Abstract Soil is the largest sink of microplastic (MP) in terrestrial ecosystems, yet little is known about the transport of MP in soils and its effect on water flow. Since pristine MP can generally be considered water repellent, the transport of MP and water flow are subject to feedback processes. To investigate MP transport in porous media and the interactions with water dynamics, we applied neutron and X‐ray imaging methods to columns filled with mixtures of sand (700–1200 µm) and MP (polyethylene terephthalate, 20–75 µm). Simultaneous neutron and X‐ray CT were used to image MP distribution in 3D before and after each experiment, while time‐series neutron radiography was applied to image water distribution during repeated wetting and drying cycles. The imaging revealed that MP impacted water infiltration. High local MP content caused an initial delay in infiltration, plausibly due to water repellency, but was eventually bypassed by water flow. Rapid and preferential propagation of the wetting front as well as reduced local water saturation above the wetting front were the outcome. The discrepancy between the initial delay and rapid stage infiltration was increased with an increase in total MP content. No significant vertical transport of MP could be observed during two wetting and drying cycles, probably since water bypassed regions containing MP. In conclusion, the interactions between hydrophobic MP and water impact the infiltration of water, with implications for the retention and fate of MP in soils.

Abstract Soil moisture regulates plant water supply and drought sensitivity in tropical forests, yet its vertical and topographic variation remains poorly characterized. We combined high‐frequency time‐domain reflectometry measurements from 5 to 100 cm across plateau, slope, and valley landforms at the Zona Florestal 2 research site north of Manaus, Central Amazonia, to quantify how soil moisture memory, timing of responses to rainfall, dry‐down rates ( τ ), and soil–water depletion vary across these contrasting landforms. Landform‐specific soil moisture calibration curves ensured accurate volumetric water content estimates in these highly weathered soils. During the 2023 dry‐to‐wet transition (August–November), soil moisture memory showed strong topographic contrasts, with valley profiles increasing from ∼47 h at 5 cm to ∼154 h at 100 cm, while plateaus exhibited higher near‐surface persistence (∼124 h at 5 cm) but weaker memory at depth. Dry‐down behavior reinforced these differences as valley soils exhibited τ values exceeding ∼200 h, more than double the characteristic τ of plateau soils (∼90 h). Rainfall–soil moisture correlations indicated immediate responses at shallow depths in valleys and progressively longer lags with depth on plateaus and slopes. These hydrologic patterns were mirrored in depletion profiles, which declined sharply below 30 cm on plateaus but remained high and sustained throughout the upper meter in slopes and valleys. Together, these findings provide the first depth‐resolved field measurements of soil moisture memory, rainfall coupling, dry‐down constants, and depletion dynamics across major upland landforms in Central Amazonia and offer clear observational benchmarks for improving land‐surface and ecosystem model representations of soil–water processes.

Abstract Surface soil moisture is essential for understanding energy exchange between land and atmosphere. Mesoscale modeling and remote sensing are two methods that can provide useful surface soil moisture information for helping characterize these exchanges. While ground‐based sensors are also used, many sensor technologies and network designs lack measurements at the 5‐cm depth often targeted by land surface models and satellite‐based sensors. If deeper soil moisture measurements could be leveraged to accurately estimate near‐surface conditions, the utility of in situ networks operating at various depths would be significantly improved. This study develops a lifted 5‐cm soil moisture in the Upper Missouri River Basin to estimate 5‐cm soil moisture from 10‐cm and 20‐cm sensor data and antecedent precipitation indices, using statistical and machine learning approaches. Model selection was based on three rigorous testing scenarios designed to evaluate different data availability conditions. Across these testing scenarios, we found that statistical multiple linear regression was the most suitable approach for this task due to its simplicity, interpretability, and excellent performance with unbiased root mean square error of 0.018–0.027 m 3 /m 3 . At least 87% of the soil moisture 5‐cm estimates across the three testing scenarios achieved errors under 0.04 m 3 /m 3 . The estimated soil moisture at 5 cm can be effectively used for evaluating Soil Moisture Active and Passive soil moisture accuracy. This study provides a method to significantly increase the availability of 5 cm soil moisture estimates in in situ networks, especially in the Montana Mesonet.

Abstract Five column experiments were conducted using a mixture of 10 per‐ and polyfluoroalkyl substances (PFASs) to explore sampling techniques for the PFAS injection solution concentration in order to accurately determine the tracer mass within the experimental system and close the mass balance. Samples of the injection solution were first taken by the method of pouring from the injection container into sampling tubes and analyzed for PFAS concentration. The results showed a drastic increase in the concentration of certain PFASs over the course of injection (up to 763%), leading to incomplete mass balances and low recovery rates. By contrast, when the injection solution was sampled by pipetting away from the air–water interface, the mass balance greatly improved (PFAS recovery percentages increased from an average of 62%–102%). The observed concentration increase by the method of pouring can be attributed to the attraction of certain PFASs to the air–water interface. Factors such as carbon chain length and solubility were found to impact this attraction most, while surprisingly, the air–water interfacial sorption coefficients did not appear to be significantly influential. This phenomenon is important to column experiments, as elevated local concentrations may promote micelle or hemi‐micelle formation, consequently affecting the transport and sorption characteristics of the compounds under study. These aspects should be thoroughly considered in future studies to ensure a correct calculation of mass balance and therefore to correctly characterize the behavior of PFASs in the environment, and may also be relevant in remediation investigations, such as column tests with granular activated carbon.

