Water Content Distribution Research Articles

A new method for estimating the soil-water diffusivity of unsaturated soils based on the Boltzmann transform and the principle of stationary action

Accurately measuring the nonlinear soil–water diffusivity remains a complex and time-consuming task. This study extended a simple method for estimating the soil–water diffusivity of unsaturated soils based on the Boltzmann transform and the principle of stationary action. The power relationship between the Boltzmann transform variable and diffusivity is explicitly expressed, with the corresponding power exponent commonly assigned a value between 1 and 3. By comparing the analytical and numerical results, it was found that the predictions are consistent when the power exponent is taken as 2. The proposed method in this study is more concise and convenient for computation when compared to the existing methods. It directly describes the relationship between the soil–water diffusivity and the water content distribution profiles. The proposed theoretical model has limited predictive ability for certain soil types, such as loam and clay. To improve the methodology, more data from different soil types should be collected in the future to better predict soil–water diffusivity. In conclusion, the method proposed in this paper provides a concise and effective method that will have a wide range of applications in future practice in the fields of soil science, agricultural engineering, and environmental research.

Geophysical visualization of water content distribution in bentonite by joint seismic and radar tomography

SUMMARY Bentonite is often considered as buffer material for deep geological radioactive waste repositories. To support decision making and safety assessment of radioactive waste repositories, international agencies and research institutions proposed the implementation of monitoring programmes. While the overall concepts of such monitoring programmes have been largely developed, the selection of key observations parameters, such as temperature, pressure and water content, and the technical implementation are still under development. The direct measurement of such parameters requires the placement of sensors inside a repository, which can significantly affect its safety functions and only provides information at the typically sparse sensor locations. Geophysical tomography can help gaining valuable insights into the state of the repository non-invasively by providing images of the distribution of geophysical parameters from measurements that are purely taken from the outside. However, the extracted geophysical parameters are often difficult to interpret and the geophysical tomography problem is non-unique, meaning that there exist multiple models that explain the data equally well. Here, we demonstrate that this non-uniqueness can be significantly reduced by simultaneously employing multiple geophysical methods in a joint tomography scheme. We simultaneously invert seismic and ground penetrating radar (GPR) traveltimes and amplitudes by imposing structural similarity constraints on the tomographic velocity and attenuation images. The resulting, estimated geophysical parameter maps show a strongly improved correlation when compared to results obtained from individual inversions, which in turn facilitates the establishment of constitutive relationships between the geophysical parameters (seismic and GPR velocity and attenuation) with the water content, as key parameter for the evaluation of the state of a radioactive waste repository. Using data from the full-scale emplacement (FE) experiment, we employ a supervised machine-learning model that enables the translation of the tomographic velocity and attenuation images obtained in bentonite to an image of the distribution of the water content inside the repository, where the machine learning model is trained using direct point measurements of the water content at sparse locations inside the tomographic plane. Due to the lack of direct water content sensors in the FE experiment, we use neutron log data (which are directly linked to water content) to train the machine learning model. Ultimately, this enables us to extrapolate the sparse neutron log data to a spatially cohesive distribution inside the repository corresponding to a visualization of the spatial distribution of water content.

Evaluating the surface relaxivity and movable fluid of low-permeability sandstones based on low-field nuclear magnetic resonance

Comprehensive characterization of pore structure and fluid distribution is beneficial for efficiently exploring and developing low-permeability sandstone reservoirs. As a conversion parameter, the surface relaxivity is significant for characterizing the pore structure of porous media and evaluating fluid mobility. The surface relaxivity indicates the strength of the interaction between the fluid and the solid during the relaxation process. This paper conducts mercury intrusion porosimetry, low-temperature nitrogen adsorption, and nuclear magnetic resonance-centrifugation experiments on low-permeability sandstones, providing insight into the evolution of pore size and water content distribution. Combining mercury intrusion porosimetry with nuclear magnetic resonance, the surface relaxivity of samples is measured to be 9.57–23.79 μm/s. The surface relaxivity ranges from 0.70 to 3.72 μm/s utilizing low-temperature nitrogen adsorption and nuclear magnetic resonance. Based on the movable water saturation through the critical radius, the calculated surface relaxivities using two methods are compared. The result indicates that surface relaxivity determined by low-temperature nitrogen adsorption is smaller than that obtained through mercury intrusion porosimetry. This is attributed to overestimating the ratio of pore surface and pore volume in the low-temperature nitrogen adsorption, which is difficult to capture information about macropores. Conversely, the similar principle between mercury intrusion porosimetry and centrifugation leads to consistent movable water saturation, minimizing discrepancies in evaluating surface relaxivity. Therefore, the surface relaxivity determined by mercury intrusion porosimetry-nuclear magnetic resonance is more suitable for characterizing the pore structure and fluid mobility of low-permeability sandstones. In addition, the ink-bottle effect retains water in the macropore during centrifugation experiments.

