Publications

Abstract The constitutive relations of the Richardson-Richards equation encode the macroscopic properties of soil water retention and conductivity. These soil hydraulic functions are commonly represented by models with a handful of parameters. The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free-form approach to learning the constitutive relations using physically constrained neural networks. We implemented the inverse modeling framework in a differentiable modeling framework, JAX, to ensure scalability and extensibility. For efficient gradient computations, we implemented implicit differentiation through a nonlinear solver for the Richardson-Richards equation. We tested the framework against synthetic noisy data and demonstrated its robustness against varying magnitudes of noise and degrees of freedom of the neural networks. We applied the framework to soil moisture data from an upward infiltration experiment and demonstrated that the neural network-based approach was better fitted to the experimental data than a parametric model and that the framework can learn the constitutive relations.

Keywords: inverse modeling, soil hydraulic functions, physics-informed machine learning, neural networks, soil moisture

Water retention curve (WRC) and hydraulic conductivity function (HCF) are essential information to model the movement of water in the soil using the Richardson-Richards equation (RRE). Although laboratory measurement methods of WRC and HCF have been well established, the lab-based WRC and HCF can not be used to model soil moisture dynamics in the field because of the scale mismatch. Therefore, it is necessary to derive the inverse solution of the RRE and estimate WRC and HCF from field measurement data. We are proposing a physics-informed neural networks (PINNs) framework to obtain the inverse solution of the RRE and estimate WRC and HCF from only volumetric water content measurements. The PINNs was constructed using three feedforward neural networks, two of which were constrained to be monotonic functions to reflect the monotonicity of WRC and HCF. The PINNs was trained using noisy synthetic volumetric water content data derived from the simulation of soil moisture dynamics for three soils with distinct textures. The PINNs could reconstruct the true soil moisture dynamics from the noisy data. As for WRC, the PINN could not precisely determine the WRCs. However, it was shown that the PINNs could estimate the HCFs from only the noisy volumetric water content data without specifying initial and boundary conditions and assuming any information about the HCF (e.g., saturated hydraulic conductivity). Additionally, we showed that the PINNs framework could be used to estimate soil water flux density with a broader range of estimation than the currently available methods.

Keywords: inverse method, machine learning , partial differential equation,physics‐informed neural networks,soil moisture,soil water flux density

Water retention curve (WRC) and hydraulic conductivity function (HCF) are essential information to model the movement of water in the soil using the Richardson-Richards equation (RRE). Although laboratory measurement methods of WRC and HCF have been well established, the lab-based WRC and HCF can not be used to model soil moisture dynamics in the field because of the scale mismatch. Therefore, it is necessary to derive the inverse solution of the RRE and estimate WRC and HCF from field measurement data. We are proposing a physics-informed neural networks (PINNs) framework to obtain the inverse solution of the RRE and estimate WRC and HCF from only volumetric water content measurements. The PINNs was constructed using three feedforward neural networks, two of which were constrained to be monotonic functions to reflect the monotonicity of WRC and HCF. The PINNs was trained using noisy synthetic volumetric water content data derived from the simulation of soil moisture dynamics for three soils with distinct textures. The PINNs could reconstruct the true soil moisture dynamics from the noisy data. As for WRC, the PINN could not precisely determine the WRCs. However, it was shown that the PINNs could estimate the HCFs from only the noisy volumetric water content data without specifying initial and boundary conditions and assuming any information about the HCF (e.g., saturated hydraulic conductivity). Additionally, we showed that the PINNs framework could be used to estimate soil water flux density with a broader range of estimation than the currently available methods.

Keywords: soil water repellency, hydrophobicity ,contact angle, sorptivity, infiltration, post‐fire runoff

Saturated hydraulic conductivity (Ks) is a fundamental soil property that regulates the fate of water in soils. Its measurement, however, is cumbersome and instead pedotransfer functions (PTFs) are routinely used to estimate it. Despite much progress over the years, the performance of current generic PTFs estimating Ks remains poor. Using machine learning, high‐performance computing, and a large database of over 18,000 soils, we developed new PTFs to predict Ks. We compared the performances of four machine learning algorithms and different predictor sets. We evaluated the relative importance of soil properties in explaining Ks. PTF models based on boosted regression tree algorithm produced the best models with root‐mean‐squared log‐transformed error in ranges of 0.4 to 0.3 (log10(cm/day)). The 10th percentile particle diameter (d10) was found to be the most important predictor followed by clay content, bulk density (ρb), and organic carbon content (C). The sensitivity of Ks to soil structure was investigated using ρb and C as proxies for soil structure. An inverse relationship was observed between ρb and Ks, with the highest sensitivity at around 1.8 g/cm3 for most textural classes. Soil C showed a complex relationship with Ks with an overall positive relation for fine‐textured and midtextured soils but an inverse relation for coarse‐textured soils. This study sought to maximize the extraction of information from a large database to develop generic machine learning‐based PTFs for estimating Ks. Models developed here have been made publicly available and can be readily used to predict Ks.

Keywords: soil hydraulic conductivity, machine learning, pedotransfer function, soil structure, bulk density, organic carbon

A new method was developed to measure soil consolidation by capillary suction in organic soils. This method differs from previous methods of measuring soil consolidation in that no external load is utilized and only the forces generated via capillary suction consolidate the soil matrix. This limits the degree of consolidation that can occur, but gives a more realistic ecological perspective on the response of organic soils to desiccation in the field. This new method combines the principles behind a traditional triaxial cell (for measurements of volume change), a pressure plate apparatus, (to facilitate drainage by capillary suction), and the permeameter, (to measure saturated hydraulic conductivity), and allows for simultaneous desaturation of the soil while monitoring desiccation induced volume change in the soil. This method also enables detection of historic limit of dryness. The historic limit of dryness is a novel concept that is unique to soils that have never experienced drying since their formation. It is fundamentally equivalent to the pre-compression stress of externally loaded soils. This method is particularly important for forecasting structural and hydrologic changes that may occur in soils that were formed in very wet regimes (e.g., wet meadows at the foot of persistent snow packs and permafrost peats) as they respond to a changing climate.

Keywords: Ecosystems, Seasons, Soil respiration, Productivity, Spring, Winter, Carbon dioxide, Ecosystem functioning

Prediction of water flow and transport processes in soils susceptible to structural alteration such as compaction of tilled agricultural lands or newly constructed landfills rely on accurate description of changes in soil unsaturated hydraulic conductivity. Recent studies have documented the critical impact of aggregate contact characteristics on water flow rates and pathways in unsaturated aggregated soils. We developed an analytical model for aggregate contact size evolution as a basis for quantifying effects of compression on saturated and unsaturated hydraulic conductivity of aggregated soil. Relating confined one‐dimensional sample strain with aggregate deformation facilitates prediction of the increase in interaggregate contact area and concurrent decrease in macropore size with degree of sample compression. The hydrologic component of the model predicts unsaturated hydraulic conductivity of a pack of idealized aggregates (spheres) on the basis of contact size and saturation conditions under prescribed sample deformation. Calculated contact areas and hydraulic conductivity for pairs of aggregates agreed surprisingly well with measured values, determined from compaction experiments employing neutron and X‐ray‐radiography and image analysis. Model calculations for a unit cell of uniform spherical aggregates in cubic packing were able to mimic some of the differences in saturated and unsaturated hydraulic conductivity observed for aggregates and bulk soil.

One potential consequence of global climate change and rapid changes in land use is an increased risk of flooding. Proper understanding of floodwater infiltration thus becomes a crucial component of our preparedness to meet the environmental challenges of projected climate change. In this paper, we present the results of a long‐term infiltration experiment performed on fractured ash flow tuff. Water was released from a 3 × 4 m2 infiltration plot (divided into 12 square subplots) with a head of ∼0.04 m, over a period of ∼800 days. This experiment revealed peculiar infiltration patterns not amenable to current infiltration models, which were originally developed for infiltration into soils over a short duration. In particular, we observed that in part of the infiltration plot, the infiltration rate abruptly increased a few weeks into the infiltration tests. We suggest that these anomalies result from increases in fracture permeability during infiltration, which may be caused by swelling of clay fillings and/or erosion of infill debris. Interaction of the infiltration water with subsurface natural cavities (lithophysal cavities) could also contribute to such anomalies. This paper provides a conceptual model that partly describes the observed infiltration patterns in fractured rock and highlights some of the pitfalls associated with direct extension of soil infiltration models to fractured rock over a long period.

