Recent advances in Earth observation, data science, artificial intelligence (AI), and computational modeling are transforming soil physics by enabling multi-scale monitoring and integrated data–model approaches. The combination of remote sensing, innovative measurement techniques, and process-based models provides new opportunities to estimate soil physical properties, assimilate heterogeneous data sources, quantify uncertainties, and improve the understanding of soil–water–atmosphere interactions.
This session aims to bring together researchers working on measurements, modeling, and data-driven approaches to advance soil physics across scales. It bridges traditional soil physics concepts with emerging technologies, fostering interdisciplinary exchange among soil scientists, hydrologists, and Earth observation researchers.
Topics include, but are not limited to: innovative laboratory and field measurement techniques; infiltration experiments; multi-scale remote sensing of soil moisture and physical properties; preferential flow and macropore processes; coupling AI and machine learning with process-based models; data assimilation, inverse modeling, pedotransfer functions, and data fusion; numerical and analytical models accounting for complex soil processes; uncertainty analysis; and case studies supporting climate impact assessments, sustainable land management, and hydrological prediction. Early-career and interdisciplinary contributions are especially encouraged.
How to cite: Rigon, R.: A Meta-Dynamics Framework for Non-Equilibrium Soil Moisture and Unified Matrix-Macropore Flow., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5803, https://doi.org/10.5194/egusphere-egu26-5803, 2026.
One dimensional dual-permeability models are used to simulate preferential flow and transport in soils. In these models, soil structure, including large biopores and cracks, is accounted for in a simple way by splitting the total porosity into a micropore and a macropore domain with first-order equations governing the transfer of water and solutes between the domains. The transfer equations are central in dual-permeability models because the values of the included parameters have a strong influence on the degree of simulated preferential flow and transport. These transfer terms include a parameter describing the characteristic length of the soil matrix structure. This characteristic length is well defined for idealised pore geometries. However, such idealised pore geometries are poor representations of macropore networks in intact soil. Our objective was to test if values of parameters governing the degree of preferential transport in dual-permeability models could be estimated from measures of soil structure derived from X-ray tomography images. To achieve this, we calibrated the dual-permeability model MACRO against non-reactive solute breakthrough curves obtained at two flow rates from 33 intact soil columns sampled from a field with large variation in soil properties. Relations between measures of soil structure derived from images of the same columns and values for parameters governing preferential transport were then evaluated. The MACRO model could reproduce all BTCs well except those for three sandy soils. When the saturated water content of the soil matrix, here used to account for possible water repellency, was included in the calibration also the BTCs for the sandy soils were well reproduced. Preliminary results indicate that the fractal dimension of the total imaged pore network is the strongest predictor for the characteristic length of the soil matrix. The other included model parameters were not strongly correlated with any measures of the total imaged pore network. We will also present results for the imaged percolating macropore networks (i.e. the parts of the pore networks connected to both the top and bottom of the imaged region of interest), which is a better representation of the pore network that was active during the transport experiments.
How to cite: Larsbo, M. and Fukumasu, J.: The relation between X-ray-derived measures of soil structure and dual-permeability model parameter values, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9525, https://doi.org/10.5194/egusphere-egu26-9525, 2026.
Accurate soil hydraulic properties (SHP) are essential for groundwater modeling with Richards’ equation. In coastal soils affected by sea-level rise, SHP shift in response to changing salinity and repeated wetting-drying cycles, yet the combined effects of soil salinity and drying memory remain poorly quantified. We conducted a controlled laboratory study to investigate these interactions across the full moisture range in three soils of different textures (sand, sandy loam, and silt loam). Soils were saturated with artificial seawater at three salinity levels, 0, 15, and 30 dS m⁻¹. SHP were measured using HYPROP evaporation experiments and WP4C dewpoint potentiometry over repeated drying cycles. Salinity induced strong, texture-dependent responses. Although salinity imposed a limited influence on the water retention behavior of the sandy silt loam, it resulted in a pronounced increase in unsaturated hydraulic conductivity, consistent with salt-driven flocculation and pore-domain reorganization. Across all textures, SHP exhibited non-linear dependencies on salinity, texture, and drying memory. Model fitting of the measured data showed that the Peters-Durner-Iden (PDI) model outperformed the van Genuchten model, due to its explicit representation of adsorptive water and film flow. The resulting dataset provides a mechanistic foundation for next-generation SHP models that incorporate dynamic soil structure and texture-specific coupling between matric and osmotic effects.
How to cite: Naseri, M. and Jin, Y.: Drying-memory effects and texture-dependent salinity responses revealed by full-range measurements of soil hydraulic properties, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6028, https://doi.org/10.5194/egusphere-egu26-6028, 2026.
A fundamental challenge in soil physics is understanding how particle size distribution and structural organization jointly determine hydraulic behavior. Traditional analytical methods systematically destroy structural context to isolate "pure" texture measurements, eliminating the very relationships we seek to understand. While we know texture and structure interact, quantifying their relative contributions and functional interdependence across diverse soils remains elusive. We use interpretable machine learning as a discovery tool to disentangle texture and structure effects on soil water retention and hydraulic conductivity. Through staged training experiments, we systematically isolate texture-only predictions (sand, silt, clay) from structure-mediated modifications (bulk density, organic carbon). By freezing model components that interpret hydraulic behavior and controlling input availability during training, we extract learned representations that reveal how structural context alters the hydraulic meaning of identical particle size distributions. Our approach incorporates physical constraints while learning representations that capture functional complexity beyond what simple texture classes encode. Initial analyses suggest that structural inputs progressively reorganize texture-based patterns in the learned embedding space, with the magnitude of structural modulation varying systematically across soil types. Soils with identical particle size distributions occupy distinct functional spaces depending on bulk density and organic matter content—texture acquires hydraulic meaning only through structural context. These learned representations align with physical intuition: structural effects dominate precisely where classical pedotransfer functions show highest uncertainty. This demonstrates how interpretable AI can recover relationships eliminated by reductionist analytical protocols, transforming machine learning from a prediction tool into an instrument for scientific insight. Beyond improving hydraulic property estimation, the methodology offers a framework for investigating other soil properties where composition and organization interact to determine function—challenging us to rethink what we measure and how we interpret it.
How to cite: Ghezzehei, T.: Deciphering Texture-Structure Interactions in Soil Hydraulic Behavior Through Interpretable Neural Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14966, https://doi.org/10.5194/egusphere-egu26-14966, 2026.
Infiltration events, such as rain-on-dry soil or snowmelt over frozen ground, often introduce water at a temperature different from the surrounding soil and air. In such cases, Local Thermal Non-Equilibrium (LTNE) conditions arise, where water, air, ice, and the solid matrix maintain distinct temperatures over extended periods. This is especially true in macropores systems, where rapid flow enhances thermal decoupling.
We present a novel dual-permeability model that resolves water, air, and solid temperatures independently under LTNE conditions. The framework captures the dynamic behavior of macropores and micropores during infiltration and freeze-thaw cycles and is validated against controlled laboratory experiments.
