Search Results (72)

Since farmers in the inland valley region of the Niger Delta mostly rely on experience rather than empirical evidence when it comes to irrigation, flood irrigation being the most popular technique, the region’s agricultural sector needs more efficient water management. In order to better understand the intricate hydrodynamics of water flow through the soil subsurface, this study aimed to develop a soil column laboratory experimental setup for soil water infiltration. The objective was to measure the soil water content and soil matric potential at 10 cm intervals to study the soil water characteristic curve as a relationship between the two hydraulic parameters, mimicking drip soil subsurface micro-irrigation. A specially designed cylindrical vertical soil column rig was built, and an EQ3 equitensiometer of Delta-T Devices was used in the laboratory as a precision sensor to measure the soil matric potential Ψ (kPa), and the volumetric soil water content θ (%) was measured using a WET150 sensor of Delta-T Devices. The relationship between the volumetric soil water content and the soil matric potential resulted in the generation of the soil water characteristic curve. Two separate monoliths of undisturbed soil samples from Ivrogbo and Oleh in the Nigerian inland valley of the Niger Delta, as well as a uniformly packed sample of soil from Aberdeen, UK, for comparison, were used in gravity-driven flow experiments. In each case, tests were performed once on the monoliths of undisturbed soil samples. In contrast, the packed sample was subjected to an experiment before being further agitated to simulate ploughing and then subjected to an infiltration experiment, resulting in a total of four samples. The Van Genuchten model of the soil water characteristic curve was used for the verification of the experimental results. Comparing the four samples’ volumetric soil water contents and soil matric potentials at various depths revealed a significant variation in their behaviour. However, compared to the predicted curve, the range of values was narrower. Compared to n = 2 in the Van Genuchten curve, the value of n at 200 mm depth was found to be 15, with θr of 0.046 and θs of 0.23 for the packed soil sample, resulting in a percentage difference of 86.7%. Additionally, n = 10 for the ploughed sample resulted in an 80% difference, yet θr = 0.03 and θs = 0.23. For the Ivrogbo sample and the Oleh sample, the range of the matric potential was relatively too small for the comparison. The pre-experiment moisture content of the soil samples was part of the cause of this, in addition to differences in the soil types. Furthermore, the data revealed a remarkable agreement between the measured behaviour and the projected technique of the soil water characteristic curve. Full article

Accurately determining the hydraulic properties of soilless growing media is essential for optimizing water management in container-based horticulture and agriculture. The very rapid estimation of hydraulic properties using a Mini Disk Infiltrometer has great potential for practical use compared to the very time-consuming standard methods. The objectives of this study were (1) to calibrate simulated cumulative stepwise infiltration under different suctions with the measured data from Mini Disk Infiltrometer, (2) to evaluate the efficiency of the Hydrus-2D inverse model to predict water dynamics through substrates, (3) to compare the substrate hydraulic parameters obtained through the numerical inversion model to those obtained via laboratory methods, and (4) to provide recommendations on how to effectively use the MDI-based method for practical applications. This study employs numerical inversion of Mini Disk Infiltrometer (MDI) data to estimate the hydraulic parameters of three different growing media, namely white peat, thermally treated wood fibre (WF4), and Seedling substrate. Infiltration experiments were conducted under suction-controlled conditions using varying initial moisture contents, followed by numerical simulations using the Hydrus-2D model and the Van Genuchten equation to describe the hydraulic parameters. The results demonstrated strong agreement between observed and simulated infiltration data, particularly under moistened conditions, with high R² > 0.9 values indicating the model’s effectiveness. However, discrepancies were observed for substrates in their initial dry state, suggesting limitations in capturing early-stage infiltration dynamics. The findings highlighted the potential of numerical inversion methods for estimating substrate hydraulic properties but also revealed the need for methodological refinements. Modifying the Van Genuchten model or exploring alternative approaches such as the Brooks and Corey model may enhance accuracy. Extending the suction range of measurement techniques is also recommended to improve parameter estimation. This study provides important evidence that the inverse method based on MDI is an effective tool for rapidly determining the hydraulic functions of substrates, which are important in promoting sustainable horticultural practices. Future research should focus on refining parameter estimation methods and addressing model limitations to enhance the reliability of hydraulic property assessments in soilless growing media. Full article

