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The two-term infiltration equation (Formula presented.) is commonly used to determine the sorptivity, (Formula presented.) (Formula presented.), and product, (Formula presented.) (Formula presented.), of the dimensionless multiple (Formula presented.) and saturated soil hydraulic conductivity (Formula presented.) (Formula presented.) from cumulative vertical infiltration measurements (Formula presented.) (L) at times (Formula presented.) (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 (Formula presented.) and (Formula presented.). 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 (Formula presented.) and (Formula presented.). 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 (Formula presented.) and (Formula presented.). 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.

Despite previous reports on European growing seasons lengthening due to global warming, evidence shows that this trend has been reversing in the past decade due to increased transpiration needs. To asses this, we used an innovative method along with space-based observations to determine the timing of greening and dormancy and then to determine existing trends of them and causes. Early greening still occurs, albeit at slower rates than before. However, a recent (2011–2020) shift in the timing of dormancy has caused the season length to decrease back to 1980s levels. This shortening of season length is attributed primarily to higher atmospheric water demand in summer that suppresses transpiration even for soil moisture levels as of previous years. Transpiration suppression implies that vegetation is unable to meet the high transpiration needs. Our results have implications for future management of European ecosystems (e.g., net carbon balance and water and energy exchange with atmosphere) in a warmer world.

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, (Formula presented.), of cumulative infiltration measurements using curve fitting techniques. The two-term infiltration equation, (Formula presented.), 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, (Formula presented.), beyond (Formula presented.) 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 (Formula presented.) relationship for (Formula presented.), 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.

Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs.

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.

Multi-walled carbon nanotubes (MWCNTs) are increasing used in commercial applications and may be released into the environment with anionic surfactants, such as sodium dodecylbenzenesulfonate (SDBS), in sewer discharge. Little research has examined the transport, retention, and remobilization of MWCNTs in the presence or absence of SDBS in porous media with controlled chemical heterogeneity, and batch and column scale studies were therefore undertaken to address this gap in knowledge. The adsorption isotherms of SDBS on quartz sand (QS), goethite coated quartz sand (GQS), and MWCNTs were determined. Adsorption of SDBS (MWCNTs » GQS > QS) decreased zeta potentials for these materials, and produced a charge reversal for goethite. Transport of MWCNTs (5 mg L-1) dramatically decreased with an increase in the fraction of GQS from 0 to 0.1 in the absence of SDBS. Conversely, co-injection of SDBS (10 and 50 mg L-1) and MWCNTs radically increased the transport of MWCNTs when the GQS fraction was 0, 0.1, and 0.3, especially at a higher SDBS concentration, and altered the shape of retention profile. Mathematical modeling revealed that competitive blocking was not the dominant mechanism for the SDBS enhancement of MWCNT transport. Rather, SDBS sorption increased MWCNT transport by increasing electrostatic and/or steric interactions, or creating reversible interactions on rough surfaces. Sequential injection of pulses of MWCNTs and SDBS in sand (0.1 GQS fraction) indicated that SDBS could mobilize some of retained MWCNTs from the top to deeper sand layers, but only a small amount of released MWCNTs were recovered in the effluent. SDBS therefore had a much smaller influence on MWCNT transport in sequential injection than in co-injection, presumably because of a greater energy barrier to MWCNT release than retention. This research sheds novel insight on the roles of competitive blocking, chemical heterogeneity and nanoscale roughness, and injection sequence on MWCNT retention and release.

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.

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 a 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.

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]

Electromagnetic (EM) coupling effects including both inductive and capacitive coupling have long been an essential problem in broad-band spectral electrical impedance tomography (sEIT) measurements at the field scale. Efforts have been made to remove EM coupling numerically or to suppress the effects by modified data acquisition strategies. For near-surface applications with relatively small survey layouts, inductive coupling can be well removed in the mHz to kHz frequency range. With the use of shielded coaxial cables and so-called active electrodes where the amplifiers are mounted at the electrodes, capacitive coupling in sEIT measurements can also be reduced. However, it remains challenging to cope with capacitive coupling between the cable shield and the ground, especially in resistive field conditions. The aim of this study is to deal with this type of capacitive coupling effect by identifying and filtering out sEIT measurements that are strongly affected by capacitive coupling. Based on a correction method for capacitive coupling proposed in a previous study, an approach to estimate measurement errors due to capacitive coupling is presented first. In the second step, a workflow was proposed to calculate the capacitive coupling strength (CCS) for each electrode configuration, which is defined as the ratio of the imaginary part of the impedance induced by capacitive coupling and the imaginary part of the impedance due to the subsurface electrical conductivity. In the final step, measurements with low CCS were selected for inversion and the results were compared with inversion results obtained using the previously developed correction approach. It was found that the filtering method based on CCS is more capable in tackling capacitive coupling compared to using model-based corrections. Spectrally consistent sEIT results up to kHz were obtained using the newly developed filtering method, which were not achieved in previous work using model-based correction. [ABSTRACT FROM AUTHOR]

Abstract. Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March–July 2012) of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for the Rollesbroich site. The large systematic underestimation of water storage at 50 cm depth by VIC-3L during the first few months of the evaluation period questions, in part, the validity of its fixed water table depth at the bottom of the modeled soil domain.