In the first stage, we simulate unsaturated infiltration into soils featuring laboratory-defined macropore configurations under non-isothermal boundary conditions. Sensitivity analyses identified the mass exchange coefficient (γ=10-4 , 10-2 [s-1]) and the macropore volume fraction (ωMa= 0.2, 0.3 [-]) as key parameters controlling thermal equilibration between pore domains. Results show that thermal disequilibrium persists significantly longer in macropores than in micropores, reflecting the dominance of advective transport in larger pore structures. To expand this investigation, we apply the model to freezing and thawing scenarios in cold-region soils. By integrating a three-phase formulation (liquid, ice, solid) and freezing point depression, we reproduce key phenomena such as delayed freezing fronts, preferential flow paths during thaw, and pore-wall ice formation in macropores. These results demonstrate the importance of domain-specific phase dynamics and the need for LTNE frameworks in frozen soil simulations.
This work provides a numerical approach for calibrating thermo-hydraulic dual-permeability models, highlighting how structural features like macropores influence the transient thermal regime during both infiltration and freeze-thaw cycles. Our approach can be extended to support multi-scale modeling and soil temperature prediction under climate-sensitive scenarios.
How to cite: Khanahmadi, H., Heinze, T., Baselt, I., and Bauer, J.: Modeling Heat and Mass Transfer under Local Thermal Non-Equilibrium Conditions in Structured Soils: A Dual-Permeability Approach for Infiltration and Freeze-Thaw Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7095, https://doi.org/10.5194/egusphere-egu26-7095, 2026.
In the literature, most of the scientific approaches that appeared in the related Soil Moisture (SM) have been generated by trying to model physical interactions between the sampled parameters and their effect on their environment. Classical approaches in this direction have been physics-based models, such as the water balance model, which describe hydrological processes [Hu, Xiande et.al.2025]. These models use physics-based equations and require high-quality input data. Furthermore, their high computational cost limits their use in large-scale applications. Statistical methods were subsequently incorporated to enhance model adaptability [Fu, Rong, et.al., 2023]. Through data-driven approaches, empirical relationships between soil moisture and environmental parameters can be established with efficient computational costs.
In contrast to these well-known areas, this work is trying to develop a comprehensive survey of the most popular data driven algorithms reported in the literature. These algorithms could be further categorized as: a) classical machine learning models (e.g., Random Forests and Support Vector Machines), b) deep learning models (e.g., Long Short-Term Memory, Artificial Neural Networks and Convolutional Neural Networks), c) statistical models (Multiple Linear Regression and Autoregressive Integrated Moving Average) and d) geostatistical models (Kriging). As the name indicates, these models use data as input, which are either historical data of SM, or environmental data, or both to predict soil classification such as wet or dry soil, or continuous soil moisture estimation using regression. The construction of such models typically entails an initial exploration of the data, the evaluation of several candidate models, and the final selection and training of a model using an appropriate learning algorithm [Ding et. al.2018].
As an overall conclusion, the most common physical parameters utilized in data drive models that affect SM variation include air temperature, precipitation, air relative humidity, solar radiation, soil type, topography, and vegetation cover data. GPS location data is also important for allowing generality and adaptability in the field. Thus, we are aiming to create a novel generic data driven model, as depicted in Figure1, that will take into consideration all the previous parameters to generalize the estimation of the SM and expand its applicability in other fields without real field measurements. For achieving this, the first potential candidate as a data driven learning model will be the Long Short-Term Memory (LSTM).
Figure 1: A block diagram of the proposed Generic Data-Driven Model.
References
Hu, Xiande et.al. "Urban rainwater resource utilization: A sustainable environmental impact assessment using life cycle assessment (LCA) and water balance model." Desalination and Water Treatment 322 (2025): 101094.
Fu, Rong, et.al. "A soil moisture prediction model, based on depth and water balance equation: A case study of the Xilingol League Grassland." International Journal of Environmental Research and Public Health 20, no. 2 (2023): 1374.
Ding, Jie, et.al. "Model selection techniques: An overview." IEEE Signal Processing Magazine 35, no. 6 (2018): 16-34.
How to cite: Sumathy, V., Tsimpidi, I., and Nikolakopoulos, G.: A Generic Data-driven model for Soil Moisture Prediction , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9587, https://doi.org/10.5194/egusphere-egu26-9587, 2026.
The infiltration of urban water is an increasingly adopted practice downstream of wastewater treatment plants for the disposal of treated effluents. To maintain optimal infiltration conditions, materials such as gravel are commonly added to soils receiving the treated water. However, these materials involve significant economic and environmental costs, and their long-term effectiveness when combined with soil remains limited.
This study investigates the use of woodchips as an alternative material for the infiltration of treated wastewater. This material, primarily composed of organic matter, is less costly and may offer advantageous properties to sustain soil infiltration over time, both in terms of hydraulic performance and treatment capacity.
To this end, two pilot columns composed solely of a low-infiltration-capacity soil layer and two columns composed of the same soil layer overlain by a woodchip layer were hydraulically monitored over a four-year period. The objective was to assess the potential of woodchips to maintain or enhance infiltration in soils over time and to follow water infiltration into the two systems soil (control) and soil with an upper layer of woodchips. Two large columns—one consisting of soil alone and the other of soil overlain by woodchips—were subjected to successive infiltrations of treated wastewater volumes (mimicking a wastewater treatment plant outlet). Water infiltration, storage, and drainage were monitored in both systems. One-dimensional hydraulic modeling of the columns was performed using HYDRUS-1D to solve Richards’ equation and simulate the system behavior. The modeling was based on the van Genuchten–Mualem formulation for the water retention and hydraulic conductivity functions, as commonly adopted. Fitting the experimentally measured quantities enabled the estimation of intrinsic soil hydraulic parameters and the characterization of their temporal evolution over the four years of operation.
In addition to inversion and parameter estimation, a sensitivity analysis of the hydraulic parameters—namely saturated hydraulic conductivity (Kₛ), α, and n—was performed to strengthen the reliability of the modeling results and parameter estimates. This analysis highlights the predominant influence of Kₛ on variations in the soil water retention curves, leading to its selection as a key indicator of the evolution of soil infiltration performance. Furthermore, electrical resistivity tomography (ERT) measurements were used to monitor water distribution within the columns during feeding and resting phases. These data also served to further calibrate the model and gave insights on processes at the interface between the woodchips and the soil below, then improving system representation.
The modeling results demonstrate the significant role of woodchips in sustaining infiltration capacity. The Kₛ values estimated for soils amended with woodchips are consistently higher than those obtained for soils without woodchips. Such benefits is expected to result to the release of organic matter with a benefic effect on the soil structure.
How to cite: Coisnon, M., Louis, P., Chatain, V., Lassabatere, L., and Clement, R.: Effect of woodchips on water infiltration into soil : evidence of mitigating clogging and hydraulic conductivity reduce , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12159, https://doi.org/10.5194/egusphere-egu26-12159, 2026.
