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The water potential in drying soils, comprising both matric potential and osmotic potential components, can be measured using the dew point method (DPM). By combining DPM data with retention curve data acquired from techniques such as the suction plate method or the simplified evaporation method (SEM), it becomes possible to determine the soil water retention curve across the entire moisture spectrum. However, as the latter methods only determine the matric potential, the osmotic potential component in DPM data must either be negligible or known so that osmotic and matric potential components can be separated. This study aims to critically analyse common approaches for calculating the osmotic potential. To achieve this, we measured the water retention properties of a silt loam, a sandy loam and a sand across the entire moisture range by combining SEM and DPM. By using almost salt‐free soil material, we characterized reference water retention curves with negligible osmotic potential components. The impact of salt on water potential was analysed by conditioning soils with MgCl2 solutions of different concentrations, drying them, and measuring the water potential at different water contents using the DPM. The resulting water potentials were compared to the reference potentials and differences were interpreted as the osmotic potential component. The DPM‐measured water potentials in drying soils can be significantly affected by osmotic potential, especially at higher matric potentials (low suctions). Two models accounting for ideal and one model accounting for non‐ideal electrolyte behaviour were used to compare osmotic potential predictions with measurements. At low to medium salt concentrations, all models performed fairly well. At high concentrations, only the model accounting for non‐ideal behaviour predicted the osmotic potential satisfactorily, whereas at very high concentrations, all models underestimated the impact of osmotic potential on water potential. This suggests that the surface properties of the soil matrix, such as the specific surface area and surface charges, may lead to a decrease in osmotic potential beyond what is expected in pure solutions. [ABSTRACT FROM AUTHOR]

Climate change will lead to prolonged droughts in various regions of the world, which may significantly affect agricultural production. This is particularly problematic for soils with low water retention capacity, which cannot store sufficient water for crops. In this paper, we investigate how a change in the water‐holding capacity of the soil material, as could be achieved by increasing the soil organic carbon (SOC) amount, affects the components of the soil water balance (evaporation, transpiration, and groundwater recharge). Specifically, we state the hypothesis that an increased water‐holding capacity in a shallow soil layer, as it is achieved through SOC enrichment at the soil surface, will result in more water being stored near the soil surface and lost to unproductive evaporation, thereby reducing the amount of water available to plants and groundwater recharge. The hypothesis was tested by numerical simulations, employing the Hydrus‐1D program package to model the water balance in a soil–plant–atmosphere system for an arable crop in hydrologically contrasting years. The study considered soils with varying textures and different depths of a soil layer with increased SOC content. The soil hydraulic properties (SHP) of the soil material, including the effect of SOC on the SHP, were determined using a recently developed pedotransfer model based on data from over 500 samples. We showed that both the improved water retention by SOC and its vertical distribution affect the soil water balance in a complex manner. In sandy soils, increasing the water‐holding capacity in shallow layers up to 0.1 m led to enhanced evaporation and thus a decrease in water availability for crops. However, deeper incorporated SOC could ameliorate these negative effects. Our findings suggest that not only the amount but also the vertical SOC distribution should be considered if enrichment of SOC shall be applied to mitigate the effect of droughts. Core Ideas: Addition of soil organic carbon (SOC) alters soil material's hydraulic properties.This, in turn, impacts the water balance of soils under agricultural use.The depth at which SOC is incorporated into soils plays a crucial role in the partitioning of evapotranspiration (ET) into evaporation (E) and transpiration (T).Deeper incorporation of SOC results in greater water availability for plants. [ABSTRACT FROM AUTHOR]

To model water, solute, and energy transport in porous media, it is essential to have accurate information about the soil hydraulic properties (SHPs), i.e., the water retention curve (WRC) and the soil hydraulic conductivity curve (HCC). It is important to have reliable data to parameterize these models, but equally critical is the selection of appropriate SHP models. While various expressions for the WRC are frequently compared, the capillary conductivity model proposed by Mualem (1976a) is widely used but rarely compared to alternatives. The objective of this study was to compare four different capillary bundle models in terms of their ability to accurately predict the HCC without scaling the conductivity function by a measured conductivity value. The four capillary bundle models include two simple models proposed by Burdine (1953) and Alexander and Skaggs (1986), which assume a bundle of parallel capillaries with tortuous flow paths, and two more sophisticated models based on statistical cut-and-random-rejoin approaches, namely those proposed by Childs and Collis-George (1950) and the aforementioned model of Mualem (1976a). To examine how the choice of the WRC parameterization affects the adequacy of different capillary bundle models, we utilized four different capillary saturation models in combination with each of the conductivity prediction models, resulting in 16 SHP model schemes. All schemes were calibrated using 12 carefully selected data sets that provided water retention and hydraulic conductivity data over a wide saturation range. Subsequently, the calibrated models were tested and rated by their ability to predict the hydraulic conductivity of 23 independent data sets of soils with varying textures. The statistical cut-and-random-rejoin models, particularly the Mualem (1976a) model, outperformed the simpler capillary bundle models in terms of predictive accuracy. This was independent of the specific WRC model used. Our findings suggest that the widespread use of the Mualem model is justified. [ABSTRACT FROM AUTHOR]