The Loess Plateau of China, a region highly vulnerable to erosion and climatic variability, faces significant soil degradation exacerbated by intensive agricultural practices and anthropogenic pressures. This study investigates the impacts of incremental soil compaction (P1–P5) on hydraulic properties, pore structure, and water retention across distinct soil textures (sandy loam, loam, clay loam) to address gaps in understanding texture-specific resilience and soil organic carbon (SOC) interactions. Utilizing X-ray computed tomography (CT), soil water retention curve (SWRC) analysis, and the van Genuchten (vG) model, we quantified compaction-induced changes in porosity, connectivity, and hydraulic conductivity, while comparing unsaturated hydraulic conductivity (Kun) predictions derived from mini disc infiltrometer (MDI) and SWRC parameters. Results revealed that fine-textured, SOC-rich soils had greater compaction, preserving macropore connectivity and saturated hydraulic conductivity (Ks), whereas sandy soils pronounced macropore collapse. Compaction homogenized pore distributions, steepened SWRC, and reduced plant-available water. Integration of CT and SWRC methodologies highlighted CT sensitivity to air-filled macropores versus SWRC’s focus on water-retentive micropores. Strong correlation (R² = 0.94–0.99) between vG parameters from MDI and SWRC validated parameter robustness, though MDI slightly underestimated Kun in clay loam, while SWRC-based models aligned closely with observed data. Integrating CT and SWRC methodologies offers a framework for precision soil health monitoring. In addition to the critical role of SOC and texture in compaction mitigation, there is a need for organic amendments in sandy soil and reduced tillage. Full article

Soil controls water distribution, which is crucial for accurate hydrological modeling. MOHID-Land is a physically based, spatially distributed model that uses van Genuchten–Mualem (VGM) functions to calculate water content in porous media. The hydraulic soil parameters of VGM are dependent on soil type and are typically estimated from experimental data; however, they are often obtained using pedotransfer functions, which carry significant uncertainty. As a result, calibration is frequently required to account for both the natural spatial variability of soil and uncertainties estimation. This study focuses on a representative Atlantic Forest watershed. It assesses the sensitivity of channel flow to VGM parameters using a mathematical approach based on residuals derivative, aimed at enhancing soil calibration efficiency for MOHID-Land. The model’s performance significantly improved following calibration, considering only five parameters. The NSE improved from 0.16 on the base simulation to 0.53 after calibration. A sensitivity analysis indicated the curve adjustment parameter ($n$) as the most sensitive parameter, followed by saturated water content (${\theta }_s$) considering the 10% variation. Additionally, a combined change in ${\theta }_s$, $n$, residual water content (${\theta }_r$), curve adjustment parameter ($\alpha$), and saturated conductivity ($K_{sat}$) values by 10% significantly improves the model’s performance, by reducing channel flow peaks and increasing baseflow. Full article

The pore solution in expansive soil contains numerous chemical components that can significantly affect the soil’s water-retention properties, strength, and deformation. This study focuses on Ningming expansive soil and investigates the effects of varying concentrations of NaCl solutions on its water-retention characteristics. The soil–water characteristic curve of expansive soil over the full suction range was obtained using the pressure plate method. The microstructure of expansive soil was analyzed using mercury intrusion porosimetry and scanning electron microscopy. The results indicate that the water-retention capacity of expansive soil increases with higher concentrations of NaCl at equivalent suction levels. MIP tests demonstrated that, regardless of changes in pore solution concentration, the interparticle pores in pre-consolidated samples consistently dominate the pore structure, while agglomeration pores play a secondary role. Importantly, variations in the pore solution concentration primarily alter the characteristics of interparticle pores without significantly affecting their overall structure. SEM analysis revealed that the microstructure of pre-consolidated samples exposed to different pore solution concentrations exhibited less development than that of compactable samples, with a notable reduction in macropores. Furthermore, the arrangement of soil particles became increasingly uniform, and the stratification within the soil matrix was more pronounced. In addition, the Brooks–Corey (BC) model and van Genuchten (VG) model were employed to fit the measured data. It was found that the air entry values predicted by the two models were closely aligned with the measured data; therefore, it is recommended to utilize the average value as the air entry value corresponding to the changes in pore solution concentration of pre-consolidated Ningming expansive soil. Full article