Reliable estimates of field-saturated soil hydraulic conductivity, Kfs, are necessary for characterizing and modelling flow and solute transport. However, field determination of Kfs is challenging since this parameter is sensitive to the applied measurement method and the underlying assumptions. Infiltration experiments provide a relatively simple and low-cost way to determine Kfs, but different devices and analysis frameworks can yield method-dependent estimates. In this context, it is necessary to benchmark simple field techniques against more established methods and to quantify how the choice of the method influences estimation of Kfs before such techniques can be widely adopted.
The aim of this investigation was to compare the Kfs values obtained with two single-ring infiltrometers methods. In particular, the classical single-ring pressure infiltrometer (PI) and the bottomless bucket (BB) method were applied in three Mediterranean agricultural fields in Italy under summer dry conditions. The soils were classified as sandy-loam at two sites and clay at the third one.
At each site, 15 PI runs with two consecutively applied ponded depths of water, H (H1 = 5 cm, H2 = 10 cm), and 15 BB falling-head runs (H ranging repeatedly from 10 to 1 cm) were performed. The data obtained with the PI were analyzed using two different approaches. In particular, the Two-Ponding-Depth (TPD) approach was applied for estimating both Kfs and the site-specific sorptive number, α*. The One-Ponding-Depth (OPD) approach was also applied by using the site-specific α* value and averaging the Kfs estimates for the two ponded depths of water. The site-specific α* parameter was also used to analyze the data obtained with the BB method.
The Kfs distribution was predominantly log-normal for all developed datasets whereas the α* distribution was normal. The α* values were consistent with expectations based on soil texture for the three sites (α* ≈ 4.7 m-1 in the finer soil and 9.7–16.4 m-1 in the coarser soils).
At each site, mean Kfs values differed by up to 1.2 times in the two sandy loam soils (70-85 and 249-293 mm h-1) and by up to 1.9 times in the clay soil (150-279 mm h-1). Coefficients of variation ranged from 27% to 130%, depending on soil type and measurement method, with both extremes observed in the clay soil. Differences among soils depended on the experimental method (PI, BB) but not on the PI data analysis approach.
In conclusion, a satisfactory correspondence between the PI and BB methods can be expected in sandy-loam soils but less in clay soils. In any case, the differences between the two methods seem more appreciable with reference to Kfs data variability than the mean value of this soil hydrodynamic parameters.
How to cite: Autovino, D., Bagarello, V., Basile, A., Caltabellotta, G., Ciotta, R., Fusco, M., and Iovino, M.: Comparing field-saturated soil hydraulic conductivity determined by the single-ring pressure infiltrometer and bottomless bucket methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5167, https://doi.org/10.5194/egusphere-egu26-5167, 2026.
Understanding root zone soil moisture dynamics across seasons is essential for improving land–atmosphere coupling estimates and drought monitoring. Seasonal variability of soil moisture thresholds – the effective critical point (SWT) and effective wilting point (STD) and response (Λ) of root zone soil moisture dynamics to surface soil moisture observations are derived by adjusting the low pass filter parameter (Λ) across India's agroecological zones. Results show that SWT varies substantially across seasons and landscapes, controlled by soil texture, evaporative demand, and infiltration, while STD remains more physiologically constrained with limited spatial variability. We find that the driest regions (northwest India) exhibit the lowest thresholds (<0.12), while humid, forested zones (northeast India, Western Ghats) maintain the highest thresholds (~0.25–0.30) due to deeper soils and persistent vegetation. Seasonally, threshold (SWT) are lowest in the dry winter/premonsoon (JF–MAM) in arid areas and rise sharply during the monsoon (JJAS) when soils recharge, reaching maxima (> 0.30) under dense canopies. The analysis reveals that arid/semi-arid zones have very strong positive feedback in dry seasons ( Λ ≈ 0.8–1.0, m ≈ 8–10), whereas humid/coastal regions remain largely decoupled (Λ ≈ 0.2–0.5, m ≈2–4) year-round. Correlation with SMAP Level-4 demonstrates strong agreement, with high values (>0.75) across most regions. Areas with lower agreement align with complex terrains and dense vegetation where vertical signal propagation is less coherent. Seasonal variation in the transitional slope (m) and LP filter parameter (Λ) further reveals dynamic coupling regimes that govern evapotranspiration control. These findings emphasize the need to incorporate seasonally adaptive thresholds and infiltration parameters in land surface modeling to better represent ecohydrological processes and surface flux feedbacks.
How to cite: Adigarla, Y. N. and Singh, Dr. S.: Root Zone Soil Moisture dynamics from Terrestrial Water-Energy Coupling across Indian Agroecological Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16664, https://doi.org/10.5194/egusphere-egu26-16664, 2026.
Climate and global changes increase pressure on natural resources, particularly water. Climate change affects hydrological processes and threatens water resources in both quantity and quality. Societal adaptation therefore requires a paradigm shift in water management, including reducing human impacts on the water cycle and restoring the natural cycle. Achieving this transition relies on a detailed understanding of hydrological processes, especially those governing water infiltration into soils. Soil water infiltration modeling has been studied for decades, with approaches ranging from analytical to numerical modeling. Analytical solutions, developed as approximations of Richards’ equation, were initially favored before advances in computational capacity enabled numerical models. Despite this evolution, analytical approaches remain essential for validating and consolidating numerical developments. Among the analytical models proposed, power series expansions in time—more precisely in t1/2 - were the earliest, based on Philip’s pioneering work (Philip, 1957). Later, Haverkamp et al. (1994) introduced an implicit quasi-exact formulation for infiltration into soils with uniform initial water content, afterwards adapted to circular surface sources. These models, along with their short-time expansions, form the basis for experimental data analysis and are typically truncated after the first three terms, as higher-order contributions are negligible (Moret-Fernández et al., 2020).
This study investigates a general power series formulation for modeling water infiltration, I(t) = a1 tα1 + a2 tα2 + a3 tα3, and evaluates its ability to fit numerically generated infiltration data for different choices of exponents and coefficients. The study first demonstrates that the simultaneous estimation of all three exponents and coefficients leads to an ill-posed inversion problem due to model overparameterization. The analysis is therefore restricted to a two-term formulation, I(t) = a1 tα1 + a2 tα2, with parameters optimized sequentially to reduce non-uniqueness. One-dimensional horizontal infiltration data are first analyzed using the single-term model I(t) = a1 tα1, with several inversion strategies, including fixing the parameters to the reference values a1 = S (soil sorptivity) and α1=1/2. One-dimensional vertical infiltration and disc-source infiltration are then fitted to estimate the remaining parameters a2 and α2. Parameter estimation options, including reference values from Haverkamp et al. (1994), are evaluated across multiple soils and initial saturation conditions. Finally, the obtained parameter values are discussed in light of physical considerations. This study aims to contribute to the development and application of analytical approaches for modeling water infiltration.
