AbstractSoil moisture is a key factor that influences various aspects of ecosystem functioning. Measuring soil moisture without installing any objects in the soil is desirable because it allows for accurate characterizations of soil moisture while minimizing impacts on soil structure and ecology. In this study, we explored the potential of leaky Rayleigh waves as a proxy to contactlessly estimate soil moisture. We developed an ultrasonic system containing a transducer, receivers, and acoustic barrier. The specimens of sand, silt, and clay were utilized. Experiments were conducted over 4 months. We used a widely used soil‐embedded moisture sensor to compare and develop relationships between leaky Rayleigh waves and soil moisture. Our results showed that as soil moisture increased, the velocity and amplitude of leaky Rayleigh waves decreased because water molecules attracted to the soils led to their attenuation. However, their magnitudes were not considerable except for very dry soils. To overcome these limited relations to estimate soil moisture from leaky Rayleigh waves, we constructed authentic images based on the observed leaky Rayleigh waves and used them as inputs for a fully convolutional network. We found that the combination of the ultrasonic system and deep learning approach developed in this study were suitable for estimating soil moisture without soil disturbances (RMSE = 0.01 m3 m−3). This study suggests that leaky Rayleigh waves have the potential to serve as a reliable proxy for determining soil moisture without the need for physical contact.

AbstractMiscanthus is a productive perennial grass that is suitable as a bioenergy crop in “marginal” lands (e.g., eroded soils) with low water holding capacity. However, little is known about the impact of miscanthus residues on vapor transport and soil water budgets. Laboratory experiments were conducted to measure the vapor conductance through miscanthus residues and its effect on soil water evaporation. The ranges for the length, width, and thickness of residue elements were 0.5–9.0, 0.1–0.5, and 0.1–0.5 cm, respectively. Average residue areal, bulk, and skeletal densities were 0.88 kg m−2, 24 kg m−3, and 1006 kg m−3, respectively, giving a porosity of 0.98 m3 m−3. A power function described the decrease in conductance with increasing residue load. The corresponding conductance for a residue load of 0.88 kg m−2 was 1.6 mm s−1. During the first days of a 60‐day drying experiment, cumulative evaporation showed logarithmic decay with increasing residue load. Conversely, cumulative evaporation during the last days of the study showed little difference between treatments. Measurements indicated that there is a “critical” residue load (∼1.0 kg m−2) beyond which evaporation no longer decreases appreciably when the soil is under the stage 1 evaporation regime. Results suggest that soil water conservation in marginal lands may be accomplished by maintaining moderate amounts of bioenergy grass residue covering the soil. Determining “critical” loads for different residue types is a knowledge gap that merits further research.

AbstractEvaporation from bare soil is an important hydrological process and part of the water and energy balance of terrestrial systems. Modeling bare‐soil evaporation is challenging, mainly due to nonlinear couplings among liquid water, water vapor, and heat fluxes. Model concepts of varying complexity have been proposed for predicting evaporative water and energy fluxes. Our aim was to test a standard model of coupled water, vapor, and heat flow in the soil using data from laboratory evaporation experiments under different boundary conditions. We conducted evaporation experiments with a sand and a silt loam soil and with three different atmospheric boundary conditions: (i) wind, (ii) wind and short‐wave radiation, and (iii) wind and intermittent short‐wave radiation. The packed soil columns were closed at the bottom (no water flux) and instrumented with temperature sensors, tensiometers, and relative humidity probes. We simulated the evaporation experiments with a coupled water, vapor, and heat flow model, which solves the surface energy balance and predicts the evaporation rate. The evaporation dynamics were predicted very well, in particular the onset of stage‐two evaporation and the evaporation rates during the stage. A continuous slow decrease of the measured evaporation rate during stage‐one could not be described with a constant aerodynamic resistance. Adding established soil resistance parametrizations to the model significantly degraded model performance. The use of a boundary‐layer resistance, which takes into account the effect of point sources of moisture, improved the prediction of evaporation rates for the sandy soil, but not for the silt loam.