This study examines the impact of soil hydraulic parameterization on simulating soil water content in a drip-irrigated grapefruit orchard (Citrus paradisi Mac.) using precise laboratory measurements and the HYDRUS 2D/3D model. Undisturbed soil samples were analyzed for water retention and saturated hydraulic conductivity using high-precision instruments, and parameters were estimated with unimodal and bimodal Van Genuchten functions. Soil water dynamics under deficit (80% of crop evapotranspiration, ET_C) and full irrigation (100% ET_C) were simulated, accounting for circular drip emitters. Calibration relied on soil water content data collected at varying depths and distances from the emitters. Results from the fitting process with laboratory-measured data for water retention and hydraulic conductivity indicate that the bimodal function provided more accurate parameter estimates, yielding lower RMSE for soil water content (0.0026 cm³ cm⁻³) and hydraulic conductivity (0.1143 cm day⁻¹), compared to the unimodal (0.0047 cm³ cm⁻³ and 0.1586 cm day⁻¹). HYDRUS simulations also demonstrated superior calibration metrics for the bimodal function with RMSE, MAE, and NSE values of 0.024 cm³ cm⁻³, 0.016 cm³ cm⁻³, and 0.892 respectively, compared to 0.025 cm³ cm⁻³, 0.017 cm³ cm⁻³, and 0.883 for the unimodal function. Although differences between the functions were small, the bimodal model’s slight performance gain comes with added complexity and uncertainty in parameter estimation. These findings highlight the critical role of precise parameterization in refining irrigation strategies and ensuring sustainable water use in citrus orchards. Full article

Understanding the spatial variability in soil hydraulic properties (SHPs) and their influencing variables is critical for ecohydrological and biogeochemical studies in coastal wetlands, where complex landscapes make it challenging to accurately delineate the spatial patterns of SHPs. In this study, soil samples were collected from two transects covered by Suaeda salsa (S. salsa) and Phragmites australis (P. australis) from the Beidagang Wetland Nature Reserve in northern China, and a comprehensive dataset on soil physical properties and SHPs was obtained by laboratory experiments. The results showed that soil physical properties (e.g., soil particle size, bulk density (BD), and soil organic matter (SOM)) displayed significant spatial variability, which was related to the physiological characteristics of S. salsa and P. australis and to soil depth. As a result, SHPs, including saturated hydraulic conductivity (K_s) and parameters of the van Genuchten model (θ_s-saturated soil water content, including α, the reciprocal of the air-entry value, and n, the pore size distribution index) varied considerably along the two transects. Specifically, K_s, θ_s, and α were negatively correlated with BD and pH, while positively correlated with SOM, which promoted soil aggregation to enlarge soil pores. Soil depth was shown to significantly affect SHPs, whereas the differences in SHPs between the two transects were not statistically significant, suggesting vegetation type did not directly impact SHPs. Soil water retention capacities were noticeably higher in surface soils, especially when soil suctions were less than 1000 cm, whereas their differences between depths largely diminished with further increasing soil suctions. This study highlights the complex interplay of SHPs with surrounding environments, providing critical insight for characterizing the spatial patterns of SHPs in coastal wetlands. Full article

As the contradiction between vegetation growth and soil moisture (SM) demand in arid zones gradually expands, accurately obtaining SM data is crucial for ecological construction. Remote sensing products limit small-scale studies due to the low resolution, and the emergence of downscaling solves this problem. This study proposes an improved semi-physical SM downscaling method. The effects of environmental factors on SM in different geographical zones (Windy Sand Hills, Flood Plains, Loess Yuan, Hilly Loess, Earth-rock Hills and Rocky Mountain) were analyzed using Random Forests. Vegetation and topographic factors were incorporated into the traditional downscaling algorithm based on the Mualem–van Genuchten model by setting weights, yielding 250 m resolution SM data for the Loess Plateau. This study found the following: (1) The Normalized Difference Vegetation Index (NDVI) was the most important environmental factor in all divisions except the Flood Plain, and the Digital Elevation Model (DEM) was second only to the NDVI in the overall importance evaluation, both of which positively influenced SM. (2) SM variability increased and then decreased when SM was below 0.4 cm³/cm³, but showed a quadratic growth trend when exceeding this threshold. The Rocky Mountain division exhibited the highest variability under the same SM. (3) Validation showed that the improved algorithm, based on geographic divisions to analyze factors importance and interpolation of coarse-scale SM and variability, had the highest accuracy, with an average R of 0.753 and an average ubRMSE of 0.042 cm³/cm³. The improved algorithm produced higher resolution, more accurate SM data, and offered insights for downscaling studies in arid regions, meeting the region’s high-resolution SM needs. Full article