Insight into failure mechanisms of rainfall induced mudstone landslide controlled by structural planes: From laboratory experiments

The role of structural planes in controlling mudstone landslides is a key issue in the study of geo-disasters in the Loess Plateau of China. In this study, the effects of sliding-control structures on the mechanisms of mudstone landslides are investigated via three model experiments with different slope structures. The results show that the hydrological response and failure mode of the experimental slope vary with the structural conditions. The vertical joints serve as preferential seepage paths, which accelerate rainfall infiltration, resulting in earlier responses of volumetric water content and pore water pressure. With the incorporation of vertical joints, the slope failure mode tends to transform from shallow failure to deep-seated failure. The presence of a weak interlayer leads to significant increases in the velocity and runout of the sliding mass. The variation in the slope failure extent and deformation characteristics with varying sliding-control structures further changes the temporal and spatial distributions of volumetric water content and pore water pressure. The different slope failure modes correspond to different sliding-control mechanisms, which are dominated by the types of structural planes and their interactions with hydrological responses. In the action of these mechanisms, pore water pressure and seepage force play significant roles in the reduction of effective stress and shear strength.

Soil behavior near the PVD filter and interpretation of clogging during vacuum preloading

Clogging near the prefabricated vertical drain filter during vacuum preloading reduces the efficiency of ground treatment, and existing research has yet to agree on the cause. This study conducted a small model test to comprehensively examine the soil's water content and particle size distribution at different distances from the filter and tailwater for different preloading durations. The results showed that the particle size distribution of the soil at different selected points exhibited a slightly irregular difference. This indicates that no measurable migration of particles occurs, which was further confirmed by the particle size distribution of the soil after the one-dimensional consolidation test. The application of vacuum pressure did not significantly change the soil particle composition and, thus, its compressibility and permeability properties. The particle size distribution of tailwater revealed that a small number of fine particles with a mean size of 3–4 μm rapidly discharged with tailwater during the initial stage of vacuum preloading, which was much less than the mean size of the slurry and pore size of the filter. The decrease in water content and permeability indicates non-uniform consolidation, and the increase in the permeability coefficient ratio suggests the formation and development of the clogging zone.

Convolutional neural network-based multimodal image information fusion for moisture damage assessment of cultural heritage buildings

Water is an essential factor causing the deterioration of historical and cultural heritage, and identifying and quantifying moisture damage is essential for conserving cultural heritage buildings. This study combines infrared thermography (IRT) and microwave technology to investigate moisture-induced diseases and the variation of the spatial distribution of water content in the Yungang Grottoes of China, a World Heritage Site. Information on building humidity distribution was extracted and combined from IRT and microwave information. Furthermore, multi-feature image fusion combines different types of monitoring information through the Convolutional Neural Network (CNN), which is used to diagnose moisture distribution and content accurately. The features obtained from different non-destructive methods were then jointly analyzed using statistical analysis methods such as absolute contrast, noise ratio (SNR), and spatial stability to quantify the artifact moisture defect data, thus improving the accuracy and range of features for detecting water-damaged pathologies. The results show that the combination of IRT and microwave information technology not only qualitatively but also quantitatively analyses the moisture in the building, significantly using microwave moisture meters to study water distribution at different depths and surface characterization by IRT. The use of non-destructive monitoring tools helps to guide the diagnosis of moisture damage, restoration of heritage buildings, and the planning of intervention strategies.

