Orals: Fri, 2 May | Room -2.20
Observations are invaluable information for testing hypotheses, increasing knowledge and advancing science. Even if this might be universally accepted, it is however quite surprising, by looking at the scientific literature, how diverse is the terminology and the interpretation used for the same monitoring strategy, ranging from the assessment of the actual measurements to the sampling designs. While this might simply mirror the diversity of the underlying assumptions developed in the different scientific communities, it raises the question if this undermines a solid scientific foundation. Among others, in this contribution we focus the discussion on the representativeness of the observations in the soil-plant-atmosphere system. We show how this has been recognized as a relevant concept since long time. We then present some formulations that have been proposed but are still not regularly adopted. By using soil water content observations as an example, we discuss the effect of explicitly considering representativeness, and provide a way forward for adopting it as basic paradigm to advance future science.
How to cite: Baroni, G., Emamalizadeh, S., Gruber, A., Evans, J. G., and Oswald, S. E.: On the representativeness of the observations: a philosophical detail or a basic paradigm?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9453, https://doi.org/10.5194/egusphere-egu25-9453, 2025.
The rise of affordable, autonomous in-situ sensors and IoT technology has enabled real-time monitoring of soil moisture, opening new opportunities for sustainable irrigation management. We present SWIM² (Sensor Wielded Inverse Modelling of a Soil Water Irrigation Model), a data-driven digital twin designed for field-scale soil moisture predictions, irrigation advice, and quantification of crop water stress, crop water use efficiency, and irrigation efficiency in vegetable production. SWIM² integrates real-time soil moisture sensor data with a soil water balance model using the DREAM(zs) Bayesian inverse modeling approach to estimate 12 key parameters, including soil and crop growth characteristics, along with their uncertainty distributions. This probabilistic framework with integration of weather forecasts allows for dynamic soil moisture predictions with uncertainty estimates, enabling farmers to make informed decisions about irrigation scheduling. By providing an estimate of the water required to mitigate drought risk, SWIM² supports efficient water use while maintaining crop health.
We validated and implemented SWIM² in commercial fields and irrigation trials across Flanders to evaluate its performance and demonstrate its utility in predicting soil moisture, scheduling irrigation, and quantifying water use efficiency under diverse conditions. In 2022, SWIM² was used in real-time to guide irrigation decisions during a celery trial, and in 2023, it was applied to celery, chicory, leek, and sweet potato trials. During these irrigation trials, the model was calibrated two times a week, after which soil moisture was predicted, and irrigation scheduling was based on the predicted probability of water stress over the following four days. Retrospectively, model calibration based on the 100% irrigation treatment enabled a comparison of various irrigation treatments, providing insights into crop water stress and irrigation surpluses.
Our results show that SWIM² accurately predicts soil moisture and improves irrigation scheduling, while also providing insights into resource optimization, contributing to sustainable agricultural practices. Due to the probabilistic nature of the framework, the irrigation strategy can be tailored to suit a conservative or risk-tolerant approach, depending on the farmer's preferences and water availability. By bridging advanced modeling with practical applications, SWIM² empowers farmers to make data-driven decisions for resilient and efficient crop management.
How to cite: Hendrickx, M., Vanderborght, J., Janssens, P., and Diels, J.: SWIM²: A data-driven digital twin for field-scale soil moisture predictions, irrigation advice, and quantification of water use efficiency in vegetable production in Flanders, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4273, https://doi.org/10.5194/egusphere-egu25-4273, 2025.
Monitoring and modelling of soil hydrological processes at large scales require measuring the spatial and temporal evolution of soil volumetric water contents, θv. Direct measurement of θv can be done by sampling and laboratory analyses. Although such methods are straightforward, they require a lot of time and effort for the collection of several samples to account for the spatial and temporal variability. Time-domain reflectometry, TDR, can be a good alternative as it is used to indirectly measure θv in the field by measuring the travel time of an electromagnetic pulse in a probe. However, it is an intrusive, point-scale method making it impractical at large scales and at subsurface measurements. Other geophysical methods, such as earth resistivity tomography, ERT, and electromagnetic induction, EMI, sensors represent a practical solution for their time efficiency and the ability to measure at large scales. In particular, compared to the ERT, which still requires the insertion of several electrodes at the soil surface and their connection with a cable network, the EMI has the further advantage (in terms of measurement rapidity) of not requiring insertion in soil to take measurements. Nevertheless, the toll to pay for this larger scale applicability, is that they do not directly measure θv and require complex electromagnetic inversion models to obtain either the electrical resistivity, ρb, or the bulk electrical conductivity, σb, spatial distributions over time respectively from the pseudo-sections coming from ERT and the series of apparent electrical conductivity, ECa, coming from EMI. In this sense, these methods require further efforts to correctly translate the ρb and the σb distributions in as many θv distributions. Accordingly, this study aims at comparing thermogravimetric, EMI and ERT systems to obtain the spatio-temporal evolution of θv at a transect scale. For this purpose, a series of measurement campaigns were carried out at a transect 24 m long and 1 m wide at a sprinkler-irrigated field in Arborea area in Sardinia, Italy. A large database of spatially, vertically and temporally distributed measurements was created from auger samples, undisturbed samples, Campbell TDR-200, ERT and CMD mini-explorer EMI sensor. TDR measurements were used to find a relationship between θv and σb. Inversion models were then used to obtain the σb distribution from ERT and EMI measurements, utilizing a previously determined characterization of the soil profile, e.g., layering, depth to groundwater table, texture, etc. The TDR-obtained θv - σb relationship was then utilized to estimate the θv distributions from EMI- and ERT-based σb distributions.
How to cite: Coppola, A., Vacca, A., Deidda, G. P., Hassan, S. B. M., Da Pelo, S., Lobina, F., Abdelmaqsoud, M. S. M., Souid, F., Biddau, R., Manis, N., and Comegna, A.: Comparing thermogravimetric, TDR, ERT and EMI measurements of space and time evolution of water content along a transect during an infiltration experiment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9988, https://doi.org/10.5194/egusphere-egu25-9988, 2025.
