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Water uptake by plant roots is an important component of the soil water balance. Predicting to what extent potential transpiration from the canopy, that is, transpiration demand, can be met by supply of water from the soil through the root system is crucial to simulate the actual transpiration and assess vegetation water stress. In models that simulate the dynamics of vertical soil water content profiles as a function of water fluxes and soil water potential gradients, the root water uptake (RWU) distribution is represented by macroscopic sink terms. We present RWU functions that calculate sink terms based on a mechanistic model of water flow in the soil–root system. Based on soil–root hydraulics, we define α‐supply functions representing the maximal uptake by the root system from a certain soil depth when the root collar water potential equals the wilting point, ω‐supply factors representing the maximal supply from the entire root system, and a critical ωc factor representing the potential transpiration demand. These functions and factors are subsequently used to calculate RWU distributions directly from potential transpiration or demand and the soil water potentials. Unlike currently used approaches, which define α‐stress functions and ω factors representing ratios of actual uptake to uptake demand, the supply‐based formulations can be derived directly from soil and root hydraulic properties and can represent processes like root hydraulic redistribution and hydraulic lift. Core Ideas: Functions to calculate root water uptake (RWU) from different soil depths were derived.RWU is calculated directly using soil water potentials and the potential transpiration rate.The functions are derived using soil and root hydraulic properties.Phenomena like RWU compensation, hydraulic redistribution, and hydraulic lift are reproduced.Similarities and differences with currently used uptake functions are discussed. [ABSTRACT FROM AUTHOR]

The two‐term infiltration equation I(t)=St+At$I(t) = S\sqrt {t} + A t$ is commonly used to determine the sorptivity, S$S$(LT−1/2)$({\text{LT}}^{-1/2})$, and product, A=cKs$A = c K_{\text{s}}$(LT−1)$({\text{LT}}^{-1})$, of the dimensionless multiple c$c$ and saturated soil hydraulic conductivity Ks$K_{\text{s}}$(LT−1)$({\text{LT}}^{-1})$ from cumulative vertical infiltration measurements I∼1,...,I∼n$\tilde{I}_{1},\ldots,\tilde{I}_{n}$ (L) at times t1,...,tn$t_{1},\ldots,t_{n}$ (T). This reduced form of the quasi‐analytical power series solution of Richardson's equation of Philip enjoys a solid physical underpinning but at the expense of a limited time validity. Using simulated infiltration data, Jaiswal et al. have shown this time validity to equal about 2.5 cm of cumulative infiltration. The goals of this work are twofold. First, we investigate the extent to which cumulative infiltration measurements larger than 2.5 cm bias the estimates of S$S$ and Ks$K_{\text{s}}$. Second, we investigate the impact of epistemic errors on the inferred time validities and parameters. Partial infiltration curves up to 2.5 cm of cumulative vertical infiltration improve substantially the agreement between actual and least squares estimates of S$S$ and Ks$K_{\text{s}}$. But this only holds if the data generating infiltration process follows Richardson's equation and experimental conditions satisfy assumptions of soil homogeneity and a uniform initial water content. Otherwise, autocorrelated cumulative infiltration residuals will bias the least squares estimates of S$S$ and Ks$K_{\text{s}}$. Our findings reiterate and reinvigorate earlier conclusions of Haverkamp et al. and show that epistemic errors deteriorate the physical significance of the coefficients of infiltration functions. As a result, the parameters of infiltration functions cannot simply be used in storm water and vadose zone flow models to forecast runoff and recharge at field and landscape scales unless these predictions are accompanied by realistic uncertainty bounds. We conclude that the time validity of Philip's two‐term equation is an elusive theoretical quantity with arbitrary physical meaning. Core Ideas: Structural model errors of infiltration functions bias the values of S$S$ and Ks$K_s$.Least squares estimates of Ks$K_s$ compare poorly to measured values.Epistemic uncertainty turns coefficients of infiltration functions into fitting parameters.The time validity of Philip's two‐term equation is an elusive theoretical quantity.Treatment of epistemic errors enhances usefulness of plot‐scale infiltration experiments for large‐scale prediction. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]

Plant 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. Core Ideas: Root water uptake depends on root and soil hydraulic properties.Water uptake at root element scale was upscaled to the root system scale.The upscaled model can be implemented in one‐dimensional soil water flow models.Low conductance of dry soil prevents low nighttime plant water potentials. [ABSTRACT FROM AUTHOR]

Renewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets. Plain Language Summary: There are still approximately 580 million people in Africa without reliable electricity supply. Renewable energy is broadly accepted as an important solution for Africa to fill the power supply gap and to mitigate climate change. Renewable energy potential (REP) information is thereby imperative for the expansion of renewable energy power planning. With conventional REP estimates, challenges are often linked to the meteorological input data, due to either the relatively coarse spatial resolution, data gaps in space and time, or data quality in general. In this study, we implemented and ran the atmospheric model ICOsahedral Nonhydrostatic (ICON) from the German Weather Service and partners at 3 km high‐resolution. The renewable energy variables wind speed and solar irradiance from these simulations are evaluated against an extensive in situ observations data set, as well as satellite, and other composite data products. In a comparison with more than 200 stations from three different in situ observation networks, it can be shown that ICON can reproduce REP‐related variables with a level of sophistication that the data is likely to offer added value over conventional inputs to REP assessments. The study is an example on how numerical models can fill in gaps in data‐scarce regions to produce useable information. Key Points: A new convection‐permitting regional ICOsahedral Nonhydrostatic (ICON) model setup over southern Africa is presented and evaluatedThe spatially and temporally highly resolved ICON wind and solar fields are inputs for improved renewable energy potential estimatesICON outputs agree well with hourly in situ observations and satellite data; wind speeds are slightly overestimated [ABSTRACT FROM AUTHOR]

Data assimilation plays a dual role in advancing the "scientific" understanding and serving as an "engineering tool" for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies. Plain Language Summary: Land Data Assimilation (LDA) integrates numerical models with observation data to enhance predictions of key variables related to land surface processes, including soil moisture, snow, evapotranspiration, and groundwater. Consequently, LDA effectively tackles two of the fundamental scientific challenges in the Earth system: how to enhance the usability of exponentially increasing land observations and how to improve the predictive capability and accuracy of land models. Significant advancements have been made in the theory, methods, and applications of LDA since the 21st century. Breakthroughs have been attained in characterizing uncertainties, estimating multiple variables and parameters simultaneously, and incorporating big data analytics. LDA systems at global, regional, and catchment‐scales, along with widely used software platforms, have been developed. However, future challenges facing LDA comprise improving meteorological forcing data, creating long‐term land reanalysis, devising operational applications, and broadening its range to cover the critical zone. In the era of big data, LDA will evolve further by assimilating big Earth data and incorporating machine learning to develop digital twins from pure geophysical systems to coupled natural and human systems. Key Points: Land data assimilation (LDA) advances scientific understanding and serves as an engineering tool for Earth system sciencesLDA reflects the trend of harmonizing theory and data in the era of big data and artificial intelligenceFuture LDA research should expand the applications from pure geophysical systems to coupled natural and human systems [ABSTRACT FROM AUTHOR]

Non‐invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in‐situ time‐lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the "trend‐corrected spatial permittivity deviation of vegetated field," allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non‐root‐related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend‐corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross‐correlated with the root volume fraction, resulting in a coefficient of determination (R2) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability. Core Ideas: Analying spatial and temporal belowground GPR signal variability and root images for maize crop roots.Root images and GPR data show differences for different treatments.Linking of root volume fraction and GPR permittivity variability. [ABSTRACT FROM AUTHOR]

