U.S. Salinity Laboratory

Abstract A new and relatively simple equation for the soil‐water content‐pressure head curve, θ( h ), is described in this paper. The particular form of the equation enables one to derive closed‐form analytical expressions for the relative hydraulic conductivity, K r , when substituted in the predictive conductivity models of N.T. Burdine or Y. Mualem. The resulting expressions for K r ( h ) contain three independent parameters which may be obtained by fitting the proposed soil‐water retention model to experimental data. Results obtained with the closed‐form analytical expressions based on the Mualem theory are compared with observed hydraulic conductivity data for five soils with a wide range of hydraulic properties. The unsaturated hydraulic conductivity is predicted well in four out of five cases. It is found that a reasonable description of the soil‐water retention curve at low water contents is important for an accurated prediction of the unsaturated hydraulic conductivity.

An extensive literature review of all available salt tolerance data was undertaken to evaluate the current status of our knowledge of the salt tolerance of agricultural crops. In general, crops tolerate salinity up to a threshold level above which yields decrease approximately linearly as salt concentrations increase. Our best estimate of the threshold salinity level and yield decrease per unit salinity increase is presented for a large number of agricultural crops. The methods of measuring appropriate salinity and plant parameters to obtain meaningful salt tolerance data and the many plant, soil, water, and environmental factors influencing the plant’s ability to tolerate salt are examined.

Cation exchange capacity (CEC), usually expressed in milliequivalents per 100 g of soil, is a measure of the quantity of readily exchangeable cations neutralizing negative charge in the soil. Ideally the method to use is one that measures the soil's capacity to adsorb cations from an aqueous solution of the same pH, ionic strength, dielectric constant, and composition as that encountered in the field, since CEC varies with these parameters. It is seldom practical to determine the CEC of each soil sample with reagents appropriate to its specific field solution conditions, since the latter information is not easily obtained and each CEC determination would require unique reagents. Either two or three steps are commonly used in the conventional methods of determining the CEC of soils, and potential errors exist in each step. The three steps are saturation of cation exchange sites with a specific cation, removal of excess saturating solution, and replacement of saturating cation.

This chapter describes prevalent laboratory methods for determining salinity based on measurements of electrical conductivity (EC) or total dissolved solids after evaporation at 180°C. It discusses various methods for determining the concentrations of individual inorganic solutes in waters and soil extracts in common use in laboratories having modern instrumentation. The extraction ratios are easier to make than that of saturation, but they are less well related to field soil water composition and content. More importantly, salinity and compositional errors from dispersion, hydrolysis, cation exchange, and mineral dissolution increase as the water/soil ratio increases. Soil salinity may be estimated from measurement of the EC of the saturated soil-paste and estimates of saturation percentage. The amount of total dissolved solids in a sample is determined by weighing the residue obtained after evaporating a sample that has been filtered to remove particulate matter.

Mathematical models have become indispensable tools for studying vadose zone flow and transport processes. We reviewed the history of development, the main processes involved, and selected applications of HYDRUS and related models and software packages developed collaboratively by several groups in the United States, the Czech Republic, Israel, Belgium, and the Netherlands. Our main focus was on modeling tools developed jointly by the U.S. Salinity Laboratory of the USDA, Agricultural Research Service, and the University of California, Riverside. This collaboration during the past three decades has resulted in the development of a large number of numerical [e.g., SWMS_2D, HYDRUS‐1D, HYDRUS‐2D, HYDRUS (2D/3D), and HP1] as well as analytical (e.g., CXTFIT and STANMOD) computer tools for analyzing water flow and solute transport processes in soils and groundwater. The research also produced additional programs and databases (e.g., RETC, Rosetta, and UNSODA) for quantifying unsaturated soil hydraulic properties. All of the modeling tools, with the exception of HYDRUS‐2D and HYDRUS (2D/3D), are in the public domain and can be downloaded freely from several websites.

