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Spectral induced polarization (SIP) has the potential for monitoring reactive processes in the subsurface. While strong SIP responses have been measured in response to calcite precipitation, their origin and mechanism remain debated. Here we present a novel geo-electrical millifluidic setup designed to observe microscale reactive transport processes while performing SIP measurements. We induced calcite precipitation by injecting two reactive solutions into a porous medium, which led to highly localized precipitates at the mixing interface. Strikingly, the amplitude of the SIP response increased by 340% during the last 7% increase in precipitate volume. Furthermore, while the peak frequency in SIP response varied spatially over 1 order of magnitude, the crystal size range was similar along the front, contradicting assumptions in the classical grain polarization model. We argue that the SIP response of calcite precipitation in such mixing fronts is governed by Maxwell–Wagner polarization due to the establishment of a precipitate wall. Numerical simulations of the electric field suggested that spatial variation in peak frequency was related to the macroscopic shape of the front. These findings provide new insights into the SIP response of calcite precipitation and highlight the potential of geoelectrical millifluidics for understanding and modeling electrical signatures of reactive transport processes.

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. 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. 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 Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystems Nonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexplored Further advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory

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.

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−1over land. Likewise the simulated rsds with an ME of 50 W m−2well 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. 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. 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 A new convection‐permitting regional ICOsahedral Nonhydrostatic (ICON) model setup over southern Africa is presented and evaluated The spatially and temporally highly resolved ICON wind and solar fields are inputs for improved renewable energy potential estimates ICON outputs agree well with hourly in situ observations and satellite data; wind speeds are slightly overestimated

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. 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. 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 Land data assimilation (LDA) advances scientific understanding and serves as an engineering tool for Earth system sciences LDA reflects the trend of harmonizing theory and data in the era of big data and artificial intelligence Future LDA research should expand the applications from pure geophysical systems to coupled natural and human systems

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.

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. 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

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 Krbon 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 Krband aquifer hydraulic conductivity (Kaq) were updated through DA after the flood, the RMSE (h) was reduced by 55%. We demonstrate how updating of Krband Kaqbased on EnKF and UAV‐based observations of riverbed topography transience after a major flood event strongly improve predictions of postflood hydraulic states. 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

The kinetic fractionation factor (αK) controls to a large extent the isotopic enrichment of surface waters during evaporation (E). In contrast to the well‐known vapor‐to‐liquid isotopic equilibrium fractionation factor, αKhas still not yet been properly characterized for soil water evaporation. In this study, we investigated the αKdaily dynamics during a series of three laboratory experiments differing in soil water availability and aerodynamic conditions. For this, we applied a commonly used isotopic evaporation model and tested it in two different approaches. First, a two‐end‐member mixing model (Keeling plot) was fitted to the measured isotopic composition of the laboratory air water vapor to obtain αK. In a second approach, αKwas obtained from the slope of the evaporation linein a dual isotopic coordinate system. For both methods, the isotopic composition of the soil water was determined nondestructively and online by sampling the soil water vapor with gas‐permeable microporous tubing. Results highlighted the limitation of the first approach, as the determination of the isotopic composition of Ewith the Keeling plot was challenging with the laboratory setup. The second approach provided αKvalues within the range ( αK2H= 1.0132 ± 0.0013; αK18O= 1.0149 ± 0.0012) reported in the literature and pointed to the prevalence of turbulent water vapor transport under water‐saturated soil conditions but also at soil water content significantly lower than the saturated value. In a third experiment, temporal dynamics of the atmospheric water vapor intrusion in the topmost soil layer could be observed during an isotopic labeling pulse. Kinetic fractionation during soil evaporation was determined during three experiments differing in soil water and aerodynamic conditionsSoil and atmospheric water stable isotopic compositions were monitored nondestructivelyDaily values of the kinetic fractionation factor were obtained by inversing a well‐known isotopic linear resistance model

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.

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 Fand 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. Plants interact with soil. This is intuitive, although we know little about the subsurface structure because we cannot see it. At first glance, all soil may look the same, yet healthy plants can survive beside withered ones. We investigate the soil‐plant interaction in an agricultural field situated in an area characterized by ancient (paleo‐) river channels. These channels formed in sandy‐gravelly material due to melting water after last glaciation, were then filled up with fine aeolien sediments, overlaid with soils up to 1 m thick, and are no longer visible at the surface. However, crops grow in meandering/braiding patterns that can be seen on satellite images, for example. To explain this, the subsurface structural geometry must be known. We combine ground‐based electromagnetic induction data inversion results with airborne hyperspectral measurements to reveal the soil depths driving plant performance (photosynthetic activity and growth). Contrary to expectations, the deeper subsoil and not the plowing layer controls plant performance at the investigated site. Plants above the buried paleoriver channels find nutrients and water, whereas the surrounding plants in gravelly soil suffer, especially during drought. These results improve our understanding of soil‐plant interaction, which may improve soil‐vegetation‐atmosphere exchange process modeling and harvest predictability. Soil structures at depth were obtained by quantitative 3‐D electromagnetic induction data inversions not by apparent electrical conductivity mapsDeeper subsoil characteristics correlate with airborne Sun‐induced fluorescence data indicating soil moisture effects on plant performanceQuantitative inverted electrical conductivity model together with plant data help to inform and improve soil‐vegetation‐atmosphere models