The Community Land Model (CLM) contains many parameters whose values are uncertain and thus require careful estimation for model application at individual sites. Here we used Bayesian inference with the DiffeRential Evolution Adaptive Metropolis (DREAM(zs)) algorithm to estimate eight CLM v.4.5 ecosystem parameters using 1 year records of half-hourly net ecosystem CO2 exchange (NEE) observations of four central European sites with different plant functional types (PFTs). The posterior CLM parameter distributions of each site were estimated per individual season and on a yearly basis. These estimates were then evaluated using NEE data from an independent evaluation period and data from “nearby” FLUXNET sites at ~600 km distance to the original sites. Latent variables (multipliers) were used to treat explicitly uncertainty in the initial carbon-nitrogen pools. The posterior parameter estimates were superior to their default values in their ability to track and explain the measured NEE data of each site. The seasonal parameter values reduced with more than 50% (averaged over all sites) the bias in the simulated NEE values. The most consistent performance of CLM during the evaluation period was found for the posterior parameter values of the forest PFTs, and contrary to the C3-grass and C3-crop sites, the latent variables of the initial pools further enhanced the quality-of-fit. The carbon sink function of the forest PFTs significantly increased with the posterior parameter estimates. We thus conclude that land surface model predictions of carbon stocks and fluxes require careful consideration of uncertain ecological parameters and initial states.

Saturated soil column experiments were conducted to investigate the transport, retention, and release behavior of a low concentration (1 mg L-1) of functionalized 14C-labeled multi-walled carbon nanotubes (MWCNTs) in a natural soil under various solution chemistries. Breakthrough curves (BTCs) for MWCNTS exhibited greater amounts of retardation and retention with increasing solution ionic strength (IS) or in the presence of Ca2+ in comparison to K+, and retention profiles (RPs) for MWCNTs were hyper-exponential in shape. These BTCs and RPs were well described using the advection-dispersion equation with a term for time- and depth-dependent retention. Fitted values of the retention rate coefficient and the maximum retained concentration of MWCNTs were higher with increasing IS and in the presence of Ca2+ in comparison to K+. Significant amounts of MWCNT and soil colloid release was observed with a reduction of IS due to expansion of the electrical double layer, especially following cation exchange (when K+ displaced Ca2+) that reduced the zeta potential of MWCNTs and the soil. Analysis of MWCNT concentrations in different soil size fractions revealed that >23.6% of the retained MWCNT mass was associated with water-dispersible colloids (WDCs), even though this fraction was only a minor portion of the total soil mass (2.38%). More MWCNTs were retained on the WDC fraction in the presence of Ca2+ than K+. These findings indicated that some of the released MWCNTs by IS reduction and cation exchange were associated with the released clay fraction, and suggests the potential for facilitated transport of MWCNT by WDCs.

Batch and saturated soil column experiments were conducted to investigate sorption and mobility of two 14C-labeled contaminants, the hydrophobic chlordecone (CLD) and the sulfadiazine (SDZ), in the absence or presence of functionalized multi-walled carbon nanotubes (MWCNTs). The transport behaviors of CLD, SDZ, and MWCNTs were studied at environmentally relevant concentrations (0.1-10 mg L-1) and they were applied in the column studies at different times. The breakthrough curves and retention profiles were simulated using a numerical model that accounted for the advective-dispersive transport of all compounds, attachment/detachment of MWCNTs, equilibrium and kinetic sorption of contaminants, and co-transport of contaminants with MWCNTs. The experimental results indicated that the presence of mobile MWCNTs facilitated remobilization of previously deposited CLD and its co-transport into deeper soil layers, while retained MWCNTs enhanced SDZ deposition in the topsoil layers due to the increased adsorption capacity of the soil. The modeling results then demonstrated that the mobility of engineered nanoparticles (ENPs) in the environment and the high affinity and entrapment of contaminants to ENPs were the main reasons for ENP-facilitated contaminant transport. On the other hand, immobile MWCNTs had a less significant impact on the contaminant transport, even though they were still able to enhance the adsorption capacity of the soil.

Transport and retention behavior of multi-walled carbon nanotubes (MWCNTs) was studied in mixtures of negatively charged quartz sand (QS) and positively charged goethite-coated sand (GQS) to assess the role of chemical heterogeneity. The linear equilibrium sorption model provided a good description of batch results, and the distribution coefficients (KD) drastically increased with the GQS fraction that was electrostatically favorable for retention. Similarly, retention of MWCNTs increased with the GQS fraction in packed column experiments. However, calculated values of KD on GQS were around 2 orders of magnitude smaller in batch than packed column experiments due to differences in lever arms associated with hydrodynamic and adhesive torques at microscopic roughness locations. Furthermore, the fraction of the sand surface area that was favorable for retention (Sf) was much smaller than the GQS fraction because nanoscale roughness produced shallow interactions that were susceptible to removal. These observations indicate that only a minor fraction of the GQS was favorable for MWCNT retention. These same observations held for several different sand sizes. Column breakthrough curves were always well described using an advective-dispersive transport model that included retention and blocking. However, depth-dependent retention also needed to be included to accurately describe the retention profile when the GQS fraction was small. Results from this research indicate that roughness primarily controlled the retention of MWCNTs, although goethite surfaces played an important secondary role.

