Temperature-Corrected Calibration of GS3 and TEROS-12 Soil Water Content Sensors.

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = aKb + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm-3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm-3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites.

Limited effects of crop foliar Si fertilization on a marginal soil under a future climate scenario.

Growing crops on marginal lands is a promising solution to alleviate the increasing pressure on agricultural land in Europe. Such crops will however be at the same time exposed to increased drought and pathogen prevalence, on already challenging soil conditions. Some sustainable practices, such as Silicon (Si) foliar fertilization, have been proposed to alleviate these two stress factors, but have not been tested under controlled, future climate conditions. We hypothesized that Si foliar fertilization would be beneficial for crops under future climate, and would have cascading beneficial effects on ecosystem processes, as many of them are directly dependent on plant health. We tested this hypothesis by exposing spring barley growing on marginal soil macrocosms (three with, three without Si treatment) to 2070 climate projections in an ecotron facility. Using the high-capacity monitoring of the ecotron, we estimated C, water, and N budgets of every macrocosm. Additionally, we measured crop yield, the biomass of each plant organ, and characterized bacterial communities using metabarcoding. Despite being exposed to water stress conditions, plants did not produce more biomass with the foliar Si fertilization, whatever the organ considered. Evapotranspiration (ET) was unaffected, as well as water quality and bacterial communities. However, in the 10-day period following two of the three Si applications, we measured a significant increase in C sequestration, when climate conditions where significantly drier, while ET remained the same. We interpreted these results as a less significant effect of Si treatment than expected as compared with literature, which could be explained by the high CO2 levels under future climate, that reduces need for stomata opening, and therefore sensitivity to drought. We conclude that making marginal soils climate proof using foliar Si treatments may not be a sufficient strategy, at least in this type of nutrient-poor, dry, sandy soil.

Wildfire precursors show complementary predictability in different timescales.

In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.

Multi-year belowground data of minirhizotron facilities in Selhausen.

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil-plant continuum and the root images to develop and compare image analysis methods.

Leveraging research infrastructure co-location to evaluate constraints on terrestrial carbon cycling in northern European forests.

Integrated long-term, in-situ observations are needed to document ongoing environmental change, to "ground-truth" remote sensing and model outputs and to predict future Earth system behaviour. The scientific and societal value of in-situ observations increases with site representativeness, temporal duration, number of parameters measured and comparability within and across sites. Research Infrastructures (RIs) can support harmonised, cross-site data collection, curation and publication. Integrating RI networks through site co-location and standardised observation methods can help answers three questions about the terrestrial carbon sink: (i) What are present and future carbon sequestration rates in northern European forests? (ii) How are these rates controlled? (iii) Why do the observed patterns exist? Here, we present a conceptual model for RI co-location and highlight potential insights into the terrestrial carbon sink achievable when long-term in-situ Earth observation sites participate in multiple RI networks (e.g., ICOS and eLTER). Finally, we offer recommendations to promote RI co-location.

3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows.

Magnetic resonance imaging (MRI) is used to image root systems grown in opaque soil. However, reconstruction of root system architecture (RSA) from 3-dimensional (3D) MRI images is challenging. Low resolution and poor contrast-to-noise ratios (CNRs) hinder automated reconstruction. Hence, manual reconstruction is still widely used. Here, we evaluate a novel 2-step work flow for automated RSA reconstruction. In the first step, a 3D U-Net segments MRI images into root and soil in super-resolution. In the second step, an automated tracing algorithm reconstructs the root systems from the segmented images. We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems, by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system. We found that the U-Net segmentation offers profound benefits in manual reconstruction: reconstruction speed was doubled (+97%) for images with low CNR and increased by 27% for images with high CNR. Reconstructed root lengths were increased by 20% and 3%, respectively. Therefore, we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows. The root length derived by the tracing algorithm was lower than in both manual reconstruction methods, but segmentation allowed automated processing of otherwise not readily usable MRI images. Nonetheless, model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions. Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected.

Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects.

Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.

Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline.

Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using "RootPainter." Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer." To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r = 0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.

Root System Scale Models Significantly Overestimate Root Water Uptake at Drying Soil Conditions.

Soil hydraulic conductivity (k soil ) drops significantly in dry soils, resulting in steep soil water potential gradients (ψ s ) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300% for the coarsest soil grid of 4.0 cm and by 30% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (-20%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution.