Soil hydraulic properties, including the water retention curve (WRC) and hydraulic conductivity function (HCF), are crucial for accurately simulating hydrological processes in soils. These properties are highly variable and nonlinear, making them challenging to parameterize, particularly at field scales. This study introduces a novel physics-informed neural network (PINN) approach with constraints of Richards equation to estimate these constitutive relationships, conditioned on field soil moisture measurements in a semi-arid study area. The PINN comprises three interconnected networks: soil moisture over space and time, WRC and HCF networks. Given the high non-linearity of the soil hydraulic functions, we adopted an alternating training strategy, with an outer loop to filter the observation dataset and train the networks for the observation variable and an inner loop to train the WRC and HCF networks through the constraints of Richards equation. This two-step alternating training approach (with different loss functions) obtains reasonable observation networks, and since then it strengthens the possibility and the efficiency to learn the constitutive relations.
How to cite: Li, N., Zheng, X., and Yue, X.: Physics-informed Neural Networks for Inferring Hydraulic Properties from Field Soil Water Content Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1286, https://doi.org/10.5194/egusphere-egu25-1286, 2025.
This study evaluates the water retention effectiveness of different good farming pactices on a hilly catchment (Felső-Válicka, Hungary) using the SWAT+ hydrological model. The Felső-Válicka case study area (124 km2), the focus of the research, is predominantly used for agricultural production (~35% of the total area). Farmers in the region face increasingly severe droughts, coupled with significant erosion damage to arable land due to intense rainfall events caused by unfavourable precipitation distribution. SWAT+ enables us to analyse management practices that potentially improve water management in agricultural fields. We followed the OPTAIN R workflow (www.optain.eu) to support the process of input data preparation, model setup, model calibration and scenario simulations. In our setup: i) individual fields/parcels can send surface runoff and lateral flow to neighbour objects (contiguous object connectivity approach), and ii) characteristic management practices can be assigned to each field annually, enabling us to analyse the effectiveness of NSWRMs with respect to their individual site-specific allocation within the catchment. The modelled good farming practices align with those defined in the agricultural subsidy framework (CAP) in Hungary.
Among the investigated practices, reducing soil tillage depth and minimizing soil disturbance through reduced tillage practices had the most significant positive impact on water retention. In contrast, linear structured measures (such as riparian buffers along streams and hedges between agricultural parcels) had a less pronounced effect. The periodic planting of perennial crops (e.g., alfalfa on a three-year cycle) or land use change from cropland to pasture yielded mixed results regarding the selected hydrological indicators.
These findings underline the potential of optimized tillage practices as a key strategy for improving water retention in agricultural landscapes.
This work was funded by the Széchenyi Plan Plus program, supported by the RRF-2.3.1-21-2022-00008 project and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862756, project OPTAIN.
How to cite: Kassai, P., Braun, P., Kolcsár, R., Farkas-Iványi, K., Mészáros, J., and Szabó, B.: Assessing the effects of good farming practices on water retention on a hilly catchment in Hungary using SWAT+, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9348, https://doi.org/10.5194/egusphere-egu25-9348, 2025.
As aridity increases, the importance of focused groundwater recharge becomes more significant. It is well-recognized that groundwater recharge in arid areas primarily occurs during flash floods in ephemeral streams. However, previous studies suggest that diffuse recharge—spatially distributed groundwater replenishment from precipitation or irrigation—may also play a substantial role in aquifers recharge. This study aims to quantify the contribution of diffuse groundwater recharge through unsaturated fractured Chalk under the arid conditions of the Negev Desert.
Three models were evaluated to quantify and understand the processes of diffuse groundwater recharge: the Richards equation, the dual porosity model, and the dual permeability model. The models were calibrated against three unsaturated zone tritium profiles. Climate data and tritium concentrations in the rain of 1960 to 2023 were prescribed as the atmospheric boundary conditions. The model calibration involved running multiple simulations, incorporating 800 combinations of hydraulic parameters generated by the Latin hypercube sampling method. Model selection and performance were evaluated using statistical metrics, including reduced root mean square error (RRMSE) and the Akaike information criteria. The simulation results indicated that the dual porosity model outperformed the dual permeability and Richards’ equations. This suggests that water flow and solute transport occur within the loess layers and chalk fractures through preferential pathways while highlighting the exchange of water and solutes between the flowing system and the immobile matrix. The calibrated dual porosity models allowed for studying the relationship between diffusive recharge and precipitation. Two cross-correlation analyses (CCA) were conducted: one between yearly rainfall and yearly potential recharge at a depth of 5 meters, and another between all precipitation events above the 95th quantile and the yearly potential recharge at 5 meters depth. Both rain statistical characteristics exhibited similar CCA trends, while the quantile-95 values demonstrated stronger correlation coefficients. This illustrates that heavy rainfall drives deep water infiltration, ultimately replenishing the aquifer.
How to cite: Jmili, H., Weisbrod, N., and Turkeltaub, T.: How important is diffuse recharge in the Chalk aquitard under desert conditions?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10381, https://doi.org/10.5194/egusphere-egu25-10381, 2025.