References
- Haverkamp, R., Ross, P.J., Smettem, K.R.J., Parlange, J.Y., 1994. Three-dimensional analysis of infiltration from the disc infiltrometer. 2. Physically based infiltration equation. Water Resour. Res. 30, 2931–2935.
- Moret-Fernández, D., Latorre, B., López, M.V., Pueyo, Y., Lassabatere, L., Angulo-Jaramilo, R., Rahmati, M., Tormo, J., Nicolau, J.M., 2020. Three- and four-term approximate expansions of the Haverkamp formulation to estimate soil hydraulic properties from disc infiltrometer measurements. Hydrol. Process. 34, 5543–5556. https://doi.org/10.1002/hyp.13966
- Philip, J., 1957. The theory of infiltration: 1. The infiltration equation and its solution. Soil Sci. 83, 345–358.
This project has received funding from European Union’s HORIZON EUROPE research and innovation program GA N°101072777-PlasticUnderground HEUR-MSCA-2021-DN-01.
How to cite: Lassabatere, L., Autovino, D., Bagarello, V., and Iovino, M.: Use of generalized power series for the modeling of 1D and 3D (disc-source) water infiltration into soils, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8364, https://doi.org/10.5194/egusphere-egu26-8364, 2026.
How to cite: Lopes, J., Vlasimsky, M., Migliori, A., Dercon, G., Kanaki, K., Melquiades, F., and Andrello, A.: MIRS and XRF Data Fusion for Improving Soil Fertility Attributes Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-433, https://doi.org/10.5194/egusphere-egu26-433, 2026.
The rapid increase in sealed surface in urban area have contributed to the alteration of climatic conditions. The use of vegetation integrated with buildings and other forms of vegetation provides a sustainable solution to these problems, bringing numerous benefits, including the decrease of CO2 emissions consequent to reduction of electricity consumption to regulate the temperature comfort inside buildings and the reduction of surface runoff into urban sewage systems. The main objective of this work was to study the thermal and hydraulic behavior of vegetated and not-vegetated extensive green roofs. For the purpose, two green roof plots were installed on the roof of the building of the Faculty of Agriculture at the University of Palermo. The experiment was carried out in the spring-summer season of 2025. Meteorological data were acquired through a weather station installed on the roof and substrate temperature and volumetric water content were monitored by Teros 12® capacitive probes (Meter Group GmbH) embedded into the green roof plots. Drainage was collected into tanks equipped with ultrasonic transducers for automatic acquisition of water level measurements. A commercial substrate (TMT, Harpo VerdePensile s.r.l) was used with expanded clay as drainage layer. Water retention and hydraulic conductivity function were determined from the laboratory measurements carried out with the evaporation method (Schindler, 1980) using HYPROP apparatus (Meter Group GmbH). Substrate thermal conductivity function parameters were determined according to the Chung and Horton model (1987) on data acquired through the THERMOLINK (Meter Group GmbH). Thermal and hydraulic processes that occur on the roof were then simulated considering both 2D and 3D flow domain by the Hydrus 2D/3D software. Comparison between simulated and measured data during either rainfall events and inter-rainfall periods allowed to highlight the role that vegetation plays on the thermo-hydrological processes and support the use of HYDRUS as a predictive tool in green infrastructure planning and management.
References
Chung, S., Horton, R., 1987. Soil heat and water flow with a partial surface mulch. Water Resources Research 23, 2175–2186. https://doi.org/10.1029/WR023i012p02175
Schindler, U., 1980. Ein Schnellverfahren zur Messung der Wasserleitfähigkeit im teilgesättigten Boden an Stechzylinderproben. Archiv für Acker- und Pflanzenbau und Bodenkunde 24 (1): 1-7.
How to cite: Ciotta, R., Autovino, D., Bondì, C., Iovino, M., and Kodešová, R.: Thermal and hydraulic behavior of substrates for extensive green roofs in mediterranean urban area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5918, https://doi.org/10.5194/egusphere-egu26-5918, 2026.
Mining deposits form anthropogenic vadose zones with physical, hydrological, and chemical characteristics that diverge strongly from natural soils. Observations show that these deposits rarely undergo spontaneous revegetation, although surrounding landscapes recover without intervention. The mechanisms controlling this failure remain poorly understood, quantitative datasets on these materials are scarce, and no framework currently links deposit genesis, material properties, hydrological functioning, and plant viability. This knowledge gap is critical in countries such as Namibia, where mining underpins the national economy but also causes severe ecological disturbance. As the driest nation in Sub-Saharan Africa, restoration of mine deposits in Namibia is not only constrained by toxicity and altered physical soil properties, but also water scarcity.
The WaMiSAR project aims to develop a toolbox for sustainable, climate-adapted water management within the mining sector of the Southern African Region by jointly addressing water scarcity and restoration of disturbed substrates. A combination of field monitoring, laboratory analyses, and process-based modeling is needed to identify the dominant factors limiting plant growth and to evaluate remediation strategies and irrigation effects in mine residue deposits. The central hypothesis is that plant-available water, rather than chemical contamination, constitutes the primary limiting factor for vegetation establishment on mine deposits in the region, particularly during early seedling stages.
To date, three field campaigns have quantified chemical, physical, and hydrological properties at two contrasting sites in Namibia: (i) Tsumeb, a decommissioned copper mine in the semi-arid north, and (ii) Rosh Pinah, an active zinc-lead mine in the arid south. Initial observations indicate that spontaneous vegetation occurs almost exclusively on sandy surface materials, whereas silt-rich layers, salt crusts, and gravelly substrates remain largely unvegetated. Where vegetation is present, roots extend several decimeters into the substrate. Elevated concentrations of copper, zinc, or lead do not appear to inhibit plant growth, whereas strong contrasts in texture and water-holding capacity are evident. Soil moisture sensors installed at multiple depths capture vadose zone dynamics. The usage of low-cost, humidity-based sensors enable the characterization of water retention in the ultra-dry range, overcoming limitations of conventional techniques.
The project generates a quantitative hydro-physical dataset for mining residues across the full moisture spectrum, identify key constraints on plant establishment, and improve hydraulic parameterizations for dry, anthropogenically altered substrates. These outcomes will support scientifically grounded remediation strategies and form the basis for an operational framework linking deposit origin, climate, substrate properties, and appropriate restoration interventions.
How to cite: Kny, K., Norra, S., Eiche, E., Kamundu, K., Nagombe, P., Russ, S., Sengl, R., and Wassenaar, T.: Integrated Assessment of Vadose Zone Physical and Hydraulic Properties in Semiarid and Arid Mining Landscapes in Namibia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6728, https://doi.org/10.5194/egusphere-egu26-6728, 2026.