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Soil water status, which refers to the wetness or dryness of soils, is crucial for the productivity of agroecosystems, as it determines nutrient cycling and uptake physically via transport, biologically via the moisture‐dependent activity of soil flora, fauna, and plants, and chemically via specific hydrolyses and redox reactions. Here, we focus on the dynamics of nitrogen (N), phosphorus (P), and sulfur (S) and review how soil water is coupled to the cycling of these elements and related stoichiometric controls across different scales within agroecosystems. These scales span processes at the molecular level, where nutrients and water are consumed, to processes in the soil pore system, within a soil profile and across the landscape. We highlight that with increasing mobility of the nutrients in water, water‐based nutrient flux may alleviate or even exacerbate imbalances in nutrient supply within soils, for example, by transport of mobile nutrients towards previously depleted microsites (alleviating imbalances), or by selective loss of mobile nutrients from microsites (increasing imbalances). These imbalances can be modulated by biological activity, especially by fungal hyphae and roots, which contribute to nutrient redistribution within soils, and which are themselves dependent on specific, optimal water availability. At larger scales, such small‐scale effects converge with nutrient inputs from atmospheric (wet deposition) or nonlocal sources and with nutrient losses from the soil system towards aquifers. Hence, water acts as a major control in nutrient cycling across scales in agroecosystems and may either exacerbate or remove spatial disparities in the availability of the individual nutrients (N, P, S) required for biological activity. [ABSTRACT FROM AUTHOR]

Current databases of soil hydraulic properties (SHPs) have typically been used to develop pedotransfer functions (PTFs) to estimate water retention [θ(h)] assuming a unimodal pore‐size distribution. However, natural soils often show the presence of bimodal to multimodal pore‐size distributions. Here, we used three widely spread databases for PTF development: UNsaturated SOil hydraulic DAtabase (UNSODA) 2.0, Vereecken, and European hydropedological data inventory (EU‐HYDI), to analyze the presence of structural effects in both θ(h) and hydraulic conductivity [K(h)]. Only undisturbed samples were included in the analysis that contained enough datapoints for both θ(h) and K(h) properties, especially in the wet range. One‐hundred ninety‐two samples were suitable for our analysis, which is only 1% of the total samples in the three databases. Results showed that 65% of the samples exhibited a bimodal pore‐size distribution, and bimodality was not limited to fine‐textured but also coarser‐textured soils. The Mualem–van Genuchten (MvG) expression for both unimodal and bimodal soils was not able to predict the observed unsaturated K. Only a joint fitting of measured θ(h) and K(h) functions provided parameter estimates that were able to describe unsaturated K for uni‐ and bimodal soils. In addition, we observed a negative relationship between α and n in the case of low sand content (<52%) for both unimodal and bimodal matrix domain properties, contradicting the classical notion. The ratio of α for the macropore and matrix domain was positively correlated with the fraction of macropores and sand content. We anticipate that the results will contribute to deriving PTF for structured soils and avoid unrealistic combinations of MvG parameters. Core Ideas: Only 1% of the samples in the three databases was suitable for analyzing the presence of bimodality.Bimodality was seen in 65% of the samples, both in fine‐textured and coarser‐textured soils.Joint fitting of θ(h) and K(h) data outperforms the fitting of only θ(h) and using MvG function to estimate K(h).Contradicting the classical notion, a negative relationship between the shape parameters α and n was observed for both uni‐ and bimodal properties.α for macropore and matrix domains was positively correlated with the fraction of macropores and sand content. [ABSTRACT FROM AUTHOR]

Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux‐related performance of a set of crop models. The aim was to predict weighing lysimeter‐based crop (i.e., agronomic) and water‐related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014–2018) were from the Dedelow (Dd), Bad Lauchstädt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop‐ and soil‐related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi‐model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site‐specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil‐related data (i.e., water fluxes and system states) when simulating soil–crop–climate interrelations in changing climatic conditions. Core Ideas: We demonstrate the use of high precision weighable lysimeter for full model calibration and validation.Lysimeter data from translocated soils represent effects of changing climatic conditions.We compare calibration with blind forward simulations (fixed soil and calibrated crop parameter).We compare individual crop model predictions with multi‐model mean.We test the predictive ability of crop models and multi‐model mean. [ABSTRACT FROM AUTHOR]

Integrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Simulations with these models are affected by uncertainties of model parameters, initial and boundary conditions, atmospheric forcings and the biophysical processes. Data assimilation (DA) can quantify and reduce the uncertainty. This has been tested intensively for single compartment models, but far less for integrated models with multiple compartments. We constructed a virtual reality (VR) with a coupled land surface‐subsurface model under the Terrestrial Systems Modeling Platform, which mimics the Neckar catchment in southern Germany. Soil moisture and groundwater level (GWL) data extracted from the simulated VR are used as measurements to be assimilated with state‐only/state‐hydraulic parameter estimation. Soil moisture DA improves soil moisture characterization in the vertical profile and the neighboring grid cells, with a 40 ∼ 60% reduction of root mean square error (RMSE) over the observation points. In spite of a small ensemble size of 64 members, assimilating soil moisture data improved saturated hydraulic conductivity estimation around the measurement locations. The characterization of evapotranspiration and river discharge only show limited improvements (1% at observation points and less than 0.1% in RMSE at 3 selected gauge locations respectively). GWL DA not only improves the GWL characterization (76 ∼ 88% RMSE reduction at observation locations) but also soil moisture for some cases. In addition, a clear improvement in GWL characterization is observed up to 8 km from the observations, and updating the model states of the saturated zone only instead of the complete domain gives better performance. Key Points: Groundwater level (GWL) and soil moisture data assimilation (DA) improve characterization of subsurface statesGWL DA improves GWL characterization up to 8 km from observation locationsEvapotranspiration and discharge hardly better estimated, despite better estimates of soil moisture and GWL [ABSTRACT FROM AUTHOR]

In his seminal paper on the solution of the infiltration equation, Philip (1969), https://doi.org/10.1016/b978-1-4831-9936-8.50010-6 proposed a gravity time, tgrav, to estimate practical convergence time and the time domain validity of his infinite time series expansion, TSE, for describing the transient state. The parameter tgrav refers to a point in time where infiltration is dominated equally by capillarity and gravity as derived from the first two (dominant) terms of the TSE. Evidence suggests that applicability of the truncated two‐term equation of Philip has a time limit requiring higher‐order TSE terms to better describe the infiltration process for times exceeding that limit. Since the conceptual definition of tgrav is valid regardless of the infiltration model used, we opted to reformulate tgrav using the analytic implicit model proposed by Parlange et al. (1982), https://doi.org/10.1097/00010694-198206000-00001 valid for all times and related TSE. Our derived gravity times ensure a given accuracy of the approximations describing transient states, while also providing insight about the times needed to reach steady state. In addition to the roles of soil sorptivity (S) and the saturated (Ks) and initial (Ki) hydraulic conductivities, we explored the effects of a soil specific shape parameter β, involved in Parlange's model and related to the type of soil, on the behavior of tgrav. We show that the reformulated tgrav (notably tgrav=F(β)S2/Ks−Ki2, ${t}_{\text{grav}}=\,F(\beta){S}^{2}/{\left({K}_{s}-{K}_{i}\right)}^{2},$ where F(β) is a β‐dependent function) is about three times larger than the classical tgrav given by tgrav,Philip=S2/Ks−Ki2 $\,{t}_{\text{grav},\text{Philip}}={S}^{2}/{\left({K}_{s}-{K}_{i}\right)}^{2}$. The differences between the classical tgrav,Philip and the reformulated tgrav increase for fine‐textured soils, attributed to the time needed to attain steady‐state infiltration and thus i + nfiltration for inferring soil hydraulic properties. Results show that the proposed tgrav is a better indicator of time domain validity than tgrav,Philip. For the attainment of steady‐state infiltration, the reformulated tgrav is suitable for coarse‐textured soils. Still neither the reformulated tgrav nor the classical tgrav,Philip are suitable for fine‐textured soils for which tgrav is too conservative and tgrav,Philip too short. Using tgrav will improve predictions of the soil hydraulic parameters (particularly Ks) from infiltration data compared to tgrav,Philip. Key Points: A new formulation for infiltration characteristic time, tgrav, is providedThe reformulated tgrav seems to be a better criterion for convergence time of Philip's truncated infiltration equationsThe usage of reformulated tgrav improves predictions of soil hydraulic parameters [ABSTRACT FROM AUTHOR]