Enhancing whipped cream anti-freeze properties: A dual plant system with aquafaba and mung bean protein

The objective of this study was to substitute sodium caseinate (SC) with a dual plant-based alternative to produce a refreshing sour whipped cream at pH 3.0, which could maintain stability when paired with low-pH foods. Aquafaba (AQ) was utilized as a novel foam booster, enhancing the whipped properties of the whipped cream, while pH12-shifting treated mung bean protein (BMBP) functioned as an interfacing stabilizer, improving its stability. Freeze-thaw stability of the aerated emulsion and the protein-protein interactions in the emulsion were characterized by analyzing water distribution, thermodynamics, serum loss, and protein retention rates. The high bound water content and dense bubble distribution of whipped cream prepared by AQ and native mung bean protein (NMBP) delivered superior freeze-thaw stability, shown as reduced serum loss, higher protein retention, and increased hardness. In addition, the internal fat network of the whipped cream made from a 1:1 combination of NMBP and BMBP showed moderate agglomeration with a comparable γmax value as SC-based whipped cream, indicating enhanced resistance to deformation and improved short-term shape retention. In conclusion, the combination of AQ and MBP provided viable options for the replacement of SC in commercial whipped cream.

Investigating Arctic Permafrost Dynamics Using Electrical Resistivity Imaging and Borehole Measurement in Svalbard

This study utilized electrical resistivity imaging (ERI) to investigate subsurface characteristics near Nicolaus Copernicus University Polar Station on the western Spitsbergen-Kaffiøyra Plain island in the Svalbard archipelago. Surveys along two lines, LN (148 m) collected in 2022 and 2023, and ST (40 m) collected in 2023, were conducted to assess resistivity and its correlation with ground temperatures. The LN line revealed a 1- to 2-m-thick resistive unsaturated outwash sediment layer, potentially indicative of permafrost. Comparing the LN resistivity result between 2022 and 2023, a 600 Ohm.m decrease in the unsaturated active layer in 2023 was observed, attributed to a 5.8 °C temperature increase, suggesting a link to global warming. ERI along the ST line depicted resistivity, reaching its minimum at approximately 1.6 m, rising to over 200 Ohm.m at 4 m, and slightly decreasing to around 150 Ohm.m at 7 m. Temperature measurements from the ST line’s monitoring strongly confirmed that the active layer extends to around 1.6 m, with permafrost located at greater depths. Additionally, water content distribution in the ST line was estimated after temperature correction, revealing a groundwater depth of approximately 1.06 m, consistent with measurements from the S4 borehole on the ST line. This study provides valuable insights into Arctic subsurface dynamics, emphasizing the sensitivity of resistivity patterns to climate change and offering a comprehensive understanding of permafrost behavior in the region.

Experimental investigations of the shear strength and deformation behavior of a frozen unsaturated silt

There are limited studies in literature with respect to the deformation and shear strength behavior of frozen unsaturated soils that consider sensitive changes in temperatures below 0 °C. For this reason, the focus of the present study is directed towards investigating the shear strength and deformation behavior of a frozen unsaturated silt. As a part of the study, one-dimensional freezing tests were performed on compacted silt samples with various initial water contents and dry densities. The pulsed nuclear magnetic resonance (P-NMR) method was used to obtain distribution of unfrozen water content of silt samples; this information was used to explain the deformation behavior during freezing. The volume change behavior in unsaturated soils due to freezing can be associated to three primary factors: expansion caused by the ice-water phase transition and shrinkage induced by ice-cementation and dehydration. A critical water saturation was also observed at which the frost heave is minimum, or no volume change occurs. In addition, conventional direct shear tests were performed on compacted unsaturated silt samples both in frozen and unfrozen conditions to determine and quantify the shear strength contribution arising from the different cohesion components. The investigations in this study provide valuable information that can be used in rational explanation of the strength and deformation behavior of unsaturated frozen soils.