Inversely obtained hydrologic parameters are always uncertain (nonunique) because of errors associated with the measurements and the invoked conceptual model, among other factors. Quantification of this uncertainty in multidimensional parameter space is often difficult because of complexities in the structure of the objective function. In this study we describe parameter uncertainties using uniform distributions and fit these distributions iteratively within larger absolute intervals such that two criteria are met: (i) bracketing most of the measured data (>90%) within the 95% prediction uncertainty (95PPU) and (ii) obtaining a small ratio (<1) of the average difference between the upper and lower 95PPU and the standard deviation of the measured data. We define a model as calibrated if, upon reaching these two criteria, a significant R 2 exists between the observed and simulated results. A program, SUFI‐2, was developed and tested for the calibration of two bottom ash landfills. SUFI‐2 performs a combined optimization and uncertainty analysis using a global search procedure and can deal with a large number of parameters through Latin hypercube sampling. We explain the above concepts using an example in which two municipal solid waste incinerator bottom ash monofills were successfully calibrated and tested for flow, and one monofill also for transport. Because of high levels of heavy metals in the leachate, monitoring and modeling of such landfills is critical from environmental points of view.

Substantial advances in the measurement of water content and bulk soil electrical conductivity (EC) using time domain reflectometry (TDR) have been made in the last two decades. The key to TDR's success is its ability to accurately measure the permittivity of a material and the fact that there is a good relationship between the permittivity of a material and its water content. A further advantage is the ability to estimate water content and measure bulk soil EC simultaneously using TDR. The aim of this review is to summarize and examine advances that have been made in terms of measuring permittivity and bulk EC. The review examines issues such as the effective frequency of the TDR measurement and waveform analysis in dispersive dielectrics. The growing importance of both waveform simulation and inverse analysis of waveforms is highlighted. Such methods hold great potential for obtaining far more information from TDR waveform analysis. Probe design is considered in some detail and practical guidance is given for probe construction. The importance of TDR measurement sampling volume is considered and the relative energy storage density is modeled for a range of probe designs. Tables are provided that compare some of the different aspects of commercial TDR equipment, and the units are discussed in terms of their performance and their advantages and disadvantages. It is hoped that the review will provide an informative guide to the more technical aspects of permittivity and EC measurement using TDR for the novice and expert alike.

A conceptual model for colloid transport is developed that accounts for colloid attachment straining, and exclusion. Colloid attachment and detachment is modeled using first-order rate expressions, whereas straining is described using an irreversible first-order straining term that is depth dependent. Exclusion is modeled by adjusting transport parameters for colloid-accessible pore space. Fitting attachment and detachment model parameters to colloid transport data provided a reasonable description of effluent concentration curves, but the spatial distribution of retained colloids at the column inlet was severely underestimated for systems that exhibited significant colloid mass removal. A more physically realistic description of the colloid transport data was obtained by simulating both colloid attachment and straining. Fitted straining coefficients were found to systematically increase with increasing colloid size and decreasing median grain size. A correlation was developed to predict the straining coefficient from colloid and porous medium information. Numerical experiments indicated that increasing the colloid excluded volume of the pore space resulted in earlier breakthrough and higher peak effluent concentrations as a result of higher pore water velocities and lower residence times, respectively. Velocity enhancement due to colloid exclusion was predicted to increase with increasing exclusion volume and increasing soil gradation.

This chapter discusses the procedures for the determination of quantity, reactivity and equilibrium relations of the carbonate minerals and gypsum. Inorganic carbonate in soil occurs predominantly as the sparingly soluble alkaline-earth carbonates, calcite and dolomite. Calcite is usually the dominant form in active pedogenic environments. A modification of the pressure-calcimeter procedure based on the relative rates of reaction of calcite and dolomite has been used to quantitatively determine calcite and dolomite in mixed-phase systems. In the volumetric calcimeter method, the carbonates are treated with excess acid and the CO2 is determined volumetrically. Acetic acid dissolution procedure provides a means of obtaining quantitative determinations of soil carbonate with readily available equipment, but it is subject to errors to which the CO2 evolution procedures are not subject. The pH-stat procedure for carbonate reactivity provides an excellent means of comparing soils with respect to relative carbonate reactivity.