Keywords: Infiltration, fractures, rock

Application of time domain reflectometry (TDR) in soil hydrology often involves the conversion of TDR‐measured dielectric permittivity to water content using universal calibration equations (empirical or physically based). Deviations of soil‐specific calibrations from the universal calibrations have been noted and are usually attributed to peculiar composition of soil constituents, such as high content of clay and/or organic matter. Although it is recognized that soil disturbance by TDR waveguides may have impact on measurement errors, to our knowledge, there has not been any quantification of this effect. In this paper, we introduce a method that estimates this error by combining two models: one that describes soil compaction around cylindrical objects and another that translates change in bulk density to evolution of soil water retention characteristics. Our analysis indicates that the compaction pattern depends on the mechanical properties of the soil at the time of installation. The relative error in water content measurement depends on the compaction pattern as well as the water content and water retention properties of the soil. Illustrative calculations based on measured soil mechanical and hydrologic properties from the literature indicate that the measurement errors of using a standard three‐prong TDR waveguide could be up to 10%. We also show that the error scales linearly with the ratio of rod radius to the interradius spacing.

Keywords: TDR, water content, compaction, measurement error

The Gardner and van Genuchten models of relativepermeability are widely used in analytical and numerical solutions toflow problems. However, the applicability of the Gardner model to realproblems is usually limited, because empirical relative permeability datato calibrate the model are not routinely available. In contrast, vanGenuchten parameters can be estimated using more routinely availablematric potential and saturation data. However, the van Genuchten model isnot amenable to analytical solutions. In this paper, we introducegeneralized conversion formulae that reconcile these two models. Ingeneral, we find that the Gardner parameter alpha G is related to the vanGenuchten parameters alpha vG and n by alpha G/alpha vG  ; 1.3 n. Thisconversion rule will allow direct recasting of Gardner-based analyticalsolutions in the van Genuchten parameter space. The validity of theproposed formulae was tested by comparing the predicted relativepermeability of various porous media with measured values.

Flow diversion around subsurface cavities in unsaturated fractured media is important to numerous environmental and engineering applications. This paper provides analytical solutions to partial and complete flow diversion around cavities intersected by fractures under steady state conditions. It is focused on a typical trifracture junction located upstream from a cavity surface. Fractures are modeled as two‐dimensional porous media with an exponential relationship between the capillary pressure and unsaturated hydraulic conductivity. The solutions show that the vertical distance between the fracture end and the nearest junction (Z) and the slope of the unsaturated hydraulic conductivity (α) are by far the most important determinants of flow diversion. In fact, the product of Z and α enters the threshold flux and liquid entry flux equations as a dimensionless sorptive length (s). This relationship between Z and α is shown to have important implications for uncertainty and scalability of calibrated model parameters. The solutions given in this paper are expected to be directly applicable to cavities on the order of the fracture spacing.

In recent years, significant advances have been made in our understanding of the complex flow processes in individual fractures, aided by flow visualization experiments and conceptual modeling efforts. These advances have led to the recognition of several flow regimes in unsaturated individual fractures subjected to different initial and boundary conditions. For an idealized smooth fracture surface the most important regimes are film flow, rivulet flow, and sliding of droplets. The existence of such significantly dissimilar flow regimes has been a major hindrance in the development of self-consistent conceptual models of flow for single fracture surfaces that encompass all the flow regimes. The objective of this study is to delineate the existence of the different flow regimes in individual fracture surfaces. For steady state flow conditions, we developed physical constraints on the different flow regimes that satisfy minimum energy configurations, which enabled us to segregate the wide range of fracture flux (volumetric flow rate per fracture width) into several flow regimes. These are, in increasing order of flow rate, flow of adsorbed films, flow of sliding drops, rivulet flow, stable film flow, and unstable (turbulent) film flow. The scope of this study is limited to wide-aperture smooth fractures with the flow on the opposing sides of fracture being independent.

Flow processes in unsaturated fractures considerably differ from flow in rock matrix because of the dominance of gravitational forces, accentuated by variations in fracture geometry. This gives rise to liquid fragmentation, fingering, and intermittent flow regimes that are not amenable to standard continuum representation. We develop an alternative modeling framework to describe the onset of liquid fragmentation and subsequent flow behavior of discrete liquid clusters. The transition from a slowly growing anchored liquid element to a finger‐forming mobile liquid element is estimated from the force balance between retarding capillary forces dominated by contact angle hysteresis and suspended liquid weight. A model for liquid fragmentation within the fracture plane (smooth and parallel walled fractures) for given a steady input flux and aperture size is developed and tested. Predictions of sizes and detachment intervals of liquid elements are in good agreement with experimental results. The results show that the mass of detached liquid element is only weakly related to flow rate but increases with fracture aperture size. Periodic discharge similar to that experimentally observed is a result of the interplay between capillary, viscous, and gravitational forces. We show that the presence of even a few irregularities in a fracture plane may induce complicated flux patterns downstream. Similar erratic fluxes are observed in studies involving gravity‐driven unsaturated flow.

Keywords: fracture, intermittent, episodic, finger, dripping, vadose zone

This study deals with deformation of small pores in wet soils of relatively high bulk density such as in the final settlement phase of tilled or disturbed soils. Pore deformation was modeled by volume reduction of spherical voids embedded in a homogenous soil matrix. External constant stress and overburden were considered as steady stresses because the change in interaggregate contact stress under overburden is slow compared to the associated strain rate. In contrast, stress due to passage of farm implements was considered as transient because the rate of change of interaggregate stress is comparable with the strain rate. Rheological behavior of the soil matrix under steady and transient stresses was obtained from independent rheological measurements. Experimental data from the literature were used to illustrate the model. Model predictions of relative density compared favorably with experimental data for constant stress application as well as for constant strain rate experiments. Results showed that the rate of densification decreased as the relative density approached unity (complete pore closure) and the relative stress required for driving densification increased exponentially with increasing relative density.

Tillage operations disrupt surface layers of agricultural soils, creating a loosened structure with a substantial proportion of interaggregate porosity that enhances liquid and gaseous exchange properties favorable for plant growth. Unfortunately, such desirable soil tilth is structurally unstable and is susceptible to change by subsequent wetting and drying processes and other mechanical stresses that reduce total porosity and modify pore size distribution (PSD). Ability to model posttillage dynamics of soil pore space and concurrent changes in hydraulic properties is important for realistic predictions of transport processes through this surface layer. We propose a stochastic modeling framework that couples the probabilistic nature of pore space distributions with physically based soil deformation models using the Fokker‐Planck equation (FPE) formalism. Three important features of soil pore space evolution are addressed: (1) reduction of the total porosity, (2) reduction of mean pore radius, and (3) changes in the variance of the PSD. The proposed framework may be used to provide input to hydrological models concerning temporal variations in near‐surface soil hydraulic properties. In a preliminary investigation of this approach we link a previously proposed mechanistic model of soil aggregate coalescence to the stochastic FPE framework to determine the FPE coefficients. An illustrative example is presented which describes changes in interaggregate pore size due to wetting‐drying cycles and the resulting effects on dynamics of the soil water characteristic curve and hydraulic conductivity functions.

Water dripping into subterranean cavities within fractured porous media is studied in order to improve estimates of dripping rates, drop sizes, and chemical composition of droplets that could affect long‐term integrity of waste disposal canisters placed in caverns. Steady state liquid flux in fracture surfaces supported by flow in partially liquid‐filled grooves and liquid films in adjacent planes was calculated as a function of the matric potential (vapor pressure) of the fracture. At an intersection of a vertical fracture with a wider cavity the liquid flux feeds a growing pendant drop that eventually detaches. Equilibrium state size and approximate shape of liquid drops suspended from the cavity ceiling were determined from lateral and vertical force balance considering capillarity, gravity, and hydrostatic pressure. A one‐dimensional, viscous extension model with appropriate gravitational and surface tension components was employed to determine dripping rate from specified fracture roughness geometry as a function of matric potential (flux). The effect of evaporation from drop surface during drop formation was incorporated; the resulting alterations in drop volume, dripping rate, and drop solute concentration were determined. To facilitate experimental testing of the proposed model, a decoupled solution that considers independently controlled flux and evaporation is presented. Under evaporative conditions, dripping in finite period is possible only when volumetric flux exceeds evaporative demand. Calculations indicate that dripping rate and solute concentration are extremely sensitive to ambient matric potential. The results of this work may be extended to study other phenomena including formation and growth of stalactites and rivulet flow in cave ceilings.