Land use is a key driver of differences in runoff generation and play an important role in water retention and the mitigation of surface runoff during high-intensity rainfall events. Perennial crops’ permanent root system and persistent mulch layers can substantially modify soil physical properties in comparison to annual crops, thereby influencing soil hydraulic functioning and runoff generation processes. While existing studies commonly compare the effects of perennial crops on infiltration, runoff, or water balance at longer temporal scales, experimental data on runoff responses under short, high-intensity rainfall events are quite scarce. In particular the hydrological behavior of agricultural fields cultivated with perennial crops remains poorly understood. For Miscanthus x giganteus, a perennial crop commonly grown for bioenergy production and increasingly investigated as a sustainable building material, observations on hillslopes in the Erft catchment during heavy rainfall events in 2016 and 2022 suggest runoff retention effects. Therefore, we quantified the influence of Miscanthus on runoff generation during heavy rainfall events. A series of different artificial rainfall experiments were conducted on three different 10 x 10 m plots cultivated with Miscanthus, winter wheat and permanent pasture serving as reference land-use type. Surface and subsurface runoff were measured at the bottom of each hillslope plot under different rainfall intensities producing in total 36 experiments. To separate the plant-induced effects on soil structure and hydrological processes, the experiments were also simulated with the process-based runoff-generation model RoGeR, which includes various preferential flow processes. The results from the measurements indicate a pronounced retention effect of Miscanthus during the runoff initiation phase, leading to lower runoff rates than winter wheat. However, no clear differences in total surface runoff volumes were observed under the applied rainfall conditions. These findings indicate that the persistent mulch layer associated with perennial crops such as Miscanthus can substantially modify runoff dynamics during high-intensity rainfall events and reduce soil erosion. Ongoing modelling of the experiments with RoGeR aims to quantify the contribution of different preferential flow paths and to investigate why Miscanthus does not reduce total surface runoff relative to tall, fully developed winter wheat. The results will improve process-level understanding of the effects of perennial crops on runoff generation under extreme rainfall events and have implications for the assessment of nature-based solutions and land-management strategies aimed at flood mitigation.
How to cite: Bucher, F., Tuschen, M., Evers, M., and Weiler, M.: Disentangling Plant-Induced Soil Effects on Runoff: Experiments and Modeling of Miscanthus, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10008, https://doi.org/10.5194/egusphere-egu26-10008, 2026.
Hysteresis in capillary pressure and relative permeability relationships with saturation degree, is an important phenomenon essential for accurately predicting multiphase retention and flow in porous media, relevant for hydrological applications such as groundwater management and soil water dynamics. The observed hysteretic loops reflect complex, history-dependent interactions between fluids and pore structures.
We used three-dimensional (3D) pore network models to systematically investigate how media properties (pore size distribution, correlation, and connectivity) and different imbibition and drainage modes determine the combined and decoupled quasi-static mechanisms of hysteresis: geometric ink bottle effects, non-wetting fluid trapping, and network-dependent effects arising from complex pore accessibility. We decoupled these mechanisms by leveraging controlled, simulated drainage and imbibition scenarios (e.g., invasion vs random percolation, bond vs site-governed displacement, and with vs without trapping).
The different mechanisms present distinct effects on the hysteretic loops, where trapping primarily affects retention curves at high wetting saturation (Sw) and dramatically reduces wetting phase relative permeability (kWr). Ink bottle hysteresis, driven by pore geometry, is visible across the entire capillary head (hc) range. In contrast, network hysteresis significantly shapes the retention (Sw(hc)) loop and drives kr(Sw) hysteresis at low Sw.
Furthermore, the impact of these mechanisms is highly dependent on media structure. Increasing pore size distribution variability enhances non-wetting phase trapping volume while mitigating ink bottle effects. Correlation between pore bodies' and throats' radii strongly increases the impact of trapping on kWr. Conversely, increasing connectivity (i.e., higher coordination number) reduces the trapped fluid fraction, and generally mitigates ink bottle and network hysteresis effects in the two-phase retention.
These results provide necessary mechanistic understanding, supporting the inverse interpretation of measured hysteretic loops to deduce the underlying topological structure of porous media.
How to cite: Ben-Noah, I., Friedman, S., and Mualem, Y.: Evaluating hysteresis mechanisms through pore networks simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3480, https://doi.org/10.5194/egusphere-egu26-3480, 2026.
Dry drainage systems enhance the leaching and migration of soil salinity in cultivated areas through evaporation in fallow zones (low-lying wastelands), improving soil quality in a cost-effective and environmentally friendly manner. However, the ongoing expansion of arable land reduces the extent of such wastelands, underscoring the need to optimize their management for maximal salt discharge. This study examines the contribution of wasteland to regional salt removal and its subsequent effects on arable land distribution, land-use conversion, and salinization dynamics. Taking the Hetao Irrigation District—a large irrigation area located in the upper reaches of the Yellow River and the largest designed irrigation area in China—as the research area, we conducted a salt inversion analysis based on Landsat remote sensing data and land use datasets to extract the distribution, location, and salinization levels of different land types. Our findings reveal substantial changes in both arable land and wasteland in the Hetao Irrigation District. From 2003 to 2023, arable land remained the dominant land-use type in the Hetao Irrigation District, characterized by widespread distribution and relatively large contiguous patches. In contrast, wasteland was primarily distributed in the western and northern regions in 2003, but after 2008, its area decreased significantly, exhibiting a trend toward smaller, more fragmented, and dispersed patches. Specifically, arable land expanded from about 7,800 km² to 8,600 km², accounting for 76.51% of the total area. Wasteland area declined annually from 2008 to 2018 but showed signs of recovery from 2018 to 2023. Although wasteland occupies a relatively small area, it acts as a major salinity sink, concentrating approximately 15.74%–35.09% of the total soil salts in the region. The spatial dispersion of wasteland fluctuated over the observation period. The distribution of wasteland showed the highest dispersion in 2003, followed by alternating phases of aggregation and re-dispersion, without forming a clear long-term trend and maintaining an overall dynamic equilibrium. This suggests that the spatial distribution of wasteland exhibits temporal elasticity, with its dispersion and aggregation significantly influenced by short-term factors, yet no systematic large-scale expansion or contraction occurred. Due to elevated salinization, some cultivated land became unsuitable for crops and transitioned primarily into wasteland or grassland. Spatially, wasteland shifted westward from 2003 to 2018, then returned eastward by 2023, with minimal north–south movement. The salinity dynamics in these discharge zones are influenced by climate, groundwater, and evaporation, which collectively alter salinization patterns and land suitability. Newly formed wastelands continue to absorb salts from surrounding soils, thereby modulating regional salinity levels and influencing land-use configurations. Overall, this study not only provides critical insights into the interactions between arable land and wasteland but also emphasizes the necessity of sustainable land management practices to address salinization challenges. Our findings can inform policymakers and land managers in developing strategies aimed at optimizing land use while preserving soil health and enhancing agricultural resilience in the face of increasing salinity pressures.
How to cite: Yan, S.: The role of wasteland in salt discharge and its impact on the distribution and transformation of arable land, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7350, https://doi.org/10.5194/egusphere-egu26-7350, 2026.