Saturated soil column experiments were conducted to explore the influence of colloid size and soil grain size distribution characteristics on the transport and fate of colloid particles in saturated porous media. Stable monodispersed colloids and porous media that are negatively charged were employed in these studies. Effluent colloid concentration curves and the final spatial distribution of retained colloids by the porous media were found to be highly dependent on the colloid size and soil grain size distribution. Relative peak effluent concentrations decreased and surface mass removal by the soil increased when the colloid size increased and the soil median grain size decreased. These observations were attributed to increased straining of the colloids; i.e., blocked pores act as dead ends for the colloids. When the colloid size is small relative to the soil pore sizes, straining becomes a less significant mechanism of colloid removal and attachment becomes more important. Mathematical modeling of the colloid transport experiments using traditional colloid attachment theory was conducted to highlight differences in colloid attachment and straining behavior and to identify parameter ranges that are applicable for attachment models. Simulated colloid effluent curves using fitted first‐order attachment and detachment parameters were able to describe much of the effluent concentration data. The model was, however, less adequate at describing systems which exhibited a gradual approach to the peak effluent concentration and the spatial distribution of colloids when significant mass was retained in the soil. Current colloid filtration theory did not adequately predict the fitted first‐order attachment coefficients, presumably due to straining in these systems.

The adsorption and stability of arsenite [As(III)] on goethite (α-FeOOH) was investigated using a combination of standard batch techniques and X-ray absorption spectroscopy (XAS). The reactivity of As(III) with α-FeOOH at varying pH and As(III) concentration provided macroscopic evidence for strong complexation on the α-FeOOH surface. Extended X-ray absorption fine structure (EXAFS) spectroscopy gave an average As(III)−Fe interatomic distance of 3.378 ± 0.014 Å, which is indicative of bidentate binuclear bridging As(III) complexes on the α-FeOOH surface and which is similar to other oxyanions which adsorb on α-FeOOH by an inner-sphere mechanism. X-ray absorption near-edge structure (XANES) analysis indicated that the As(III)−α-FeOOH surface complex is stable toward heterogeneous oxidation to As(V), as determined by the energy position of the X-ray absorption edge. The structural information from EXAFS was included in the description of As(III) adsorption on the α-FeOOH surface using a surface complexation model (the constant capacitance model). These results suggest that As(III) surface complex formation on iron(III) oxides may play an important role in the environmental behavior of arsenic.

Abstract Changes in climate patterns are dramatically influencing some agricultural areas. Arid, semi‐arid and coastal agricultural areas are especially vulnerable to climate change impacts on soil salinity. Inventorying and monitoring climate change impacts on salinity are crucial to evaluate the extent of the problem, to recognize trends and to formulate irrigation and crop management strategies that will maintain the agricultural productivity of these areas. Over the past three decades, Corwin and colleagues at the U.S. Salinity Laboratory (USSL) have developed proximal sensor and remote imagery methodologies for assessing soil salinity at multiple scales. The objective of this paper is to evaluate the impact climate change has had on selected agricultural areas experiencing weather pattern changes, with a focus on the use of proximal and satellite sensors to assess salinity development. Evidence presented in case studies for Californiaʼs San Joaquin Valley (SJV) and Minnesotaʼs Red River Valley (RRV) demonstrates the utility of these sensor approaches in assessing soil salinity changes due to changes in weather patterns. Agricultural areas are discussed where changes in weather patterns have increased root‐zone soil salinity, particularly in areas with shallow water tables (SJV and RRV), coastal areas with seawater intrusion (e.g., Bangladesh and the Gaza Strip) and water‐scarce areas potentially relying on degraded groundwater as an irrigation source (SJV and Murray‐Darling River Basin). Trends in salinization due to climate change indicate that the infrastructure and protocols to monitor soil salinity from field to regional to national to global scales are needed. Highlights Climate change will have a negative impact on agriculture, particularly in arid regions. Proximal/remote sensors are useful to assess climate change impact on soil salinity across scales. Salt‐water intrusion, shallow water tables and degraded water reuse will increase soil salinity. Infrastructure and protocols to monitor soil salinity across multiple scales are needed.

Soil salinity is described and characterized in terms of the concentrations of soluble salts. The management and need for reclamation of saline soils are evaluated from measurements of such concentrations. The appropriate method of measuring soil salinity must be selected for the specific condition and purpose. To monitor soil water salinity as the soil dries between irrigations, the salinity sensor is recommended. When determination of a particular solute is needed, then either collection or extraction of soil samples or collection of water samples is required. For some needs, knowing the composition of solutes in soil water at field water contents is desirable. However, present methods of obtaining soil water samples at usual field water contents are not practical for routine purposes. For these reasons, crop tolerance to salinity is often related to the electrical conductivity, or total electrolyte concentration, of the saturation extract.