The desired soil structure following tillage of agricultural soils is often unstable and susceptible to coalescence of aggregates and reduction of interaggregate porosity due to wetting and drying cycles. This process of aggregate rejoining was modeled by equating the rate of work done by liquid‐vapor menisci, to the rate of energy dissipation due to viscous deformation of a pair of spherical aggregates. The nonlinearity of wet soil viscous flow behavior was accounted for by introducing a Bingham rheological model. A natural outcome of the analysis was the formulation of a mathematical condition for the onset and termination of coalescence based on soil strength at specified water content. The condition states that sufficient energy in excess of soil strength (yield stress) must be available for coalescence to proceed. The rate of aggregate coalescence is proportional to available energy and is inversely related to the coefficient of plastic viscosity. Transport of wet soil to the periphery of the interaggregate contact by viscous flow leads to smoothing of the neck, resulting in pore closure, on the one hand, and restricting the minimum matric potential that can be achieved, on the other. The interplay between rheology and geometry prevent coalescence from proceeding indefinitely. Independently determined soil rheological properties were used to illustrate the use of the model. Coalescence under constant water content and during wetting‐drying cycles was calculated. Comparison of data from experiments on one‐dimensional, aggregate bed settlement has shown reasonable agreement with the model predictions.

Application of biochar amendments in agricultural systems has received much attention in recent years. In this study, we assess the 5-year impacts of biochar application on soil water and plant interactions for an irrigated fresh market tomato (Solanum lycopersicum) and a rainfed pasture (Poaceae) cropping system. In particular, we focus on three varieties of locally produced biochar from agricultural waste materials—almond shell, walnut shell, and almond pruning residues that are pyrolyzed using a mobile pyrolysis unit. We used the soil hydrological model HYDRUS-1D to explicitly track seasonal and annual soil water fluxes through changes in water retention, drainage, evaporation, and plant water uptake under biochar application. Modeling results show that the application of biochar at 5% increased soil water availability within the top 20 cm for a rainfed system, irrespective of biochar amendment type. This is clearly indicative of higher plant water uptake and greater water use efficiency (WUE) under biochar application. In contrast, a similar biochar amendment for the irrigated system did not affect WUE, instead reducing seasonal soil evaporation loss and thereby reducing irrigation demand. In both cropping systems, year-to-year variability in precipitation significantly impacted the total amount of water saved under biochar application with certain amendments retaining more water than others. Given that biochar application increased water retention irrespective of cropping systems, we further used a simple approach to determine yield trade-off, if any, between control and biochar treatments. Our economic balance clearly demonstrates that the water saved by amending soil with biochar does not offset the yield disparity if compensated with carbon credits and therefore, application of biochar should be actively considered for both its direct and indirect benefits to potential greenhouse gas mitigation (e.g., diverting orchard waste from open burning), water savings, and soil health.

Despite the vast extent of desert soils on the Earth’s surface, our understanding of the moisture dynamics of near-surface desert soils (top centimeters to a few meters) remains limited. A recent study introduced a HYDRUS-1D model to simulate water redistribution in a bare, sandy desert soil as a function of infiltration and evaporation. For soil conditions drier than pF 2, the model consistently underestimated evaporative fluxes and subsequently overestimated moisture content in the near-surface soil. The goal of this study was to explore the use of the Peters-Durner-Iden (or PDI) instead of the original bimodal van Genuchten (or BVG) water retention and hydraulic conductivity functions to improve water redistribution simulations for drier soils in desert environments. By comparing measured and simulated moisture redistribution data, we found that the simulations of moisture redistribution were improved by employing PDI soil water retention functions instead of BVG soil water retention functions. In particular, simulations for volumetric moisture contents ranging between 6% and 10% (suction heads between pF 2 and pF 3.8 and saturation degrees between 19% and 32%, respectively) improved using PDI. Interestingly, using PDI instead of BVG hydraulic conductivity functions had no noticeable effect on the simulation results. This study also emphasized the importance of good-quality soil water retention data for the relevant soil moisture content range. In conclusion, the HYDRUS-1D model using PDI hydraulic functions can accurately predict moisture redistribution for bare, sandy soil at volumetric moisture contents as low as 6% (pF 3.8 or 19% saturation, respectively).

Although desert soils cover approximately one third of the Earth’s land surface, surprisingly little is known about their physical properties and how those properties affect the ecology and hydrology of arid environments. The main goal of this study was to advance our understanding of desert soil hydrodynamics. For this purpose, we developed a process-based component within HYDRUS-1D to describe the moisture dynamics of an arid zone soil as a function of water fluxes through the soil surface. A modified van Genuchten model for the dry end of the soil water retention curve was developed to better capture the basic flow processes for very dry conditions. A scaling method was further used to account for variabilities in water retention because of changes in the bulk density vs. depth. The model was calibrated and validated using hourly soil moisture, temperature, and mass data from a 3-m-deep weighing lysimeter of the Scaling Environmental Processes in Heterogeneous Arid Soils facility at the Desert Research Institute (Las Vegas, NV). Measurements and simulations during a 1-yr period agreed better under precipitation (wetting) than under evaporation (drying) conditions. Evaporation was better simulated for wet than for dry soil surface conditions. This was probably caused by vapor-phase exchange processes with the atmosphere, which were unaccounted for and need to be further explored. Overall, the model provides a promising first step toward developing a more realistic numerical tool to quantify the moisture dynamics of arid ecosystems and their role in climate change, plant growth, erosion, and recharge patterns.

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

Keywords: Akaike information criterion, Bayesian model evidence, Bayesian information criterion, Bayesian model averaging, CLM, Community Land Model, DEM, digital elevation model, EnKF, Ensemble Kalman Filter, ET, evapotranspiration, GHG, greenhouse gases, GIS, geographic information system, GPS, global positioning system, IC, information criteria, ISMC, International Soil Modeling Consortium, KIC, Kashyap information criterion, LIDAR, Light Detection and Ranging, MCMC, Markov chain Monte Carlo, MRI, magnetic resonance imaging, MW, microwave spectrum, MWIR, mid-wave infrared spectrum, NIR, near-infrared spectrum, OTU, operational taxonomic units, pdf, probability density function, PSS, proximal soil sensing, PTF, pedotransfer function, SAR, Synthetic Aperture Radar, SDA, sequential data assimilation, SVAT, soil–vegetation–atmosphere transfer, SWIR, short-wave infrared spectrum, TE, treated effluents, TIR, thermal infrared spectrum, UAV, unmanned air vehicles, μCT, microcomputed tomography, VIS, visible spectrum, VSP, virtual soil platform

Plant roots exude approximately 10% of the carbon assimilated through photosynthesis into the soil, a process referred to as rhizodeposition. Here, we show that the mucilaginous fraction of the rhizodeposits, referred to as mucilage, plays a crucial role on soil–plant water relation and it has the potential to increase plant drought tolerance. Mucilage is a gel that can absorb large volumes of water, altering the physical properties of the rhizosphere and maintaining the rhizosphere wet and conductive when the soil dries. It is hypothesized that mucilage acts as a hydraulic bridge between roots and the soil, facilitating root water uptake and maintaining transpiration in dry soils. By employing a simplified model of root water uptake coupled with mucilage dynamics, we found that in a sandy soil the benefit of mucilage in maintaining root water uptake commenced to manifest when the soil matric potential dropped below approximately −0.8 MPa. This critical matric potential varied with transpiration rate, root length, and exudation rate. Below the critical potential, mucilage maintained photosynthesis and resulted in a net gain of carbon. In summary, rhizodeposition modifies the physical soil environment and has an impact on transpiration and photosynthesis. In other words: water for carbon, but also carbon for water.