Tillage practices significantly impact soil structure, pore-size distribution (PSD), and soil-water retention curves (SWRC). The SWRC, which represents the relationship between soil water content and soil water potential, is important for various studies involving plants, soil, environment, irrigation, drainage, modeling, and hydrology. In this study, the HYPROP method was used to measure SWRCs and estimate soil physical and hydraulic properties under conventional tillage (CT), strip tillage (ST), and no-tillage (NT) systems in clay loam soil. Undisturbed soil cores were collected from 0–15 cm and 15–30 cm depths within sugarbeet rows, with sampling replicated five times following a randomized block design. Soil-water retention curves were modeled using the van Genuchten (vG) model for each depth under each tillage system. The results showed that none of the soil parameters from the vG equation, plant-available soil water content, or pore-size distribution were significantly influenced by tillage type. This lack of significant difference may be attributed to considerable soil disturbance from sugarbeet root harvesting, freeze and thaw cycles between tillage and sampling, or soil displacement caused by beet root growth. However, small differences in soil parameters among the three tillage systems were noted at both soil depths, due to minor variations in soil porosity and pore-size distribution. Regardless of the tillage system, understanding SWRC is essential for insights into soil and water processes such as water flow, soil water storage, and water availability for plants. Full article

The seepage characteristics of clay core walls are crucial for the seepage safety of core rockfill dams, with the permeability coefficient in the unsaturated zone being nonlinear. To accurately determine the unsaturated seepage parameters in clay core rockfill dams, this paper first proposes an enhanced reptile search algorithm (ERSA) by applying three improvement strategies: Arnold’s cat chaotic map, nonlinear evolutionary factor, and adaptive Cauchy–Gaussian mutation with variable weight. Then, by integrating the ERSA with the unsaturated seepage finite element method, an inverse modeling approach is developed. This approach is applied to an actual rockfill dam with operational monitoring data to determine the unsaturated seepage parameters of the clay core. Results indicate that the ERSA outperforms the original RSA in test functions, and the calculation results of the seepage parameters determined through inversion are consistent with the monitoring data, showing an overall mean absolute error of 1.086 m. The inverse modeling approach provides a valuable reference for determining unsaturated seepage parameters in similar clay core rockfill dams. Full article

Relating soil moisture content to soil suction, the soil–water characteristic curve (SWCC) represents an essential feature in unsaturated soil mechanics that enables estimation of different unsaturated soil property functions and modeling of the macro-scale soil behavior. However, depending on the soil and processes under consideration, proper hydraulic characterization of a soil through direct laboratory measurements can be difficult, time-consuming, and involve many uncertainties. In the case of uniformly graded sands, there is a highly nonlinear and steep shape of the SWCC, with only a few kPa of soil suction separating saturated and residual soil moisture conditions, which makes measurements for determinations of SWCC especially challenging. This study encompasses an investigation of the sandy type of soil’s behavior and presents some preliminary results and experiences on the determination of SWCC through the use of physical slope model tests. The 30 cm deep slope, inclined at 35 degrees and instrumented with soil moisture and pore water pressure sensors, was exposed to series of rainfall intensities, ranging from 37 up to 300 mm/h. The results indicated that the data on hydraulic response in monitored points are not only useful for the determination of SWCC, but that the approach is useful for investigation of hydraulic hysteresis phenomena, as well as its effects on soil moisture and pore water pressure conditions, which also affects the stability conditions of a slope. In particular, the best-fit parameters of the van Genuchten model suggested air entry values of 1.6 and 1.1 kPa for the drying and the wetting curves of the SWCC, respectively, with the two branches shifted by about 1 kPa of soil suction. Full article

This paper presents the results of a geotechnical investigation regarding the slope stability in a pit lake, emphasizing the impact of water level variations. Advanced analysis techniques were utilized for this study. The research was performed by using fully coupled flow-deformation analyses. For the fully coupled approach, Bishop’s effective-stress equation was used, and for the description of soil hydraulic behavior, the Van Genuchten’s model was applied. The analysis of slope stability associated with reservoir water level changes revealed that the slope tended to become unstable as the water level decreased; the stability factor was negatively related to the rate of water level reduction. Concerning the water level fluctuations, the analyses revealed that the soil mass seemed to become less stable as the rate of water level change increased. Under a specific range of rates of water level variation, the safety factor became higher as the number of fluctuations increased. Additionally, the simulation results concerning the water level rising indicate that the pressure due to the external water level acts on the slope surface with a positive impact on the stability factor. The results obtained reflect the effects under a specific site condition, but they can be used as a reference for evaluating slope stability in a pit lake design. Full article