Abstract Recent research has demonstrated that field soil salinity can be inferred from four‐electrode soil electrical conductivity (EC a ), if the soil profile is near “field capacity” and calibration curves, based on saturation extract salinity (EC e ), are available. To extend the use of this field method to arbitrary water contents, we studied EC in the laboratory as a function of water content (θ) and in situ soil water conductivity (EC w ). We collected undisturbed cores of four soil types (fsl, vfsl, l, cl) using Lucite column inserts, which were tapped for later insertion of electrodes. The cells were equilibrated with waters of a desired EC w and, using a pressure membrane apparatus, adjusted to a desired θ. Values of EC a were calculated for each EC w − θ equilibration from measured four‐electrode resistances and an appropriate cell constant. Our results indicated that over the θ‐range of practical concern, EC a = (transmission coefficient) × θ × EC w + surface conductivity. This relationship is derived using a simple capillary model, which assumes that liquid phase and surface conductivities (via exchangeable cations) behave as resistors in parallel.

Arsenic adsorption on amorphous Al and Fe oxides and the clay minerals, kaolinite, montmorillonite, and illite was investigated as a function of solution pH and As redox state, i.e., arsenite [As(III)] and arsenate [As(V)]. Arsenic adsorption experiments were carried out in batch systems to determine adsorption envelopes, amount of As(III), As(V), or both adsorbed as a function of solution pH per fixed total As concentration of 20 μ M As. Arsenate adsorption on oxides and clays was maximal at low pH and decreased with increasing pH above pH 9 for Al oxide, pH 7 for Fe oxide and pH 5 for clays. Arsenite adsorption exhibited parabolic behavior with an adsorption maximum around pH 8.5 for all materials. There was no competitive effect of the presence of equimolar arsenite on arsenate adsorption. The competitive effect of equimolar arsenate on arsenite adsorption was small and apparent only on kaolinite and illite in the pH range 6.5 to 9. The constant capacitance model was able to fit the arsenate and arsenite adsorption envelopes to obtain values of the intrinsic As surface complexation constants. These intrinsic surface complexation constants were then used in the model to predict competitive arsenate and arsenite adsorption from solutions containing equimolar As(III) and As(V) concentrations. The constant capacitance model was able to predict As adsorption from mixed As(III)‐As(V) solutions in systems where there was no competitive effect.

Adsorption and oxidation reactions of arsenite (As(III)) at the mineral−water interface are two important factors affecting the fate and transport of arsenic in the environ ment. Numerous studies have concluded that As(III) is more soluble and mobile than arsenate (As(V)) in soils, though very little experimental work has demonstrated the differences in reactivity and stability of As(III) and As(V) at the mineral−water interface. In this investigation, As(III) adsorption on kaolinite, illite, montmorillonite, and amorphous aluminum hydroxide (am-Al(OH) 3 ) was studied as a function of pH and ionic strength and was compared with As(V) adsorption. High-performance liquid chromatog raphy−hydride generation atomic absorption spectrophotometry (HPLC−HGAAS) was employed for direct determination of As(III) and As(V). In addition, surface complexation modeling was used to describe As(III) and As(V) adsorption on the four minerals. It was revealed that alkaline solutions (pH > 9) without mineral solids caused homogeneous oxidation of As(III) to As(V). In addition, recovery of adsorbed As from As(III)-treated clay mineral solids showed that oxidation of As(III) to As(V) was enhanced by heterogeneous oxidation on kaolinite and illite surfaces.

Abstract The solution of many field‐scale flow and transport problems requires estimates of unsaturated soil hydraulic properties. The objective of this study was to calibrate neural network models for prediction of water retention parameters and saturated hydraulic conductivity, K s , from basic soil properties. Twelve neural network models were developed to predict water retention parameters using a data set of 1209 samples containing sand, silt, and clay contents, bulk density, porosity, gravel content, and soil horizon as well as water retention data. A subset of 620 samples was used to develop 19 neural network models to predict K s . Prediction of water retention parameters and K s generally improved if more input data were used. In a more detailed investigation, four models with the following levels of input data were selected: (i) soil textural class, (ii) sand, silt, and clay contents, (iii) sand, silt, and clay contents and bulk density, and (iv) the previous variables and water content at a pressure head of 33 kPa. For water retention, the root mean square residuals decreased from 0.107 for the first to 0.060 m 3 m ‐3 for the fourth model while the root mean square residual K s decreased from 0.627 to 0.451 log(cm d ‐1 ). The neural network models performed better on our data set than four published pedotransfer functions for water retention (by ≈0.01–0.05 m 3 m ‐3 ) and better than six published functions for K s (by ≈0.1–0.9 order of magnitude). Use of the developed hierarchical neural network models is attractive because of improved accuracy and because it permits a considerable degree of flexibility toward available input data.

