Water Flow & Hydraulic Properties

Understanding how temporal patterns of moisture variability control biogeochemical responses remains a fundamental challenge in Earth system science. Jensen’s inequality provides a mathematical framework for quantifying when episodic environmental events dominate over mean conditions. We applied this framework to continental-scale AmeriFlux data (134.5 million hourly observations from 2,004 soil moisture sensors) to quantify how moisture distribution patterns control soil respiration responses across environmental gradients. Sensors in dry regions show large Jensen’s inequality effects (median temporal averaging difference of -63.6%) because they experience highly skewed moisture distributions where brief wet periods drive disproportionate respiratory responses. Wet regions show minimal effects (median -27.1%) because they have more uniform moisture distributions. Data density analysis reveals that sensors operating at θ≈0.05 exhibit severe temporal averaging effects, while sensors at θ≈0.45 show minimal effects, demonstrating the mechanistic basis for where ecosystems operate on the moisture-respiration relationship. Climate gradient analysis shows systematic transitions from severe effects in arid systems to moderate effects in humid systems. Depth analysis reveals that surface soils experience maximum episodic event importance while deeper soils show reduced effects due to environmental buffering. Moisture-temperature coupling demonstrates systematic negative correlations in water-limited systems, indicating that environmental co-variation modulates biogeochemical responses. Jensen’s inequality emerges as a diagnostic tool for identifying when moisture variability patterns dominate biogeochemical processes, with continental-scale patterns revealing fundamental controls on episodic event importance across ecosystems.

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 conducted two 64-day incubation experiments to assess how locally produced almond-shell biochar influences soil physical and hydraulic properties. Biochar was created using slow pyrolysis at different temperatures (350 °C or 700 °C), separated into different particle sizes (<250 μm or 1–2 mm), and applied at 10 ton/ha or 60 ton/ha to a coarse-textured soil. While our analysis shows that biochar yielded greater cation exchange capacity (CEC) and specific surface area (SSA) with increasing pyrolysis temperature and finer particle size, its contributions to improving soil hydraulic properties were marginal. In the first experiment, the addition of biochar at high rates slightly improved water stable aggregate (WSA) (3.8%–5.3% increase) but has no effect on saturated hydraulic conductivity (Ksat). Soil respiration measured throughout the experiment were not significantly different among treatments. In the second experiment, the addition of biochar increased soil infiltration rate at the initial stage (8.18E–4 cm/s), but this effect diminished over time. WSA was lower for biochar amended soil and lowest at high application rates (5%–21% reduction). Cumulative carbon dioxide (CO2) flux varied between biochar particle sizes and rates. Additionally, a significant difference between the two experiments was also observed, with cumulative CO2 (38%–56% greater) and WSA (11%–40%) being inversely correlated. Our findings suggest that almond-shell derived biochar has a limited impact on arable loamy sand soil properties, specifically for water retention under short-term conditions.

The growing water scarcity jeopardizes crop production for global food security, a problem poised to worsen under climate change–induced drought. Amending soils with locally derived biochar from pyrolyzed agricultural residues may enhance soil moisture retention and resilience, in addition to climate change mitigation. However, prior studies on the hydrologic benefits of biochar focused on optimal moisture, not water-limited conditions where biochar’s large wettable surface area could aid plants and microbes. We hypothesized that biochars differing in feedstocks would positively augment soil moisture and respiration, with overall impacts most beneficial under drier conditions. Using water vapor sorption isotherms, we used film theory to estimate the specific surface area (SSA) of biochars. We then modeled and tested the moisture retention of a coarse-textured soil amended with biochar. Additionally, a 109-day lab incubation experiment was also conducted to examine biochar effects on respiration across a moisture range spanning optimal to wilting point. Among seven tested biochars, almond shell biochar significantly increased soil moisture and yield the second highest SSA. Despite drying treatments, the amended soil maintained higher respiration than the control, indicating enhanced biological activity. The results demonstrate biochars counter drying effects in coarse soils through physical and biological mechanisms linked to increased sorptive capacity. Our findings contribute to the development of sustainable water and waste management strategies tailored to the needs of California Central Valley, where the potential for biochar application is substantial. Above all, our research fills a crucial gap by providing context-specific insights that can inform the effective utilization of locally produced biochars in the face of increasing water scarcity and excess biomass challenges.

