Abstract Lysimeters are widely used to investigate soil‐plant‐atmosphere interactions and to assess the impact of management practices on water and nutrient fluxes. These interactions and fluxes are strongly impacted by local scale variability in soil water content and soil hydraulic properties. However, centimeter‐scale imaging of subsurface processes and the controlling variables such as soil water content remains challenging, and conventional approaches often rely on simplifying assumptions that smooth heterogeneities. This study presents a theoretical investigation of full‐waveform ground penetrating radar (GPR) modeling for cylindrical lysimeters, with the objective of assessing the lysimeter‐induced effects on GPR data and their implications for data analysis and inversions. Numerical experiments were conducted for three scenarios: homogeneous dry and saturated sand, heterogeneous soil, and a simplified experimental model, using different antenna frequencies and explicitly accounting for the three‐dimensional lysimeter geometry. The results demonstrate that incorporating the cylindrical geometry is essential, particularly for dry soil conditions and high‐frequency data. The modeled wavefields reveal strong interference among direct, reflected, and refracted waves, consistent with observations from experimental GPR data. While wave‐type distinction is more straightforward in saturated soils, increased attenuation limits signal penetration. A simplified layered model inside the lysimeter geometry was used to reproduce dominant observed features of experimental data from a layered sand‐filled lysimeter, providing a robust foundation for future FWI applications in lysimeter studies.
Abstract Conventional volumetric indices, such as easily available water (EAW), quantify the amount of water retained in growth media but do not account for the energy required by plants to extract that water. Therefore, this study evaluated integral and hydraulic‐energy indices, obtained from the integration of water retention curve, in several inorganic and organic substrates, and their biochars. Biochars of agricultural wastes were produced by slow pyrolysis at 300°C (B‐300) and 500°C (B‐500). Organic substrates and biochars of sawdust, wheat straw (WS), rice hull, date palm bunches, and sugarcane bagasse (SC), and inorganic substrates of vermiculite, pumice, perlite, rockwool, sand, and zeolite were used. Integral energy ( E I ), integral water storage ( W I ), absolute aeration energy ( A a ), absolute water retention energy (WR a ), relative aeration energy ( A r ), relative water retention energy (WR r ), relative air‐water energy (AW r ) and Dexter's index of physical quality ( S ) were calculated. The E I values in the growth media were lower than their values in soils due to the larger pores of the media. Growth media with the same EAW had different E I values. Highest and lowest E I values in the EAW range were observed in vermiculite and zeolite 1–2 mm, respectively. The WR a of biochars produced at 500°C was higher than those produced at 300°C. The lowest and highest WR a values were obtained for the zeolite of 1–2 mm and SC‐B‐500, respectively. The substrates with higher porosity and smaller pores had higher saturated water content ( θ s ) and A a . The S index had a negative relation with E I and W I , implying more water availability for plants. Based on the W I , E I , and W I / E I values, the water availability of WS is the highest among the investigated substrates. Overall, integral and hydraulic‐energy indices offer a more sensitive and mechanistic assessment of soilless substrates than conventional volumetric metrics, providing improved criteria for evaluating substrate physical quality and optimizing irrigation management in controlled environments. These findings may support the screening and formulation of greenhouse growing substrates with improved water availability and reduced energy demand for root water uptake.
Abstract Accurate modeling of subsurface flow and transport processes in the vadose zone, governed by the Richards equation and advection‐dispersion equation, respectively, poses major numerical challenges due to the strong nonlinearity of the governing partial differential equations, spatial heterogeneity and anisotropy of the media parameters, and complex coupling between the physical processes. This paper presents an extensive review of the state‐of‐the‐art numerical modeling techniques, evaluating both traditional Eulerian frameworks and Lagrangian approaches. The paper quantitatively examines the global and local mass conservation characteristics of traditional numerical methods—finite difference, finite element, and finite volume (FV), through a comparative mass balance error analysis. The results demonstrate the high mass balance efficiency of the FV method, highlighting its suitability and robustness for highly nonlinear vadose zone modeling. Furthermore, this review work explores advanced modeling paradigms for capturing non‐Fickian transport phenomena resulting from the strong heterogeneity of natural porous media. The paper also critically assesses the emergence of artificial intelligence and machine learning (AI/ML) surrogates, specifically physics‐informed neural networks and operator learning, in hydrogeological modeling. Overall, this paper identifies critical future research avenues, emphasizing the necessity for fully coupled multi‐physics models (flow, mass, and heat transport) and the establishment of novel benchmark problems to simulate emerging contaminants, reactive transport, and complex hydrodynamics in data‐scarce subsurface environments.
Abstract Afforestation in drylands is an effective approach to mitigating soil degradation and erosion while promoting ecological restoration in disturbed ecosystems. This study evaluated the long‐term effects of Prosopis juliflora afforestation on soil physical, chemical, and biological properties in Iranian drylands. Four afforested stands representing two age classes (10 and 30 years) and a nearby non‐afforested control area were selected. A total of 40 soil samples were collected from two depths (0–15 and 15–30 cm). Soil properties were analyzed using two‐way analysis of variance, a generalized linear model, principal component analysis, and Duncan and Tamhane post hoc tests. The interaction between afforestation age and soil depth significantly affected pH, electrical conductivity (EC), total nitrogen (N), organic carbon (OC), sand and silt content, basal respiration, and soil quality index (SQI) (p < 0.05). EC, clay content, and bulk density did not differ significantly between soil depths, whereas saturated moisture point, OC, total nitrogen, available phosphorus, silt content, and basal respiration showed significant depth‐related differences. In surface soils (0–15 cm), afforestation age had no significant effect on pH, EC, bulk density, or clay content. However, afforestation improved several soil quality indicators, with fertility enhancement beginning in younger stands and increasing over time. Compared with younger stands, older stands exhibited 45% higher soil C, 33% higher soil N, and a 16% increase in SQI. The afforestation positively influenced soil quality, particularly in surface layers, by enhancing nutrient status and biological activity while contributing to the stabilization of loose sandy soils in arid environments.