Proteomic analysis revealed the deterioration of surimi gelling capability to fish stress during transportation

Live silver carp with various transportation durations (3 h, 6 h, 12 h and 24 h) was processed into surimi. Proteomics and other technical methods were applied to investigate the changes in protein composition and structure aiming to explore the mechanism through which transportation stress affects gelling capability of surimi. As the transportation time was prolonged from 3 to 24 h, the gel strength of surimi gel decreased by 28.97 %, and the whiteness value decreased significantly (p < 0.05). Moreover, malondialdehyde content in fish serum and surface hydrophobicity of surimi also increased significantly (p < 0.05), suggesting that oxidation induced protein unfolding. Proteomic analysis identified eleven significantly differential proteins in the samples after 24 h of transportation compared to those transported for 3 h. Notably, the expression levels of myosin heavy chain and glutathione peroxidase were significantly down-regulated. Additionally, the PI3K-Akt signaling pathway was activated. Prolonged transportation time resulted in a looser microstructure of surimi gels, increased free water content and uneven water distribution. These results indicated that the altered properties of surimi gel due to transportation stress are primarily related to oxidative stress which leads to oxidative denaturation of proteins and the degradation of myosin.

Effects of coal mining disturbance on spatial and temporal distribution of soil water content in Northwest China‐based on 3D EBK model

AbstractNorthwest China is both an important coal storage base and an ecologically fragile area, and soil water content (SWC) is a key factor limiting the ecological development of Northwest China. Revealing the characteristics of spatial and temporal distribution changes of soil water content in mining areas under coal mining disturbance and the influencing mechanism is crucial for the protection of water resources in mining areas. In this study, the soil water content (depth 0–1000 cm) of a typical coal mine subsidence area in the western part of Shendong Coal Group was monitored in situ for 1 year after mining, and the absolute value, variability, and spatial distribution of soil water content were temporally analysed by combining classical statistics, 2D Ordinary Kriging and 3D Empirical Bayesian kriging spatial interpolation. sequence analysis. The results showed that the shallow SWC (0 ~ 60 cm) was distributed horizontally in bands, and gradually increased along the direction from northwest to southeast; with the increase of coal mining time, the absolute value of SWC decreased by 0.81% ~ 33.58%, and the coefficient of variation decreased and then increased, with the range of variation from 2.19% ~ 34.49%. The deep SWC (100 ~ 1000 cm) was stratified vertically and increased with soil depth, and the shallow and deeper soil moisture would gradually migrate to the middle layer under the influence of coal mining. In addition, this paper accurately portrays the three‐dimensional spatial and temporal distribution of soil water content by the 3D EBK model, which further reveals the mechanism of coal mining's influence on soil water content. This study can provide technical and data support for predicting and evaluating the potential impacts of mining activities on the water cycle, and help mining areas to formulate policies for managing and protecting water resources.

Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region

AbstractQuantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic.

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng

Red ginseng (RG) has been traditionally valued in Northeast Asia for its health-enhancing properties. Recent advancements in hyperspectral imaging (HSI) offer a non-destructive, efficient, and reliable method to assess critical quality indicators of RG, such as reducing sugar content (RSC), water content (WC), and hollow rate (HR). This study developed predictive models using HSI technology to monitor these quality indicators over the spectral range of 400–1700 nm. Image features were enhanced using Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), followed by classification through Spectral Angle Mapping (SAM). The best-performing model for RSC achieved an R2 value of 0.6198 and a root mean square error (RMSE) of 0.013. For WC, the optimal model obtained an R2 value of 0.6555 and an RMSE of 0.014. The spatial distribution of RSC, WC, and HR was effectively visualized, demonstrating the potential of HSI for on-site quality control of RG. This study provides a foundation for real-time, non-invasive monitoring of RG quality, addressing industry needs for rapid and reliable assessment methods.

Characterizing preferential infiltration of loess using geostatistical electrical resistivity tomography

Preferential infiltration is prevalent in loess and is pivotal in disasters such as landslides. However, the inherent multi-scale heterogeneity of loess makes traditional in-situ monitoring techniques challenging for characterizing the preferential infiltration process. This study employed the Geostatistical Electrical Resistivity Tomography (GERT) to examine the spatial distribution of mass water content (ωs) in loess strata to understand the preferential infiltration processes in loess. This study improved stimulus-response data quality and used GERT to characterize the spatial-temporal distribution of electrical conductivity (σ) and estimate ωs through the σ-ωs relationship. This study indicates that the surface loess layer has higher ωs than deeper layers, rapidly declining as depth increases. Under preferential infiltration, early-stage water forms a spherical saturated zone, transforming into an ellipsoid as it descends. Using equivalent homogeneous parameters as prior information effectively improved σ estimation. This study found a 15.9% error in the total change in water content based on the GERT survey compared to the known amount of injected water. It explores the possible impacts of the uncertainty of the unknown relationship between σ and ωs, its spatial variability, and the accuracy of the survey on the error. The formation of an electric double-layer structure within the loess as the saturation increases, which reduces the sensitivity of GERT to changes in water content, is likely another cause. Finally, these areas are essential for advancing future studies on applying geophysical tools to accurately estimate water distributions in loess formations.