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale. For the application of pedotransfer functions in current Earth system models, and specifically for the different fluxes of water, solutes, and gas between soil and atmosphere, subject of the land surface models, recent developments of knowledge are entered in a new generation of pedotransfer functions. Methods for development and evaluation of pedotransfer functions are described in this comprehensive review, and perspectives for future developments in different Earth system science disciplines are presented. Challenges are still present for the application in some extreme environments of the Earth. We argue that a comprehensive set of pedotransfer functions can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Even though methodological challenges are still present for extrapolation and scaling, as outlined, integration and validation in global‐scale models is an achievable goal. Methods for development and evaluation of pedotransfer functions are described, and perspectives in different Earth system science disciplines presentedNovel applications are present for the different fluxes of water, solutes, and gas between soil and atmosphere, subject of the land surface modelsMethodological challenges are still present for extrapolation and scaling, but integration and validation in global‐scale models is an achievable goal

The use of biochar as a soil amendment attracts increasing research interest. However, the lack of methods to detect and monitor biochar in situlimits the validation of the field‐scale application of biochar. Spectral induced polarization is a potential tool to characterise biochar in soil. The aim of this study is to investigate the sensitivity of spectral induced polarization to biochar in sand and to understand how the physicochemical properties of both the biochar and the surrounding matrix influence the spectral induced polarization response. To this end, spectral induced polarization measurements were conducted on four types of biochar with different mass fractions disseminated in saturated sand as a host media with changing electrical conductivity. In addition, it was investigated how the spectral induced polarization response depends on the particle size of biochar. The measured SIP data were interpreted by Debye decomposition to obtain values for the peak relaxation time, τpeak; total chargeability, M; and normalised total chargeability, Mn. Spectral induced polarization showed a clear and specifically differentiated response to the presence of all four types of biochars. Mwas found to be proportional to the mass fraction of biochars, although relationships varied for each type of biochars. τpeakof biochars increased with increasing particle size. Increased electrolyte concentration enhanced Mnfor all biochars, although again, the specific response was different for each biochar. In addition, higher electrolyte concentrations decreased τpeakfor biochars derived from wood through pyrolysis but did not affect τpeakof biochar derived from miscanthus through hydrothermal carbonisation. It was concluded that the spectral induced polarization response of pyrolytic biochars resembled that of conductors or semiconductors, whereas the spectral induced polarization response of hydrothermal carbonisation biochar more closely resembled that of clay. Overall, the findings in this study suggest that spectral induced polarization is a promising method for the detection and characterisation of biochar in soil.

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 CO2exchange (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. The CLM parameters, estimated separately for four plant functional types, correlated with initial carbon‐nitrogen poolsParameter estimates improved model performance for an independent evaluation period and at independent sitesParameter estimates were more reliable for forest than for C3‐crop and C3‐grass PFTs and less dependent on the initial model states

Limited knowledge about the spatial distribution of aquifer properties typically constrains our ability to predict subsurface flow and transport. Here we investigate the value of using high resolution full‐waveform inversion of cross‐borehole ground penetrating radar (GPR) data for aquifer characterization. By stitching together GPR tomograms from multiple adjacent crosshole planes, we are able to image, with a decimeter scale resolution, the dielectric permittivity and electrical conductivity of an alluvial aquifer along cross sections of 50 m length and 10 m depth. A logistic regression model is employed to predict the spatial distribution of lithological facies on the basis of the GPR results. Vertical profiles of porosity and hydraulic conductivity from direct‐push, flowmeter and grain size data suggest that the GPR predicted facies classification is meaningful with regard to porosity and hydraulic conductivity, even though the distributions of individual facies show some overlap and the absolute hydraulic conductivities from the different methods (direct‐push, flowmeter, grain size) differ up to approximately one order of magnitude. Comparison of the GPR predicted facies architecture with tracer test data suggests that the plume splitting observed in a tracer experiment was caused by a hydraulically low‐conductive sand layer with a thickness of only a few decimeters. Because this sand layer is identified by GPR full‐waveform inversion but not by conventional GPR ray‐based inversion we conclude that the improvement in spatial resolution due to full‐waveform inversion is crucial to detect small‐scale aquifer structures that are highly relevant for solute transport. Highly resolved tomographic images of the subsurface are obtained from GPR full‐waveform inversionLogistic regression is used to translate GPR electrical properties into hydrogeological faciesThe distribution of GPR facies explains the plume splitting observed in a tracer experiment

Experiments using electrical resistivity tomography (ERT) have shown promising results in reducing the uncertainty of solute plume characteristics related to estimates based on the analysis of local point measurements only. To explore the similarities and differences between two cross-borehole ERT inversion approaches for characterizing salt tracer plumes, namely the classical smoothness-constrained inversion and a geostatistically based approach, we performed two-dimensional synthetic experiments. Simplifying assumptions about the solute transport model and the electrical forward and inverse model allowed us to study the sensitivity of the ERT inversion approaches towards a variety of basic conditions, including the number of boreholes, measurement schemes, contrast between the plume and background electrical conductivity, use of a priori knowledge, and point conditioning. The results show that geostatistically based and smoothness-constrained inversions of electrical resistance data yield plume characteristics of similar quality, which can be further improved when point measurements are incorporated and advantageous measurement schemes are chosen. As expected, an increased number of boreholes included in the ERT measurement layout can highly improve the quality of inferred plume characteristics, while in this case the benefits of point conditioning and advantageous measurement schemes diminish. Both ERT inversion approaches are similarly sensitive to the noise level of the data and the contrast between the solute plume and background electrical conductivity, and robust with regard to biased input parameters, such as mean concentration, variance, and correlation length of the plume. Although sophisticated inversion schemes have recently become available, in which flow and transport as well as electrical forward models are coupled, these schemes effectively rely on a relatively simple geometrical parameterization of the hydrogeological model. Therefore, we believe that standard uncoupled ERT inverse approaches, like the ones discussed and assessed in this paper, will continue to be important to the imaging and characterization of solute plumes in many real-world applications.

