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Iran is in a serious freshwater shortage crisis because its major part is located in arid and semi‐arid areas. This research evaluated the groundwater quality in terms of potable and irrigation uses in the wet and dry seasons in the Kazeroon plain, southern Iran. In this study, a total of 408 groundwater samples were gathered from 68 boreholes to measure water quality indices, such as acidity (pH), electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), bicarbonate (HCO3−), chloride (Cl−), sulphate (SO42−), calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+). Geographical information system technology was also applied to draw maps of spatial changes of water quality parameters. The results showed that groundwater quality indices had the minimum and maximum values of pH (6.9–8.6), EC (414–9,813 μmho cm−1) and TDS (278–6,180), TH (175–3250), HCO3− (152–518), Cl− (5–1950), SO42− (17–2371), Ca2+ (30–681), Mg2+ (12–607), Na+ (1–1303) and K+ (0.8–18) (mg L−1). Based on World Health Organization standards, the results indicated that all of the aquifer water in the plain except for the northern region was of poor and very poor quality for potable usages. Also, the United States Salinity Laboratory diagram showed that the groundwater quality is doubtful for irrigation. Therefore, the cultivation pattern in this plain should be switched towards salt‐tolerant crops.

Characterization and measurement of the hydraulic properties of unsaturated porous media is still a challenge in natural environments, although exact knowledge of the soil's hydraulic properties [unsaturated soil hydraulic conductivity (K), volumetric water content (θ), soil matric head (h)] is crucial for solving many soil, hydrological, and environmental issues. The main purpose of this study was to establish a modified multistep outflow technique that facilitates laboratory operations and reduces time and costs. We also focused on inverse parameter optimization of the Mualem-van Genuchten (MVG) model with laboratoryprovided data for three fine-grained soils. This modified version was assessed by inverse modeling of the MVG model and the generalized reduced gradient algorithm. The results represent the best estimate of the adjusted parameters and the very low uncertainty associated with the fitted values. The soil hydraulic functions were simulated well up to pressures of 1,500 kPa and their uncertainties were extremely low. The R 2 values were 88 to 97 and 81 to 96% for the soil water characteristics and the unsaturated hydraulic conductivity functions, respectively. The simultaneous optimization of the nonlinear hydraulic functions from a single transient flow experiment agreed well with their observed data for each soil examined. Besides, by eliminating the use of tensiometers and having no independent measurements of saturated soil hydraulic conductivity and saturated volumetric water content, the modified version was faster and easier than the conventional method. The proposed protocol appears to be suited for the vadose zone flow simulation of fine-grained soils.

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Soil bulk density (ρ b ) is an important indicator of soil quality, productivity, compaction and porosity. Despite its importance, ρ b is often omitted from global datasets due to the costs of making many direct ρ b measurements and the difficulty of direct measurement on rocky, sandy, very dry, or very wet soils. Pedotransfer functions (PTFs) are deployed to address these limitations. Using readily available soil properties, PTFs employ estimator equations to fit existing datasets to estimate properties like ρ b . However, PTF performance often declines when applied to soils outside those in the training dataset.Potentially, recalibrating existing PTFs using new observations would leverage the power of large datasets used in the original PTF derivation, while updating information based on new soil observations. Here, we evaluate such a recalibration approach for ρ b estimation, benchmarking its performance against two alternatives: the original, uncalibrated PTFs, and novel, local PTFs derived solely from new soil observations. Using a ρ b dataset of N = 360 total observations obtained in West Azerbaijan, Iran, we varied the local dataset size (with N = 15, 30, 60, and 360) and recalibrated four existing PTFs with these data.Local PTFs were generated based on stepwise multiple linear regression for the same datasets. The same PTFs (original, recalibrated, and local) were also applied to the study area, and the resulting ρ b estimates were compared with the global SoilGrids dataset. Recalibration of PTFs reduced errors relative to the original uncalibrated PTFs; for instance, the NSE increased from À22.07 to 0.30 (uncalibrated) to 0.20-0.41 (recalibrated), and RMSE decreased from 0.12 to 0.60 Mg m À3 (uncalibrated) to 0.10-0.13 Mg m À3 (recalibrated). The recalibrated PTFs performance was comparable to or better than local PTFs applied to the same data. Recalibration of existing PTFs with local/regional uses provides a viable alternative to the use of global datasets or the development of local PTFs in data-scarce regions. Highlights• Existing global PTFs were calibrated and tested using a small dataset for local utilisation.• Several new local PTFs were also developed using the same datasets.