Capacitance sensors are widely used in agriculture for irrigation and soil management purposes. However, their use under saline conditions is a major challenge, especially for sensors operating with low frequency. Their dielectric readings are often biased by high soil electrical conductivity. New calculation approaches for soil water content (θ) and pore water electrical conductivity (ECp), in which apparent soil electrical conductivity (ECa) is included, have been suggested in recent research. However, these methods have neither been tested with low-cost capacitance probes such as the 5TE (70 MHz, Decagon Devices, Pullman, WA, USA) nor for field conditions. Thus, it is important to determine the performance of these approaches and to test the application range using the 5TE sensor for irrigated soils. For this purpose, sandy soil was collected from the Jemna oasis in southern Tunisia and four 5TE sensors were installed in the field at four soil depths. Measurements of apparent dielectric permittivity (Ka), ECa, and soil temperature were taken under different electrical conductivity of soil moisture solutions. Results show that, under field conditions, 5TE accuracy for θ estimation increased when considering the ECa effect. Field calibrated models gave better θ estimation (root mean square error (RMSE) = 0.03 m³ m⁻³) as compared to laboratory experiments (RMSE = 0.06 m³ m⁻³). For ECp prediction, two corrections of the Hilhorst model were investigated. The first approach, which considers the ECa effect on K’ reading, failed to improve the Hilhorst model for ECp > 3 dS m⁻¹ for both laboratory and field conditions. However, the second approach, which considers the effect of ECa on the soil parameter K₀, increased the performance of the Hilhorst model and gave accurate measurements of ECp using the 5TE sensor for irrigated soil.

Water scarcity and quality degradation represent real threats to economic, social, and environmental development of arid and semi-arid regions. Drip irrigation associated to Deficit Irrigation (DI) has been investigated as a water saving technique. Yet its environmental impacts on soil and groundwater need to be gone into in depth especially when using brackish irrigation water. Soil water content and salinity were monitored in a fully drip irrigated potato plot with brackish water (4.45 dSm⁻¹) in semi-arid Tunisia. The HYDRUS-1D model was used to investigate the effects of different irrigation regimes (deficit irrigation (T1R, 70% ETc), full irrigation (T2R, 100% ETc), and farmer’s schedule (T3R, 237% ETc) on root water uptake, root zone salinity, and solute return flows to groundwater. The simulated values of soil water content (θ) and electrical conductivity of soil solution (ECsw) were in good agreement with the observation values, as indicated by mean RMSE values (≤0.008 m³·m⁻³, and ≤0.28 dSm⁻¹ for soil water content and ECsw respectively). The results of the different simulation treatments showed that relative yield accounted for 54%, 70%, and 85.5% of the potential maximal value when both water and solute stress were considered for deficit, full. and farmer’s irrigation, respectively. Root zone salinity was the lowest and root water uptake was the same with and without solute stress for the treatment corresponding to the farmer’s irrigation schedule (273% ETc). Solute return flows reaching the groundwater were the highest for T3R after two subsequent rainfall seasons. Beyond the water efficiency of DI with brackish water, long term studies need to focus on its impact on soil and groundwater salinization risks under changing climate conditions.

Estimating crop evapotranspiration (ET_a) is an important requirement for a rational assessment and management of water resources. The various remote sensing products allow the determination of crops’ biophysical variables integrated in the evaluation of ET_a by using surface energy balance (SEB) models. This study compares ET_a estimated by the simplified surface energy balance index (S-SEBI) using Landsat 8 optical and thermal infra-red spectral bands and transit model HYDRUS-1D. In semi-arid Tunisia, real time measurements of soil water content (θ) and pore electrical conductivity (EC_p) were made in the crop root zone using capacitive sensors (5TE) for rainfed and drip irrigated crops (barley and potato). Results show that HYDRUS model is a fast and cost-effective assessment tool for water flow and salt movement in the crop root layer. ET_a estimated by S-SEBI varies according to the available energy resulting from the difference between the net radiation and soil flux G₀, and more specifically according to the assessed G₀ from remote sensing. Compared to HYDRUS, the ET_a from S-SEBI was estimated to have an R² of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d⁻¹) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d⁻¹).

Classifying dams is important to ensure proper management, safety, and maintenance based on their size, purpose, and risk level. This helps in planning for emergency responses, structural inspections, and efficient water resource utilization. This study used the analysis of variance (ANOVA) technique to categorize the main Tunisian dams according to their precipitation to potential evapotranspiration (P/PET) ratio. The data were obtained from the NASA POWER platform, with potential evapotranspiration estimated using the Oudin model. Despite the violation of the normality assumption, the robustness of the ANOVA test for classification purposes remained unaffected. A comparison between Duncan’s test (parametric) and the Kruskal–Wallis test (non-parametric) revealed similar class structures, although Duncan’s test provided greater precision. The analysis identified four primary dam classes, reflecting regional differences in water availability and evaporative demand, and included dams in north-west Tunisia, considered the ‘water tower’ of the country, and those in semi-arid and arid regions.