Beneficial Management Practices (BMPs) are designed to reduce nitrate leaching from agricultural fields and protect groundwater quality. However, temporal groundwater monitoring results from wells beneath or downgradient agricultural fields often fail to show evidence for nitrate reduction even years after BMP implementation, and the mechanisms underlying this delayed response remain poorly understood. This study conducted high-resolution characterization and monitoring to investigate nitrate transport from soil to groundwater in a 7-hectare potato rotation field in Prince Edward Island, Canada. The site features fine sandy loam soil underlain by 7–9 m of glacial till, which overlies a regional fractured “red-bed” sandstone aquifer. The water table fluctuates seasonally between 2 and 6 m below ground surface (bgs). Multi-depth groundwater monitoring was conducted over 5 years from 2011 to 2016. Historically, the field was uniformly managed under a grain-forage-potato rotation. For this study, it was divided into four management zones (A–D). Zone D was removed from crop production to eliminate agricultural nitrogen inputs, while Zones A–C continued the crop rotation. This ensured that results from Zone D were not influenced by active cropping. Additionally, the up-gradient areas of Zones C and D were forested, minimizing lateral nitrate input from outside the study area. Multilevel wells were installed along a transect in Zone D to measure nitrate concentrations at various aquifer depths bi-weekly, while water levels were monitored daily using transducers. Rock core collection with detailed core sub-sampling for nitrate distribution was conducted in 2012 to track legacy nitrate in the subsurface. Soil sampling was conducted in each zone during spring and fall. Daily tile drainage samples for nitrate analysis were collected in Zone B using an ISCO sampler. Initial soil and tile drainage sampling detected exceptionally high residual nitrate levels following the 2011 potato harvest. Using this nitrate pulse as a marker, rock coring identified it at ~3 m bgs in December 2012, while piezometer sampling detected it at the water table in spring 2014. Despite seasonal recharge, these results indicate that nitrate required approximately 2.5 years to travel through the 6-m-thick vadose zone to the aquifer. Seasonal recharge processes pushed older nitrate stored in the vadose zone downward via hydraulic pressure, creating a piston-like movement. This caused a rapid water table response but a delayed nitrate concentration response in the aquifer, highlighting that uniform rather than preferential flow dominated nitrate transport through the glacial till vadose zone. By 2016, the nitrate plume in Zone D had disappeared. The short presence of a nitrate plume in the groundwater zone suggested that aquifer matrix diffusion had a minor influence on nitrate transport at this site. Instead, the delayed response of groundwater nitrate levels to surface remediation was attributed to processes occurring in the vadose zone. This study underscores the critical role of vadose zone dynamics in governing the time lag between implementing BMPs and observing groundwater quality improvements. High-resolution monitoring of soil, drainage, and aquifer systems is essential for understanding these processes and accurately predicting the outcomes of agricultural nitrate mitigation efforts.
How to cite: Jiang, Y., Nyiraneza, J., Chapman, S., Malatesta, A., and Parker, B.: Deciphering the Role of Vadose Zone Processes in Delayed Groundwater Nitrate Reductions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2879, https://doi.org/10.5194/egusphere-egu25-2879, 2025.
Nitrate pollution of groundwater is often attributed to excess fertilisation in agriculture. Low quality water, enriched by nitrate and other pollutants percolates down from the root zone through the unsaturated zone to the water table. Accordingly, the vadose zone holds the footprint of all possible groundwater pollution events occurring at the land surface before the pollution imprint arrives in the groundwater. Here we present a study were detailed long-term monitoring of the unsaturated zone reveals the groundwater pollution potential of various representative agricultural setups over the island of Malta. Malta is a semi-arid island in the Mediterranean Sea where groundwater, which is the only natural freshwater resource, suffers from nitrate pollution due to the intensive agricultural landscape. A national monitoring network, comprising of 16 Vadose zone Monitoring Systems (VMS) were installed under the different agricultural setups which represent Malta’s main agricultural practices. The VMS enables continuous monitoring of variations in the unsaturated zone water content, as indication to percolation processes, and frequent sampling of the sediment pore water for chemical analysis and characterisation of pollutant transport across the unsaturated zone.
Results show that the mean nitrate concentrations in the vadose zone underlying fields of potato, forage, mixed outdoor vegetables, greenhouses, vineyards, and orchards were 923 mg/L, 673 mg/L, 416 mg/L, 416 mg/L, 252 mg/L and 33.6 mg/L, respectively. Spatial distribution of the different agricultural setups shows that forage and potato fields are among the most common agricultural setups, with higher occurrence in the central and eastern areas of the island (>50% of the agricultural land area). This agricultural land use distribution spatially links to the high average nitrate concentrations in groundwater under those fields (ranging from 75 to 200 mg/L). On the other hand, lower proportions of potato and wheat fields are cultivated in the north-western areas (25-50% of the total agricultural land area). In the north-western areas, a clay layer situated in the unsaturated zone creates a shallow perched aquifer with a rock matric thickness ranging from 20 to 50 m, which impedes water fluxes from the agricultural fields to the main groundwater system below. The mean nitrate concentrations in the shallow perched aquifer are relatively high ranging from 200 to 350 mg/L due to the aquifer’s low water storage. On the other hand, nitrate concentrations in the regional aquifer underlying the perched aquifer are relatively low ranging from 25 to 100 mg/L.
In conclusion, the results show that potato and wheat fields are likely to have the greatest impact on nitrate pollution in the vadose zone and eventual groundwater nitrate contamination. Furthermore, these agricultural land uses are among the most common land uses cultivated in Malta. This implies the significant potential spatial impact of potato and wheat fields on groundwater nitrate pollution. With data being made available from this vadose zone monitoring network we can increasingly understand the pollution potential of different agricultural land uses on groundwater.
How to cite: Laudi, L., Dahan, O., Sapiano, M., Schembri, M., and Turkeltaub, T.: Using vadose zone data to determine agricultural impact on groundwater pollution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13344, https://doi.org/10.5194/egusphere-egu25-13344, 2025.