In desert soils, water vapor transport is a key mechanism driving soil moisture dynamics. This transport occurs along vapor density gradients that arise from variations in soil matric potential and temperature in the soil. In hyper-arid soils, extremely high negative matric potentials across the near surface lead to uniform matric potential gradients (∇Ψₘ). Therefore, in these soils, variations in temperature (∇T) primarily drive vapor movement by affecting vapor concentration. In cases where soil moisture is high enough so that the relative humidity in the air-filled pores approaches 100%, vapor flows from warmer to cooler soil sections. This is because an increase in temperature under these conditions causes an exponential rise in vapor concentration, which triggers vapor migration toward cooler soil regions. We hypothesize that in very dry soils, the opposite is the case. When the relative humidity in the air-filled pores is much lower than 100%, an increase in temperature does not translate to an increase in water vapor because there is no liquid water to evaporate. In contrast, the increase in temperature results in a decrease in air density, forming a lower water vapor concentration compared to a cooler soil, resulting in water vapor flow from cooler to warmer soil sections. To test this hypothesis, we conducted an in-situ experiment in the Negev Desert, Israel, where the total soil-atmosphere water flux was measured by lysimeters, and the soil water content at depths of 0.5, 2, 5 and 10 cm were measured using temperature and relative humidity sensors. Water vapor transport was also simulated using a HYDRUS 1D numerical model. We found that vapor transport in these hyper-arid soils is dominated by thermally driven vapor flux (total soil water flux ≈ thermal vapor flux), while liquid fluxes (thermal and isothermal) and isothermal vapor fluxes are negligible. While the experimental data support our hypothesis, the HYDRUS configuration does not allow for an influx of water vapor from the atmosphere, nor does it allow for water vapor to move from cooler to warmer soil layers, both of which may limit the model’s prediction accuracy. These results highlight the need to reconsider the description of water vapor flow in extremely dry soils in HYDRUS and potentially other land-surface and hydrological models.
How to cite: Gurebiyaw, K., Kool (RIP), D., and Agam, N.: Water vapor transport in extremely dry soils – does vapor always flow from high to low temperature? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3825, https://doi.org/10.5194/egusphere-egu26-3825, 2026.
Soil salinity commonly exhibits strong within-field variability, and operational diagnosis requires scaling point-based reference measurements to spatially continuous estimates. This study evaluates a hierarchical, cascading modelling framework to map soil salinity at field scale in agricultural plots in Belorado (Burgos, Spain), integrating laboratory reference data with diffuse reflectance spectroscopy, proximal apparent electrical conductivity sensing, and very-high-resolution multispectral UAV products.
In each plot, 40 sampling locations are established and georeferenced using GNSS RTK. Soil samples collected at these locations are analysed in the laboratory to obtain salinity reference values. On the same day, and prior to the commencement of sampling operations, a multispectral UAV survey (DJI P4 Multispectral) is conducted to generate high-resolution orthomosaics and derived spectral variables. This is followed by a full-coverage proximal survey using a VERIS Q2800 system to measure apparent electrical conductivity (ECa) continuously across each plot. After these surveys, sampling is performed and diffuse reflectance spectra are acquired in situ at the GNSS-referenced locations using a NeoSpectra (Si-Ware) instrument. In addition, laboratory spectroscopy is repeated on air-dried samples to quantify moisture effects and to assess the consistency between field and laboratory spectral acquisitions.
The upscaling strategy is implemented as a cascade of transfer models: (i) laboratory salinity and diffuse reflectance spectroscopy, (ii) diffuse reflectance spectroscopy and ECa (VERIS), and (iii) ECa and UAV-derived spectral variables, enabling plot-wide prediction. Model performance is assessed using cross-validation, including spatially explicit schemes, and uncertainty propagation along the cascade is examined where feasible. The outcome is a reproducible workflow for producing field-scale salinity maps and quantifying the added value of each sensing layer in the hierarchical framework.
How to cite: Salgado, L., Martín, A., Peña, V., Soto-Gómez, D., Rad, C., Cambra, C., Gallego, J. L. R., and Barros, R.: Hierarchical modelling to map soil salinity using proximal sensing and a multispectral UAV, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12963, https://doi.org/10.5194/egusphere-egu26-12963, 2026.
The wide availability of high-performance computing resources has opened possibilities to employ complex, physically-based soil-water flow models at larger scales. While detailed 1D models like SWAP (Soil-Water-Atmosphere-Plant) are ideal for modelling unsaturated flow and crop growth at field scale, their traditional design tailored for single-column applications limits their scalability. This legacy structure creates a significant challenge when attempting to perform quasi-3D distributed simulations or when coupling with regional groundwater models such as MODFLOW, where thousands of interacting columns must be solved simultaneously.
This poster presents a refactoring effort that enables SWAP to run many independent 1D columns truly in parallel within one process, supporting high-throughput simulations with minimal I/O bottlenecks. This modernization targets the needs of modern environmental data science, such as (i) memory-efficient integration with spatial libraries (e.g., NetCDF), and (ii) seamless compatibility with parameter estimation and uncertainty frameworks (e.g., PEST) which require thousands of iterative model calls. By enabling direct memory access to state variables, the updated framework removes the overhead of disk operations, facilitating autocalibration and sensitivity analysis even at scale.
A motivating application is the Kinrooi subirrigation experiment, where spatial variability in soil hydraulic properties and boundary conditions influences the effective recharge rate. The poster shows how the updated parallel approach makes it practical to explore these spatial patterns while preserving the mechanistic detail of the original model.
How to cite: Zawadzki, M.: Scaling up SWAP: multi-instance parallel execution for soil-water–atmosphere-plant simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15486, https://doi.org/10.5194/egusphere-egu26-15486, 2026.
Field-scale soil moisture retrieval from Sentinel-1 synthetic aperture radar (SAR) is rather well established for bare soils, winter crops, and grasslands, but its applicability during summer cropping periods remains uncertain due to dense vegetation and complex vegetation structure. This study evaluates the potential and limitations of the Sentinel-derived soil moisture product (S²MP, El Hajj et al. (2017)), based on a neural network that uses Sentinel-1 VV backscatter and Sentinel-2 NDVI, for surface soil moisture estimation during summer cropping.
The first part of this study evaluates S²MP against in situ measurements at 10 cm depth over several winter and summer crops in a Mediterranean context (Bazzi et al., 2023). Results show that Sentinel-1 mainly senses the top few centimetres of soil, leading to strong underestimation in dry conditions (up to ~20 vol.%) and smaller biases under moderately wet conditions, while performance degrades again in very wet soils. Reliable soil moisture retrievals are limited to low–moderate vegetation cover (NDVI < 0.7), with crop-dependent biases under dense canopies, and accuracy improves at lower radar incidence angles (< 35°).