Abstract Azouani–Olson–Titi data assimilation (often referred to as AOT, after its authors) is a computationally‐efficient algorithm that has been shown analytically and computationally to recover the true solution for a wide variety of regimes exponentially fast in time, in addition to being robust with respect to noisy data, stochastic forcing, and errors in parameters. In this paper, we apply AOT data assimilation to the Richards equation, a nonlinear degenerate system that models fluid flow in unsaturated soil. We examine convergence of the AOT approach computationally, explicitly in the case of unsaturated flow, assuming unknown initial data and sparse in time and space observations of soil water content, and show matching convergence‐in‐time and superior CPU (central processing unit) efficiency of this approach as compared to the ensemble Kalman filter algorithm. We further use the AOT algorithm to study the number, type, frequency, and placement of observations for optimal network design, and make recommendations accordingly.

Abstract In soils contaminated with petroleum hydrocarbons (PHCs), water table fluctuations (WTFs) affect the kinetics of PHC biodegradation and the generation and transport of carbon dioxide (CO 2 ) and methane (CH 4 ). In this study, a 300‐day‐long column experiment was conducted to simulate the effects of WTFs on the aerobic and anaerobic PHC biodegradation pathways. Eight columns were each filled with 45 cm of PHC‐contaminated soil cores. Four columns simulating WTFs were subjected to cycles of drainage and imbibition. The remaining columns remained fully saturated, simulating a static water table. Except for the controls, the columns received injections of ethanol or ethanol plus naphthalene after 111 days of pre‐equilibration. Over the duration of the experiment, soil moisture, surface CO 2 and CH 4 effluxes, dissolved concentrations and δ 13 C compositions of CO 2 and CH 4 , dissolved naphthalene concentrations, and geochemical parameters were monitored. A reactive transport model representing 13 biogeochemical reaction pathways was verified against the acquired data. The experimental and modeling results confirmed that the prevailing pathway generating CH 4 shifted from hydrogen‐based to acetate‐based methanogenesis in the ethanol and ethanol‐plus‐naphthalene‐spiked columns, while CH 4 oxidation played a key role in controlling the CH 4 efflux during the drainage periods. The WTF columns exhibited significantly faster naphthalene attenuation, while the cumulative CO 2 and CH 4 effluxes were about twice as high. These observations were attributed to the periodic incursion of air during the WTFs, which increased the porewater‐air interface area for gas transfer while also accelerating the aerobic degradation of soil organic matter and naphthalene.

Abstract Soil moisture profile sensors (SMPSs) are designed to measure the volumetric water content of soil at multiple depths simultaneously. Their vertical installation makes them particularly suitable for frequent relocation, as required for the use during cultivation of field crops. In addition to established sensor manufacturers who have been offering SMPS for some time, an increasing number of companies are now introducing new models specifically designed for agricultural applications. Beyond practical and economic factors—such as ease of installation, durability, and costs—the measurement accuracy of SMPS is a key performance criterion, yet one that end users cannot easily assess themselves. Validating sensor accuracy requires reference methods that are impractical for most users, and soil heterogeneity further complicates data interpretation. In this study, we conducted a sandbox experiment to compare the measurement accuracy of 10 SMPSs under controlled soil moisture conditions. The SMPS readings were evaluated against time‐domain reflectometry reference measurements using three SMPS devices of each type. We found that most SMPSs performed with reasonable accuracy under very dry and very wet conditions. However, in the intermediate soil moisture range, strong variations were observed with respect to slope, offset, and spread of measurements for some SMPSs, which resulted in reduced accuracy. The high intra‐sensor variability (: 0.7–3.1 vol. %) and varying measurement accuracy (: 3.1–8.3 vol. %) highlight the importance of selecting a suitable SMPS, especially for scientific applications and precision agriculture, where accurate field data are critical for informed management decisions.

Abstract Mechanical methods implemented to solve drainage issues caused by compacted layers in cultivated organic soils have produced only short‐term improvements. Limited research has been conducted on biological methods, particularly the use of plants as biological subsoilers. Consequently, this study aimed to assess the impact of willow ( Salix viminalis L.) rotation of varying duration on improving saturated hydraulic conductivity ( K sat ) under two soil degradation conditions and to determine the duration of these effects following the removal of willow plants. K sat was measured at various sites with willow rotations spanning 3–6 years to assess the rate of improvement. To determine the duration of the effects after the removal of willow plants, measurements of water table position and key soil physical properties ( K sat , bulk density, air‐filled porosity, and total porosity) were conducted for 5 and 3 years, respectively, in treatments where willow was removed after a 2‐year rotation. In addition, water table drawdown was predicted over a 24‐h period using the HYDRUS‐2D model and the Hooghoudt equation. Highly degraded soils with a shallow peat layer showed a significant improvement in K sat over a longer period of willow rotation compared to cultivated soil with a thicker peat layer. Moreover, K sat and air‐filled porosity remained significantly higher even 3 years after willow removal following a 2‐year rotation, with drainage rates exceeding 30 cm day −1 . This positive effect on drainage persisted for at least 4 years following willow destruction. Hence, short‐ to medium‐term willow rotation shows potential for improving soil physical properties and drainage in cultivated organic soils with a compact layer.