Groundwater flow paths using combined self-potential, electrical resistivity, and induced polarization signals

SUMMARY The dam of Lampy (Black Mountain, Aude, France) is considered as one of the oldest dams in France. A geophysical survey is performed to better understand the pattern of groundwater flow downstream of this dam in the granitic substratum. Induced polarization is first used to image both electrical conductivity and normalized chargeability. Eight core samples of granite from this site are measured and analysed in the laboratory. Their electrical conductivity and normalized chargeability are expressed as a function of the porosity and cation exchange capacity (CEC). The field data and the petrophysical results are used to image the water content, the CEC and the permeability distribution of the substratum. Then, self-potential is used as a complementary passive geophysical technique, which, in absence of metallic bodies, is directly sensitive to groundwater flow through the so-called streaming potential effect. Indeed, the excess of electrical charges in the vicinity of the solid grains, in the so-called double layer, is dragged by the ground water flow generating in turn an electrical (streaming) current and therefore an electrical field. A map of the resulting self-potential signals is done over the area covered by the induced polarization profiles. This map shows a large positive anomaly with an amplitude of ∼80 mV possibly associated with upwelling groundwater in an area where the soil is water-saturated. A groundwater flow simulation is performed to model this anomaly. This is done in two steps. A preliminary groundwater flow model is built using the permeability and water content distributions obtained from the induced polarization data. Then, this groundwater flow model is updated using the information contained in the self-potential data including the electrical conductivity distribution obtained through resistivity tomography. The algorithm for the inversion of the self-potential data is validated through a 2-D numerical test. This analysis yields a groundwater flow model with the flow being focused through a high permeability zone. This study shows how three geoelectrical methods (self-potential, induced polarization and electrical resistivity) can be efficiently combined to image groundwater flow in the vicinity of a dam.

Mechanical Resistance to Penetration for Improved Diagnosis of Soil Compaction at Grazing and Forest Sites

Penetrometers and penetrographers are widely used to measure soil resistance to penetration, but the results are associated with other soil properties (such as bulk density, water content, and particle size distribution). Thus, for an adequate interpretation of results, site-specific studies are necessary to identify which properties are more related to soil resistance. We aimed to measure the resistance to penetration of a Typic Paleudalf under distinct soil uses and to identify soil properties that influence soil resistance. The soil uses in this study included anthropized forest (composed of tree and shrub species), pasture (5-year-old pasture), Eucalyptus 20 (a 20-year-old Eucalyptus saligna stand), and Eucalyptus 4.5 (a 4.5-year-old Eucalyptus saligna under the second rotation). Soil resistance to penetration was measured with an impact penetrometer, and the data were correlated with other physical and mechanical properties of soil, such as the particle size, soil moisture, air permeability, saturated hydraulic conductivity, porosity, bulk density, precompression stress, and compressibility index. We observed that a resistance of 1.3 MPa matches with other soil property values corresponding to soil compaction, and values greater than 1.3 MPa were verified at depths of 0–8 cm for pasture and 8–30 cm for Eucalyptus 4.5. Analyzing all soil uses together, the correlation was significant (p &lt; 0.05) with gravel (r = 0.34), silt (r = −0.32), clay (r = 0.26), gravimetric moisture (r = −0.27), macroporosity (r = 0.24), and soil bulk density at the end of the compressibility test (r = 0.27). The penetrometer is useful for evaluating the physical conditions of soil, but we highlight that soil resistance is influenced by factors such as particle size and soil moisture, as examples. We recommend using a set of soil properties for a better interpretation of penetration resistance data and to support decision-making regarding soil management.