Inversely obtained hydrologic parameters are always uncertain (nonunique) because of errors associated with the measurements and the invoked conceptual model, among other factors. Quantification of this uncertainty in multidimensional parameter space is often difficult because of complexities in the structure of the objective function. In this study we describe parameter uncertainties using uniform distributions and fit these distributions iteratively within larger absolute intervals such that two criteria are met: (i) bracketing most of the measured data (>90%) within the 95% prediction uncertainty (95PPU) and (ii) obtaining a small ratio (<1) of the average difference between the upper and lower 95PPU and the standard deviation of the measured data. We define a model as calibrated if, upon reaching these two criteria, a significant R2 exists between the observed and simulated results. A program, SUFI-2, was developed and tested for the calibration of two bottom ash landfills. SUFI-2 performs a combined optimization and uncertainty analysis using a global search procedure and can deal with a large number of parameters through Latin hypercube sampling. We explain the above concepts using an example in which two municipal solid waste incinerator bottom ash monofills were successfully calibrated and tested for flow, and one monofill also for transport. Because of high levels of heavy metals in the leachate, monitoring and modeling of such landfills is critical from environmental points of view.

The oxidation of arsenite (As(III)) by manganese oxide is an important reaction in both the natural cycling of As and the development of remediation technology for lowering the concentration of dissolved As(III) in drinking water. This study used both a conventional stirred reaction apparatus and extended X-ray absorption fine structure (EXAFS) spectroscopy to investigate the reactions of As(III) and As(V) with synthetic birnessite (MnO2). Stirred reactor experiments indicate that As(III) is oxidized by MnO2 followed by the adsorption of the As(V) reaction product on the MnO2 solid phase. The As(V)-Mn interatomic distance determined by EXAFS analysis for both As(III)- and As(V)-treated MnO2 was 3.22 A, giving evidence for the formation of As(V) adsorption complexes on MnO2 crystallite surfaces. The most likely As(V)-MnO2 complex is a bidentate binuclear corner sharing (bridged) complex occurring at MnO2 crystallite edges and interlayer domains. In the As(III)-treated MnO2 systems, reductive dissolution of the MnO2 solid during the oxidation of As(III) caused an increase in the adsorption of As(V) when compared with As(V)-treated MnO2. This suggested that As(III) oxidation caused a surface alteration, creating fresh reaction sites for As(V) on MnO2 surfaces.

Abstract The mobility of As in soils depends on several factors including redox potential, soil mineralogy, pH, and the presence of other oxyanions that compete with As for soil retention sites. We investigated the effects of pH and competing anions on the adsorption of arsenate [As(V)] on α‐FeOOH (goethite) and γ‐Al(OH) 3 (gibbsite). Batch equilibrium As(V) adsorption experiments were conducted with P and Mo as competing anions in order to produce single‐anion [As(V), P, and Mo] and binary‐anion [As(V)/P and As(V)/Mo] adsorption envelopes (adsorption vs. solution pH). Arsenate and P single‐anion adsorption envelopes were similar with substantial adsorption occurring across a wide pH range, including pH values above the points of zero charge of the oxides. Maximum Mo adsorption occurred across a narrower pH range (pH 4–6). On both oxides, equimolar P concentrations decreased As(V) adsorption within the pH range 2 to 11, whereas Mo decreased As(V) adsorption only below pH 6. The constant capacitance model was used to predict competitive surface complexation behavior between As(V)/P and As(V)/Mo using intrinsic equilibrium constants [ K anlon (int)] optimized from single‐anion data. In addition, the model was applied using one‐site (monodentate) and two‐site (monodentate + bidentate) conceptualizations of the oxide surface. The two approaches gave comparable fits to experimental adsorption data and were consistent with competitive adsorption observed in binary adsorption envelopes.