The second part analyses summer vegetable case studies in Flanders, comparing S²MP with in situ observations across irrigated and rainfed fields. S²MP successfully captures rainfall and irrigation signals during early growth stages and differentiates irrigated from non-irrigated areas, but performance under dense canopies strongly depends on crop type. Crops with complex canopy structures (e.g. beans podding stage, pumpkins) show reduced or inconsistent sensitivity, while onions and carrots retain detectable soil moisture dynamics even at high NDVI. These results demonstrate that NDVI alone is insufficient to characterise vegetation effects on SAR soil moisture retrievals and highlight the need for crop-specific parameterisation and complementary longer-wavelength SAR observations.
References:
El Hajj, M., Baghdadi, N., Zribi, M., & Bazzi, H. (2017). Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sensing 2017, Vol. 9, Page 1292, 9(12), 1292. https://doi.org/10.3390/RS9121292
Bazzi, H., Baghdadi, N., Nino, P., Napoli, R., Najem, S., Zribi, M., & Vaudour, E. (2023). Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer. Water 2024, Vol. 16, Page 40, 16(1), 40. https://doi.org/10.3390/W16010040
How to cite: Bazzi, H., Hendrickx, M., Baghdadi, N., and Najem, S.: Sentinel-derived soil moisture retrievals during winter and summer cropping: Potential and limitations of S²MP, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19250, https://doi.org/10.5194/egusphere-egu26-19250, 2026.
Understanding the dynamics of water and salt migration in seasonally frozen agricultural soils is critical for effective arid land management. This study combined field monitoring with a three-method extraction strategy for stable isotope analyses (direct vapor equilibration, centrifugation, and cryogenic vacuum distillation). By integrating resulting isotope signatures into an Isotope Mass Balance (IMB) model, we quantitatively differentiated phase-state water pools. The results confirmed that freezing induces significant Rayleigh fractionation, enriching ice in heavy isotopes relative to mobile water. In contrast, the bound water fraction remains hydraulically isolated and isotopically distinct, requiring its exclusion from phase-change calculations. Coupling with geochemical modelling (FREZCHEM) revealed that salt migration is controlled by the interplay between thermally driven convective fluxes and concentration-driven diffusive fluxes, although individual ion exhibited distinct redistribution pathways.
Cryogenic precipitation regulates soil salt transport regime: extensive surface crystallization reduces dissolved ion concentrations, thereby maintaining the steep upward gradient required for continuous salt accumulation. The model demonstrated that crystallization accounted for up to 40.5 % of the total salt load incorporated into solid phases during freezing. These solid salts create a "geochemical trap" in which re-dissolution lags behind the initial spring meltwater pulse, significantly reducing leaching efficiency. Consequently, sustainable salinity management cannot rely on hydraulic regulation alone. Effective irrigation strategies must integrate groundwater management with the specific composition of the salt load to overcome these persistent geochemical constraints.
How to cite: Zhang, H., Wan, C., Skrzypek, G., and Gibson, J. J.: Soil water and salt transport in seasonally frozen cropland: isotopic tracing method with geochemical modelling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8667, https://doi.org/10.5194/egusphere-egu26-8667, 2026.
The interaction between water, vapor, and heat transport in soils plays a pivotal
role in regulating soil moisture and thermal regimes in forest ecosystems, yet
these processes are often modeled independently. Such approach may overlook
their inter-dependent dynamics, particularly under forest canopies where inter-
ception, evaporation, and energy exchange are strongly correlated. In this study,
a physics-based canopy interception model was developed and calibrated using
throughfall monitoring data from the AMALIA experimental site in central Bo-
hemia. The simulated intercepted rainfall is subsequently used as an upper
boundary condition for the Saito–Sakai model which couples the transport of
water, vapor, and heat in the soil profile. Surface energy balance was applied
as the thermal boundary condition, accounting for coupled heat and vapor ex-
change, while precipitation served as the moisture flux boundary. The model
was calibrated and validated against month-long soil temperature and moisture
measurements across three soil horizons, with meteorological forcing derived
from ERA5-Land hourly reanalysis data interpolated to match the observational
time step. Model performance demonstrated good agreement with observations,
successfully reproducing soil temperature and moisture dynamics beneath the
forest canopy and highlighted the importance of interception-induced delays of
rainfall inputs. Results demonstrate that neglecting canopy–soil interactions
can lead to biased estimates of near-surface soil states, particularly during wet-
ting and drying events. The proposed approach provides a physically consistent
link between canopy processes and subsurface thermal–hydrological dynamics
and can improve the representation of forest soil conditions in land-surface and
ecohydrological models.
How to cite: Steinbach, V., Kuráž, M., and Kuželkova, M.: Soil thermal and moisture regime beneath forest canopy: A coupled modeling approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19493, https://doi.org/10.5194/egusphere-egu26-19493, 2026.
Soil health and moisture availability are critical for increased productivity and sustainable agricultural systems, particularly in the face of increasing environmental variability and land degradation. The Joint FAO/IAEA Centre Coordinated Research Project (CRP) D12015 brings together 12 Member States to advance gamma-ray spectrometry (GRS) as an innovative, non-invasive nuclear technique for high-resolution soil property mapping. The objective is to advance GRS integrated with digital and remote sensing technologies, such as drone and satellite imagery, environmental sensors, and machine learning, for precise and scalable mapping of soil properties including texture, organic carbon, nitrogen, and moisture. These efforts support the evaluation of erosion risk, improve resource efficiency, and strengthen resilient farming systems.
The first Research Coordination Meeting (Vienna, December 2025) established a clear roadmap for advancing GRS as a nuclear tool for soil monitoring. Key priorities include developing standardized and transferable protocols for GRS deployment and calibration across diverse agro-ecological zones, defining minimal datasets for robust conversion models, and validating both empirical and physics-based predictive approaches for soil physical properties and moisture. The CRP emphasizes the integration of GRS with complementary digital technologies, such as drone-based multispectral and thermal imaging, satellite remote sensing, and geospatial data fusion, to deliver high-resolution soil maps and decision-support tools.
Applications span three primary areas: (i) the synergistic use of stationary and mobile GRS for precision agriculture mapping, (ii) the fusion of GRS and remote sensing to support irrigation efficiency and drought resilience, and (iii) predictive soil property mapping using multi-sensor datasets and machine learning. Discussions highlighted the need for harmonized methodologies, transparent conversion of radionuclide signals to soil attributes, and rigorous uncertainty quantification to ensure reproducibility and trust in science-based GRS evidence. When implemented with rigor and data traceability, GRS offers a transformative pathway for Sustainable and Resilient Agriculture. Planned outputs include illustrated guidelines, validated case studies, and user-friendly decision-support platforms integrating GRS outputs with crop and water management models. These tools will strengthen Member State capacity to apply nuclear and digital technologies for sustainable soil management.
How to cite: Said, H., Fujisawa, M., Dercon, G., Jose Chagas, C., Palacios Fernández, D. F., Gong, D., Baroni, G., Bezuidenhout, J., Gaspar Ferrer, L., Zibar, M. C. L., Modou, M., Mohd Amin Mohamad, S., Mansfield, N., Strauss, P., Strati, V., and Blake, W.: Combining Gamma Ray Sensing and Digital Technology for Soil Moisture and Soil Property Mapping: advancing integrated tools for sustainable soil management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21574, https://doi.org/10.5194/egusphere-egu26-21574, 2026.