Soil bulk density (ρb) is an important indicator of soil quality, productivity, compaction and porosity. Despite its importance, ρb is often omitted from global datasets due to the costs of making many direct ρb measurements and the difficulty of direct measurement on rocky, sandy, very dry, or very wet soils. Pedotransfer functions (PTFs) are deployed to address these limitations. Using readily available soil properties, PTFs employ estimator equations to fit existing datasets to estimate properties like ρb. However, PTF performance often declines when applied to soils outside those in the training dataset. Potentially, recalibrating existing PTFs using new observations would leverage the power of large datasets used in the original PTF derivation, while updating information based on new soil observations. Here, we evaluate such a recalibration approach for ρb estimation, benchmarking its performance against two alternatives: the original, uncalibrated PTFs, and novel, local PTFs derived solely from new soil observations. Using a ρb dataset of N = 360 total observations obtained in West Azerbaijan, Iran, we varied the local dataset size (with N = 15, 30, 60, and 360) and recalibrated four existing PTFs with these data. Local PTFs were generated based on stepwise multiple linear regression for the same datasets. The same PTFs (original, recalibrated, and local) were also applied to the study area, and the resulting ρb estimates were compared with the global SoilGrids dataset. Recalibration of PTFs reduced errors relative to the original uncalibrated PTFs; for instance, the NSE increased from −22.07 to 0.30 (uncalibrated) to 0.20–0.41 (recalibrated), and RMSE decreased from 0.12 to 0.60 Mg m−3 (uncalibrated) to 0.10–0.13 Mg m−3 (recalibrated). The recalibrated PTFs performance was comparable to or better than local PTFs applied to the same data. Recalibration of existing PTFs with local/regional uses provides a viable alternative to the use of global datasets or the development of local PTFs in data‐scarce regions. Highlights: Existing global PTFs were calibrated and tested using a small dataset for local utilisation.Several new local PTFs were also developed using the same datasets.Recalibration of existing global PTFs is comparable to or more accurate than developing new PTFs. [ABSTRACT FROM AUTHOR]

Fast and accurate large‐scale localization and quantification of harmfully compacted soils in recultivated post‐mining landscapes are of particular importance for mining companies and the following farmers. The use of heavy machinery during recultivation imposes soil stress and can cause irreversible subsoil compaction limiting crop growth in the long term. To overcome or guide classical point‐scale methods to determine compaction, fast methods covering large areas are required. In our study, a recultivated field of the Garzweiler mine in North Rhine‐Westphalia, Germany, with known variability in crop performance was intensively studied using non‐invasive electromagnetic induction (EMI) and electrode‐based electrical resistivity tomography (ERT). Additionally, soil bulk density, volumetric soil water content and soil textures were analysed along two transects covering different compaction levels. The results showed that the measured EMI apparent electrical conductivity (ECa) along the transects was highly correlated (R2 >.7 for different dates and depths below 0.3 m) to subsoil bulk density. Finally, the correlations established along the transects were used to predict harmful subsoil compaction within the field, whereby a spatial probabilistic map of zones of harmful compaction was developed. In general, the results revealed the feasibility of using the EMI derived ECa to predict harmful compaction. They can be the basis for quick monitoring of the recultivation process and implementation of necessary melioration to return a well‐structured soil with good water and nutrient accessibility, and rooting depths for increased crop yields to the farmers. [ABSTRACT FROM AUTHOR]

Many different equations have been proposed to describe quantitatively one‐dimensional soil water infiltration. The unknown coefficients of these equations characterize soil hydraulic properties and may be estimated from a n record, {t∼i,I∼i}i=1n$\{ {\tilde t_i},{\tilde I_i}\} _{i = 1}^n$, of cumulative infiltration measurements using curve fitting techniques. The two‐term infiltration equation, I(t)=St+cKst$I(t) = S\sqrt t + c{K_{\rm{s}}}t$, of Philip has been widely used to describe measured infiltration data. This function enjoys a solid mathematical–physical underpinning and admits a closed‐form solution for the soil sorptivity, S [L T−1/2], and multiple, c [−], of the saturated hydraulic conductivity, Ks [L T−1]. However, Philip's two‐term equation has a limited time validity, tvalid [T], and thus cumulative infiltration data, I∼(t∼)$\tilde I(\tilde t)$, beyond t=tvalid$t = {t_{{\rm{valid}}}}$ will corrupt the estimates of S and Ks. This paper introduces a novel method for estimating S, c, Ks, and tvalid of Philip's two‐term infiltration equation. This method, coined parasite inversion, use as vehicle Parlange's three‐parameter infiltration equation. As prerequisite to our method, we present as secondary contribution an exact, robust and efficient numerical solution of Parlange's infiltration equation. This solution admits Bayesian parameter estimation with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and yields as byproduct the marginal distribution of Parlange's β parameter. We evaluate our method for 12 USDA soil types using synthetic infiltration data simulated with HYDRUS‐1D. An excellent match is observed between the inferred values of S and Ks and their "true" values known beforehand. Furthermore, our estimates of c and tvalid correlate well with soil texture, corroborate linearity of the c(β)$c({{\beta}})$ relationship for 0≤t≤tvalid$0 \le t \le {t_{{\rm{valid}}}}$, and fall within reported ranges. A cumulative vertical infiltration of about 2.5 cm may serve as guideline for the time‐validity of Philip's two‐term infiltration equation. Core Ideas: We describe a method that can determine the time validity of infiltration equations.This method infers as well the hydraulic properties of unsaturated soils.We present a robust and efficient numerical solution of Parlange's three‐parameter infiltration equation.Posterior distribution determines how well parameters are defined by calibration to data.Two and a half centimeters of cumulative infiltration is a good proxy for time validity Philip's two‐term equation. [ABSTRACT FROM AUTHOR]

In the last decade, time‐domain crosshole ground‐penetrating radar full‐waveform inversion has been applied to several different test sites and has improved the resolution and reconstruction of subsurface properties. The full‐waveform inversion requires several diligent executed pre‐processing steps to guarantee a successful inversion and to minimize the risk of being trapped in a local minimum. Thereby, one important aspect is the starting models of the full‐waveform inversion. Generally, adequate starting models need to fulfil the half‐wavelength criterion, which means that the modelled data based on the starting models need to be within half of the wavelength of the measured data in the entire investigation area. Ray‐based approaches can provide such starting models, but in the presence of high contrast layers, such results do not always fulfil this criterion and need to be improved and updated. Therefore, precise and detailed data processing and a good understanding of experimental ground‐penetrating radar data are necessary to avoid erroneous full‐waveform inversion results. Here, we introduce a new approach, which improves the starting model problem and is able to enhance the reconstruction of the subsurface medium properties. The new approach tames the non‐linearity issue caused by high contrast complex media, by applying bandpass filters with different passband ranges during the inversion to the modelled and measured ground‐penetrating radar data. Thereby, these bandpass filters are considered for a certain number of iterations and are progressively expanded to the selected maximum frequency bandwidth. The resulting permittivity full‐waveform inversion model is applied to update the effective source wavelet and is used as an updated starting model in the full‐waveform inversion with the full bandwidth data. This full‐waveform inversion is able to enhance the reconstruction of the permittivity and electrical conductivity results in contrast to the standard full‐waveform inversion results. The new approach has been applied and tested on two synthetic case studies and an experimental data set. The field data were additionally compared with cone penetration test data for validation. [ABSTRACT FROM AUTHOR]