Improved characterization of subsurface heterogeneity is important to better understand a range of key processes in the hydrologic cycle, such as overland flow, infiltration into the soil, evaporation to the atmosphere, and transpiration by plants. Recently, synthetic modelling studies have shown that information on subsurface heterogeneity can be obtained from the anisotropy in electrical resistivity. The objective of this paper is to experimentally validate the findings of this synthetic modelling study. In order to do so, we developed a new measurement procedure to determine the effective complex electrical resistivity from a set of current injections and voltage measurements on a heterogeneous sample. A synthetic modelling study showed that this new measurement procedure was able to reproduce the results of the previous study that showed how the electrical properties and the correlation length ratio of bimodal distributions of two materials can be obtained from the effective complex electrical resistivity measured in two perpendicular directions. After validation of the new measurement approach, we constructed two bimodal distributions in a 2D measurement cell and applied the newly developed measurement strategy. We were able to estimate the electrical properties, the volume fraction, and the correlation length ratio with good accuracy from the complex resistivity measurements in two directions. The remaining differences were attributed to variations in sediment thickness that occurred during sample preparation. We conclude that anisotropy should not be ignored but embraced when dealing with subsurface heterogeneity and that proper interpretation of anisotropy may actually be used to characterize subsurface heterogeneity.

Due to the limited solubility of phosphorus (P) in soil, understanding its binding in fine colloids is vital to better forecast P dynamics and losses in agricultural systems. We hypothesized that water‐dispersible P is present as nanoparticles and that iron (Fe) plays a crucial role for P binding to these nanoparticles. To test this, we isolated water‐dispersible fine colloids (WDFC) from an arable topsoil (Haplic Luvisol, Germany) and assessed colloidal P forms after asymmetric flow field‐flow fractionation coupled with ultraviolet and an inductively coupled plasma mass spectrometer, with and without removal of amorphous and crystalline Fe oxides using oxalate and dithionite, respectively. We found that fine colloidal P was present in two dominant sizes: (i) in associations of organic matter and amorphous Fe (Al) oxides in nanoparticles <20 nm, and (ii) in aggregates of fine clay, organic matter and Fe oxides (more crystalline Fe oxides) with a mean diameter of 170 to 225 nm. Solution 31P‐nuclear magnetic resonance spectra indicated that the organically bound P predominantly comprised orthophosphate‐monoesters. Approximately 65% of P in the WDFC was liberated after the removal of Fe oxides (especially amorphous Fe oxides). The remaining P was bound to larger‐sized WDFC particles and Fe bearing phyllosilicate minerals. Intriguingly, the removal of Fe by dithionite resulted in a disaggregation of the nanoparticles, evident in higher portions of organically bound P in the <20 nm nanoparticle fraction, and a widening of size distribution pattern in larger‐sized WDFC fraction. We conclude that the crystalline Fe oxides contributed to soil P sequestration by (i) acting as cementing agents contributing to soil fine colloid aggregation, and (ii) binding not only inorganic but also organic P in larger soil WDFC particles. Improved quantification of P bonding forms in soil nanoparticles and their aggregates.Most P in water dispersible fine colloids is associated with (amorphous) Fe oxides.Crystalline Fe oxides contribute to soil P sequestration.Crystalline Fe oxides act as cementing agents of aggregation for soil fine colloids.Crystalline Fe oxides bind not only inorganic but also organic P in larger soil colloid particles.

Previous studies of streamwater transit time distributions (TTDs) used isotope tracer information from open precipitation (OP) as inputs to lumped watershed models that simulate the stream isotopic composition to estimate TTD. However, in forested catchments passage of rainfall through the canopy will alter the tracer signature of throughfall (TF) via interception. Here we test the effect of using TF instead of OP on TTD estimates. We sampled a 0.39 km2catchment (Wüstebach, Germany) for a 19 month period using weekly precipitation and stream isotope data to evaluate changes in stream isotope simulation and TTDs. We found that TF had different effects on TTDs for δ18O and δ2H, with TF leading to up to 4 months shorter transit times. TTDs converged for both isotopes only when using TF. TF improved the stream isotope simulations. These results demonstrate the importance of canopy‐induced isotope tracer changes in estimating streamwater TTDs in forested catchments. TTDs are affected by throughfall isotope dataA simple correction factor can partly account for throughfall effectsUsing throughfall isotope data is necessary for accurate TTD estimates

Information about the soil–water retention curve is necessary for modeling water flow and solute transport processes in soils. Soil spectroscopy in the visible, near‐infrared, and shortwave infrared (Vis‐NIR‐SWIR) range has been widely used as a rapid, cost‐effective and nondestructive technique to predict soil properties. However, less attention has been paid to predict soil hydraulic properties using soil spectral data. In this paper, spectral reflectances of soil samples from the Zanjanrood watershed, Iran, were measured in the Vis‐NIR‐SWIR ranges (350–2500 nm). Stepwise multiple linear regression coupled with the bootstrap method was used to construct predictive models and to estimate the soil–water retention curve. We developed point and parametric transfer functions based on the van Genuchten (VG) and Brooks‐Corey (BC) soil hydraulic models. Three different types of transfer functions were developed: (i) spectral transfer functions (STFs) that relate VG/BC hydraulic parameters to spectral reflectance values, (ii) pedotransfer function (PTFs) that use basic soil data as input, and (iii) PTFs that consider spectral data and basic soil properties, further referred to as spectral pedotransfer functions (SPTFs). We also derived and evaluated point transfer functions which estimate soil–water contents at specific matric potentials. The point STFs and SPTFs were found to be accurate at low and intermediate water contents (R2> 0.50 and root mean squared error [RMSE] < 0.018 cm3cm−3), while the point PTFs performed better close to saturation. The parametric STFs and SPTFs of both the VG and BC models performed similarly to parametric PTFs in estimating the retention curve. The best predictions of soil–water contents were obtained for all the three transfer functions when the VG and BC retention models were fitted to the retention points estimated by the point transfer functions. Overall, our findings indicate that spectral data can provide useful information to predict soil—water contents and the soil–water retention curve. However, there is a need to extend and validate the derived transfer functions to other soils and regions.