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

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 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

Finding feasible solutions for sustainable food production is challenging. Here we try to understand the balance between crop productivity and ecological stewardship using agroecological-based soil management strategies. We evaluated the potential of different organic materials such as dairy manure compost and different biochar manure co-composts, derived locally from agricultural wastes, to enhance soil ecosystem services. We assessed their potential impact on soil moisture and nutrient retention, greenhouse gas emissions, and crop productivity using data collected from an outdoor tomato column study. Results from the experiment showed potential of biochar co-composts to positively affect soil health by lessening loss of essential nutrients such as NO3−-N and NH4+-N, sustained tomato yield, and uphold crop water use efficiency. However, yield response to soil organic amendment is constrained by external factors such as irrigation strategies, with treatments under deficit irrigation greatly impacted. Overall, we observed a positive effect of adding biochar manure co-composts to soil, although best management practices are needed to optimize crop productivity and avoid unintentional consequences.

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.

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.

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.

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

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

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.

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

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.

Conservation agriculture offers a pathway for enhancing soil health with climate co-benefits in Mediterranean agricultural systems. This study examined long-term impacts of combining no-till management with cover cropping over 20 years in California’s Central Valley, providing rare insights into soil system equilibrium under sustained conservation management. We assessed soil physical, chemical, and structural properties comparing reduced tillage with cover crops to standard tillage without cover crops, employing density fractionation and spectroscopic analysis to understand carbon protection mechanisms. After two decades, conservation agriculture achieved dynamic equilibrium characterized by fundamental shifts in carbon stabilization pathways. Water-stable aggregate analysis revealed the most pronounced management effects, with conservation practices exhibiting 136% greater stability, indicating substantial improvements in soil structural integrity. These structural enhancements corresponded with a reorganization of carbon protection mechanisms, demonstrating that physical protection within aggregates becomes a dominant carbon stabilization pathway under long-term conservation management. Mineral-associated organic carbon saturation analysis revealed that both management systems remained well below theoretical maximum capacity, indicating substantial remaining potential for carbon sequestration even after reaching equilibrium. Physical property improvements included 15% lower bulk density and 13% greater water retention at field capacity. Our findings demonstrate that two decades of conservation agriculture fundamentally transforms soil functioning through aggregate-mediated physical protection.

Understanding which soil properties control water retention at different moisture states remains a critical challenge for predicting how soils respond to management and environmental change. Traditional pedotransfer functions predict parameters of equations like van Genuchten’s model, but parameter correlations create ill-posed inverse problems that propagate errors and reduce prediction accuracy. Fundamentally, these parameter-based approaches cannot reveal which properties matter most at different water potentials or how texture-structure interactions vary across the moisture spectrum—knowledge essential for mechanistic understanding and management applications. We present Hierarchical Attention-Based Pedotransfer Function (HABIT) to address these limitations through two objectives: (1) achieve superior prediction accuracy through direct moisture prediction that circumvents parameter correlation errors, and (2) quantify property importance and interactions across moisture regimes through interpretable attention mechanisms. The model employs neural network attention architectures that dynamically weight soil properties based on water potential, enforces physical constraints including monotonicity, and accommodates incomplete soil characterization through hierarchical training. We evaluate HABIT against traditional parameter-based approaches using comprehensive international soil databases spanning global textural and structural variability. Attention weight analysis reveals moisture-dependent property interactions, including asymmetric texture-structure information flow, organic carbon’s dual structural and sorptive roles, and saturated hydraulic conductivity’s diagnostic function in integrating pore network information. These patterns generate testable predictions while explaining how direct prediction with adaptive property weighting captures soil-specific retention behaviors that parametric forms miss. HABIT demonstrates that properly designed machine learning architectures serve as prediction tools and scientific discovery platforms, providing frameworks for understanding texture-structure interactions that remain qualitatively understood but quantitatively elusive.