Evaluation of Soil Hydraulic Properties in Northern and Central Tunisian Soils for Improvement of Hydrological Modelling

The hydrological cycle is strongly affected by climate changes causing extreme weather events with long drought periods and heavy rainfall events. To predict the hydrological functioning of Tunisian catchments, modelling is an essential tool to estimate the consequences on water resources and to test the sustainability of the different land uses. Soil physical properties describing water flow are essential to feed the models and must therefore be determined all over the watershed. A simple but robust ring infiltration method combined with particle size distribution (PSD) analysis (BEST method) was used to evaluate and derive the retention properties and the hydraulic conductivities. Physically based and statistical pedotransfer functions based on PSD were compared to test their potential use for different types of Tunisian soils. The functional sensitivity of these parameters was assessed by employing the Hydrus-1D software (PC Progress, Prague, Czech Republic) for water balance computations. This evaluation process involved testing the responsiveness and accuracy of the parameters in simulating various water balance components within the model. The evaluation of soil hydraulic parameters across the three used models highlighted significant variations, demonstrating distinct characteristics in each model. While notable differences were evident overall, intriguing similarities emerged, particularly regarding saturated hydraulic conductivity between BEST and Rosetta, and the shape parameter ( n ) between Arya–Paris and Rosetta. These parallels indicate shared hydraulic properties among the models, underscoring areas of agreement amid their diverse results. Significant differences were shown for scale parameter α for the various methods employed. Marginal differences in evaporation and drainage were observed between the BEST and Arya–Paris methods, with Rosetta distinctly highlighting a disparity between physically based models and statistical models.