In recent years, the coastal areas of the Mediterranean have experienced extreme climatic events, including the intrusion of the salt wedge into freshwater systems. In Italy, this phenomenon is causing an increasing salinisation of coastal agricultural soils. Among these, the Lazio coast, characterized by dune, fluvial-marsh and marine wind deposits, represents a particularly vulnerable area. This territory, historically reclaimed with landfills and drainage, sees agriculture as one of the main socioeconomic drivers. This research aims to evaluate the extent of salinisation in the coastal area around the Tevere River and its potential impact on agriculture. Monitoring was conducted during the 2024 agricultural season through monthly sampling campaigns and measuring soil and surface water parameters. A particular focus was placed on analyzing the salt wedge intrusion in water bodies and in cultivated soils to understand its progression and impacts on crops. Among the techniques used, time domain reflectometry (TDR) proved essential for collecting spatial and temporal data on salt concentration variability. Using TDR both volumetric water content (VWC%) and Electrical conductivity (EC dS/m) were measured, with EC linked soil properties such as water content, texture and organic matter. Additionally, to soil sampling, leaching tests were carried out in the laboratory to assess the release of salts from the soil into water. Preliminary chemical analyses of major ions in the leachates were used to quantify the contribution of soil salinity to water salinization. Monthly soil samples collected from 10 monitoring points showed in several cases salinity levels exceeding the minimum threshold of 2 dS/m indicated by the FAO. This can adversely affect the growth and yield of various cultivated species. The results highlight the importance of continuous monitoring of parameters such as electrical conductivity, temperature and soil humidity to track salinization trends. Understanding these dynamics is fundamental for developing adaptation and mitigation strategies to preserve the agricultural productivity of the Lazio coast.
Keywords: Lazio Coast, Soil Salinization, Coastal Agriculture, Seawater Intrusion.
How to cite: Burbulea, R., Marchina, C., Bartoletta, S., Manoni, A., and Tarolli, P.: Analysis of Soil Salinization Induced by Seawater Intrusion on the Coastal Agriculture of Lazio Region (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9637, https://doi.org/10.5194/egusphere-egu25-9637, 2025.
Soil erosion damages many fertile regions world-wide. At the same time, scientists expect more frequent and intense erosive precipitation events and droughts with proceeding climate change.
The Erosion and Runoff Laboratory (EARL) of the Bavarian State Research Center for Agriculture (LfL) is an extraordinary experimental site under construction in Lower Bavaria (Germany) to study the physical, social, and economic factors driving the change in landscape water balance, as well as the increase in surface runoff and erosion under agricultural use. During the twenty-year study period, future-proof cropland systems (i.e., a combination of crop rotation and soil management regimes) will be investigated in terms of their effect on water retention, erosion protection, as well as contaminant and nutrient leaching in the hills.
Volumetric water content and matric potential are measured at three depths in 36 plots, each measuring 330 m² (55 m long and 6 m wide), and the data are validated using a central cosmic ray neutron scattering sensor. Surface and interflow runoff are monitored continuously for each precipitation event. Runoff samples collected automatically by samplers will be analyzed in the laboratory regarding the transported materials and substances. Root growth scans and regular drone flights provide data on phenological growth and soil conditions, while soil water sampling and an extensive meteorological station including distrometers and precipitation-impulse gauges ensure the boundary conditions.
We believe that this design enables comprehensive process-based modeling and a validated balance of energy, water, and material flows on the hillside scale for each individual plot. As the EARL is designed as a collaborative research facility, the created dataset will enable the scientific community to understand critical processes within agricultural soils’ vadose zone better.
Comparatively high financial resources for long-term monitoring projects in the vadose zone are very rarely available. However, the precise measurement of water fluxes in the vadose zone is difficult, the availability of non-destructive measuring instruments is limited, and the results are often compromised by individual unconsidered aspects. Consequently, the risk of an erroneous measurement design and the associated bad investment due to the naturally limited perspective of a small research group is high. Therefore, we want to present our measurement concept for the vadose zone in detail and discuss it with experts before the final installation of the measuring equipment in summer 2025.
How to cite: Mitterer, J. and Ebertseder, F.: Monitoring of water balance in soils dominated by arable farming – development of a measurement concept for the Erosion and Runoff Laboratory (EARL), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20353, https://doi.org/10.5194/egusphere-egu25-20353, 2025.
Soil organic carbon (SOC) stock is critical in mitigating global warming by sequestering carbon and enhancing soil fertility. This study focuses on Campania, a region of southern Italy covering about 13,700 km2, and addresses the challenging task of estimating SOC stock at relatively large spatial scales in a sustainable manner. A practical outcome is to provide public bodies and stakeholders with as many reliable SOC stock maps as possible, allowing for the uncertainties associated with the techniques employed. The assessment of SOC stock requires the knowledge of SOC content, oven-dry bulk density, soil depth, and rock fragment.
To accomplish this task, the following soil physical and chemical properties were directly measured by collecting 3,316 soil samples: particle-size distribution, soil textural classes (i.e., the sand, silt, and clay contents), oven-dry soil bulk density, soil organic content, pH, and calcium carbonate. However, direct measurements of SOC content and especially oven-dry soil bulk density are labor-demanding, time-consuming, and expensive. Therefore, we explored the use of soil spectroscopy in the visible, near-infrared, and shortwave infrared (vis-NIR-SWIR in the range 400-2500 nm) range to estimate these input properties. The spectral reflectance in the vis-NIR-SWIR range was measured on co-located 3,316 air-dried soil samples, sieved at 2 mm, whereas Spectro-Transfer Functions (STFs) have been developed to predict the SOC stock using advanced statistical methods, including neural networks, partial least square regression, and linear/nonlinear regression models.
Our findings demonstrate the superior performance of neural networks and partial least square regression in accurately estimating SOC stocks. However, we also emphasize the value of simpler linear/nonlinear regression models for their reproducibility and ease of implementation. These results highlight the potential of spectral-based approaches to estimate SOC stocks at large scales efficiently and cost-effectively, thereby improving the implementation of carbon management strategies and enhancing the assessment of agroecosystem resilience to global warming.
How to cite: Mazzitelli, C., Romano, N., Hermansen, C., de Jonge, L. W., Ben Dor, E., and Nasta, P.: Evaluating spectro-transfer functions to estimate soil organic carbon stock at large spatial scales , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10600, https://doi.org/10.5194/egusphere-egu25-10600, 2025.