Evaporation is an important process in the global water cycle and its variation affects the near surface soil water content, which is crucial for surface hydrology and climate modelling. Soil evaporation rate is often characterized by two distinct phases, namely, the energy limited phase (stage‐I) and the soil hydraulic limited period (stage‐II). In this paper, a laboratory experiment was conducted using a sand box filled with fine sand, which was subject to evaporation for a period of twenty three days. The setup was equipped with a weighting system to record automatically the weight of the sand box with a constant time‐step. Furthermore, time‐lapse air‐launched ground penetrating radar (GPR) measurements were performed to monitor the evaporation process. The GPR model involves a full‐waveform frequency‐domain solution of Maxwell’s equations for wave propagation in three‐dimensional multilayered media. The accuracy of the full‐waveform GPR forward modelling with respect to three different petrophysical models was investigated. Moreover, full‐waveform inversion of the GPR data was used to estimate the quantitative information, such as near surface soil water content. The two stages of evaporation can be clearly observed in the radargram, which indicates qualitatively that enough information is contained in the GPR data. The full‐waveform GPR inversion allows for accurate estimation of the near surface soil water content during extended evaporation phases, when a wide frequency range of GPR (0.8–5.0 GHz) is taken into account. In addition, the results indicate that the CRIM model may constitute a relevant alternative in solving the frequency‐dependency issue for full waveform GPR modelling.

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. 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 Groundwater level (GWL) and soil moisture data assimilation (DA) improve characterization of subsurface states GWL DA improves GWL characterization up to 8 km from observation locations Evapotranspiration and discharge hardly better estimated, despite better estimates of soil moisture and GWL

In his seminal paper on the solution of the infiltration equation, Philip (1969), https://doi.org/10.1016/b978-1-4831-9936-8.50010-6proposed 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 tgravrefers 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 tgravis valid regardless of the infiltration model used, we opted to reformulate tgravusing the analytic implicit model proposed by Parlange et al. (1982), https://doi.org/10.1097/00010694-198206000-00001valid 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 tgravgiven 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,Philipand the reformulated tgravincrease 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 tgravis a better indicator of time domain validity than tgrav,Philip. For the attainment of steady‐state infiltration, the reformulated tgravis suitable for coarse‐textured soils. Still neither the reformulated tgravnor the classical tgrav,Philipare suitable for fine‐textured soils for which tgravis too conservative and tgrav,Philiptoo short. Using tgravwill improve predictions of the soil hydraulic parameters (particularly Ks) from infiltration data compared to tgrav,Philip. A new formulation for infiltration characteristic time, tgrav, is providedThe reformulated tgravseems to be a better criterion for convergence time of Philip's truncated infiltration equationsThe usage of reformulated tgravimproves predictions of soil hydraulic parameters A new formulation for infiltration characteristic time, tgrav, is provided The reformulated tgravseems to be a better criterion for convergence time of Philip's truncated infiltration equations The usage of reformulated tgravimproves predictions of soil hydraulic parameters

Atrazine (2‐chloro‐4‐ethylamino‐6‐isopropylamino‐1,3,5‐triazine) groundwater monitoring in the Zwischenscholle aquifer in western Germany revealed concentrations exceeding the threshold value of 0.1 μg L−1and increasing concentration trends even 20 yr after its ban. Accordingly, the hypothesis was raised that a continued release of bound atrazine residues from the soil into the Zwischenscholle aquifer in combination with the low atrazine degradation in groundwater contributes to elevated atrazine in groundwater. Three soil cores reaching down to the groundwater table were taken from an agricultural field where atrazine had been applied before its ban in 1991. Atrazine residues were extracted from eight soil layers down to 300 cm using accelerated solvent extraction and analyzed using liquid chromatography–tandem mass spectrometry. Extracted atrazine concentrations ranged between 0.2 and 0.01 μg kg−1for topsoil and subsoil, respectively. The extracted mass from the soil profiles represented 0.07% of the applied mass, with 0.01% remaining in the top layer. A complete and instantaneous remobilization of atrazine residues and vertical mixing with the groundwater body below would lead to atrazine groundwater concentrations of 0.068 μg L−1. Considering the area where atrazine was applied in the region and assuming instantaneous lateral mixing in the Zwischenscholle aquifer would result in a mean groundwater concentration of 0.002 μg L−1. A conservative estimation suggests an atrazine half‐life value of about 2 yr for the soil zone, which significantly exceeds highest atrazine half‐lives found in the literature (433 d for subsurface soils). The long‐term environmental behavior of atrazine and its metabolites thus needs to be reconsidered.

Banana plantations in tropical humid regions require a high input of pesticides. Given the long history of this practice, a number of pesticides that are now banned have accumulated in these soils. Little is known about the sorption of two banned insecticides, chlordecone and cadusafos, which are known to cause adverse environmental and health effects. We studied the sorption-desorption characteristics (Freundlich sorption-desorption coefficient, Kf, and partition coefficient, Kd) of these two molecules in tropical volcanic soils with different soil properties. In particular, we observed the effect of the chemical nature of the soil organic carbon (SOC). The sorption of chlordecone (35.56