Pore-Scale Mechanisms for Spectral Induced Polarization of Calcite Precipitation Inferred from Geo-Electrical Millifluidics.

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.

Investigating Soil-Root Interactions with the Numerical Model R-SWMS.

In this chapter, we present the Root and Soil Water Movement and Solute transport model R-SWMS, which can be used to simulate flow and transport in the soil-plant system. The equations describing water flow in soil-root systems are presented and numerical solutions are provided. An application of R-SWMS is then briefly discussed, in which we combine in vivo and in silico experiments in order to decrypt water flow in the soil-root domain. More precisely, light transmission imaging experiments were conducted to generate data that can serve as input for the R-SWMS model. These data include the root system architecture, the soil hydraulic properties and the environmental conditions (initial soil water content and boundary conditions, BC). Root hydraulic properties were not acquired experimentally, but set to theoretical values found in the literature. In order to validate the results obtained by the model, the simulated and experimental water content distributions were compared. The model was then used to estimate variables that were not experimentally accessible, such as the actual root water uptake distribution and xylem water potential.

KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models-II: model description, implementation and testing.

New knowledge on soil structure highlights its importance for hydrology and soil organic matter (SOM) stabilization, which however remains neglected in many wide used models. We present here a new model, KEYLINK, in which soil structure is integrated with the existing concepts on SOM pools, and elements from food web models, that is, those from direct trophic interactions among soil organisms. KEYLINK is, therefore, an attempt to integrate soil functional diversity and food webs in predictions of soil carbon (C) and soil water balances. We present a selection of equations that can be used for most models as well as basic parameter intervals, for example, key pools, functional groups' biomasses and growth rates. Parameter distributions can be determined with Bayesian calibration, and here an example is presented for food web growth rate parameters for a pine forest in Belgium. We show how these added equations can improve the functioning of the model in describing known phenomena. For this, five test cases are given as simulation examples: changing the input litter quality (recalcitrance and carbon to nitrogen ratio), excluding predators, increasing pH and changing initial soil porosity. These results overall show how KEYLINK is able to simulate the known effects of these parameters and can simulate the linked effects of biopore formation, hydrology and aggregation on soil functioning. Furthermore, the results show an important trophic cascade effect of predation on the complete C cycle with repercussions on the soil structure as ecosystem engineers are predated, and on SOM turnover when predation on fungivore and bacterivore populations are reduced. In summary, KEYLINK shows how soil functional diversity and trophic organization and their role in C and water cycling in soils should be considered in order to improve our predictions on C sequestration and C emissions from soils.

KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.

A framework for modelling soil structure dynamics induced by biological activity.

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

Call for Participation: Collaborative Benchmarking of Functional-Structural Root Architecture Models. The Case of Root Water Uptake.

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models, such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore, defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute toward improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.

Soil structure is an important omission in Earth System Models.

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.

Investigating the root plasticity response of Centaurea jacea to soil water availability changes from isotopic analysis.

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

Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data.

Multi-coil electromagnetic induction (EMI) systems induce magnetic fields below and above the subsurface. The resulting magnetic field is measured at multiple coils increasingly separated from the transmitter in a rigid boom. This field relates to the subsurface apparent electrical conductivity (σa), and σa represents an average value for the depth range investigated with a specific coil separation and orientation. Multi-coil EMI data can be inverted to obtain layered bulk electrical conductivity models. However, above-ground stationary influences alter the signal and the inversion results can be unreliable. This study proposes an improved data processing chain, including EMI data calibration, conversion, and inversion. For the calibration of σa, three direct current resistivity techniques are compared: Electrical resistivity tomography with Dipole-Dipole and Schlumberger electrode arrays and vertical electrical soundings. All three methods obtained robust calibration results. The Dipole-Dipole-based calibration proved stable upon testing on different soil types. To further improve accuracy, we propose a non-linear exact EMI conversion to convert the magnetic field to σa. The complete processing workflow provides accurate and quantitative EMI data and the inversions reliable estimates of the intrinsic electrical conductivities. This improves the ability to combine EMI with, e.g., remote sensing, and the use of EMI for monitoring purposes.

Co-transport of multi-walled carbon nanotubes and sodium dodecylbenzenesulfonate in chemically heterogeneous porous media.

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 ¼â€¯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.