Aug 2026· Hydrological Processes· Vol 40· 0 citations· 20 references
Abstract
Soil hydraulic properties govern the movement and storage of water in soils and are essential inputs for hydrological and land surface models, influencing water‐holding capacity, matter and energy transport, erosion susceptibility and plant water availability. However, direct measurement of these properties is labour‐intensive and thus often limited in spatial coverage. Therefore, pedotransfer functions (PTFs) are widely used to estimate soil hydraulic properties from readily available soil characteristics. We present a spatial dataset of soil hydraulic properties for Europe at 0–20 cm depth, developed using harmonised soil property maps derived from the LUCAS dataset and the improved PTFs (EUPTFv2) proposed by Szabó et al., which are developed on the European Hydropedological Data Inventory. The derived dataset includes water content at saturation, field capacity (at −10 and −33 kPa), wilting point (−1500 kPa), saturated hydraulic conductivity and their associated uncertainty maps. In addition, we derived available water capacity and air capacity (air‐filled porosity) from the predicted properties. The input datasets (LUCAS‐based soil maps) used in this study are derived from harmonised data in terms of measurement methods and spatial consistency. This is expected to improve the reliability of the resulting maps compared with existing products. The generated dataset is freely available and can therefore support a wide range of environmental and modelling applications.
Soil controls processes within the hydrological cycle, which are fundamental to environmental sustainability and agricultural production, such as infiltration, redistribution, and storage of water. Numerical models capable of estimating soil water content at low operational costs are potential tools for the management of water resources. This study evaluated the HYDRUS-1D model for simulating soil water content in five tropical soil classes representative of the Brazilian Federal District, at two soil depths, using statistical indicators. Model parameterization was based on the determination of soil hydraulic and physical properties using field and laboratory methods, including soil water retention curves described by the van Genuchten–Durner dual-porosity model and saturated hydraulic conductivity. Model performance was assessed using RMSE, RSR, R², NSE, and PBIAS statistics. Overall, HYDRUS-1D showed high accuracy in predicting the temporal soil moisture dynamics across different soil classes and depths, indicating its suitability for applications in Cerrado soils. In particular, the use of the dual-porosity approach for describing the soil water retention curve contributed to improved prediction of soil hydraulic behavior. However, some statistical metrics indicated lower performance for the Rhodic Ferralsol in the subsurface layer, influenced by the sensitivity of the metrics to low data variability. The results highlight the importance of soil-specific determination of soil hydraulic parameters for the model performance. Despite the potential limitations, the study shows that the HYDRUS-1D model can be a valuable tool for soil water and land management in the Brazilian Cerrado and in the Federal District.
Keywords: dual-porosity, HYDRUS-1D, soil moisture, soil water retention curve, vadose zone.
Márcio Leonardo de Sousa Coimbra, T. P. Leão, Manuel Pereira de Oliveira Junior· Ambiente E Agua - An Interdi...· 0 citations
Accurate information on soil properties throughout the soil profile is essential for effective soil and water management in dryland regions. This study aimed to digitally map key soil physical and chemical properties important for plant growth and water retention in dry environments from the surface to 1.5 m depth in the Central Dry Zone of Myanmar and to evaluate the contribution of Landsat spectral data to model performance across different soil depths. A total of 4,059 soil samples from 800 sites were collected across seven depth intervals (0–150 cm). Soil properties, including texture, organic carbon, pH, electrical conductivity, effective cation exchange capacity, exchangeable cations, exchangeable sodium percentage, rock fragment content, were modelled using Random Forest algorithms, and soil depth was modelled separately using a Cubist model. Landsat spectral bands and terrain attributes derived from a digital elevation model were used as covariates. Landsat data capture vegetation, moisture, and surface reflectance, representing organisms, and soil condition, while terrain attributes represent relief-driven processes such as water redistribution, erosion, and deposition. Profile available water capacity was estimated using pedotransfer functions based on predicted soil properties. Soil properties showed strong vertical organisation, with organic carbon and sand concentrated in surface layers and increasing clay content with depth. Incorporating Landsat spectral data significantly improved prediction accuracy for surface and upper subsoil layers (0–30 cm), particularly for texture, organic carbon, and effective cation exchange capacity. Model improvements declined progressively with depth. Spatial predictions revealed coherent landscape-scale patterns in soil properties, soil depth, rock fragments, and available water capacity. The results demonstrate that optical remote sensing enhances digital soil mapping in surface and upper subsoil layers but has limited value at greater depths. The study provides the first region-wide depth-explicit digital maps of soil properties and available water capacity maps for the Central Dry Zone of Myanmar and offers a transferable framework for soil assessment in data-scarce dryland regions.