Soils with high stone content represent a challenge to root development, as each stone is an obstacle to root growth. A high stone content also affects soil properties such as temperature or water content, which in turn affects root growth. We investigated the effects of all soil properties combined on root development in the field using both experiments and modeling. Field experiments were carried out in rhizotron facilities during two consecutive growing seasons (wheat [Triticum aestivum L.] and maize [Zea mays L.]) in silty loam soils with high (>50%) and low (<4%) stone contents. We extended the CPlantBox root architecture model to explicitly consider the presence of stones and simulated root growth on the plot scale over the whole vegetation period. We found that a linear increase of stone content resulted in a linear decrease of rooting depth across all stone contents and developmental stages considered, whereas rooting depth was only sensitive to cracks below a certain crack density and at earlier growth stages. Moreover, the impact of precipitation‐influenced soil strength had a relatively stronger impact on simulated root arrival curves during the vegetation periods than soil temperature. Resulting differences between stony and non‐stony soil of otherwise the same crop and weather conditions show similar trends as the differences observed in the rhizotron facilities. The combined belowground effects resulted in differences in characteristic root system measures of up to 48%. In future work, comparison of absolute values will require including shoot effects—in particular, different carbon availabilities. Core Ideas: We present a dynamic root architecture model that considers macroscopic soil properties.Root elongation rate and preferred growth direction are influenced by stones.We simulate the effects of soil properties on root architecture development using field data.Only belowground effects are considered by the root architecture model.Similar trends in measured and simulated root growth are observed in two different soils. [ABSTRACT FROM AUTHOR]

Detailed knowledge of the intra‐field variability of soil properties and crop characteristics is indispensable for the establishment of sustainable precision agriculture. We present an approach that combines ground‐based agrogeophysical soil and aerial crop data to delineate field‐specific management zones that we interpret with soil attribute measurements of texture, bulk density, and soil moisture, as well as yield and nitrate residue in the soil after potato (Solanum tuberosum L.) cultivation. To delineate the management zones, we use aerial drone‐based normalized difference vegetation index (NDVI), spatial electromagnetic induction (EMI) soil scanning, and the EMI–NDVI data combination as input in a machine learning clustering technique. We tested this approach in three successive years on six agricultural fields (two per year). The field‐scale EMI data included spatial soil information of the upper 0–50 cm, to approximately match the soil depth sampled for attribute measurements. The NDVI measurements over the growing season provide information on crop development. The management zones delineated from EMI data outperformed the management zones derived from NDVI in terms of spatial coherence and showed differences in properties relevant for agricultural management: texture, soil moisture deficit, yield, and nitrate residue. The combined EMI–NDVI analysis provided no extra benefit. This underpins the importance of including spatially distributed soil information in crop data interpretation, while emphasizing that high‐resolution soil information is essential for variable rate applications and agronomic modeling. Core Ideas: Electromagnetic induction (EMI) enables delineation of high‐resolution management zones.Management zone delineation using aerial crop data depends on acquisition time.Management zone delineation based on EMI data seems time independent.Important for variable rate applications, EMI‐based delineated zones differ agronomically. [ABSTRACT FROM AUTHOR]

Recently, a novel approach with excellent performance based on the concept of the characteristic infiltration time, the characteristic time method (CTM), is proposed to infer soil sorptivity (S) and saturated hydraulic conductivity (Ks) from one‐dimensional (1D) cumulative infiltration. The current work provides a simplified version of the CTM, called the SCTM, by eliminating the necessity of the iteration method used in CTM and providing a similar accuracy as the original method when estimating S and Ks. We used both synthetic and experimental data to evaluate SCTM in comparison with the original CTM, as well as Sharma (SH) and curve‐fitting methods. In the case of synthetically simulated infiltration experiments, the predicted S and Ks values showed an excellent agreement with their theoretical values, with Nash–Sutcliffe (E) values higher than 0.9 and RMSE values of 0.11 cm h1/2 and 0.35 cm h–1, respectively. In the case of experimental data, the SCTM showed E values larger than 0.73 and RMSE values of 0.64 cm h1/2 and 0.35 cm h–1, respectively. The accuracy and the robustness of SCTM was comparable with the original CTM when applied on synthetic infiltration curves as well as on experimental data. Similar to the original CTM, the simplified approach also does not require the knowledge of the time validity, which is needed when using approaches based on Philip's infiltration theory. The method is applicable to infiltrations with durations from 15 min to 24 h. The supplemental material presents the calculation of S and Ks using SCTM in an Excel spreadsheet. Core Ideas: A simplified version of the CTM is proposed to estimate the S and Ks from a 1D infiltration curve.The SCTM is nearly as accurate as the original CTM in prediction of S and Ks.The SCTM eliminates the necessity of the iteration method used in original CTM. [ABSTRACT FROM AUTHOR]

Modeling of the land surface water‐, energy‐, and carbon balance provides insight into the behavior of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large‐scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyze the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS‐1D were simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in‐depth analysis of the soil SHPs and derived soil characteristics was performed to analyze why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time‐integrated behavior of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter‐comparison studies to avoid artifacts originating from the choice of PTF rather from different model structures. Plain Language Summary: Hydrological models need information about the soil physical characteristics (soil hydraulic parameters), which are in general not available if the models are applied at larger scales (region to global scale). Therefore, pedotransfer functions (PTFs) are classically used, which relate easily available soil properties such as sand‐, silt‐, clay‐content, soil organic carbon content, and soil bulk density, which are available from soil maps, to the soil hydraulic parameters. Unfortunately, there are many different PTFs available in literature. In the study presented, we analyzed the impact of different PTFs on the simulation results of water fluxes and found, that the choice of PTF impacts the simulation results. Further, some PTFs were identified as being less robust compared to others. In general, the study shows that harmonizing PTFs in model‐inter‐comparisons is needed to avoid artifacts originating from the choice of PTF rather from different model structures. Key Points: Using different PTFs in hydrological models causes substantial variability in predicted fluxesWe strongly recommend to harmonize the PTFs used in model inter‐comparison studies [ABSTRACT FROM AUTHOR]

Multiannual data (2016–2018) from 12 weighed lysimeters (four soil types with textures ranging from sandy loam to silt loam, three replicates) of the TERENO SOILCan network were used to evaluate if evaporation (E) rates could be predicted from weather data using the FAO Penman–Monteith (PM) method combined with soil water flow simulations using the Richards equation. Soil hydraulic properties (SHPs) were estimated either from soil texture using the ROSETTA pedotransfer functions, from in situ measured water retention curves, or from soil surface water contents using inverse modeling. In all years, E was water limited and the measured evaporation rates (Em) surprisingly did not vary significantly among the four different soil types. When SHPs derived from pedotransfer functions were used, simulated evaporation rates of the finer textured soils overestimated the measured ones considerably. Better agreement was obtained when simulations were based on in situ measured or inversely estimated SHPs. The SHPs estimated from pedotransfer functions represented unrealistically large characteristic lengths of evaporation (Lc), and Lc was found to be a useful characteristic to constrain estimates of SHPs. Also, when soil evaporation was water limited and Em rates were below Epot (PM evaporation scaled by an empirical coefficient), the diurnal dynamics of Em followed those of Epot. The Richards equation that considers only isothermal liquid water flow did not reproduce these dynamics caused by temperature dependent vapor transport in the soil. [ABSTRACT FROM AUTHOR]