Seawater intrusion (SWI), driven by climate change and anthropogenic pressure, is a critical issue in coastal regions, with soil salinization as one of its most severe consequences. In low-lying agricultural areas, such as the Po River Delta in Northeast Italy, SWI-induced salinization degrades soil quality by altering structure, reducing porosity, and suppressing microbial activity, ultimately threatening agricultural productivity. The combination of prolonged summer droughts, high evapotranspiration rates, and reduced river flow intensifies SWI, facilitating salt accumulation in soils. Understanding the mechanisms and impacts of soil salinization is essential to understand SWI-related challenges. Accumulated salts in soils not only induce stress in vegetation, impairing metabolic functions and crop cycles, but also accelerate soil degradation, with long-term risks such as micro-desertification. Addressing these challenges requires precise soil measurements to monitor salinity levels and fluctuations over time, especially in the critical summer months. This study aims to integrate multi-temporal on-site observations of soil moisture, electrical conductivity (EC), and temperature with the collection and analysis of EC and ion content from water extracts, conducted throughout the summer of 2023 (July to September). In selected study areas within the Po Delta territory, point measurements of soil temperature, moisture, and electrical conductivity, obtained biweekly using a Time Domain Reflectometry (TDR) probe, were spatially interpolated. These interpolated data were then compared with spatially interpolated values of electrical conductivity derived from water extracts of soil samples collected at the same locations on the same biweekly schedule. In addition, leaching tests were performed to detect major ions in the leachates. This approach allowed for a detailed assessment of the link between soil salinity and other soil properties across the study areas. This information, combined with precipitation data, facilitates the detection of salinization patterns, enabling the identification of the most affected zones.
How to cite: Ghirardelli, A., Marchina, C., and Tarolli, P.: Integrating Time Domain Reflectometry and Soil Sample Analysis to Monitor Soil Salinization in the Po River Delta, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9339, https://doi.org/10.5194/egusphere-egu25-9339, 2025.
Understanding soil-moisture-atmosphere interactions is essential for a range of applications like accurate weather forecasting and climate modelling. However, current models often overlook critical soil properties, particularly soil hydraulic parameters (SHP), limiting their accuracy. The Sahel region, characterized by intense evaporation-precipitation feedback and recycling of up to 95% of rainfall, provides an ideal setting to study these dynamics. Despite its significance, data on SHP remains scarce for this region.
In the context of the DakE-project, which operates a high-resolution network of weather stations including soil moisture monitoring in Western Senegal, this study investigates the influence of SHP on heat fluxes across five soil types, covered by the network. SHP were characterized by constructing water retention curves based on on-site observations of soil moisture and soil suction and sampled physical soil properties. A coupled water and heat transport model was developed in HYDRUS-1D, incorporating vegetation effects to explore the influence of SHP, particularly on latent heat flux. Site-specific initial and boundary conditions were applied to construct and calibrate five models against observed soil moisture data. Model outputs from the different study sites were compared to evaluate the role of SHP on heat flux partitioning
Preliminary results, derived from Pearson correlation test, indicate a positive correlation between clay content and latent heat flux, with higher clay content increasing latent heat and the probability of precipitation. The same relationship is observed for saturated hydraulic conductivity, which is strongly influenced by soil texture and structure. These findings, while limited to data from only five study sites and influenced by interconnected nature of soil properties, highlight the potential importance of soil texture and structure as key parameters for precipitation modelling. As we show in this study, the drastic seasonal differences in soil wetness behaviour – from desert-like in dry season to prolonged inundation periods during wet season – makes the choice of model set up not trivial and can make model calibration challenging. Our findings underscore the need to better understand the soil water-atmosphere interactions in the Sahel region.
Besides the small spatial scale considered in this study, our study emphasizes the importance of SHP for process understanding not only in soil hydrology but also in atmospheric sciences. Interdisciplinary approach is imperative to incorporate soil texture and structure into current climate models, improving their representation of soil-atmosphere feedbacks in the Sahel and beyond.
How to cite: Kny, K., Dimitrova-Petrova, K., and Haerter, J. O.: Influence of soil properties on latent and sensible heat transport in the major soil types in Western Senegal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-406, https://doi.org/10.5194/egusphere-egu25-406, 2025.
The study investigates the hydrological cycle mechanisms in the Taihu Basin plain to support the development of next-generation, physically-based hydrological models for plain regions. Comprehensive monitoring at two experimental sites captured key hydrometeorological variables, including rainfall, evaporation, groundwater depth, soil moisture, and outlet flow. Analysis of runoff processes revealed that saturation-excess runoff dominates the rainfall-runoff mechanism, while infiltration-excess and mixed runoff models occur under specific conditions. Initial soil moisture and groundwater depth significantly influence runoff coefficients, with groundwater depth exhibiting a parabolic relationship. Furthermore, despite the relatively flat terrain, micro-topography markedly impacts runoff pathways, convergence times, and water distribution. Simulations using existing models highlighted the critical roles of micro-topography and runoff patterns in shaping hydrological responses, offering theoretical support for advancing refined hydrological models and improving water resource management in plain areas.
How to cite: Chen, G.: Rainfall-Runoff Generation Patterns and Key Influencing Factors in the Plain of the Taihu Lake Basin, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6414, https://doi.org/10.5194/egusphere-egu25-6414, 2025.
