in the field. Volumetric and gravimetric water contents are easy and cheap to measure in the field, but can be a poor proxy of plant-available water. Soil water content can be transformed to water potential using soil moisture retention curves. We provide empirically derived soil moisture retention curves for seven soil types in the Kruger National Park, South Africa. Site-specific curves produced excellent estimates of soil water potential from soil water content values. Curves from soils derived from the same geological substrate were similar, potentially allowing for the use of one curve for basalt soils and another for granite soils. It is anticipated that this dataset will help hydrologists and ecophysiologists understand water dynamics, carbon cycling and biosphere–atmosphere interactions under current and changing climatic conditions in the region.

Online appendix 1: Soil water retention curves for the major soil types of the Kruger National Park

Soil water potential is crucial to plant transpiration and thus to carbon cycling and biosphere–atmosphere interactions, yet it is difficult to measure in the field. Volumetric and gravimetric water contents are easy and cheap to measure in the field, but can be a poor proxy of plant-available water. Soil water content can be transformed to water potential using soil moisture retention curves. We provide empirically derived soil moisture retention curves for seven soil types in the Kruger National Park, South Africa. Site-specific curves produced excellent estimates of soil water potential from soil water content values. Curves from soils derived from the same geological substrate were similar, potentially allowing for the use of one curve for basalt soils and another for granite soils. It is anticipated that this dataset will help hydrologists and ecophysiologists understand water dynamics, carbon cycling and biosphere–atmosphere interactions under current and changing climatic conditions in the region.