Tomographic inversions of cross‐hole ground‐penetrating radar provide images of electromagnetic properties of the shallow subsurface and are used in a wide range of applications. Whereas the resolutions of ray‐based methods like first‐arrival traveltime and first‐cycle amplitude tomography are limited to the scale of the first Fresnel zone, full‐waveform inversions incorporate precise forward modelling using the full recorded signal for a solution of Maxwell’s equation, which results in sub‐wavelength resolutions. In practice, the method can be time‐consuming in data acquisition and expensive in computational costs. To overcome these expenses, a semi‐reciprocal acquisition setup with a reduced number of transmitters and an interchange of transmitter and receiver boreholes instead of a one‐sided equidistant setup in either borehole yielded promising results. Here, this optimized, semi‐reciprocal acquisition setup is compared to a dense, equidistant, one‐sided acquisition setup measured at the field site Krauthausen, Germany. The full‐waveform inversion results are evaluated using the checkerboard test as a capable resolution analysis tool to determine resolvabili‐ties. We introduced also a new method of time‐zero correction by a cross‐correlation of a zero‐offset profile with corresponding horizontal traces of each multi‐offset gather. The obtained experimental results from Krauthausen combined with the checkerboard analysis indicate the main three‐permittivity layers that correspond with different porosities. Also fine‐layered structures within these main layers were reliably imaged. We conclude that the use of the semi‐reciprocal setup is optimum for acquisition speed, inversion speed and obtained permittivity inversion results. Our results indicate that conductivity results are better for denser transmitter‐receiver setups.

Ground‐penetrating radar (GPR) data affected by waveguide dispersion are not straightforward to analyse. Therefore, waveguide dispersed common midpoint measurements are typically interpreted using so‐called dispersion curves, which describe the phase velocity as a function of frequency. These dispersion curves are typically evaluated with deterministic optimization algorithms that derive the dielectric properties of the subsurface as well as the location and depth of the respective layers. However, these methods do not provide estimates of the uncertainty of the inferred subsurface properties. Here, we applied a formal Bayesian inversion methodology using the recently developed DiffeRential Evolution Adaptive Metropolis DREAM(ZS)algorithm. This Markov Chain Monte Carlo simulation method rapidly estimates the (non‐linear) parameter uncertainty and helps to treat the measurement error explicitly. We found that the frequency range used in the inversion has an important influence on the posterior parameter estimates, essentially because parameter sensitivity varies with measurement frequency. Moreover, we established that the measurement error associated with the dispersion curve is frequency dependent and that the estimated model parameters become severely biased if this frequency‐dependent nature of the measurement error is not properly accounted for. We estimated these frequency‐dependent measurement errors together with the model parameters using the DREAM(ZS)algorithm. The posterior distribution of the model parameters derived in this way compared well with inversion results for a reduced frequency bandwidth which is an alternative, yet subjective method to reduce the bias introduced by this frequency‐dependent measurement error. Altogether, our inversion procedure provides an integrated and objective methodology for the analysis of dispersive GPR data and appropriately treats the measurement error and parameter uncertainty.

Physically‐based ground‐penetrating radar (GPR) data processing is essential for quantitative characterization of soils and materials. A novel near‐field GPR antenna model coupled with layered media Green's functions was used to investigate the effect of antenna‐medium coupling in the analysis of GPR data. The radar antennas are modelled using an equivalent set of infinitesimal electric dipoles and characteristic, frequency‐dependent, global reflection and transmission coefficients. These coefficients determine through plane wave decomposition, wave propagation between the radar reference plane, point sources and field points. We calibrated an actual commercial 400 MHz time‐domain antenna, from which synthetic GPR data sets were generated. We observed that, depending on the model configuration, antenna effects may affect the topography of the objective function in full‐waveform inverse problems. In addition, antenna‐medium coupling has a significant impact on the medium surface reflection, whether in terms of amplitude or propagation time (which usually defines the so‐called time zero). We also showed that an effective source cannot be used for simulating near‐field radar data as the antenna‐medium coupling strongly depends on medium properties. In this respect, numerical experiments demonstrated promising perspectives for simultaneous estimates of medium permittivity and conductivity from antenna‐medium coupling.

Understanding the long‐term sequestration of veterinary antibiotics into soil fractions with different bioavailability is important in terms of assessing their eco‐toxicological impact. We performed 60‐d batch sorption experiments with radiolabeled sulfadiazine (SDZ) using samples from two agricultural soils. Sequential extraction with CaCl2/MeOH (easily accessible fraction), microwave (residual fraction, RES), and combustion (nonextractable residues, NER) was used to quantify the sequestration dynamics of the 14C‐derived SDZ‐equivalent concentration. Multiple harsh extractions allowed us to mathematically extrapolate to the amount of SDZ equivalents that can be potentially extracted, resulting in halving the NER fraction after 60 d. A modified two‐stage model with irreversible sorption combined with global parameter optimization was able to display the sequestration dynamics. We demonstrated this with sterilized samples in which no transformation of the parent compound was observed. This also showed that transformation was primarily biologically driven. These modeling results verified the procedure, which was then applied to nontreated samples from both soils to estimate effective parameter values for SDZ‐derived equivalents. Observed initial sorption, to which up to 20% of the kinetic sorption sites attributed, was included in the model. Both the RES and NER fractions reached a sorption plateau, with NER occupying about 30% of the kinetic fraction (RES+NER) for all soils. The sorption and sequestration of SDZ were soil‐specific and dominated by kinetics. Sequestration in the RES fraction was much slower (characteristic time: 60 d) than the redistribution in the NER fraction (characteristic time: <6 d). The work presented here contributes to the prediction of the dynamics of (bio‐)availability.