Nitrogen leaching in agricultural systems is a major environmental risk resulting from irrigation-fertilization practices. Global losses of fertilized agricultural systems are estimated to be about 30% of the applied nitrogen fertilizer. Nitrate leaching, the most predominant form of loss, results in groundwater contamination impacting the quality of drinking water. In the context of climate change, food, and water security, it is imperative to develop strategies that optimize fertilization application (and irrigation) to mitigate adverse environmental effects of nitrogen losses while maximizing grain production. Developing and testing such strategies remains challenging, partly because soil functions strongly depend on pedoclimatic conditions, soil degradation, and crop type; and all these variables may largely differ even for different regions across a same country and for different years. The ongoing Horizon Europe FARMWISE project aims at addressing this challenge by developing a decision support system based on combining: (1) multi-scale data collection and data fusion, (2) development of optimised fertilization-irrigation practices and sensors for monitoring them, (3) process-based modelling of agricultural systems. In this presentation, we introduce how the agroecosystem modelling is integrated in FARMWISE, and discuss the numerical modelling approach to predict crop yield and nitrate leaching as a function of the applied fertilization rate and for different soil types. As part of this, we present a toy-model example simulating winter wheat for 15 fertilization rates from 0 to 150 kg N/ha/yr and for clay contents of 15%, 25%, and 35%. Preliminary simulations predicted a maximum increase in yield of 74%, 97% and 93% achieved at applications rates of 90, 130 and 110 kg N/ha/yr for 15%, 25%, and 35%, respectively. The modelling framework presented in this work, in combination with European observations of current and new agricultural management practices, has the potential on filling information gaps of data for different pedoclimatic conditions and assisting decision making through providing a tool to predict the efficacy of such practices for different conditions and in the context of climate change.
How to cite: Romero-Ruiz, A. and Halloran, L.: Impacts of fertilization rate in nitrate leaching in agricultural soils: Insights from process-based modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6964, https://doi.org/10.5194/egusphere-egu25-6964, 2025.
Accurately predicting the distribution of soil organic carbon (SOC) is essential for sustainable land management and climate change mitigation. However, due to the significant spatial variability of SOC and the complex interactions among soil factors, precise prediction remains a challenging task. With advancements in remote sensing technologies and increased data availability, various types of data have been utilized for SOC prediction. Nevertheless, traditional machine learning models often rely on single-modal data, which limits their ability to fully capture the complexity of SOC dynamics. Recent developments in deep learning have shown promise in improving environmental modeling by integrating multiple data sources. However, the effective integration of multi-modal data for SOC distribution prediction has not been fully explored. In this study, we proposed a multi-modal convolutional neural networks (MM-CNN) model that integrates satellite imagery and topographic variables derived from DEM to improve SOC prediction in the Walnut Creek watershed (WCW). Spatial features were extracted using CNN from an optical RGB band image captured by Google Earth on June 5, 2011, while 16 terrain variables derived from DEM were processed using artificial neural network (ANN) and concatenated with CNN features. The target variables include SOC density, Cesium-137 (137Cs) inventory, and soil redistribution (SR) rate, which were obtained from 100 soil samples collected in WCW. To evaluate the performance of MM-CNN, we compared it with single-modal models, including CNN, ANN, and XGBoost, using the coefficient of determination (R2) and root mean squared error (RMSE) as performance metrics. Considering the spatial variability of SOC distribution, various image patch sizes centered on soil sampling points were used for both MM-CNN and CNN. The results of this study would show comparisons between MM-CNN and various single modal models predictions to inform the potential benefits of integrating complementary information from satellite imagery and topographic variables. The findings from this study would provide valuable insights of a multi modal approach for practical applications in environmental and agricultural fields.
How to cite: Lee, Y., Jeong, H., Lee, B., Du, L., W. McCarty, G., and Lee, S.: Application of multi modal deep learning framework for predicting the distribution of soil organic carbon, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7738, https://doi.org/10.5194/egusphere-egu25-7738, 2025.
Changes in groundwater levels due to climate change and human activities affect the process of soil water and salt transport in the vadose zone of drylands, which further influences vegetation growth and community succession. Groundwater level, soil structure, and the salt-endurance mechanism of halophytes are essential factors affecting this process. To further understand the interactions of these factors and their effects on the water-salt transport process in the vadose zone, the study was conducted to investigate the impact of halophytes and soil structure on soil water-salt transport in the root zone of plants by constructing an observation site for water-salt transport, selecting three soil types, namely, sandy soil, sandy loam, and clay, as well as two typical halophytes, namely, Nitraria tangutorum and Tamarix ramosissima, and carrying out water-salt observation experiments with the help of water-salt sensors for water-salt observation and data collection, and conducting spot observation experiments under different treatments. The study will investigate the effects of halophytes and soil structure on soil water and salt transport in the root zone of plants and determine the parameters of water and salt transport under different treatments to serve the establishment of water and salt transport modeling in arid zones and the construction of ecological protection forest system in arid oases.
How to cite: Xue, X., Tuyishimire, E., and You, Q.: Coupled Effects of Halophytes and Soil Texture on Water and Salt Movement in Unsaturated Saline Soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9045, https://doi.org/10.5194/egusphere-egu25-9045, 2025.
Unsaturated soil hydraulic conductivity (UHC) plays a crucial role in natural systems, as it governs the flow of water in soil under partial saturation conditions, which are typical for most environmental processes. One of the laboratory methods used for UHC determination is Instantaneous Profile Method (IPM). IPM requires the measurement of soil water flux at least at one end of a soil sample, alongside the measurements of soil water content (SWC) and soil water potential (SWP) at various points along the height of the soil sample.
This study analyzes the accuracy of UHC determination using IPM, based on simulated desaturation experiments for 460 types of soils. The parameters of soils used in the analysis were sourced form the Global Database of Soil Hydraulic Properties (GSHP) to reflect the realistic properties of the particular soils. The desaturation of a 5 cm-high soil sample is simulated using Richard’s equation. Simulations were performed for various experimental scenarios using axis translation method, where overpressure is used for enforcing the particular boundary conditions. Different overpressure causes different drying rates. Following boundary conditions were applied at the bottom of the sample: constant soil water potential of -10 mH2O over 8 days, a linear decrease in soil water potential from 0 mH2O to -10 mH2O over 2.5 days, followed by maintenance at -10 mH2O until the 8th day, and a linear decrease in soil water potential to -10 mH2O over 5 days, followed by maintenance at this level until the 8th day of simulation.