Purpose. Accurate characterization of water infiltration into the vadose zone requires estimating key soil hydrodynamic properties, including the macroscopic capillary length (λc) and saturated hydraulic conductivity (Ks). These parameters quantify the contributions of capillarity and gravity (λc) and gravity-driven flow (Ks) during infiltration. Both λc and Ks can be estimated in the field using a simple Beerkan infiltration test, which requires minimal equipment, limited water, and no specialized operators. Their calculation, however, depends on “integral” shape parameters that vary strongly with soil type. In this study, we present new formulations based on integrating the hydraulic conductivity function expressed in terms of pressure head. These formulations allow accurate estimation of shape parameters even under dry soil conditions, providing an alternative to previous methods that rely on diffusivity or conductivity expressed as a function of water content.
Method. We applied the new formulations to calculate soil-dependent shape parameters for the twelve USDA textural classes. Their performance in estimating λc and Ks was evaluated using synthetic cumulative infiltration curves generated with HYDRUS-2D/3D and compared with results obtained using default literature values. For practical applications, we propose two approaches to select appropriate shape parameters: (i) based on soil samples to determine textural class, and (ii) using texture-dependent parameter maps for site-specific selection. Both approaches were tested using a dataset of 167 Beerkan infiltration experiments across seven sites in Burundi, Ghana, Italy, and Senegal.
Results. The sample-based approach provides higher accuracy in estimating λc and Ks, whereas the map-based approach eliminates the need for laboratory analysis and still outperforms default literature values, making it suitable for large-scale studies. To support the map-based method, we provide parameter maps at 250 m resolution for six countries (Burundi, Cameroon, Ghana, Italy, Kenya, and Senegal), alongside complementary soil property maps from the SoilGrids database (clay, sand, silt content, dry bulk density, and USDA soil texture classes), all freely accessible. Additionally, we propose a simplified method for estimating λc using a new empirical relationship that requires only the Mualem–van Genuchten shape parameter n, which can be derived from SoilGrids texture data using pedotransfer functions such as Rosetta3. Complementary maps of all Mualem–van Genuchten parameters are also provided.
Conclusions. This work improves and simplifies the field estimation of key soil hydrodynamic properties by providing shape parameters for all USDA texture classes and accessible maps for parameter extraction. The approach facilitates the hydraulic characterization of large areas and extensive datasets, supporting both local and regional-scale infiltration studies.
Data availability
Soil property and parameter maps at 250 m resolution for Burundi, Cameroon, Ghana, Italy, Kenya, and Senegal are available in the open-access digital repository Zenodo at https://doi.org/10.5281/zenodo.17397791
Funding
This work was supported through the project GALILEO ― Strengthening rural livelihoods and resilience to climate change in Africa: innovative agroforestry integrating people, trees, crops and livestock (project number: 101181623), funded by the European Union.
How to cite: Di Prima, S., Do, F., Roupsard, O., and Lassabatere, L.: Mapping Infiltration Shape Parameters for Enhanced Soil Hydraulic Characterization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12069, https://doi.org/10.5194/egusphere-egu26-12069, 2026.
The use of the automated Beerkan infiltrometer (10.1016/j.compag.2015.09.022) represents an interesting alternative to the classical Beerkan infiltration test. The device enables the measurement of field infiltration rates at a higher temporal resolution than manually conducted tests. Although the infiltrometer can be easily implemented in the field and provides good measurement reproducibility, the analysis of raw experimental data requires appropriate processing to ensure reliable estimation of soil hydraulic parameters using the BEST algorithms.
The automated infiltrometer operates as follows. Water infiltrates at the soil surface, and when the water depth decreases below a given threshold, the Mariotte bottle of the infiltrometer is activated and allows an air bubble to enter the reservoir. Consequently, the increase in air pressure in the reservoir releases an amount of water. Under ideal conditions, monitoring of the air pressure in the system produces a piecewise-constant step function. Each plateau corresponds to a constant water height in the reservoir and is separated from the next by oscillations caused by the passage of the air bubble. Therefore, the signal must be filtered using appropriate filtering techniques in order to identify the points that properly define the cumulative infiltration curve. In the ideal case, the selected points should correspond to the end of each plateau, which defines the exact time at which the infiltrometer supplies water. However, in many cases the signal is noisy, making filtering a challenging task.
This study compares the performance of two mathematical approaches commonly used to process raw pressure transducer data: (i) an automatic filtering method based on first and second derivatives to detect plateaus (i.e., periods of constant water height in the reservoir), and (ii) the commonly used moving average technique. Experimental data collected on a well-structured, cultivated sandy soil in Senegal were used to assess the impact of the two filtering approaches on the determination of cumulative infiltration and on the estimation of saturated soil hydraulic conductivity (Ks) and soil sorptivity (S), using the three BEST algorithms (BEST-slope, BEST-intercept, and BEST-steady).
We expect that comparison of the cumulative infiltration curves obtained with the two methods will reveal discrepancies, and that the automated filtering approach will better preserve infiltration dynamics, as suggested by preliminary results. In contrast, the moving average method may excessively smooth the data, potentially leading to biased estimates of hydraulic parameters, particularly under conditions of strong capillary effects. A synthesis of the results from both methods will help identify the most appropriate filtering approach.
How to cite: Waly, F., Didier, O., Frederic, D., Olivier, R., and Awa, N.: Automated Filtering versus Moving Average in the Analysis of Automated Beerkan Infiltrometer Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17481, https://doi.org/10.5194/egusphere-egu26-17481, 2026.
Observations of soil states obtained from in-situ or remote sensors have various sources of errors. A crude way to represent these errors is to assume that part of the error is purely random whereas another part persists and does not change over time. Since the persistent part does not cancel out when more data become available over time whereas the random part does, the partitioning of the error into a persistent and random part is important to assess the uncertainty of model parameters and model predictions that are derived from these observations. Two approaches can be followed to represent these systematic errors in model parameter estimation. The first approach represents the systematic error as an additional parameter representing the bias that is estimated using additional unbiased observations, which we assumed to have only random errors. A second approach represents the systematic error as a covariance in the error-covariance matrix. The uncertainty of the model predictions in the first approach consists of a term that represents the uncertainty of the bias estimation, which is independent of the magnitude of the bias and depends only on the uncertainty of the unbiased additional observations. When additional unbiased measurements are included in the second approach, which represents bias as error covariance, smaller model prediction uncertainty is obtained than using the first approach. This is especially the case when the covariance representing the bias is smaller than the variance of the average error of the random observations. Including prior knowledge about the bias in the error covariance, reduces the model parameter and prediction uncertainty.
How to cite: Vanderborght, J., Hendrickx, M., Diels, J., and Janssens, P.: Effects of systematic errors in observations on model prediction uncertainty., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6243, https://doi.org/10.5194/egusphere-egu26-6243, 2026.