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]

Based on small-scale laboratory and field-scale lysimeter experiments, the sorption and biodegradation of sulfonamide sulfadiazine (SDZ) were investigated in unsaturated sandy and silty-clay soils. Sorption and biodegradation were low in the laboratory, while the highest leaching rates were observed when SDZ was mixed with manure. The leaching rate decreased when SDZ was mixed with pure water, and was smallest with the highest SDZ concentrations. In the laboratory, three transformation products (TPs) developed after an initial lag phase. However, the amount of TPs was different for different mixing-scenarios. The TP 2-aminopyrimidine was not observed in the laboratory, but was the most prevalent TP at the field scale. Sorption was within the same range at the laboratory and field scales. However, distinctive differences occurred with respect to biodegradation, which was higher in the field lysimeters than at the laboratory scale. While the silty-clay soil favored sorption of SDZ, the sandy, and thus highly permeable, soil was characterized by short half-lives and thus a quick biodegradation of SDZ. For 2-aminopyrimidine, half-lives of only a few days were observed. Increased field-scale biodegradation in the sandy soil resulted from a higher water and air permeability that enhanced oxygen transport and limited oxygen depletion. Furthermore, low pH was more important than the organic matter and clay content for increasing the biodegradation of SDZ. A numerical analysis of breakthrough curves of bromide, SDZ, and its TPs showed that preferential flow pathways strongly affected the solute transport within shallow parts of the soil profile at the field scale. However, this effect was reduced in deeper parts of the soil profile. Due to high field-scale biodegradation in several layers of both soils, neither SDZ nor 2-aminopyrimidine was detected in the discharge of the lysimeter at a depth of 1m. Synthetic 50 year long simulations, which considered the application of manure with SDZ for general agricultural practices in Germany and humid climate conditions, showed that the concentration of SDZ decreased below 0.1 μg/L in both soils below the depth of 50 cm.

This comment challenges Gao et al. (2023)'s perspective rejecting the role of soil processes in hydrology. We argue that the authors present a false dichotomy between soil-centric and ecosystem-centric views. These two views of hydrology are complementary and reflect on the inherent multiscale complexity of hydrology where soil processes dominate at certain scales but other processes may become important at catchment scale. We recognize the need for a new scale aware framework that reconciles the interplay between soil processes at small scales with emergent behaviors driven by vegetation, topography and climate at large scales. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters. [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]

Column experiments were conducted with undisturbed loamy sand soil under unsaturated conditions (around 90% saturation degree) to investigate the retention of surfactant stabilized silver nanoparticles (AgNPs) with various input concentration (Co), flow velocity, and ionic strength (IS), and the remobilization of AgNPs by changing the cation type and IS. The mobility of AgNPs in soil was enhanced with decreasing solution IS, increasing flow rate and input concentration. Significant retardation of AgNP breakthrough and hyperexponential retention profiles (RPs) were observed in almost all the transport experiments. The retention of AgNPs was successfully analyzed using a numerical model that accounted for time- and depth-dependent retention. The simulated retention rate coefficient (k1) and maximum retained concentration on the solid phase (Smax) increased with increasing IS and decreasing Co. The high k1 resulted in retarded breakthrough curves (BTCs) until Smax was filled and then high effluent concentrations were obtained. Hyperexponential RPs were likely caused by the hydrodynamics at the column inlet which produced a concentrated AgNP flux to the solid surface. Higher IS and lower Co produced more hyperexponential RPs because of larger values of Smax. Retention of AgNPs was much more pronounced in the presence of Ca(2+) than K(+) at the same IS, and the amount of AgNP released with a reduction in IS was larger for K(+) than Ca(2+) systems. These stronger AgNP interactions in the presence of Ca(2+) were attributed to cation bridging. Further release of AgNPs and clay from the soil was induced by cation exchange (K(+) for Ca(2+)) that reduced the bridging interaction and IS reduction that expanded the electrical double layer. Transmission electron microscopy, energy-dispersive X-ray spectroscopy, and correlations between released soil colloids and AgNPs indicated that some of the released AgNPs were associated with the released clay fraction.

Column experiments were conducted in undisturbed and in repacked soil columns at water contents close to saturation (85-96%) to investigate the transport and retention of functionalized (14)C-labeled multi-walled carbon nanotubes (MWCNT) in two natural soils. Additionally, a field lysimeter experiment was performed to provide long-term information at a larger scale. In all experiments, no breakthrough of MWCNTs was detectable and more than 85% of the applied radioactivity was recovered in the soil profiles. The retention profiles exhibited a hyper-exponential shape with greater retention near the column or lysimeter inlet and were successfully simulated using a numerical model that accounted for depth-dependent retention. In conclusion, results indicated that the soils acted as a strong sink for MWCNTs. Little transport of MWCNTs is therefore likely to occur in the vadose zone, and this implies limited potential for groundwater contamination in the investigated soils.

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.