IPM is based on measurements of SWC (using TDR method) and SWP (using microtesiometers) in subsequent layers in the soil core. The influence of the height of the layer (from 1 cm to 2.33 cm) on the accuracy of UHC was analyzed. Time interval between subsequent measurements was also examined in the context of the accuracy. The TDR measurements naturally introduces noise. Two denoising methods were evaluated: the Bézier curve method and the B-spline method.
In conclusion, selecting an appropriate denoising method is critical for accurate UHC determination. This study provides a comparative analysis of the effectiveness of different denoising techniques for TDR data. Results indicate that thinner soil layers allow for better estimation of UHC. However, the determination of UHC in subsequent layers introduces greater errors due to the propagation of flux estimation errors from earlier layers. The most accurate UHC value is determined for the first layer. Moreover, the experiment setup in which the boundary condition is changing slowly allows for better estimation of UHC in comparison with the other scenarios. These findings underscore the importance of careful experimental design and the selection of suitable methods to minimize cumulative errors in UHC determination.
Acknowledgments:
Research was founded by the National Science Centre within the contract 2021/43/B/ST10/03143
How to cite: Kozyra, M., Lamorski, K., and Sławiński, C.: Instantaneous profile method - the impact of experimental procedure on measurement results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9535, https://doi.org/10.5194/egusphere-egu25-9535, 2025.
There is a recurring question in environmental science, whether the spatial heterogeneity of soils is also accompanied by notable variation of soil hydrological behaviour. Our aim was to investigate this so-called “structural heterogeneity versus functional homogeneity” issue by using variably saturated zone simulations and the Hungarian soil database MARTHA v3.1.4. The purpose of the applied functional classification method is to simulate the water balance components of different soils under the same meteorological forcing and then cluster them based on their hydrological response.
We used 2552 soil samples with adequate data availability (fitted van Genuchten parameters of the soil moisture retention curve, saturated hydraulic conductivity) to set up and run 200 cm deep homogeneous soil profile models in Hydrus-1D, which differed only in their soil hydraulic parametrization. The simulations covered a 1 year period with daily time steps. Two types of upper boundary were applied: (i) 1 mm/day constant precipitation for 30 days then no precipitation, (ii) measured precipitation time series from Hungary over the whole period. The bottom boundary condition was free drainage.
Hydrological indicators were derived from the simulation results (surface runoff, average root zone saturation, storage change, bottom boundary flux, flowthrough volume at 40 cm depth, break through curve characteristics for the constant precipitation). These indicators were used to classify the soil profiles using k-means clustering.
1984 simulations were successful, from which 9 clusters were formed. These represent distinct hydrological behaviour for the same forcing time series, indicating the applicability of the proposed classification method.
Key words: soil hydrology, functional evaluation, Hydrus-1D, MARTHA database
The research presented in the article was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project.
How to cite: György, M., Ács, T., Decsi, B., Kolcsár, R., Bakacsi, Z., Makó, A., Szabó, B., and Kozma, Z.: Structural heterogeneity versus functional homogeneity - hydrological soil clustering of the MARTHA database, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11586, https://doi.org/10.5194/egusphere-egu25-11586, 2025.
Understanding forest water cycles and the processes influencing them is critical for predicting how environmental changes may impact forest hydrology. Soil-Vegetation-Atmosphere Transfer (SVAT) models are essential tools for simulating and understanding water fluxes within forest ecosystems. However, the accuracy and reliability of these models are often limited by the quality and availability of input data, particularly soil hydraulic parameters. Consequently, assumptions made in the modeling process can lead to underestimations or overestimations of key water balance components, such as groundwater recharge. To improve the predictive accuracy of SVAT models, observed soil moisture data are commonly used to validate model parameterization, ensuring that simulated soil moisture levels match the observations. Nonetheless, determining which measured values or how many observations are adequate for model calibration poses a challenge due to the high spatial and vertical variability of soil moisture. This variability is driven by heterogeneity in soil properties and forest structure across the studied area.
For this reason, the study presented investigates the variability of soil moisture observations across two diverse forest environments differing primarily in soil matrix homogeneity and tree species composition. The influence of soil moisture variability on input parameter set adjustments required for effective SVAT model calibration was analyzed based on recorded soil moisture by a sensor network installed at both sites. Specifically, the study examined whether significant modifications to the parameter set are necessary to match simulated and observed soil moisture from identified soil moisture clusters at the sites.
The results confirmed that soil moisture variability was greater at the site with a more heterogeneous soil matrix, both spatially and with depth. At such locations, significant adjustments to input parameters are needed to match simulated and observed soil moisture, substantially affecting the simulation of individual water balance components. Using a mean soil moisture value for model validation proved inadequate for capturing the full range of variability at such locations. Conversely, at the site with a more homogenous soil matrix, soil moisture variability was low and adjustments to input parameters were negligible. Here, using the mean soil moisture value for model calibration is sufficient to represent the water balance accurately. The results highlight the importance of considering soil moisture variability when calibrating SVAT models, particularly in heterogeneous environments.
How to cite: Fichtner, T., Aguilar Avila, Y. J., Hartmann, A., Seeger, S., Maier, M., and Raspe, S.: Significance of soil moisture variability in forest on Soil-Vegetation-Atmosphere Transfer model results , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15624, https://doi.org/10.5194/egusphere-egu25-15624, 2025.