Soil degradation is a worsening global phenomenon driven by socio‐economic pressures, poor land management practices and climate change. A deterioration of soil structure at timescales ranging from seconds to centuries is implicated in most forms of soil degradation including the depletion of nutrients and organic matter, erosion and compaction. New soil–crop models that could account for soil structure dynamics at decadal to centennial timescales would provide insights into the relative importance of the various underlying physical (e.g. tillage, traffic compaction, swell/shrink and freeze/thaw) and biological (e.g. plant root growth, soil microbial and faunal activity) mechanisms, their impacts on soil hydrological processes and plant growth, as well as the relevant timescales of soil degradation and recovery. However, the development of such a model remains a challenge due to the enormous complexity of the interactions in the soil–plant system. In this paper, we focus on the impacts of biological processes on soil structure dynamics, especially the growth of plant roots and the activity of soil fauna and microorganisms. We first define what we mean by soil structure and then review current understanding of how these biological agents impact soil structure. We then develop a new framework for modelling soil structure dynamics, which is designed to be compatible with soil–crop models that operate at the soil profile scale and for long temporal scales (i.e. decades, centuries). We illustrate the modelling concept with a case study on the role of root growth and earthworm bioturbation in restoring the structure of a severely compacted soil. [ABSTRACT FROM AUTHOR]

Modeling soil hydraulic properties requires an effective representation of capillary and noncapillary storage and conductivity. This is made possible by using physically comprehensive yet flexible soil hydraulic property models. Such a model (Brunswick [BW] model) was introduced by Weber et al. (2019, https://doi.org/10.1029/2018WR024584), and it overcomes some core deficiencies present in the widely used van Genuchten‐Mualem (VGM) model. We first compared the performance of the BW model to that of the VGM model in its ability to describe water retention and hydraulic conductivity data on a set of measurements of 402 soil samples with textures covering the entire range of classes. Second, we developed a simple transfer function to predict BW parameters based on VGM parameters. Combined with our new function, any existing pedotransfer function for the prediction of the VGM parameters can be extended to predict BW model parameters. Based on information criteria, the smaller variance of the residuals, and a 40% reduction in mean absolute error in the hydraulic conductivity over all samples, the BW model clearly outperforms VGM. This is possible as the BW model explicitly accounts for hydraulic properties of dry soils. With the new pedotransfer function developed in this study, better descriptions of water retention and hydraulic conductivities are possible. We are convinced that this will strengthen the utility of the new model and enable improved field‐scale simulations, climate change impact assessments on water, energy and nutrient fluxes, as well as crop productivity in agroecosystems by soil‐crop and land‐surface modeling. The models and the pedotransfer function are included in an R package spsh (https://cran.r‐project.org/package=spsh). [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow‐up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

To investigate transient dynamics of soil water redistribution during infiltration, we conducted horizontal borehole and surface ground penetrating radar measurements during a 4‐day infiltration experiment at the rhizontron facility in Selhausen, Germany. Zero‐offset ground penetrating radar profiling in horizontal boreholes was used to obtain soil water content information at specific depths (0.2, 0.4, 0.6, 0.8 and 1.2 m). However, horizontal borehole ground penetrating radar measurements do not provide accurate soil water content estimates of the top soil (0–0.1 m depth) because of interference between direct and critically refracted waves. Therefore, surface ground penetrating radar data were additionally acquired to estimate soil water content of the top soil. Due to the generation of electromagnetic waveguides in the top soil caused by infiltration, a strong dispersion in the ground penetrating radar data was observed in 500 MHz surface ground penetrating radar data. A dispersion inversion was thus performed with these surface ground penetrating radar data to obtain soil water content information for the top 0.1 m of the soil. By combining the complementary borehole and surface ground penetrating radar data, vertical soil water content profiles were obtained, which were used to investigate vertical soil water redistribution. Reasonable consistency was found between the ground penetrating radar results and independent soil water content data derived from time domain reflectometry measurements. Because of the improved spatial representativeness of the ground penetrating radar measurements, the soil water content profiles obtained by ground penetrating radar better matched the known water storage changes during the infiltration experiment. It was concluded that the combined use of borehole and surface ground penetrating radar data convincingly revealed spatiotemporal soil water content variation during infiltration. In addition, this setup allowed a better quantification of water storage, which is a prerequisite for future applications, where, for example, the soil hydraulic properties will be estimated from ground penetrating radar data. [ABSTRACT FROM AUTHOR]

Summary: Root water uptake is a key ecohydrological process for which a physically based understanding has been developed in the past decades. However, due to methodological constraints, knowledge gaps remain about the plastic response of whole plant root systems to a rapidly changing environment.We designed a laboratory system for nondestructive monitoring of stable isotopic composition in plant transpiration of a herbaceous species (Centaurea jacea) and of soil water across depths, taking advantage of newly developed in situ methods. Daily root water uptake profiles were obtained using a statistical Bayesian multisource mixing model.Fast shifts in the isotopic composition of both soil and transpiration water could be observed with the setup and translated into dynamic and pronounced shifts of the root water uptake profile, even in well watered conditions.The incorporation of plant physiological and soil physical information into statistical modelling improved the model output. A simple exercise of water balance closure underlined the nonunique relationship between root water uptake profile on the one hand, and water content and root distribution profiles on the other, illustrating the continuous adaption of the plant water uptake as a function of its root hydraulic architecture and soil water availability during the experiment. [ABSTRACT FROM AUTHOR]

Electromagnetic induction (EMI) data are often used to investigate spatial and temporal patterns of soil texture, soil water content and soil salinity. We hypothesized that the EMI methodology might thus also offer potential to detect agricultural legacy effects originating from fertilizer application and irrigation of different fields. Therefore, we performed EMI measurements on two long‐term field experiments (LTFE) at Thyrow near Berlin (Germany) that differed in agricultural management with regard to long‐term irrigation in combination with mineral (NPK and lime) and organic amendments (straw and farmyard manure). Two different rigid‐boom multi‐coil EMI instruments were used to measure simultaneously the apparent electrical conductivity (ECa) over nine different depth ranges to study the entire soil profile from topsoil to deep subsoil. Additionally, soil samples were taken from the different treatments to ground‐truth the measurements and disentangle the nutrient application or irrigation effects from natural soil heterogeneity. The soil samples indicated a rather homogenous soil and the correlation between soil parameters or states were not significant. However, the treatments showed significant differences in measured ECa values. In general, ECa values were largest on regularly irrigated as well as on mineral and organic fertilized plots, with regular irrigation exhibiting the largest impact on EMI records even though the last application was months before the EMI measurement. Overall, this study reveals that EMI data can support the classical in situ assessment of agricultural management effects within LTFE, while offering new potentials in detecting and understanding legacy effects of agricultural management on spatial soil properties at farm level. [ABSTRACT FROM AUTHOR]