Recognition of cross-regional loess water content based on machine vision: Case study of three regions

Robot technology equipped with vision system is expected to facilitate deep, in-hole, in-situ nondestructive recognition of water content in loess slopes. This advancement could considerably aid in the study of thespatial and temporal evolution of loess water content. However, a robust image recognition method capable of accurately recognizing the water content in loess across different regions is essential for the widespread adoption of this technology. Thus, we collected loess samples from the western (Lanzhou), central (Lantian), and eastern (Yan’an) parts of the Chinese Loess Plateau as a case study to explore the feasibility of a cross-regional intelligent recognition method for loess water content. Initially,we simulated the environmental conditions encountered during in-hole recognition to design an image collection platform, and subsequently prepared a loess water content dataset comprising 32,940 images from these three regions. Based on domain adaptation, we proposed a deep learning recognition method, namely, self-attention-based domain adaptation with Deep EXpectation (DA-DEX Swin). The proposed method demonstrates reasonable accuracy in estimating the water content of loess across regions, with a mean absolute error of 0.807%–1.137%, mean absolute percentage error of 0.074–0.154, and root mean square error of 0.939%–1.546%. The findings of this study provide valuable insights for addressing cross-regional issues, and DA-DEX Swin shows promise for application in robot detection technology to monitor variations and distributions of water content within slopes.

Karakteristik Indeks Air Menggunakan Normalized Difference Water Index (NDWI) Pada DAS Negeri Rutong Kota Ambon

The objectives of this study are analyzing the transformation of the water content of the NDWI index and the spatial pattern of its mapping in the Rutong State Watershed of Ambon City and analyzing the influence of environmental attributes on spatial patterns of NDWI index water content. The results of the transformation of the NDWI index water content and the distribution pattern of the NDWI water content for the 2010-2022 period show that changes in the distribution of NDWI water content have shifted ways that extend to coastal areas towards the mainland. The difference between the watery area and the dry area around it is visible. High water content dominates with the water content of tree vegetation begins to experience decreasing changes. In contrast, low and moderate water content increases and spreads along the coast, moving towards land areas. The spatial distribution pattern of low and moderate NDWI water content extends from coastal and inland areas to hills and mountains. The results of the analysis of the influence of environmental attributes on the spatial pattern of the NDWI index water content showed that the results of the ANOVA F-count (36,051) were more significant than the F-table (2,479), indicating that the parameters of temperature, humidity, vegetation density and type of vegetation cover had a considerable influence simultaneously on the water content of the NDWI index at a significance level (0.000) smaller than α 0.05. The results of the partial test (t-test) show that the parameters of temperature, humidity, and vegetation density have a partially significant influence on the water content of the NDWI index. While the variable type of vegetation cover has an insignificant impact on water content, the NDWI index is at a significance level of α 0.05.

Effect of aerogel incorporation on water content distribution and capillary water absorption in pore size intervals of cementitious composites

Effect of aerogel incorporation on water content distribution and capillary water absorption in pore size intervals of cementitious composites

Approximate analytical solution of a soil water movement equation under different ponding radii on the basis of numerical simulation

AbstractThis study addresses the problem of 2D soil water movement under ponding radii of 1, 2, and 3 cm. The soil water movement characteristics (shape parameters of the water content profile, ratio of horizontal wetting front to vertical wetting front, relationship between infiltration time and horizontal wetting front, and relationship between infiltration time and cumulative infiltration) under the above three kinds of water ponding radius were analyzed. On the basis of the assumption that the soil wetting body is a semi‐ellipse and the analytical solution of the 1D soil water movement equation at any angle, the approximate analytical solution of the 2D soil water movement equation under ponding conditions is optimized. The function relationships between infiltration time, wetting front, and cumulative infiltration are established. We applied the numerical data simulated by HYDRUS‐3D to validate the parameters in proposed analytical solutions and evaluated the relationships between the wetting front and hydraulic parameters. The results indicate that as the water ponding radius increases, the wetting body and 2D water content distribution becomes larger. When the water ponding radius was 2 cm, the numerical and analytical solution of 1D soil water distribution showed the best comparison results, and the model error was the smallest. The ratio of wetting fronts was linearly increased with the increase of air‐entry suction with R2 = 0.9969.