Akari Win, B. Minasny, A. Ringrose-Voase et al.· Journal of soil science and...· 0 citations
The productivity of tropical dryland soils is constrained not only by limited water availability and low soil fertility but also by unfavorable soil physical properties, which are critical for achieving sustainable and climate-resilient agriculture. This study identified the characteristics and physical constraints of soils under several land use types (LUTs) in dryland areas of Aceh Besar Regency, Indonesia. Undisturbed and disturbed soil samples for physical analysis were collected using sample rings at a depth of 0–20 cm. Each LUT consisted of 5–8 sample plots (20 m × 20 m), with five soil samples taken from each plot. The results showed that soil physical properties in suboptimal drylands vary across land uses. Soil texture, bulk density, porosity, permeability, aggregate stability index, and water holding capacity (WHC) under forest vegetation, cultivated forests, and dryland agricultural systems were relatively better than those in uncultivated areas. In contrast, uncultivated lands exhibited several physical constraints, including coarse texture, high bulk density (1.51±0.01 Mg m
−3
), low porosity (49.13±5.21%), moderately rapid permeability (21.48±3.12 cm h
−1
), unstable soil aggregates (0.43±0.0021), and low WHC. Improving soil physical quality is essential for enhancing productivity and strengthening climate resilience in dryland systems. This can be achieved through the application of organic amendments such as biochar, compost, manure, and mulch (cover crops), which contribute to sustainable soil management and improved ecosystem functioning.
S. Sufardi, H. Helmi, K. Khairullah et al.· BIO Web of Conferences· 0 citations
This study assessed the physicochemical properties and nutrient status of soils at the Krishi Vigyan Kendra (KVK) farm, Sakhigopal, Puri district, Odisha, using GPS- and GIS-based soil fertility mapping to identify site-specific soil-related constraints and develop appropriate nutrient- and land-management strategies. A plot-wise soil survey was conducted using 35 GPS-referenced composite surface soil samples (0–15 cm) collected from upland (plots 1–5), medium land (plots 6–15), and lowland (plots 16–35) physiographic units. Samples were analysed for texture, pH, electrical conductivity (EC), soil organic carbon (SOC), available nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) using standard analytical procedures. Geographic coordinates and laboratory data were integrated in ArcGIS to generate thematic soil fertility maps. Soil texture ranged from sand to sandy loam, with mean clay content decreasing from upland (8.80%) to medium land (7.72%) and lowland (7.04%). Soil pH ranged from 5.00 to 7.13, with a mean of 6.53, while EC values ranged from 0.05 to 0.60 dS m⁻¹, indicating non-saline conditions throughout the farm. SOC ranged from 3.2 to 10.3 g kg⁻¹ and increased down the toposequence, from 3.2–4.7 g kg⁻¹ in uplands to 8.3–10.3 g kg⁻¹ in lowlands. Available N (173–260 kg ha⁻¹) and P (5.6–24.5 kg ha⁻¹) were low to medium, K (115–535 kg ha⁻¹) was medium to high, and S (2.2–7.3 kg ha⁻¹) was predominantly low. The principal soil-related constraints were sandy texture and low clay content, low SOC and acidity in uplands, widespread sulphur deficiency, upland erosion, and waterlogging in lowlands. Based on the observed soil physicochemical and nutrient status, the study suggests physiography-based land use, organic-matter enrichment, need-based liming, sulphur supplementation, and soil-test-based site-specific fertiliser management as potential strategies to improve nutrient-use efficiency and support long-term soil health.