Cavities excavated in unsaturated geological formations are important to activities such as nuclear waste disposal and mining. Such cavities provide a unique setting for simultaneous occurrence of seepage and evaporation. Previously, inverse numerical modeling of field liquid-release tests and associated seepage into cavities were used to provide seepage-related large-scale formation properties, ignoring the impact of evaporation. The applicability of such models was limited to the narrow range of ventilation conditions under which the models were calibrated. The objective of this study was to alleviate this limitation by incorporating evaporation into the seepage models. We modeled evaporation as an isothermal vapor diffusion process. The semiphysical model accounts for the relative humidity (RH), temperature, and ventilation conditions of the cavities. The evaporation boundary layer thickness (BLT) over which diffusion occurs was estimated by calibration against free-water evaporation data collected inside the experimental cavities. The estimated values of BLT were 5 to 7 mm for the open underground drifts and 20 mm for niches closed off by bulkheads. Compared with previous models that neglected the effect of evaporation, this new approach showed significant improvement in capturing seepage fluctuations into open cavities of low RH. At high relative-humidity values (>85%), the effect of evaporation on seepage was very small.

Modeling water flow in unsaturated soils is vital for describing various hydrological and ecological phenomena. Soil water dynamics is described by well-established physical laws (Richardson–Richards equation – RRE). Solving the RRE is difficult due to the inherent nonlinearity of the processes, and various numerical methods have been proposed to solve the issue. However, applying the methods to practical situations is very challenging because they require well-defined initial and boundary conditions. Recent advances in machine learning and the growing availability of soil moisture data provide new opportunities for addressing the lingering challenges. Specifically, physics-informed machine learning allows both the known physics and data-driven modeling to be taken advantage of. Here, we present a physics-informed neural network (PINN) method that approximates the solution to the RRE using neural networks while concurrently matching available soil moisture data. Although the ability of PINNs to solve partial differential equations, including the RRE, has been demonstrated previously, its potential applications and limitations are not fully known. This study conducted a comprehensive analysis of PINNs and carefully tested the accuracy of the solutions by comparing them with analytical solutions and accepted traditional numerical solutions. We demonstrated that the solutions by PINNs with adaptive activation functions are comparable with those by traditional methods. Furthermore, while a single neural network (NN) is adequate to represent a homogeneous soil, we showed that soil moisture dynamics in layered soils with discontinuous hydraulic conductivities are correctly simulated by PINNs with domain decomposition (using separate NNs for each unique layer). A key advantage of PINNs is the absence of the strict requirement for precisely prescribed initial and boundary conditions. In addition, unlike traditional numerical methods, PINNs provide an inverse solution without repeatedly solving the forward problem. We demonstrated the application of these advantages by successfully simulating infiltration and redistribution constrained by sparse soil moisture measurements. As a free by-product, we gain knowledge of the water flux over the entire flow domain, including the unspecified upper and bottom boundary conditions. Nevertheless, there remain challenges that require further development. Chiefly, PINNs are sensitive to the initialization of NNs and are significantly slower than traditional numerical methods.

This study investigates the ability of machine learning models to retrieve the surface soil moisture of a grassland area from multispectral remote sensing carried out using an unoccupied aircraft system (UAS). In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture. We tested four different machine learning algorithms and interrogated the models to rank the importance of different variables and to understand their relationship with surface soil moisture. All the machine learning algorithms we tested were able to predict soil moisture with good accuracy. The boosted regression tree algorithm was marginally the best, with a mean absolute error of 3.8 % volumetric moisture content. Variable importance analysis revealed that the four most important variables were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

Paleosols form when soils are buried through deposition by aeolian, colluvial, alluvial or other processes. Burial of former topsoil isolates soil organic matter (SOM) from surface conditions, allowing carbon to accumulate and potentially remain stable for millennia. In this study, SOM composition, distribution, and persistence were analyzed in the Brady Soil of Nebraska, USA to compare SOM spatial variability in modern and buried soils, as well as the impact of erosional exposure on SOM stability. The Brady Soil, formed as a surface soil during the Pleistocene-Holocene transition and now a paleosol buried up to 6 m deep (or more) by loess deposition during the Holocene, was sampled along burial (up to 5.8 m depth) and erosional (up to 1.8 m depth) transects to compare SOM dynamics in different geomorphic settings. Fourier Transform Infrared Spectroscopy (FTIR) and Fourier Transform ion cyclotron resonance mass spectrometry (FTICR-MS) were used to analyze SOM composition, while δ13C isotope analyses identified SOM sources and radiocarbon values were used to estimate turnover rates. Results confirmed a vegetation shift from C3 to C4 plants after Brady Soil formation, reflecting warming climatic conditions. Increasing SOM age and decreasing δ13C and δ15N values with depth indicated slowing of decomposition rate in buried soils. Higher pH in the Brady Soil suggested greater base cation content, supporting SOM stabilization through organo-mineral associations and aggregate formation. However, exposure of the Brady Soil due to surface erosion caused faster SOM turnover. This result suggested susceptibility of buried SOM to losses via decomposition upon erosional exposure, possibly accelerated by priming in response to modern SOM inputs. These findings highlight the potential loss of carbon stocks in buried soils under future climate change, as shifts in soil physicochemical properties may destabilize long-preserved SOM.

Keywords: Erosion, Paleosols, Soil organic matter composition, Soil carbon persistence, Subsoil

Machine learning (ML) applications in soil science have significantly increased over the past two decades, reflecting a growing trend towards data-driven research addressing soil security. This extensive application has mainly focused on enhancing predictions of soil properties, particularly soil organic carbon, and improving the accuracy of digital soil mapping (DSM). Despite these advancements, the application of ML in soil science faces challenges related to data scarcity and the interpretability of ML models. There is a need for a shift towards Soil Science-Informed ML (SoilML) models that use the power of ML but also incorporate soil science knowledge in the training process to make predictions more reliable and generalisable. This paper proposes methodologies for embedding ML models with soil science knowledge to overcome current limitations. Incorporating soil science knowledge into ML models involves using observational priors to enhance training datasets, designing model structures which reflect soil science principles, and supervising model training with soil science-informed loss functions. The informed loss functions include observational constraints, coherency rules such as regularisation to avoid overfitting, and prior or soil-knowledge constraints that incorporate existing information about the parameters or outputs. By way of illustration, we present examples from four fields: digital soil mapping, soil spectroscopy, pedotransfer functions, and dynamic soil property models. We discuss the potential to integrate process-based models for improved prediction, the use of physics-informed neural networks, limitations, and the issue of overparametrisation. These approaches improve the relevance of ML predictions in soil science and enhance the models’ ability to generalise across different scenarios while maintaining soil science principles, transparency and reliability.

Keywords: Artificial Intelligence, Process-based models, Physics Informed Neural Networks, Informed Machine Learning, Mechanistic models, Pedology

The effect of irrigation with treated wastewater (TWW) on soil physico-chemical and hydraulic properties was evaluated in this study. Field treatments were: non-irrigated (rain-fed) plot (control), rain-fed plot for the first three years and irrigated with TWW for the last two years (2 yr) and plot irrigated with TWW for five years (5 yr). Soil samples were collected from two depth intervals (0–15 and 15–30 cm) in five replicates. Irrigation with TWW significantly increased aggregate stability (AS), exchangeable sodium percentage (ESP), organic matter (OM), and electrical conductivity (EC). Both hydraulic conductivity (HC) and cumulative infiltration (F(t)) were decreased significantly with TWW use and period of application. Moreover, reduction of HC at different tension revealed that pore clogging occurred at both, macro and micro scale. Scanning electron microscopy (SEM) images showed that soil pores were clogged partially and/or fully as a result of suspended particulates and organic matter. Enhanced AS of treated areas indicated that infiltration was more affected by pore clogging than soil dispersion and swelling.