The environmental risks caused by the use of fluoroquinolone antibiotics in human therapeutics and animal husbandry are associated with their persistence and (bio)accessibility in soil. To assess these aspects, we administered difloxacin to pigs and applied the contaminated manure to soil. We then evaluated the dissipation and sequestration of difloxacin in soil in the absence and presence of plants within a laboratory trial, a mesocosm trial, and a field trial. A sequential extraction yielded antibiotic fractions of differing binding strength. We also assessed the antibiotic's effects on nitrogen turnover in soil (potential nitrification and denitrification). Difloxacin was hardly (bio)accessible and was very persistent under all conditions studied (dissipation half‐life in bulk soil, >217 d), rapidly forming nonextractable residues. Although varying environmental conditions did not affect persistence, dissipation was accelerated in soil surrounding plant roots. Effects on nitrogen turnover were limited due to the compound's strong binding and small (bio)accessibility despite its persistence.

The quantitative retrieval of soil apparent electrical conductivity using electromagnetic induction (EMI) has remained limited due to strong simplifications regarding EMI antenna modelling. In this paper, a new technique for EMI antenna modelling is applied for the common‐offset EMI systems. The EMI system is efficiently described using global transmission and reflection coefficients and Green's functions are used to describe wave diffusion for horizontal and vertical dipole modes. We performed EMI measurements along a 180‐metre‐long transect with two different instrument heights above the soil surface, as well as with different orientations and frequencies. To ensure proper retrieval of the soil apparent electrical conductivity, the reference values were obtained from electrical conductivity data measured from 11 undisturbed soil cores taken along the EMI transect. The apparent electrical conductivity values calculated by applying the proposed model have a good agreement with reference values, however some discrepancies can be observed that are mainly attributed to the presence of local heterogeneities and also errors due to the variations in the height of the EMI instruments above the ground. The proposed method appears to be promising for quantitative retrieval of soil apparent electrical conductivity and resolving calibration issues that are typically encountered using EMI. In addition, the model calibration (antenna transfer functions determination) was successfully accomplished using conductivity values measured from the soil cores.

We applied inverse modelling of zero‐offset, air‐raised ground‐penetrating radar (GPR) data to measure soil surface water contents over a bare agricultural field. The GPR system consisted of a vector network analyser combined with a low‐frequency 0.2–2.0 GHz off‐ground monostatic horn antenna, thereby setting up an ultra‐wideband stepped‐frequency continuous‐wave radar. A fully automated platform was created by mounting the radar system on a truck for real‐time data acquisition. An antenna calibration experiment was performed by lifting the whole setup to different heights above a perfect electrical conductor. This calibration procedure allowed the flittering out of the antenna effects and antenna‐soil interactions from the raw radar data in the frequency domain. To avoid surface roughness effects, only the lower frequency range of 0.2–0.8 GHz was used for signal processing. Inversions of the radar data using the Green’s functions were performed in the time domain, focusing on a time window containing the surface reflection. GPR measurements were conducted every 4 m along a transect of 100 m. In addition, five time‐domain reflectometry measurements were randomly recorded within the footprint of the GPR antenna. A good agreement was observed between the GPR and time‐domain reflectometry soil water content estimates, as compared to the previous study performed at the same test site using a higher frequency 0.8–1.6 GHz horn antenna. To monitor the dynamics of soil water content, a pair of time‐domain reflectometry probes was installed at 8 cm depth near the footprint of the GPR antenna and both time‐domain reflectometry and GPR measurements were carried out for a period of 20 days. A good agreement of the trend was observed between the time‐domain reflectometry and GPR time‐lapse data with respect to several precipitation events. The proposed method and truck‐mounted setup appear to be promising for the real‐time mapping and monitoring of surface soil moisture contents at the field scale.

Antibiotics, such as sulfadiazine (SDZ), may enter arable soil by spreading of manure of medicated husbandry or directly by the excrement of grazing animals. Knowledge of the fate of antibiotics in soils is crucial for assessing the environmental risk of these compounds, including possible transport to ground water. Kinetic sorption of 14C‐labeled SDZ (4‐amino‐N‐pyrimidin‐2‐yl‐benzenesulfonamide) was investigated using the batch technique. The batch sorption–desorption experiments were conducted at various concentration levels (0.044–13 mg L−1initial solute concentration) and time scales (0.75–272 d). Sorption of 14C‐SDZ in the investigated silty loam was time dependent and strongly nonlinear in the solution phase concentration. The time to reach an apparent sorption equilibrium was about 20 d. However, desorption was very slow, and 41 d were insufficient to reach the desorption equilibrium. An inverse modeling technique was used to identify relevant sorption processes of 14C‐SDZ during the batch experiments. Among the investigated two‐ and three‐domain sorption models, adsorption and desorption of 14C‐SDZ were best described with a new model defining two sorption domains and four parameters. Whereas sorption in the first sorption domain was nonlinear and instantaneous, solute uptake in the second sorption domain was rate limited following first‐order kinetics. Desorption followed the same rate law until an equilibrium distribution was reached. After that, desorption was assumed to be impossible due to partly irreversible sorption. Although the proposed model needs further validation, it contributes to the discussion on complex sorption processes of organic chemicals in soils.

The unknown consequences and potential impacts of mankind's ability to destroy, alter, or manipulate ecosystems on a vast scale drives our need to better understand the earth system. A fundamental challenge for soil science in the 21st century is to understand the role of soil processes in relation to the function of the earth system. The rationale for developing a definition of soil natural capital stems from the premise that we value ‘things’ based on their perceived value to human well‐being. As a consequence, ignorance of the value of a resource, or system, may lead to its neglect and omission from decision making. Therefore, there is a need to develop a definition of soil natural capital, fitting within a broad framework, which can be used to assess soil ecosystem services that contribute to the function of the earth system. Though various definitions of soil natural capital have been proposed, mostly in the agricultural context, it still remains a nebulous and ill‐defined term. The objective of this paper is to develop an embracing definition of soil ‘natural capital’ focusing on (i) mass, (ii) energy, and (iii) organization/entropy. Mass is further subdivided into solid, liquid and gas phases, and organization into physicochemical, biotic, and spatiotemporal structure. We differentiate between two aspects of capital, the quantity and the quality. As a result of our definition, soil moisture, temperature, and structure emerge as valuable stocks, alongside the more traditionally viewed stocks such as inorganic (mineralogy, texture) and organic materials (OM content). We go on to demonstrate how natural capital fits within the ecosystem services framework, and how using integrated valuation and process based models it can be evaluated. Finally we discuss measurement and monitoring needs that fit with this vision of evaluation.