AbstractDetailed soil information is increasingly sought after for watershed‐scale hydrological modeling to better understand the soil–water interactions at a landscape level. In South Africa, 8% of the surface area is responsible for 50% of the mean annual runoff. Thus, understanding the soil–water dynamics in these catchments remains imperative to future water resource management. In this study, the value of hydropedological information is tested by comparing a detailed hydropedological map based on infield soil information to the best readily available soil information at five different catchment sizes (48, 56, 174, 674, and 2421 km2) using the soil and water assessment tool (SWAT)+ model in the Sabie catchment, South Africa. The aim was to determine the value of hydropedological information at different scales as well as illustrate the value of hydropedology as soft data to improve hydrological process representation. Improved hydropedological information significantly improved long‐term streamflow simulations at all catchment sizes, except for the largest catchment (2421 km2). It is assumed that the resulting improved streamflow simulations are a direct result of the improved hydrological process representation achieved by the hydropedological information. Here, we argue that hydropedological information should form an important soft data tool to better understand and simulate different hydrological processes.

AbstractWater retention and hydraulic conductivity characteristics are key input data in studies on soil water dynamics in the vadose zone. The most well‐known analytical functions to describe these characteristics are those given by Mualem and van Genuchten, where van Genuchten showed that both can be described by a limited set of shared parameters. Analytically, there are no restrictions on the range of pressure heads for which these characteristics can be used. Experience, however, has shown that for certain sets of parameters, the hydraulic conductivity cannot be computed accurately at low‐pressure heads. This is due to the accuracy of (double precision) floating point operations in computer code. It is shown that for low‐pressure heads, the Mualem function approaches a power function. An adapted version of the Mualem–van Genuchten (MvG) expression for the hydraulic conductivity is proposed: between saturation and a soil‐dependent critical pressure head, the classical Mualem expression is valid and below this critical pressure head a power function is used. The power function is defined such that it matches the Mualem value at the critical pressure head. No accuracy problems will occur when using the power function until the result approaches the smallest possible (double precision) floating point value that significantly differs from zero.

AbstractThe Reynolds Creek Experimental Watershed (RCEW) and Critical Zone Observatory (CZO), located south of the western Snake River Plain in the Intermountain West of the United States, is the site of over 60 years of research aimed at understanding integrated earth processes in a semi‐arid climate to aid sustainable use of environmental resources. Meteoric water lines (MWLs) are used to interpret hydrologic processes, though equilibrium and nonequilibrium processes affect the linear function and can reveal seasonal and climatological effects, necessitating the development of local meteoric water lines (LMWLs). At RCEW‐CZO, an RCEW LMWL was developed using non‐volume‐weighted, orthogonal regression with assumed error in both predictor and response variables from several years of precipitation (2015, 2017, 2019, 2020, and 2021) primarily at three different elevations (1203, 1585, and 2043 m). As most precipitation is evaporated or intercepted by vegetation in the driest months, an RCEW LMWL for groundwater recharge (RCEW LMWL‐GWR) was also developed using precipitation from the wettest months (November through April). The RCEW LMWL (δ2H = 7.41 × δ18O – 3.09) is different from the RCEW LMWL‐GWR (δ2H = 8.21 × δ18O + 9.95) and compares favorably to other LMWLs developed for the region and climate. Comparative surface, spring, and subsurface water datasets within the RCEW‐CZO are more similar to precipitation during the wettest months than dry months, illustrating that some semi‐arid hydrologic systems may most appropriately be compared to MWLs developed from precipitation only from the wettest season.

AbstractOxygen (O2) availability in soils is vital for plant growth and productivity. The transport and consumption of O2 in the root zone is closely linked to soil moisture content, the spatial distribution of roots, as well as structure and heterogeneity of the surrounding soil. In this study, we measure three‐dimensional root system architecture and the spatiotemporal dynamics of soil moisture (θ) and O2 concentrations in the root zone of maize (Zea mays) via non‐invasive imaging, and then construct and parameterize a reactive transport model based on the experimental data. The combination of three non‐invasive imaging methods allowed for a direct comparison of simulation results with observations at high spatial and temporal resolution. In three different modeling scenarios, we investigated how the results obtained for different levels of conceptual complexity in the model were able to match measured θ and O2 concentration patterns. We found that the modeling scenario that considers heterogeneous soil structure and spatial variability of hydraulic parameters (permeability, porosity, and van Genuchten α and n), better reproduced the measured θ and O2 patterns relative to a simple model with a homogenous soil domain. The results from our combined imaging and modeling analysis reveal that experimental O2 and water dynamics can be reproduced quantitatively in a reactive transport model, and that O2 and water dynamics are best characterized when conditions unique to the specific system beyond the distribution of roots, such as soil structure and its effect on water saturation and macroscopic gas transport pathways, are considered.