Keywords: Aggregate stability, SEM, Infiltration rate, Hydraulic conductivity, Pore clogging

Paleosols formed by the burial of topsoil during landscape evolution can sequester substantial amounts of soil organic carbon (SOC) over millennia due to protection from surface disturbances. We investigated the moisture sensitivity of buried SOC storage in the Brady paleosol, a loess-derived soil in Nebraska, USA, where historical aeolian deposition during the Pleistocene–Holocene transition buried soils up to 6 m deep. Topsoils from erosional (up to 1.8 m depth) and depositional (up to 5.8 m depth) transects were incubated under two moisture regimes – continuous wetting (60 % water-holding capacity) and repeated drying–rewetting – to assess SOM vulnerability to changing hydrologic conditions. SOC decomposition rates modeled from CO2 fluxes were consistently higher in erosional than depositional settings, with surface re-exposure of Brady soils enhancing microbial accessibility and destabilization. A two-pool model showed that >96 % of SOC was stored in a slow-cycling pool, particularly in deeply buried soils where stabilization was linked to mineral association, fine particles, and Ca-mediated flocculation. However, this pool decomposed more rapidly in shallower Brady soils (higher turnover rate relative to buried soil), reflecting increased microbial responsiveness to surface-driven processes. Drying–rewetting cycles caused greater SOC losses from Brady soils than continuous wetting, despite the dominance of the slow pool and depletion of labile SOC. These cycles also accelerated fast pool decay in modern soils and erosional transects, whereas burial dampened variability in Brady soils. Although continuous wetting increased overall decay in burial transects during the incubation period, wet–dry cycles destabilized the slow pool, which may result in greater long-term SOC loss. Together, these results underscore the importance of burial depth, geomorphic context, and moisture regime in shaping the long-term vulnerability of ancient SOC under climate change.

We studied the long-term impact of contrasting tillage and cover cropping systems on soil structure and hydraulic properties. Complete water retention and conductivity curves for the top (0–5 cm) and subsurface (20–25 cm) soils were characterized and contrasted. Dynamic water storage and retention were evaluated using numerical simulations in HYDRUS-2D software. Compared with standard-till (ST) and no-cover-crop (NO) systems, soils under no-till (NT) and cover cropping (CC) systems showed improved soil structure in terms of pore size distribution (PSD). Changes in hydraulic conductivity (K) under these systems led to an increased infiltration rate and water retention. However, NT and CC plots had lower water content at field capacity (33 kPa suction) and lower plant-available water (PAW) compared with ST and NO plots. Numerical simulations, however, showed that NT and CC plots have higher water storage (albeit marginal in magnitude) and water availability following irrigation. Because the numerical simulations considered retention and conductivity functions simultaneously and dynamically through time, they allow the capture of hydraulic states that are arguably more relevant to crops. The study concludes that the long-term practices of NT and CC systems were beneficial in terms of changes to the PSD. NT and CC systems also marginally improved soil water conductivity and storage at the plot scale.

Subsurface carbon stocks are a prime target for efforts to increase soil carbon storage for climate change mitigation and improving soil health. However, subsurface carbon (C) dynamics are not well understood, especially in soils under long term intensive agricultural management. We compared subsurface C dynamics in tomato-corn rotations after 25 years of differing C and nutrient management in the California Central Valley: CONV (mineral fertilizer), CONV+WCC (mineral fertilizer + cover crops) and ORG (composted poultry manure + cover crops). Our results showed a  19 Mg/ha increase in SOC stocks down to 1 m under ORG systems, no significant SOC increases under CONV+WCC or CONV systems, and the accumulation of carboxyl rich C in the subsurface (60–100 cm) horizons of all systems. Systems also had greater amounts of aromatic carbon in the order ORG>CONV+WCC>CONV. We identified a potential interaction between cover crops and compost, theorizing that increased macropores from cover crop roots facilitate the transport of soluble C and nutrients into the subsurface, thereby increasing stocks. These results demonstrate the potential for subsurface carbon storage in tilled agricultural systems and highlight a potential pathway for increasing carbon transport and storage in subsurface soil layers.

Soil science is at a critical juncture in defining its disciplinary identity. This paper critically examines a recent proposal to define the field primarily through its societal contributions, arguing that such an approach risks constraining soil science’s scientific identity. By analyzing historical perspectives and drawing parallels with other scientific disciplines, we demonstrate that transformative solutions often emerge from fundamental research. We propose a definition that positions soil science as a natural science studying the complex planetary surfaces, encompassing both living and nonliving systems, and maintaining intellectual freedom while remaining responsive to environmental challenges.

Soil science is one of the least diverse subdisciplines within the agricultural, earth, and natural sciences. Representation within soil science does not currently reflect demographic trends in the U.S. We synthesize available data on the representation of historically marginalized groups in soil science in the U.S. and identify historical mechanisms contributing to these trends. We review education and employment information within academic and the federal government, land‐grant university participation, and available Soil Science Society of America (SSSA) membership data to gain insight into the current state of representation within soil sciences and implications for the future of this discipline. Across all domains of diversity, historically marginalized groups are underrepresented in soil science. We provide recommendations toward recognizing diversity within the field, improving and encouraging diversity within the SSSA, and suggested responses for both individuals and institutions toward improving diversity, equity, and inclusion.

Keywords: antiracist, diversity, hostile climates, race, racism

Silt-sized aggregates (2–53 μm) can store a high percentage of organic matter (OM) in agricultural soils. This study aimed to determine whether additions of charcoal and CaCO3 may enhance the retention of organic C (OC) and total N (Nt) in silt-sized aggregates. We used artificial soil mixtures without a silt component (89% sand, 10% clay, 1% OM) to emulate sandy soils with little natural structure. Charcoal and/or CaCO3 were added, and the resulting mixtures were incubated for 16 wk in the dark. The newly formed silt-sized fraction was separated and analyzed for OC and Nt concentrations and characterized using FTIR and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). Compared to the control treatment, CaCO3 addition had no positive effects on C and N retention in the silt-sized fraction (17–20 g kg−1 OC, 0.15–0.17 g kg−1 Nt) whereas the silt-sized fraction from treatments with charcoal additions showed significantly higher OC and Nt concentrations (50–56 g kg−1 OC, 0.31–0.85 g kg−1 Nt). Silt-sized fractions from the charcoal treatments also showed a significant increase in the proportion of C=O groups. These initial results justify more detailed investigations into the improvement of the structure and nutrient retention of sandy soils by charcoal and CaCO3 applications.

Direct characterization of hysteresis in water retention (WR) is typically cumbersome and time consuming. Thus, such data are scarce, and even when available are typically ignored in unsaturated flow modeling. One reason for disregard of this ubiquitous and significant feature of WR is lack of a universally applicable index for the degree of hysteresis that allows a priori assessment of its effect on flow. In this note we show that the mismatch between the hydraulic capacity functions of the primary drainage and imbibition curves can serve as generalized index for degree of hysteresis (H). Moreover, we showed that hysteresis indices of a broad range of soils are linearly related with the natural-logarithm of the van Genuchten n parameter (r^2 = 0.73). This model allows predicting the degree of hysteresis and the missing hysteresis branch using only wetting or drying water retention data. The robustness of the proposed index was illustrated by comparing it with error in simulated moisture redistribution in a horizontal column that arises from ignoring hysteresis.

The loose and fragmented soil structure that results from tillage operations provides favorable physical conditions for plant growth. This desirable state is structurally unstable and deteriorates with time because of overburden, external stresses, and capillary forces. The objective of this study was to model these structural changes by coupling soil intrinsic rheological properties with geometry and arrangement of aggregates represented as monosized spheres. Calculations of interaggregate stresses and strains, and associated changes in density and porosity, were performed for a rhombohedral unit cell. Soil rheological properties determined by application of steady shear stress were used for calculations of strains under steady interaggregate stresses. The models developed herein correspond to the initial stage of deformation when discrete aggregates exist. At strains exceeding 0.12 the interaggregate voids are isolated and the current model no longer applies and an alternative approach is presented elsewhere. Unit cell calculations were up scaled to an aggregate-bed scale by considering a one-dimensional stack of unit cells, which allows only vertical stress transmission. The stress acting at an interaggregate contact is fully accommodated (dissipated) by viscous flow when it exceeds the yield stress (strength) of the aggregates. The stress is fully transmitted to subsequent unit cells when it is less than the yield stress. Plausibility of the models was demonstrated by illustrative examples that highlight the different features of the models. The results were in qualitative agreement with observations from the literature for deformation of either loose structure, and for highly dense cases close to maximal bulk density.