Many different equations ranging from simple empirical to semi‐analytical solutions of the Richards equation have been proposed for quantitative description of water infiltration into variably saturated soils. The sorptivity, S, and the saturated hydraulic conductivity, Ks, in these equations are typically unknown and have to be estimated from measured data. In this paper, we use so‐called characteristic time (tchar) to design a new method, referred to as the characteristic time method (CTM) that estimates S, and Ks, from one‐dimensional (1D) cumulative infiltration data. We demonstrate the usefulness and power of the CTM by comparing it with a suite of existing methods using synthetic cumulative infiltration data simulated by HYDRUS‐1D for 12 synthetic soils reflecting different USDA textural classes, as well as experimental data selected from the Soil Water Infiltration Global (SWIG) database. Results demonstrate that the inferred values of S and Ks are in excellent agreement with their theoretical values used in the synthetically simulated infiltration experiments with Nash–Sutcliffe criterion close to unity and RMSE values of 0.04 cm h−1/2 and 0.05 cm h−1, respectively. The CTM also showed very high accuracy when applied on synthetic data with added measurement noise, as well as robustness when applied to experimental data. Unlike previously published methods, the CTM does not require knowledge of the time validity of the applied semi‐analytical solution for infiltration and, therefore, is applicable to infiltrations with durations from 5 min to several days. A script written in Python of the CTM method is provided in the supplemental material. [ABSTRACT FROM AUTHOR]

Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models. [ABSTRACT FROM AUTHOR]

Weighable lysimeters were used to study the relation between soil water content (SWC) and the actual evapotranspiration (ETa) of grassland under two different climate regimes of Rollesbroich and Selhausen but for an identical soil from Rollesbroich. All components of the water balance were determined from 2012 until 2018. Budyko analysis was used to characterize the hydrological status of the studied sites. Wavelet analysis was also applied to study the power spectrum of ETa, vegetationheight- adjusted reference evapotranspiration (ETcrop), and water stress index (WSI) defined as ETa/ETcrop, as well as SWC at three different depths and the coherence between SWC and ETa and WSI. The Budyko analysis showed that 2018 resulted in a shift of both locations towards more water-limited conditions, although Rollesbroich remained an energy-limited system. Based on the power spectrum analysis, the annual timescale is the dominant scale for the temporal variability of ETa, ETcrop, and SWC. The results also showed that increasing dryness at the energy-limited site led to more temporal variability of SWC at all depths at the annual timescale. Wavelet coherence analysis showed a reduction of the phase shift between SWC and ETa at an annual scale caused by the increase in dryness during the measurement period. We found that phase shifts between SWC and ETa and SWC and WSI were stronger at the water-limited site than at the energy-limited site. The wavelet coherence analysis also showed that from 2014 to 2018, the control of ETa and WSI on SWC increased due to higher dryness of soil. [ABSTRACT FROM AUTHOR]

For vadose zone studies, it is essential to characterize the soil heterogeneity. However, manual soil coring is time consuming and lacks spatial coverage. Ground-penetrating radar (GPR) has a high potential tomap these parameters. However, with conventional common-offset profile (COP) measurements, soil layer changes are only detected as a function of time, and no exact determination of velocities, and thus permittivity, is possible. For velocity estimation, time-consuming point-scale common midpoint (CMP) or wide-angle reflection and refraction (WARR) measurements are necessary. Recently, a novel simultaneous multi-offset multichannel (SiMoc) GPR system was released, enabling rapid profiling with virtually continuous acquisition of WARR gathers. For this system, we developed a new processing approach. First, time shifts caused by the different cables and receivers were eliminated by a novel calibration method. In the obtained CMP gathers, groundwave and (when present) reflection velocities were determined with an automated semblance approach. The obtained velocity can be converted to permittivity and soil water content. We tested SiMoc GPR with a synthetic study and time-lapse field measurements. In the synthetic study, the accuracy of velocity and layer thickness were within 0.02 m ns-1 and 2 cm. The SiMoc field results (spatial sampling of 5 cm) are consistent with coarse sampled single-channel data (spatial sampling of 10 m). Soil water content changes over the different measurement days were in agreement with nearby installed sensors (one per hectare). Overall, SiMoc GPR is a powerful tool for fast imaging of spatially highly resolved permittivity, and soil water content at a large scale. [ABSTRACT FROM AUTHOR]

The article focuses on the evaluation of different methods for gap filling of long-term actual evapotranspiration time series measured by lysimeters. It explore four different gap filling methods namely potential terrestrial evapotranspiration method, the ratio method, and the FAO-based water balance method, and HYDRUS modeling. It also mentions that gap-filling methods do not perform very differently for dry and wet conditions.

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security. Key Points: Recent soil moisture measurement and monitoring techniques and estimation models from the point to the global scales and their limitations are presentedThe importance and application of soil moisture information for various Earth and environmental sciences disciplines such as forecasting weather and climate variability, modeling hydrological processes, and predicting and monitoring extreme events and their impacts on the environment and human society are presented [ABSTRACT FROM AUTHOR]

Core IdeasCosmic‐ray neutron sensing (CRNS) was used in a drip‐irrigated field.Soil water content was estimated from CRNS.Neutron transport was simulated for the drip‐irrigated field.CRNS has limitations for irrigation scheduling of drip‐irrigated fields. Irrigation is essential for maintaining food production in water‐scarce regions. The irrigation need depends on the water content of the soil, which we measured with the novel technique of cosmic‐ray neutron sensing (CRNS). The potential of the CRNS technique for drip irrigation scheduling was explored in this study for the Picassent site near Valencia, Spain. To support the experimental evidence, the neutron transport simulation URANOS was used to simulate the effect of drip irrigation on the neutron counts. The overall soil water content (SWC) in the CRNS footprint was characterized with a root mean square error <0.03 cm3/cm3, but the experimental dataset indicated methodological limitations to detect drip water input. Both experimental data and simulation results suggest that the large‐area neutron response to drip irrigation is insignificant in our specific case using a standard CRNS probe. Because of the small area of irrigated patches and short irrigation time, the limited SWC changes due to drip irrigation were not visible from the measured neutron intensity changes. Our study shows that CRNS modeling can be used to assess the suitability of the CRNS technique for certain applications. While the standard CRNS probe was not able to detect small‐scale drip irrigation patterns, the method might be applicable for larger irrigated areas, in drier regions, and for longer and more intense irrigation periods. Since statistical noise is the main limitation of the CRNS measurement, the capability of the instrument could be improved in future studies by larger and more efficient neutron detectors. [ABSTRACT FROM AUTHOR]

Core IdeasPTFs for water contents at specific pressure heads were developed.Covariate shift increased uncertainty in PTF predictions.Relative importance of predictors in machine learning PTFs was determined. There has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e., different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset into training and testing subsets, and the outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov–Smirnov test for the prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian process regression and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared with machine learning methods, which in their turn estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling. From these, 100 sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF‐predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty. [ABSTRACT FROM AUTHOR]

Core Ideas4‐yr monitoring of soil water content dynamics for different plant types.Horizontal crosshole GPR at the field‐plot scale sampled ∼65 m3.Natural, irrigated, and sheltered treatments in gravelly and clayey–silty soils.Consistent patches of soil water content were observed along horizontal slices using GPR.Results enable improved investigation of soil–plant–atmosphere interactions. Ground penetrating radar (GPR) has shown a high potential to derive soil water content (SWC) at different scales. In this study, we combined multiple horizontal GPR measurements at different depths to investigate the spatial and temporal variability of the SWC under cropped plots. The SWC data were analyzed for four growing seasons between 2014 and 2017, two soil types (gravelly and clayey–silty), two crops (wheat [Triticum aestivum L.] and maize [Zea mays L.]), and three different water treatments. We acquired more than 150 time‐lapse GPR datasets along 6‐m‐long horizontal crossholes at six depths. The GPR SWC distributions are distinct both horizontally and vertically for both soil types. A clear change in SWC can be observed at both sites between the surface layer (>0.3 m) and subsoil. Alternating patches of higher and lower SWC, probably caused by the soil heterogeneity, were observed along the horizontal SWC profiles. To investigate the changes in SWC with time, GPR and time‐domain reflectometry (TDR) data were averaged for each depth and compared with changes in precipitation, treatment, and soil type. The high‐temporal‐resolution TDR and the large‐sampling‐volume GPR show similar trends in SWC for both sites, but because of the different sensing volumes, different responses were obtained due to the spatial heterogeneity. A difference in spatial variation of the crosshole GPR SWC data was detected between maize and wheat. The results for this 4‐yr period indicate the potential of this novel experimental setup to monitor spatial and temporal SWC changes that can be used to study soil–plant–atmosphere interactions. [ABSTRACT FROM AUTHOR]