Full‐waveform inverse modelling of proximal ground‐penetrating radar was used to measure soil surface water content in irrigated areas of southern Tunisia. The ground‐penetrating radar system consisted of a hand held vector network analyser combined with an off‐ground monostatic horn antenna, thereby setting up an ultra wideband stepped‐frequency continuous‐wave radar. Inversion of the radar Green’s function was performed in the time‐domain, on a time window focused on the surface reflection only. Results were compared with volumetric and time‐domain reflectometry measurements, as well as with an improved version of the standard reflection coefficient method. Except for water contents close to saturation, good agreements were obtained between ground‐penetrating radar, time‐domain reflectometry and volumetric samples. Significant differences were observed when the soil electric conductivity was high and could not be neglected anymore in the inversion process. Accounting for electric conductivity provided better results. Remaining errors were attributed to the different scales of characterization dealt with in relation to the vertical variability of water content in the top few centimetres of the soil. The proposed method appears to be more practical and accurate than the standard reflection coefficient method and shows great promise for real‐time mapping of surface soil moisture at the field scale.

A tool has been designed to calculate the leaching potential of pesticide on a catchment scale using the one-dimensional pesticide leaching model MACRO. The tool is able to collect the input data of the study area in a grid with homogeneous cells and prepare spatially distributed data required by MACRO. Successively, the tool can run multiple MACRO simulations for each cell of the grid and organise output reports following the user’s preference, as well as generate output files compatible with GIS and other environmental software applications. The tool was tested to simulate the behaviour in the unsaturated zone of water and pesticide isoproturon used on winter wheat at 1.75 kg ha–1 of active ingredient in a German study area for 10 years of meteorological data and for different crop-soil combinations. Maps of isoproturon concentration in the liquid phase were created using the spatialised output data from the tool.

The consideration of pesticide volatilization from soil surfaces as an integral component of pesticide fate models is of importance, especially as part of the Predicted Environmental Concentrations (PEC) models used in the registration procedures for pesticides. The Pesticide Leaching Model (PELMO), which is used in the European registration process, was modified to allow for a reliable prediction of volatilization from soil. The previous PELMO version was upgraded by improving the spatiotemporal discretization at the soil surface, improving the empirical description of temperature dependence of Henry's law constants and including increased sorption of pesticides in dry soils. Comparison of predictions with experimental findings revealed the improvements of PELMO to contribute to a more realistic reflection of measurements, particularly at initial stages of the studies. The broad range of literature values of Henry's law constants was shown to have a significant effect on predicted volatilization fluxes. As a main refinement, the tendency of pesticides toward enhanced volatilization under moist conditions was correctly calculated by the improved model. Variations between model predictions and measurements were due to a lack of experimental data on soil sorption under dry conditions and indicated the need for further calibration of the model. The description of water content in the top layer was subject to uncertainty, which was exemplified by an overestimation of soil moisture during the last days of the field study. Thus, future model improvement will be dependent on experimental support to obtain more detailed information on soil–air–water partitioning of pesticides in the top soil layer.

A comparison was drawn between model predictions and experimentally determined volatilization rates to evaluate the volatilization approaches of European registration models. Volatilization rates of pesticides (14C‐labeled parathion‐methyl, fenpropimorph, and terbuthylazine and nonlabeled chlorpyrifos) were determined in a wind‐tunnel experiment after simultaneous soil surface application on Gleyic Cambisol. Both continuous air sampling, which quantifies volatile losses of 14C‐organic compounds and 14CO2separately, and the detection of soil residues allow for a mass balance of radioactivity of the 14C‐labeled pesticides. Recoveries were found to be >94% of the applied radioactivity. The following descending order of cumulative volatilization was observed: chlorpyrifos > parathion‐methyl > terbuthylazine > fenpropimorph. Due to its high air–water partitioning coefficient, nonlabeled chlorpyrifos was found to have the highest cumulative volatilization (44.4%) over the course of the experiment. Volatilization flux rates were measured up to 993 μg m−2h−1during the first hours after application. Parameterization of the Pesticide Emission Assessment at Regional and Local Scales (PEARL) model and the Pesticide Leaching Model (PELMO) was performed to mirror the experimental boundary conditions. In general, model predictions deviated markedly from measured volatilization rates and showed limitations of current volatilization models, such as the uppermost compartment thickness, making an enormous influence on predicted volatilization losses. Experimental findings revealed soil moisture to be an important factor influencing volatilization from soil, yet its influence was not reflected by the model calculations. Future versions of PEARL and PELMO ought to include improved descriptions of aerodynamic resistances and soil moisture dependent soil–air partitioning coefficients.

This paper demonstrates the possibilities of reactive transport modeling in predicting contaminant transport processes of laboratory and field scales. Basic approaches of reactive transport modeling are discussed, focusing on the incorporation of a thermodynamic description of chemical interactions into the convection-dispersion equation (CDE). Both equilibrium and kinetic approaches are presented. Chemical reaction parameters can be estimated from 1-D column experiments, by combining the CDE and the thermodynamic description of transport. A 2-D simulation with a heterogeneous flow field and ion exchange parameters obtained from column experiments is presented. The results of the 2-D model are in qualitative agreement with measured field breakthrough curves. Limitations of this approach are discussed, focusing on parameter heterogeneity and scaling effects.