S. Priyadarshini, P. K. Dash, Jatiprasad Barala et al.· Journal of Experimental Agri...· 0 citations
Accurate estimation of irrigation water use is essential for agricultural water accounting and water-resource allocation in large irrigated districts, yet existing statistics are usually available only as aggregated administrative totals and cannot adequately characterize seasonal and spatial differences in field-applied water. In this study, a soil moisture profile response-driven framework was developed to estimate spring and summer irrigation water use in the Hetao Irrigation District, a typical large-scale irrigated region in the upper Yellow River Basin. Soil property zones were first delineated using K-means clustering based on field capacity, wilting point, available water capacity, bulk density, porosity, and electrical conductivity, and season-specific soil profile response layers were identified through bootstrap stability tests using 0–100 cm daily soil moisture changes. A net water inflow response was then constructed by integrating soil water storage change, precipitation, and evapotranspiration, and six estimation models were compared, including a soil-water-response conversion model, historical-management baseline models, and recent-management baseline models. Model calibration was conducted for 2016–2021, and independent testing was performed for 2022. Spatial allocation constraints were further introduced to ensure consistency between total estimated irrigation volume and pixel-scale irrigation depth patterns. Results showed that the optimal soil profile stratification was 0–20/20–60/60–100 cm for spring irrigation and 0–20/20–50/50–100 cm for summer irrigation, indicating clear seasonal differences in profile response. For spring irrigation, the recent three-year management baseline model performed best, with a training-period RMSE of 8.8 mm and MAPE of 8.5%, and a testing-period RMSE of 8.4 mm, bias of −2.7%, and R2 of 0.97. For summer irrigation, the historical-median management baseline model was most robust, with a training-period RMSE of 8.1 mm and MAPE of 14.0%, and a testing-period RMSE of 4.4 mm, bias of −9.4%, and R2 of 0.96. Spatially, spring irrigation depths increased from 2019 to 2022 and were higher in WLBH, JFZ, and YJ, whereas summer irrigation depths were generally lower and more concentrated in western and central sub-irrigation districts. The proposed framework provides a practical approach for linking soil moisture profile response, management-based volume constraints, and spatially explicit irrigation mapping in large-scale irrigated regions.
Soil salinization, referring to the excessive accumulation of soluble salts in soils, adversely influences nutrient cycling, biodiversity, soil structure, crop production, soil health, and ecosystem functioning. Accurately assessing soil salinity via electrical conductivity (EC) is key to mitigating its impacts. Thus, developing predictive tools for soil EC at regional and continental scales is essential for sustainable soil management. Here, we apply machine learning models to predict soil EC in the European Union (EU) and United Kingdom (UK) soils using different environmental factors like soil, climate, topography, and satellite data as predictors. The model is trained by ≈40,000 soil EC data points from the 2015 and 2018 Land Use/Cover Area Frame Survey data (LUCAS) surveys, complemented by the EC observations from World Soil Information Services (WoSIS) dataset. To improve the model performance, a forward feature selection technique was used resulting in selection of 17 covariates out of initially 34 predictors. The final selected XGBoost model achieved R2 values of 0.68, 0.6, and 0.63 for the training, internal testing, and independent validation datasets, respectively. For the year 2018, we estimate ≈21.7 Mha of EU + UK land exceeds an EC of 0.6 dS/m (at a 1:5 soil to water ratio, the so-called EC1:5). This estimate should be interpreted as elevated predicted EC1:5, rather than a direct estimate of soils meeting protosalic diagnostic criteria. The output of the predictive model consists of a gridded dataset that illustrates the spatial distribution of EC1:5 throughout the study area for the year 2018, along with an associated uncertainty map with a spatial resolution of 1 km.
Mohammad Aziz Zarif, Amirhossein Hassani, Mehdi H. Afshar et al.· Science of the Total Environ...· 0 citations