Core Ideas: Transversal relaxation time spectra were a good proxy to determine pore size distribution.Chitosan provides more degradable C to the soil microbial community than Arabic gum.The water retention curve showed that 10 g Arabic gum kg−1 soil decreased plant‐available water.The same amount of chitosan increased available water content compared with the reference soil. The pore size distribution (PSD) of biopolymeric‐amended soils is rarely investigated due to difficulties in its quantification using classical methods. In this study, we analyzed the impact of biopolymeric soil amendments on the PSD of a dryland loamy soil based on its physical and biological properties using a completely randomized design with four treatments consisting of two different dosages (10 and 5 g kg−1) of two different biopolymers, (chitosan [CH] and Arabic gum [AG]) plus a reference soil. To determine the effects of CH and AG on the PSD, nuclear magnetic resonance relaxometry (NMRR) measurements were used to determine the longitudinal (T1) and transversal (T2) relaxation times. A set of soil structure–related characteristics was also determined in the laboratory. The results revealed that T2 spectra provided a good proxy to determine the PSD, showing good agreement between the PSD from T2 spectra and that calculated from the water retention curve (WRC) (R2 > 0.78; RMSE <1.38 μm). The application of CH also increased the zeta potential of the soil to −18.5 mV, compared with −20 mV obtained for the reference soil. The WRC measurements revealed that AG decreased the available water content for plant use compared with the reference soil, whereas CH increased the available water in comparison to the reference soil. Considering the parameters of the van Genuchten model, the application of AG and CH mainly affected the parameter α, confirming the dominant changes in macropores. This finding was confirmed by NMRR relaxation spectra. Furthermore, the application of CH and AG stimulated the microbial activity of the amended soil, leading to an increase in soil respiration. [ABSTRACT FROM AUTHOR]

Core Ideas: A 3D soil–root model was used to investigate root–biopore interactions.Known effects of biopores on root growth, i.e., increased root length and depth were reproduced.Despite reducing root–soil contact, biopores led to increased water uptake in dry periods.Biopores had a larger impact on water uptake for more compact and less conductive soils. Roots are known to use biopores as preferential growth pathways to overcome hard soil layers and access subsoil water resources. This study evaluates root–biopore interactions at the root‐system scale under different soil physical and environmental conditions using a mechanistic simulation model and extensive experimental field data. In a field experiment, spring wheat (Triticum aestivum L.) was grown on silt loam with a large biopore density. X‐ray computed tomography scans of soil columns from the field site were used to provide a realistic biopore network as input for the three‐dimensional numerical R‐SWMS model, which was then applied to simulate root architecture as well as water flow in the root–biopore–soil continuum. The model was calibrated against observed root length densities in both the bulk soil and biopores by optimizing root growth model input parameters. By implementing known interactions between root growth and soil penetration resistance into our model, we could simulate root systems whose response to biopores in the soil corresponded well to experimental observations described in the literature, such as increased total root length and increased rooting depth. For all considered soil physical (soil texture and bulk density) and environmental conditions (years of varying dryness), we found biopores to substantially mitigate transpiration deficits in times of drought by allowing roots to take up water from wetter and deeper soil layers. This was even the case when assuming reduced root water uptake in biopores due to limited root–soil contact. The beneficial impact of biopores on root water uptake was larger for more compact and less conductive soils. [ABSTRACT FROM AUTHOR]

Core Ideas: Land surface models (LSMs) show a large variety in describing and upscaling infiltration.Soil structural effects on infiltration in LSMs are mostly neglected.New soil databases may help to parameterize infiltration processes in LSMs. Infiltration in soils is a key process that partitions precipitation at the land surface into surface runoff and water that enters the soil profile. We reviewed the basic principles of water infiltration in soils and we analyzed approaches commonly used in land surface models (LSMs) to quantify infiltration as well as its numerical implementation and sensitivity to model parameters. We reviewed methods to upscale infiltration from the point to the field, hillslope, and grid cell scales of LSMs. Despite the progress that has been made, upscaling of local‐scale infiltration processes to the grid scale used in LSMs is still far from being treated rigorously. We still lack a consistent theoretical framework to predict effective fluxes and parameters that control infiltration in LSMs. Our analysis shows that there is a large variety of approaches used to estimate soil hydraulic properties. Novel, highly resolved soil information at higher resolutions than the grid scale of LSMs may help in better quantifying subgrid variability of key infiltration parameters. Currently, only a few LSMs consider the impact of soil structure on soil hydraulic properties. Finally, we identified several processes not yet considered in LSMs that are known to strongly influence infiltration. Especially, the impact of soil structure on infiltration requires further research. To tackle these challenges and integrate current knowledge on soil processes affecting infiltration processes into LSMs, we advocate a stronger exchange and scientific interaction between the soil and the land surface modeling communities. [ABSTRACT FROM AUTHOR]

Core Ideas: Cosmic‐ray neutron data are used to inversely estimate soil hydraulic properties.The forward neutron operator COSMIC is coupled with the vadose zone model HYDRUS‐1D.Bayesian analyses confirm the information content of cosmic‐ray neutron data. Observations of soil moisture content from remote sensing platforms can be used in conjunction with hydrological models to inversely estimate soil hydraulic properties (SHPs). In recent years, cosmic‐ray neutron sensing (CRNS) has proven to be a reliable method for the estimation of area‐average soil moisture at field scales. However, its use in the inverse estimation of the effective SHPs is largely unexplored. Thus, the main objective of this study was to assess the information content of aboveground fast‐neutron counts to estimate SHPs using both a synthetic modeling study and actual experimental data from the Rollesbroich catchment in Germany. For this, the forward neutron operator COSMIC was externally coupled with the hydrological model HYDRUS‐1D. The coupled model was combined with the Affine Invariant Ensemble Sampler to calculate the posterior distributions of effective soil hydraulic parameters as well as the model‐predictive uncertainty for different synthetic and experimental scenarios. Measured water contents at different depths were used to assess estimated SHPs. The analysis of both synthetic and actual CRNS data from homogenous and heterogeneous soil profiles, respectively, led to confident estimations of the shape parameters α and n, while higher uncertainty was observed for the saturated hydraulic conductivity. Furthermore, results demonstrated that neutron data are less influenced by local sources of uncertainty compared with near‐surface point measurements. The simultaneous use of CRNS and water content data further reduced the overall uncertainty, opening up new perspectives for the combination of CRNS with other remote sensing techniques for the inverse estimation of the effective SHPs. [ABSTRACT FROM AUTHOR]