The parameters’ inversion of saturated–unsaturated is important in ensuring the safety of earth dams; many scholars have conducted some research regarding the inversion of hydraulic conductivity based on seepage pressure monitoring data. The van Genuchten model is widely used in saturated–unsaturated seepage analysis, which considers the permeability connected to the water content of the soil and the soil’s shape parameters. A BP neural artificial network is a mature prediction technique based on enough data, and the marine predator algorithm is a new nature-inspired metaheuristic inspired by the movement of animals in the ocean. The BP neural artificial network and marine predator algorithm are applied in the permeability coefficient inversion of a core-rock dam in China; the results show that in the normal operation status, the BP network shows better accuracy, and the average of the absolute error and variance of the absolute error are both minimum values, which are 2.21 m and 1.43 m, respectively. While the water storage speed changes, the marine predator algorithm shows better accuracy; the objective function is calculated to be 0.253. So, the marine predator algorithm is able to accurately reverse the desired results in some situations. According to the actual condition, employing suitable methods for the inverse permeability coefficient of a dam can effectively ensure the safe operation of dams. Full article

A clean environment is an essential component of sustainable development, which is based on a comprehensive understanding of the behavior of water, soil, and air. The soil water retention (SWR) curve is a crucial function that describes how soil retains water, playing a fundamental role in irrigation and drainage, soil conservation, as well as water and contaminant transport in the vadose zone. This study evaluates the accuracy, performance, and prediction capabilities of 15 SWR models. A total of 140 soil samples were collected from different sites, covering all textural classes. Standard suction tests, using both hanging column and ceramic pressure plate extractors, were conducted to compile the SWR databank. 15 SWR models were selected and fitted to the SWR data points. Soil texture, bulk density, and organic matter were used to determine their effect on the performance of the SWR models. The results indicate that the Tani and Russo models exhibit the lowest levels of accuracy and performance among the selected models. Based on the Akaike and Bayesian information criteria analysis, the van Genuchten model exhibits the lowest values among the selected models, with poor prediction capabilities in estimating the SWR curve. The significance test at the 0.05 level (95% confidence interval) shows that according to the calculated p-values for the Pearson correlation coefficient between RMSE and texture, the Brooks-Corey and van Genuchten models are poorly influenced by soil properties. The performance of the models is not significantly affected by the soil organic matter. Similarly, bulk density does not significantly affect model performance except for the Brooks–Corey, van Genuchten, Tani, and Russo models. Among the SWR models considered, the double exponential, Groenevelt and Grant, and Khlosi et al. models demonstrate superior accuracy and performance in predicting the SWR curve. This is supported by lower values for RMSE, Akaike, and Bayesian information criteria. Full article

Near-surface soil hydraulic properties (SHPs) are fundamental for describing and predicting water and energy exchange processes, particularly at the soil–atmosphere interface, and regulating evapotranspiration, infiltration, and runoff in different ecosystems. In this study, a new method was proposed to estimate near-surface SHPs by combining sensor-based soil infiltrability measurements with inverse modeling using HYDRUS-2D. The infiltration rate (IR) was estimated by combining the linear source inflow method with image processing, and K_s was estimated from the near-surface steady-state IR (NSIRM). The SWRC parameters described by the van Genuchten model were estimated using the inverse modeling method of HYDRUS-2D for the fitting of sensor-measured infiltration data. Subsequently, the parameters of the van Genuchten model, including $\alpha$, $n$, and $l$, were inversely estimated. Three undisturbed soils, including two stand humus samples from cork oak (Quercus suber L.) and oleander (Pinus tabuliformis L.) stands and one sandy loam from a farmland, were sampled near the soil surface to validate the proposed method. The estimated K_s was evaluated by the constant head method (CHM). The estimated parameters of the SWRC were validated by those determined through the simultaneous measurement of the soil moisture content and water potential using sensor techniques. The results showed that the K_s estimated from the NSIRM for each soil sample were 23.40 ± 1.21, 23.86 ± 1.83, and 22.99 ± 2.26 mm h⁻¹, respectively. In comparison, the K_s determined by the CHM were 24.41 ± 1.53, 24.26 ± 0.37, and 23.81 ± 0.10 mm/h, respectively. The relative errors of the proposed method were 4.14%, 1.64%, and 3.42%, respectively. For the SWRC estimation, the normalized root mean square errors (NRMSEs) between the measurements and the estimates for each soil sample were 0.1724, 0.1454, and 0.0606, respectively. Based on this, the AWC was obtained, and K_u was deduced from the estimated K_s and SWRC parameters for each soil sample. In general, the proposed method successfully estimates near-surface SHPs, simplifies the measurement device, and provides a new perspective for the in situ determination of near-surface SHPs under field conditions in the near future. Full article