Tillage causes soil fragmentation thereby increasing the proportion of interaggregate (structural) pore space. The resulting tilled layer tends to be structurally unstable as manifested by a gradual decrease in interaggregate porosity until a new equilibrium has been reached between external loads and internal capillary forces at a rate governed by the soil rheological properties. The soil pore-size distribution (PSD) will change accordingly with time. We have previously applied the Fokker-Planck equation (FPE) to describe the evolution of the PSD as the result of drift, dispersion, and degradation processes that affect the pore space in unstable soils. In this study, we provide closed-form solutions for PSD evolution, which can be used to predict temporal behavior of unsaturated soil hydraulic properties. Solutions and moments of the PSD were obtained in case: (i) drift and degradation coefficients depend on time and the dispersivity is constant and (ii) drift and dispersivity are also linearly related to pore size. Both solutions can model the reduction in pore size during the growing season while the second solution can account for a reduction in the dispersion of the PSD. The solutions for PSD were plotted for a mathematically convenient expression for the drift and degradation coefficients and for an expression derived from a model for soil aggregate coalescence. Experimental data on the settlement of a Millville (coarse-silty, carbonatic, mesic Typic Haploxeroll) silt loam during wetting and drying cycles were used to determine time-dependent drift and degradation coefficients according to this coalescence model. The solution for the PSD was used to independently predict the water retention curve, which exhibited a satisfactory agreement with experimental retention data at the end of two drying cycles.

Tilled agricultural soils are in a constant state of change induced by variations in soil strength due to wetting and drying and compaction by farm implements. Changes in soil structure affect many hydraulic and transport properties; hence their quantification is critical for accu- rate hydrological and environmental modeling. This study highlights the role of soil rheology in determining time-dependent stress–strain relationships that are essential for prediction and analysis of structural changes in soils. The primary objectives of this study were (i) to extend a previously proposed aggregate-pair model to prediction of compaction under external steady or transient stresses and (ii) to provide experimentally determined rheological information for the above models. Rheological properties of soils and clay minerals were measured with a rotational rheometer with parallel-plate sensors. These measurements, under controlled steady shear stress application, have shown that wet soils have viscoplastic behavior with well-defined yield stress and nearly constant plastic viscosity. In contrast, rapid transient loading (e.g., passage of a tractor) is often too short for complete viscous dissipation of applied stress, resulting in an elastic (recoverable) component of deformation (viscoelastic behavior). Measured viscoelastic properties were expressed by complex viscosity and shear modulus whose components denote viscous energy dissipa- tion, and energy storage (elastic). Results show that for low water contents and fast loading (tractor speed), the elastic component of deformation increases, whereas with higher water contents, viscosity and shear modulus decrease. Steady and oscillatory stress application to an aggregate pair model illustrates potential use of rheological properties towards obtaining predictions of strains in soils.

Soil structure is a key attribute of soil quality and health that significantly impacts water infiltration. Structure can be significantly altered by natural or anthropogenic drivers including soil management practices and can in turn impact soil infiltration. Those changes in soil structure are often complex to quantify and can lead to conflicting impacts on water infiltration into soils. Here, we present a narrative systematic review (SR) of the impacts of soil structure on water infiltration. Based on inclusion and exclusion criteria, as well as defined methods for literature search and data extraction, our systematic review led to a total of 153 papers divided into two sets: experimental (131) and theoretical (22) papers. That implied a significant number of in-situ and field experiments that were conducted to assess the impacts of soil structure on water infiltration under the influence of different land uses and soil practices. Analysis of the metadata extracted from the collected papers revealed significant impacts of soil structure on water infiltration. Those effects were further attributed to land use and management, where we demonstrate the impact of three unique categories: soil amendments, crop management and tillage. Furthermore, significant correlations were established between infiltration rate and soil structural properties, with R2 values ranging from 0.51 to 0.80 and for saturated hydraulic conductivity and soil structural properties, with R2 values ranging from 0.21 to 0.78. Finally, our review highlighted the significant absence of and the need for theoretical frameworks studying the impacts of soil structure on water infiltration.

Keywords: Soil structure, Soil infiltration, Pedotransfer functions, Infiltration capacity

Scarcity of plant available water is a major challenge for rainfed agriculture throughout the Sahel. At two long-term experiments in Central and Southern Senegal, optimized intercropping with native woody shrubs, Piliostigma reticulatum (DC.) Hochst or Guiera senegalensis J.F. Gmel, (elevated densities and annual coppiced biomass returned to soils) have shown significant improvement of soil-plant-water relations, nutrient availability, and crop yields. The objective was to investigate soil physical properties to develop a mechanistic understanding for the observed improvement of water dynamics due to optimized shrub intercropping. The field experiments had a split-plot factorial design with shrubs as the main factor and fertilizer rate (0, 0.5, 1.0. 1.5 times the recommended addition of N-P-K fertilizer) as the subplot factor. This experiment was carried out at the sites of Keur Matar Arame (Keur Matar) with G. senegalensis and Nioro du Rip (Nioro) with P. reticulatum. Water retention characteristic, unsaturated hydraulic conductivity, surface evaporation, and surface infiltration were measured in the zero fertilizer treatment. At Keur Matar samples were collected from crop + shrub plots near (<0.5 m) the shrub canopy (CSn), crop + shrub plots far (>1 m) from the canopy (CSf) and in crop only plots (CO). At Nioro samples were taken in CSn, CSf, CO, and also from bare soil with no crops or shrubs growing (BS). Infiltration in CO plots compared to CSn plots was 75% and 28% higher at Keur Matar and Nioro, respectively. At Keur Matar water retention was significantly higher at wilting point (−1.5 MPa) in the CSn treatment than in the CSf treatment with values of 0.030 and 0.016 m3 m−3, respectively. At Nioro there was no significant difference in wilting point water content between treatments. These results indicate that shrubs slow down soil water as it infiltrates in the sandy soils and that the large additions of shrub biomass over a ten year period has had a small but significant effect on water retention at wilting point. This study highlights the role that shrub presence and biomass additions play in altering centimeter-scale soil properties.

Keywords: Soil structure, Sahel, Agroforestry

Tillage modifies the soil structure to create conditions favorable for plant growth. However, the resulting loose structure is susceptible to collapse by internal capillary forces and external compactive stresses with concurrent changes in soil hydraulic properties. Presently, limited understanding of these complex processes often leads to consideration of the soil plow-layer as a static porous medium. Our objective is to provide a review of recent progress in modeling soil structural dynamics at the pore-scale, based on soil mechanical and rheological properties. The basic geometrical framework of the models was a cubic arrangement of monosized spherical aggregates (other arrangements are discussed). The process of soil aggregate rejoining by capillary forces was modeled by considering the rate of energy dissipation due to viscous deformation of wet soil, and corresponding energy release due to reconfiguration of water capillary menisci. The model was complemented by independent rheological characterization of soil that provides control on the rate as well as the onset and termination of aggregate coalescence. The model was also adapted for consideration of steady stress (such as overburden) acting upon the unit cells. Unlike steady stress, transient stress (such as traffic) is applied for too short of a period to allow for total energy dissipation by viscous deformation. Hence, a portion of the deformation is elastic (with a recoverable portion of the applied energy). Rheological characterization under transient (oscillatory) stress provided coupled elastic and viscous properties under several loading frequencies. Effects of transient stresses on the geometrical model were modeled by considering a combination of (i) Hertzian-type elastic strain and (ii) viscous flow of soil at the contacts. Application of the models is demonstrated using illustrative examples and rheological measurements of Millville silt loam soil. Finally, we provide an outlook for upscaling the unit cell results to an aggregate bed scale.

Keywords: Rheology, Soil-structure, Compaction, Aggregate

Soil tillage often results in a structurally unstable soil layer with an elevated inter-aggregate porosity that is gradually decreased by the interplay of capillary and rheological processes. We have previously proposed to describe the evolution of the pore-size distribution (PSD) with the Fokker–Planck equation (FPE). The coefficients of this equation quantify the drift, dispersion, and degradation processes acting upon the PSD. An analytical solution for the PSD is presented for the case where drift and degradation coefficients depend on time, and the dispersion coefficient is proportional to the drift coefficient. These coefficients can be estimated from independent measurements of the PSD or (surrogate) water retention data or from mechanistic models. In this paper, we illustrate the application of the pore-size evolution model for: (i) a generic drift coefficient, (ii) static water retention data for soils under different tillage regimes, and (iii) dynamic hydraulic data for a soil subject to a sequence of wetting and drying cycles. These applications show the viability of our approach to model pore-size evolution. However, the development and application of the model is hampered by a lack of definitive data on soil structural and hydraulic dynamics.