Rice production is estimated to contribute around 11% of global anthropogenic methane emissions. There is consistent evidence that management practices such as drying and rewetting the soil can reduce these emissions without impacting crop yield, yet it is challenging to quantify the emission reductions in different soils and for different management practices. Robust estimates of emission reductions would enable farmers to claim carbon credits when switching from continuous flooding (CF) to alternate wetting and drying (AWD) - and advanced modelling with remotely sensed inputs can contribute to this. Two models (DNDC and Daycent) are commonly used to simulate methane emissions, but both require large numbers of parameters making them challenging to scale to new locations where parameter values are uncertain. We have developed a new model of soil methane emissions that shows comparable performance to these more complex models but reduces the required number of parameters to around 30 (depending on management actions) by focusing only on the processes key to methane. The proposed model uses a similar approach to the Daycent methane module in which the soil redox potential after flooding or drying is modelled, but we consider the parameters for this process to be influenced by soil texture and mineralogy as well as the drainage conditions (e.g. water table). To permit an application of the model to large regions of interest, our model can be linked to remotely sensed estimates of above ground biomass. Using a Bayesian Calibration approach, we show that the model can be successfully calibrated with local data from a single season, and validated for subsequent seasons. Using the model at new sites is less consistent; we present our progress and the ongoing challenges in defining relationships between the soil properties and the model parameters to improve emission reduction estimates at new sites and enable the model to be used at scale.
How to cite: Todman, L., Nooreyezdan, N., and Atzberger, C.: Modelling soil methane emissions from rice production to enable scaling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16133, https://doi.org/10.5194/egusphere-egu25-16133, 2025.
The spatial distribution of soil water content within the entire range of the unsaturated zone is imperative for several hydrologic, agricultural and geotechnical applications. Monitoring of soil water content within the unsaturated zone is conventionally conducted with TDR and FDR sensors at discrete points within the soil matrix. Although both TDR and FDR sensors provide reliable measurements of soil water content for a representative volume around the placement area, their installation at various depths is laborious and time-consuming work, causing significant disturbance to the soil matrix modifying its soil structure. Furthermore, commercial profile sensors measuring soil water content at discrete points, are typically limited to lengths of 1 m., preventing the monitoring of critical hydrologic processes below this depth. To address this limitation, custom-length waveguides made from flexible insulated flat copper wires have been developed. Unlike non-insulated rod waveguides, these custom waveguides maintain signal integrity over lengths exceeding 1 meter. They enable soil water content monitoring along their entire length by applying inverse modeling techniques to reflected signals reconstructed through profile reconstruction algorithms. The installation of custom waveguides in the field is achieved through portable drilling equipment that ensures minimal soil disturbance.
This study evaluates the performance of TDR and NanoVNA devices, connected to custom waveguides, within the extent of an experimental field. The use of low-cost NanoVNA devices offers a portable and affordable alternative to traditional TDR systems, which are often cost-prohibitive even for research purposes. NanoVNA devices function as virtual TDR instruments, making them suitable for field installations. The development of supportive Python code for waveform acquiring and inverse modeling automation, facilitates high-resolution, in-field monitoring of soil water content, providing valuable insights into soil hydrological processes throughout the length of the transmission lines. Soil hydrologic processes above shallow aquifers are dominated by capillary flow phenomena, emerging from capillary rise over the groundwater table. Spatial TDR and virtual TDR monitoring, from NanoVNA devices, with custom waveguides results to high time and spatial resolution of capillary action above shallow aquifers.
Acknowledgments
How to cite: Chrysanthopoulos, E., Perdikaki, M., Floros, E., Kofakis, P., and Kallioras, A.: Monitoring soil hydrologic processes above shallow aquifers using spatial TDR and FDR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19137, https://doi.org/10.5194/egusphere-egu25-19137, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 3
EGU25-446 | ECS | Posters virtual | VPS15
Vegetation as proxies for improving the estimation of soil water fluxesFri, 02 May, 14:00–15:45 (CEST) | vP3.22
Soil water fluxes, including soil moisture, water storage, and recharge flux, are essential components of energy exchange at the Earth's surface and are fundamental to modeling land surface processes. Accurate estimation of soil hydraulic properties (SHPs) at the field scale is critical for simulating these fluxes, particularly within the vadose zone. Consequently, a robust understanding of soil water dynamics and associated processes relies on the precise characterization of SHPs. The experimental determination of these properties at different spatial scales are challenging and often time-consuming, especially in the case of vertically heterogeneous soils. Studies showed that the vegetation indices can provide sub-surface hydrological information. For example, the Leaf area index (LAI) of forest cover was found to be strongly correlated with the groundwater levels. This indicates that vegetation has the potential to act as a proxy for understanding many surface and sub-surface soil water processes. Inverse modeling approaches provide an opportunity to use vegetation information to estimate SHPs. The present study is aimed at developing and testing methodologies for estimating SHPs for multi-layered soils, specifically field capacity and wilting point, in an agricultural watershed. This is accomplished using variables like surface soil moisture, surface soil temperature, and canopy variables (Leaf Area Index and evapotranspiration) as proxies in different weighted likelihood combinations and carrying out the inverse modeling using the soil water balance model STICS. The methodology has been developed for three layered soil profiles (0 to 10 cm, 11 to 50 cm, and 51 to 100 cm) with combinations made from four major soil textures: sandy loam, sandy clay loam, clay loam, and clay, making 12 soil combinations. A sensitivity analysis of canopy variables relative to soil water storage properties was carried out to determine the best choice of canopy variable for estimating soil water fluxes using the EASI Method. The results show that the soil moisture and canopy variables showed a strong correlation with SHPs, indicating that these variables could provide reliable estimates of soil water fluxes. In which the leaf area index shows more sensitivity towards the subsurface layers (sensitivity index~0.4). The study showed that the likelihood combinations of variables with higher weights to canopy variables provided better estimates of SHPs in the deeper layers. With the use of the likelihood combinations made by surface and canopy variables, we achieved mean relative absolute errors of 4% for the surface layer properties and 10% for the root zone SHPs, especially in water-stressed conditions. Since the variables used in this study are potentially accessible from the remote sensing data, the application of this methodology at large spatial scales is feasible, thereby generating spatial maps of sub-surface soil properties at regional scales, which can aid in the improved modeling of sub-surface soil moisture.
How to cite: Vk, A. and Krishnan, S.: Vegetation as proxies for improving the estimation of soil water fluxes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-446, https://doi.org/10.5194/egusphere-egu25-446, 2025.