The emission of [ 3-$^{14}$C] fluoranthene and [ pyridine-2-$^{14}$C] diflufenican after application to bare soil was measured and compared to model predictions. Gaseous losses were determined in a glass wind tunnel under field-like conditions. In several experiments of 13-14 days each, both compounds were sprayed onto the soil surface and the volatilization rates were determined. Measurements showed that the cumulative volatilization of fluoranthene was considerably higher than that of diflufenican. The data obtained were compared with the output of a screening model (BAM) and two estimation methods. Variations in the most sensitive model parameters (e.g. soil moisture and temperature) were investigated by changing the default values used in the model. The influence on the predicted volatilization is discussed in relation to our experimental findings. Several limitations and shortcomings in the current screening and estimation tools for volatilization are characterized and point to the need for an improved physically based model which adequately describes the relevant processes.

The paper presents a study of the diffusive transport of passive solute plumes in a two-dimensional non-homogeneous depth stratified flow domain. All the properties of the process are expressed by depth dependent deterministic functions. The method of moments, combined with the method of Green functions are chosen to determine the relevant characteristics of the flow (mass, center of mass, variance, etc.) used to describe the behaviour of the transient motion. General relationships for the n-order concentration moments are proved. Further, it is derived that the transient motion defined by time-dependent parameters tends asymptotically at large time to a stable regime whose characteristics are determined. Consequently, under certain hypotheses, an equivalence between the mean original process and a Fickian diffusive transport at large time may be established. The time required by the process to reach its asymptotic behaviour is also calculated.

Kurzfassung: In dem heterogenen Grundwasserleiter des Testfeldes Krauthausen wurden zur Untersuchung des Transports von gelösten Stoffen drei Tracerversuche (Bromid, Lithium, Uranin) durchgeführt. Um den Einfluss der natürlichen Heterogenität auf den Stofftransport zu untersuchen, wurden die transportrelevanten Parameter des Grundwasserleiters hoch aufgelöst untersucht. In den Tracerversuchen wurde bei den Stoffen Uranin und Lithium im Vergleich zu Bromid eine deutliche Retardierung festgestellt, die mit abnehmender Stoffkonzentration zunahm. Dieses Verhalten kann für Uranin und Lithium mit einer Freundlich-Sorptions-Isotherme beschrieben werden. Die longitudinale Aufweitung der Wolke von Lithium war deutlich größer als die von Uranin. Dies lässt sich durch eine höhere Einleitkonzentration von Lithium, abweichende Freundlich-Parameter und die unterschiedliche Sorptionskinetik erklären. Die Sorption von Uranin und Lithium war mit dem Tongehalt und der Kationenaustauschkapazität korrelierbar. Für die spezifische Oberfläche wurde eine Korrelation mit der Sorption von Uranin festgestellt. Die vertikalen Autokorrelationslängen der physiko-chemischen Parameter und der hydraulischen Durchlässigkeit sind von gleicher Größenordnung.

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). Soil hydraulic property models are mathematical functions, which describe the relationship between the soil water pressure head and the state of soil water saturation, on the one hand, and the soil water pressure head and the unsaturated soil hydraulic conductivity, on the other. These types of mathematical functions are flexible by adjustable parameters. With one set of model equations, the hydraulic properties of soils which may have very different properties due to their vast natural variability can be described. The models treated in this work are (i) the van Genuchten‐Mualem model, a model with well‐known problems, but still frequently applied, and (ii) a relatively new physical comprehensive model, named the Brunswick model. First of all, in a data‐based comparison of model performance, we demonstrate that the Brunswick model has systematic advantages. Second, knowledge about these above‐mentioned parameters can be determined through other mathematical functions, so‐called hydro‐pedotransfer functions, which empirically relate these parameters to observed soil properties. The information about these soil properties can be measured in the laboratory and is also recorded in soil maps. We created a new pedotransfer function to facilitate the prediction of model parameters for the new Brunswick model. A pedotransfer function is established to relate van Genuchten‐Mualem (VGM) to Brunswick (BW) soil hydraulic property model parametersThe BW model overcomes the structural deficiencies in VGM and leads to considerably better descriptions of retention and conductivity dataWith the new pedotransfer function, soil properties and information in soil maps can be used to predicted BW model parameters

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.

An adequate characterization of river bed hydraulic conductivities (L) is crucial for a proper assessment of river‐aquifer interactions. However, river bed characteristics may change over time due to dynamic morphological processes like scouring or sedimentation what can lead to erroneous model predictions when static leakage parameters are assumed. Sequential data assimilation with the ensemble Kalman filter (EnKF) allows for an update of model parameters in real‐time and may thus be capable of assessing the transient behavior of L. Synthetic experiments with a three‐dimensional finite element model of the Limmat aquifer in Zurich were used to assess the performance of data assimilation in capturing time‐variant river bed properties. Reference runs were generated where Lfollowed different temporal and/or spatial patterns which should mimic real‐world sediment dynamics. Hydraulic head (h) data from these reference runs were then used as input data for EnKF which jointly updated hand L. Results showed that EnKF is able to capture the different spatio‐temporal patterns of Lin the reference runs well. However, the adaptation time was relatively long which was attributed to the fast decrease of ensemble variance. To improve the performance of EnKF also an adaptive filtering approach with covariance inflation was applied that allowed a faster and more accurate adaptation of model parameters. A sensitivity analysis indicated that even for a low amount of observations a reasonable adaptation of Ltowards the reference values can be achieved and that EnKF is also able to correct for a biased initial ensemble of L. EnKF is able to recalibrate time‐variable river bed conductivities in real‐timeAdaptation time and accuracy can be improved with adaptive covariance inflationReasonable results were also obtained for a low number of observations

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