AbstractThis study investigates the contamination potential of herbicides to groundwater with the help of numerical modeling (HYDRUS‐1D) and stable carbon isotopes for characterizing biodegradation. Four herbicides, metolachlor, terbuthylazine, prosulfuron, and nicosulfuron, were applied over a period of 4.5 years on two lysimeters located in Wielenbach, Germany, and monitored by lysimeter drainage. These lysimeters contained soil cores dominated by sandy gravel (Ly1) and clayey sandy silt (Ly2) and were both cropped with maize (Zea mays). In the preceding study, we characterized flow within the lysimeters by using stable water isotopes and unsaturated flow models. Building up on these findings, models were extended for describing reactive transport of the herbicides and investigating process contributions. At the end of the experiment, 0.9%–15.9% of the applied herbicides (up to 20.9% if including metabolites) were recovered by lysimeter drainage. Metabolite formation and accumulation was observed, and biodegradation was also indicated by small changes in carbon isotope signals (δ13C) between applied and leached herbicides. Model setups could describe the dynamics of herbicide concentrations in lysimeter drainage well. Concentration peaks in drainage were partly also linked with strong precipitation events, indicating preferential flow influence. The soil core with the coarser texture (Ly1) showed less herbicide leaching than the finer texture (Ly2), which can be explained by a larger mobile phase in Ly1. Overall, our approaches and findings contribute to the understanding of multi‐process herbicide transport in the vadose zone and leaching potentials to groundwater, where δ13C can provide valuable hints for microbial degradation.

AbstractUnderstanding transport and fate processes in the subsurface is of fundamental importance to identify the leaching potentials of herbicides or other compounds to groundwater resources. HYDRUS‐1D was used to simulate water flow and solute transport in arable land lysimeters. Simulations were compared to observed drainage rates and stable water isotopes (δ18O) in the drainage. Four different model setups were investigated and statistically evaluated for their model performance to identify dominant processes for water flow characterization in the vadose zone under similar cultivation management and climatic conditions. The studied lysimeters contain soil cores dominated by sandy gravel (Ly1) and clayey sandy silt (Ly2), both cropped with maize located in Wielenbach, Germany. First, a single‐porosity setup was chosen. For Ly1, modeling results were satisfactory, but for Ly2, the damping observed in the isotope signature of the drainage could not be fully covered. By considering immobile water with a dual‐porosity setup for Ly2, model performance improved. This could be due to a higher fraction of fine pores in Ly2 available for water storage, leading to mixing processes of isotopically enriched summer precipitation and lighter winter water. Accounting for isotopic evaporation fractionation processes in both model setups did not lead to improved model performance. Consequentially, the difference in soil hydraulic properties between the two lysimeters seems to impact water flow processes. Knowledge of such differences is crucial to prevent contamination and mitigate potential risks to soil and groundwater.

AbstractPlant water uptake and plant and soil water status are important for the soil water balance and plant growth. They depend on atmospheric water demand and the accessibility of soil water to plant roots, which is in turn related to the hydraulic properties of the root system and the soil around root segments. We present a simulation model that describes water flow in the soil–plant system mechanistically considering both root and soil hydraulic properties. We developed an approach to upscale three‐dimensional (3D) flow in the soil toward root segments of a 3D root architecture to a model that considers one‐dimensional flow between horizontal soil layers and radial flow to root segments in that layer. The upscaled model couples upscaled linear flow equations in the root system with an analytical solution of the nonlinear radial flow equation between the soil and roots. The upscaled model avoids simplifying assumptions about root hydraulic properties and water potential drops near roots made in, respectively, soil‐ and root‐centered models. Xylem water potentials and soil–root interface potentials are explicitly simulated and show, respectively, large variations with depth and large deviations from bulk soil water potentials under dry soil conditions. Accounting for hydraulic gradients in the soil around root segments led to an earlier but slower reduction of transpiration during a drought period and a better plant water status with higher nighttime plant water potentials.