Flood events can change the riverbed topography as well as the riverbed texture and structure, which in turn can influence the riverbed hydraulic conductivity (Krb) and river‐aquifer exchange fluxes. A major flood event occurred in the Emme River in Switzerland in 2014, with major implications for the riverbed structure. The event was simulated with the fully integrated hydrological model HydroGeoSphere. The aim was to investigate the effect of the spatial and temporal variability of riverbed topography and Krb on predictions of hydraulic states and fluxes and to test whether data assimilation (DA) based on the ensemble Kalman filter (EnKF) can better reproduce flood‐induced changes to hydraulic states and parameters with the help of riverbed topography changes recorded with an unmanned aerial vehicle (UAV) and through‐water photogrammetry. The performance of DA was assessed by evaluating the reproduction of the hydraulic states for the year 2015. While the prediction of surface water discharge was not affected much by the changes in riverbed topography and in Krb, using the UAV‐derived postflood instead of the preflood riverbed topography reduced the root‐mean‐square error of predicted heads (RMSE [h]) by 24%. If, in addition to using the postflood riverbed topography, also Krb and aquifer hydraulic conductivity (Kaq) were updated through DA after the flood, the RMSE (h) was reduced by 55%. We demonstrate how updating of Krb and Kaq based on EnKF and UAV‐based observations of riverbed topography transience after a major flood event strongly improve predictions of postflood hydraulic states. Key Points: Drone information on riverbed topography and hydraulic head data provide useful information for the simulation of river‐aquifer systemsParameter estimation via data assimilation allows better estimation of hydraulic heads and slightly better estimation of river discharge [ABSTRACT FROM AUTHOR]

Abstract: Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow‐up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

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Abstract: For the first time, we combine depth‐specific soil information obtained from the quantitative inversion of ground‐based multicoil electromagnetic induction data with the airborne hyperspectral vegetation mapping of 1 × 1‐m pixels including Sun‐induced fluorescence (F) to understand how subsurface structures drive above‐surface plant performance. Hyperspectral data were processed to quantitative F and selected biophysical canopy maps, which are proxies for actual photosynthetic rates. These maps showed within‐field spatial patterns, which were attributed to paleo‐river channels buried at around 1‐m depth. The soil structures at specific depths were identified by quantitative electromagnetic induction data inversions and confirmed by soil samples. Whereas the upper plowing layer showed minor correlation to the plant data, the deeper subsoil carrying vital plant resources correlated substantially. Linking depth‐specific soil information with plant performance data may greatly improve our understanding and the modeling of soil‐vegetation‐atmosphere exchange processes. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union (EGU) in Vienna in April 2017. This event was a follow‐up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in our journals. [ABSTRACT FROM AUTHOR]

Core Ideas: Advanced sensor networks gather dense information on hydro‐meteorological variables.The Alento CZO helps assess the impact of climate and land‐use change on ecosystem services.Our research supports effective supply‐side and demand‐side adaptation strategies. In recent years, the critical zone (CZ) of catchments across the Mediterranean region has been influenced by rapid changes in both climate seasonality and land use–land cover. Rural ecosystems in southern Europe are experiencing prolonged droughts, seriously compromising water resource availability and crop yields while increasing the risk of wildfire occurrence. Rainy seasons are likely to be characterized by intense storms that trigger floods, resulting in increasing damage severity. The negative effects of anthropogenic disturbance on hydrological ecosystem services can be tempered by demand‐side adaptation options and appropriate investments to ensure water supply under drought conditions. To shed light on some of the scientific challenges related to these issues, a critical zone observatory (CZO) has been established in the Alento River catchment. Although sampling campaigns and monitoring investigations have been performed in this area for >25 yr, a more systematic research program was recently started to take comprehensive measurements in representative subcatchments of the study area. These sites are instrumented with advanced ground‐based sensor network platforms that provide hydro‐meteorological variables and fluxes in the groundwater–soil–vegetation–atmosphere system. Hydrological models of different complexity exploit the dense information gathered to assess the impact of land use and climate changes on key functions and services of the CZO in the Alento River catchment. [ABSTRACT FROM AUTHOR]

Core Ideas: To determine the water retention curve from inverse modeling, θ and ψ need to be monitored.δ18O ratios contained information to inversely estimate soil hydraulic parameters.Different observation types should be combined in a single OF to estimate parameters.Averages of local measurement could be described using effective parameters.Accurate measurement of fluxes at lysimeter boundaries improved model parameterization. Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δ18O) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δ18O) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non‐measured water flow variables. When a bromide (Br−) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br− sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br− concentrations were well predicted, which validated the dispersivity parameterization. [ABSTRACT FROM AUTHOR]

Core Ideas: Dynamics of redox potential were induced by water‐table changes in a lysimeter.The redox potential measurements well reflected the different GHG emission sources.Redox potential monitoring is a viable tool for better understanding of GHG emissions. Soil greenhouse gas (GHG) emissions contribute to global warming. To support mitigation measures against global warming, it is important to understand the controlling processes of GHG emissions. Previous studies focusing mainly on paddy rice fields or wetlands showed a strong relationship between soil redox potential and GHG emission (e.g., N2O). However, the interpretation of redox potentials for the understanding of the controlling factors of GHG emission is limited due to the low number of continuous redox measurements in most ecosystems. Recent sensor developments open the possibility for the long‐term monitoring of field‐scale soil redox potential changes. We performed laboratory lysimeter experiments to investigate how changes in the redox potential, induced by changes in the water level, affect GHG emissions from agricultural soil. Under our experimental conditions, we found that N2O emissions followed closely the changes in redox potential. The dynamics of redox potential were induced by changing the water‐table depth in a laboratory lysimeter. Before fertilization during saturated conditions, we found a clear negative correlation between redox potentials and N2O emission rates. After switching from saturated to unsaturated conditions, N2O emission quickly decreased, indicating denitrification as the main source of N2O. In contrast, the emissions of CO2 increased with increasing soil redox potentials. After fertilization, N2O emission peaked at high redox potential, suggesting nitrification as the main production pathway, which was confirmed by isotope analysis of N2O. We propose that redox potential measurements are a viable method for better understanding of the controlling factors of GHG emissions, for the differentiation between different source processes, and for the improvement of process‐based GHG models. [ABSTRACT FROM AUTHOR]

Core Ideas: Cation exchange (CE) on illite, a main sorption process for Cs+, is driven by Cs+ concentration.Undefined CE and surface complexation on OM are main sorption processes for Sr2+.137Cs+ migrated only into shallow depths of a few centimeters after 3 yr.90Sr2+ migrated into deeper layers of the soil due to competitive sorption. A process‐based knowledge of the sorption mechanisms in soils is a prerequisite for the prediction of radionuclide transport in soils, plant availability, and leaching risk into groundwater. The present study combined batch sorption experiments of Cs+ and Sr2+ in agricultural soils of differing soil texture with numerical experiments using PHREEQC to identify key processes of sorption at different temperatures. Sorption was simulated for both radionuclides using cation exchange models. In addition, surface complexation was integrated into the reaction network for Sr2+. Our geochemical simulations identified cation exchange on illite as the dominant sorption process for Cs+. Selection of the site types in Cs+ sorption was mainly driven by Cs+ concentration. At low Cs+ concentrations the highly selective frayed edge sites dominated sorption behavior, while less affine site types on illite constituted the major sorption partners at high concentrations. We identified undefined cation exchange as the dominant sorption process for Sr2+, followed by surface complexation on organic matter. These process‐based analyses were the basis for field‐scale simulations to predict the leaching risk of Cs and Sr radionuclides in agricultural soils under humid climate conditions. For both soils and radionuclides, the distribution coefficients (Kd) varied distinctly with time in shallow layers due to changes in temperature, saturation, and the prevailing dominant sorption processes. During a 3‐yr‐simulation period, 137Cs+ migrated to depths of 3.6 cm (silty loam) and 7.6 cm (sandy loam), while 90Sr2+ migrated to depths of 15.6 cm (silty loam) and 23.6 cm (sandy loam) due to competitive sorption of infiltrating Ca2+ and Mg2+ ions reducing 90Sr2+ sorption and displacing 90Sr2+ from its exchange sites. [ABSTRACT FROM AUTHOR]