Keywords: Soil hydraulic properties, Compaction, Wetting, Drying, Pore-size distribution, Analytical solution

Soil is a complex ecosystem within which many species interact and where physicochemical and geological processes occur at different spatiotemporal scales, with strong interactions taking place between ecological and management processes. Soil processes affect the qualities of the food and water that we eat and drink, the regulation of greenhouse gases, and are the foundation of our habitation and transportation infrastructures. However, it is estimated that over 2 billion hectares of lands are degraded, with a further 12 million hectares degraded each year causing the annual loss of 24 billion tons of fertile soil. Soil degradation negatively affects the well-being of over 3 billion people, costing more than 10% of the annual global GDP via the loss of ecosystem services, and reducing the productivity of 23% of the global terrestrial area. The sustainable management of soil ecosystems is, therefore, fundamental to global food, water, and energy security, especially under increasingly unpredictable weather patterns caused by climate change. The land–water–food–energy nexus is central to sustainable development and soil inextricably links these critical domains. Stakeholders and decision-makers in all four domains are necessarily focusing on the effects of soil degradation on climate change, water resource management, and food production as key to the development of sustainable agricultural practices and policies. A properly integrated approach to managing rural soils is thus required to ensure global water, food, and energy security, whilst increasing and protecting biodiversity. This special issue collects 15 papers on recent advances on soil physical-, hydrological-, and biological processes, and linkages with agroecosystem sustainability across experiments, field observations, and methodological breakthroughs.

Soil science is one of the least diverse fields within science, technology, engineering and mathematics (STEM). Because demographics of groups and institutions provide a window into the culture, climate, equity and inclusion of minoritized scholars, we discuss how lack of diversity continues to affect our science and the scientific community, and its implications for the welfare of the global population. We highlight the role of antiracist practices and policies for improving workplace climate and thereby developing a diverse and inclusive scientific community. We present this article as a starting point for discussions on issues of race and racism in our scientific community and institutions. Highlights Soil science remains one of the least diverse fields in STEM. Workplace climate plays a major role in perpetuating the lack of diversity within soil science and other fields within geosciences. Incorporation of antiracist practices and policies is urgently needed to reverse the current trend and improve representation in our scientific community.

Keywords: antiracist, diversity, hostile climates, race, racism

A long-term annual crop study in Five Points, California, shows that the combined use of no-tillage, surface residue retention, and cover crops improves soil health compared to conventional practices common to the region. Several chemical, biological, and physical soil health indicators were improved when these practices were combined. Our data suggest that farmers stand to gain multiple synergistic benefits from the integrated use of these practices by increasing soil structural stability, water infiltration and storage, and agroecosystem biodiversity, and improving the efficiencies of the carbon, nitrogen, and water cycles of their production systems.

To meet the requirements of California’s Sustainable Groundwater Management Act, there is a critical need for crop production strategies with less reliance on irrigation from surface and groundwater sources. One strategy for improving agricultural water use efficiency is reducing tillage and maintaining residues on the soil surface. We evaluated high residue no-till versus standard tillage in the San Joaquin Valley with and without cover crops on the yields of two crops, garbanzo and sorghum, for 4 years. The no-till treatment had no primary or secondary tillage. Sorghum yields were similar in no-till and standard tillage systems while no-till garbanzo yields matched or exceeded those of standard tillage, depending on the year. Cover crops had no effect on crop yields. Soil cover was highest under the no-till with cover crop system, averaging 97% versus 5% for the standard tillage without cover crop system. Our results suggest that garbanzos and sorghum can be grown under no-till practices in the San Joaquin Valley without loss of yield.

Manure is a leading source of methane (CH4), nitrous oxide (N2O), and ammonia (NH3) emissions, and alternative manure management practices can help society meet climate goals and mitigate air pollution. Recent studies show that biochar-composting can substantially reduce emissions from manure. However, most studies test only one type of biochar applied at a single application rate, leading to high variation in emission reductions between studies. Here, we measured greenhouse gas and NH3 emissions during biochar-composting of dairy manure with biochar applied at 5% or 20%, by mass, and made from walnut shells, almond shells, or almond clippings. We found little difference in emissions between biochar type. However, we found that the 20% application rates increased CH4 emissions and decreased N2O and NH3 emissions, resulting in a net reduction in global warming potential (GWP). We attribute this result to biochar increasing the formation of compost aggregates, which likely acted as anaerobic reactors for methanogenesis and complete denitrification. Biochar may have further fueled CH4 production and N2O consumption by acting as an electron shuttle within aggregates. We recommend lower application rates, as we found that the 5% treatments in our study led to a similar reduction in GWP without increasing CH4 emissions.

Keywords: ammonia, biochar, climate change mitigation, composting, livestock, manure, methane, nitrous oxide

Organic amendments, such as compost and biochar, mitigate the environmental burdens associated with wasting organic resources and close nutrient loops by capturing, transforming, and resupplying nutrients to soils. While compost or biochar application to soil can enhance an agroecosystem’s capacity to store carbon and produce food, there have been few field studies investigating the agroecological impacts of amending soil with biochar co-compost, produced through the composting of nitrogen-rich organic material, such as manure, with carbon-rich biochar. Here, we examine the impact of biochar co-compost on soil properties and processes by conducting a field study in which we compare the environmental and agronomic impacts associated with the amendment of either dairy manure co-composted with biochar, dairy manure compost, or biochar to soils in a winter wheat cropping system. Organic amendments were applied at equivalent C rates (8 Mg C ha−1). We found that all three treatments significantly increased soil water holding capacity and total plant biomass relative to the no-amendment control. Soils amended with biochar or biochar co-compost resulted in significantly less greenhouse gas emissions than the compost or control soils. Biochar co-compost also resulted in a significant reduction in nutrient leaching relative to the application of biochar alone or compost alone. Our results suggest that biochar co-composting could optimize organic resource recycling for climate change mitigation and agricultural productivity while minimizing nutrient losses from agroecosystems.

Livestock are the largest source of anthropogenic methane (CH4) emissions, and in intensive dairy systems, manure management can contribute half of livestock CH4. Recent policies such as California’s short-lived climate pollutant reduction law (SB 1383) and the Global Methane Pledge call for cuts to livestock CH4 by 2030. However, investments in CH4 reduction strategies are primarily aimed at liquid dairy manure, whereas stockpiled solids remain a large source of CH4. Here, we measure the CH4 and net greenhouse gas reduction potential of dairy manure biochar-composting, a novel manure management strategy, through a composting experiment and life-cycle analysis. We found that biochar-composting reduces CH4 by 79%, compared to composting without biochar. In addition to reducing CH4 during composting, we show that the added climate benefit from biochar production and application contributes to a substantially reduced life-cycle global warming potential for biochar-composting: −535 kg CO2e Mg–1 manure compared to −194 kg CO2e Mg–1 for composting and 102 kg CO2e Mg–1 for stockpiling. If biochar-composting replaces manure stockpiling and complements anaerobic digestion, California could meet SB 1383 with 132 less digesters. When scaled up globally, biochar-composting could mitigate 1.59 Tg CH4 yr–1 while doubling the climate change mitigation potential from dairy manure management.

Soil compaction represents one of the most ubiquitous environmental impacts of human development, decreasing bulk-scale soil porosity and hydraulic conductivity, thereby reducing soil productivity and fertility. At the aggregate-scale however, this study shows that natural root-induced compaction increases contact areas between aggregates, leading to an increase in unsaturated hydraulic conductivity of the soils adjacent to the roots. Contrary to intuition, water flow may therefore be locally enhanced due to root-induced compaction. This study investigates these processes by using recent advances in X-ray microtomography (XMT) imaging and numerical water flow modeling to show evolution in interaggregate contact and its implications for water flow between aggregates under partially saturated conditions. Numerical modeling showed that the effective hydraulic conductivity of a pair of aggregates undergoing uniaxial deformation increased following a nonlinear relationship as the interaggregate contact area increased due to increasing aggregate deformation. Numerical modeling using actual XMT images of aggregated soil around a root surrogate demonstrated how root-induced deformation increases unsaturated water flow toward the root, providing insight into the growth, function, and water uptake patterns of roots in natural soils.

Most plants derive their water and nutrient needs from soils, where the resources are often scarce, patchy, and ephemeral. In natural environments, it is not uncommon for plant roots to encounter mismatched patches of water-rich and nutrient-rich regions. Such an uneven distribution of resources necessitates plants to rely on strategies that allow them to explore and acquire nutrients from relatively dry patches. We conducted a laboratory study to provide a mechanistic understanding of the biophysical factors that enable this adaptation. We grew plants in split-root pots that permitted precisely controlled spatial distributions of resources. The results demonstrated that spatial mismatch of water and nutrient availability does not cost plant productivity compared to matched distributions. Specifically, we showed that nutrient uptake is not reduced by overall soil dryness, provided that the whole plant has access to sufficient water elsewhere in the root zone. Essential strategies include extensive root proliferation towards nutrient-rich dry soil patches that allows rapid nutrient capture from brief pulses. Using high-frequency water potential measurements, we also observed nocturnal water release by roots that inhabit dry and nutrient-rich soil patches. Soil water potential gradient is the primary driver of this transfer of water from wet to dry soil parts of the root zone, which is commonly known as hydraulic redistribution (HR). The occurrence of HR prevents the soil drying from approaching the permanent wilting point, and thus supports root functions and enhance nutrient availability. Our results indicate that roots facilitate HR by increasing root-hair density and length and deposition of organic coatings that alter water retention. Therefore, we conclude that biologically-controlled root adaptation involves multiple strategies that compensate for nutrient acquisition under mismatched resource distributions. Based on our findings, we proposed a nature-inspired nutrient management strategy for significantly curtailing water pollution from intensive agricultural systems.

Soil water status is one of the most important environmental factors that control microbial activity and rate of soil organic matter decomposition (SOM). Its effect can be partitioned into effect of water energy status (water potential) on cellular activity, effect of water volume on cellular motility and aqueous diffusion of substrate and nutrients, as well as effect of air content and gas-diffusion pathways on concentration of dissolved oxygen. However, moisture functions widely used in SOM decomposition models are often based on empirical functions rather than robust physical foundations that account for these disparate impacts of soil water. The contributions of soil water content and water potential vary from soil to soil according to the soil water characteristic (SWC), which in turn is strongly dependent on soil texture and structure.The overall goal of this study is to introducea physically based modelling framework of aerobic microbial respiration that incorporates the role of SWC under arbitrary soil moisture status.The model was tested by compariing it with published datasets of SOM decomposition under laboratory conditions.

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.

Keywords: Inverse modeling, Shortwave infrared imaging, Soil moisture, Soil hydraulic functions

Soil water infiltration is a critical process in the soil water cycle and agricultural practices, especially when wastewater is used for irrigation. Although research has been conducted to evaluate the changes in the physical and chemical characteristics of soils irrigated by treated wastewater, a quantitative analysis of the effects produced on the infiltration process is still lacking. The objective of this study is to address this issue. Field experiments previously conducted on three adjacent field plots characterized by the same clayey soil but subjected to three different irrigation treatments have been used. The three irrigation conditions were: non-irrigated (natural conditions) plot, irrigated plot with treated wastewater for two years, and irrigated plot with treated wastewater for five years. Infiltration measurements performed by the Hood infiltrometer have been used to estimate soil hydraulic properties useful to calibrate a simplified infiltration model widely used under ponding conditions, that were existing during the irrigation stage. Our simulations highlight the relevant effect of wastewater usage as an irrigation source in reducing cumulative infiltration and increasing overland flow as a result of modified hydraulic properties of soils characterized by a lower capacity of water drainage. These outcomes can provide important insights for the optimization of irrigation techniques in arid areas where the use of wastewater is often required due to the chronic shortage of freshwater.

Interplay between capillary, gravity and viscous forces in unsaturated fractures gives rise to a range of complex flow phenomena. Evidence of highly intermittent fluxes, preferential and sustainable flow pathways lead to potentially significant flow focusing of concern for regulatory and management of water resources in fractured rock formations. In previous work[Ghezzehei TA,Or D.: Water Resour. Res. In Review(2005)] we developed mechanistic models for formation, growth and detachment of liquid bridges in geometrical irregularities within fractures. Such discrete and intermittent flows present a challenge to standard continuum theories. Our focus here is on predicting travel velocities of detached liquid elements and their interactions with fracture walls. The scaling relationships proposed by Podgorski et al. [Podgorski, T., et al.: Phys. Rev. Lett. 8703(3), 6102-NIL_95 (2001)] provide a general framework for processes affecting travel velocities of discrete liquid elements in fractures, tubes, and in coarse porous media. Comparison of travel velocity and distance by discrete bridges relative to equivalent continuous film flow reveal significantly faster and considerably larger distances traversed by liquid bridges relative to liquid films. Coalescence and interactions between liquid bridges result in complex patterns of travel times and distances. Mass loss on rough fracture surfaces shortens travel distances of an element; however, results show that such retardation provides new opportunities for coalescence of subsequent liquid elements traveling along the same path, resulting in mass accumulation and formation of larger liquid elements traveling larger distances relative to smooth fracture surfaces. Such flow focusing processes may be amplified considering a population of liquid bridges within a fracture plane and mass accumulation in fracture intersections.

Keywords: Intermittent flow, Fracture ,Dripping, Liquid bridge, Transport

Aggregation is a vital characteristic of soil structure that affects its physical and biogeochemical properties. Aggregation results from the cohesion of primary minerals with organic or inorganic constituents. It depends on the dynamic balance between binding and fragmentation. There are two classes of aggregation. Mechanical aggregates are formed instantly by external forces and are often unstable. Hierarchical aggregates result from slow binding and are stable. Characterization of aggregation includes size, shape, stability, configuration, and their arrangement within soil. The nature of hierarchical aggregation is inferred from size separation of aggregates. Aggregate stability indicates their ability to persist under disruptive forces.

Keywords: Aeration, Aggregate, Cementation, Clod, Imaging, Organic matter, Rhizosheath, Rhizosphere, Root, Sequestration, Soil health, Tillage, X-racy CT

The rhizosphere, a thin layer of soil surrounding plant roots, plays a dynamic role in the hydrologic cycle by governing plant water and nutrient uptake. Study of rhizosphere soil structure formation due to mechanical processes has been limited by a lack of nondestructive techniques to quantify the dynamic nature of this region. In this chapter, we present recent developments in visualizing how growing roots modify their physical environment by moving soil particles, deforming aggregates and decreasing the amount of inter-aggregate pores while creating hydraulic pathways that connect neighboring soil aggregates using noninvasive, synchrotron X-ray microtomography (XMT). Image-processing tools were applied for quantifying root-induced rhizosphere alterations from XMT grayscale images as well as to transform XMT images into finite element meshes, building a bridge from nondestructive rhizosphere visualization to micromechanical and hydraulic simulations.

The presence of subterranean holes creates a capillary barrier in an unsaturated environment. This phenomenon has been referred to as "Drift Shadow" and indicates a region that is sheltered from the downward percolating water. If the lateral hydraulic conductivity is insufficient to divert the water, fully saturated conditions are reached locally, and seepage occurs as the capillary barrier fails. Natural heterogeneities in hydrological properties can reduce the probability of seepage only if the flux is largely diverted around the drift. Previous numerical studies have been performed investigating various aspects of capillary barrier performance in engineered or naturally layered systems. Many authors examined the impact of heterogeneity on the distribution and rate of water seepage across a capillary barrier and into a drift, but the seepage exclusion problem has not been formally analyzed for fractured formations, in which the physical processes governing seepage in porous media also represent key factors. This paper analyzes the effect that a fracture network can have on the drift shadow. In a fractured environment, the effectiveness of the capillary barrier is determined by the capability of individual fractures to hold water by capillary forces and by the permeability and connectivity of the fracture network, which allow water to be diverted around the drift. The orientation of any individual fracture in relation to the opening, the discreteness and the anisotropy of\ldots