Bijan Ghahraman

Soil salinization is one of the significant concerns regarding irrigation with saline waters as an alternative resource for limited freshwater resources in arid and semi-arid regions. Thus, the investigation of proper management methods to control soil salinity for irrigation with saline waters is inevitable. The HYDRUS-1D model is a well-known numerical model that can facilitate the exploration of management scenarios to mitigate the consequences of irrigation with saline waters, especially soil salinization. However, before using the model as a decision support system, it is crucial to calibrate the model and analyze the model’s parameters and outputs’ uncertainty. Therefore, the generalized likelihood uncertainty estimation (GLUE) algorithm was implemented for the HYDRUS-1D model in the R environment to calibrate the model and assess the uncertainty aspects for simulating soil salinity of corn root zone under saline irrigation with linear move sprinkle irrigation system. The results of the study have detected a lower level of uncertainty in the α, n, and θ_s (saturated soil water content) parameters of water flow simulations, dispersivity (λ), and adsorption isotherm coefficient (K_d) parameters of solute transport simulations comparing to the other parameters. A higher level of uncertainty was found for the diffusion coefficient as its corresponding posterior distribution was not considerably changed from its prior distribution. The reason for this phenomenon could be the minor contribution of diffusion to the solute transport process in the soil compared with advection and hydrodynamic dispersion under saline water irrigation conditions. Predictive uncertainty results revealed a lower level of uncertainty in the model outputs for the initial growth stages of corn. The analysis of the predictive uncertainty band also declared that the uncertainty in the model parameters was the predominant source of uncertainty in the model outputs. In addition, the excellent performance of the calibrated model based on 50% quantiles of the posterior distributions of the model parameters was observed in terms of simulating soil water content (SWC) and electrical conductivity of soil water (ECsw) at the corn root zone. The ranges of NRMSE for SWC and ECsw simulations at different soil depths were 0.003 to 0.01 and 0.09 to 0.11, respectively. The results of this study have demonstrated the authenticity of the GLUE algorithm to seek uncertainty aspects and calibration of the HYDRUS-1D model to simulate the soil salinity at the corn root zone at field scale under a linear move irrigation system.

Utilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems. Leaching application requires understanding the relationship between the amount of irrigation water and its quality with the dynamic of salts in the soil. The HYDRUS-1D model can simulate the dynamic of soil salinity under saline water irrigation conditions. However, these simulations are subject to uncertainty. A study was conducted to assess the uncertainty of the HYDRUS-1D model parameters and outputs to simulate the dynamic of salts under saline water irrigation conditions using the Markov Chain Monte Carlo (MCMC) based Metropolis-Hastings algorithm in the R-Studio environment. Results indicated a low level of uncertainty in parameters related to the advection term (water movement simulation) and water stress reduction function for root water uptake in the solute transport process. However, a higher level of uncertainty was detected for dispersivity and diffusivity parameters, possibly because of the study’s scale or some error in initial or boundary conditions. The model output (predictive) uncertainty showed a high uncertainty in dry periods compared to wet periods (under irrigation or rainfall). The uncertainty in model parameters was the primary source of total uncertainty in model predictions. The implementation of the Metropolis-Hastings algorithm for the HYDRUS-1D was able to conveniently estimate the residual water content (θ_r) value for the water simulation processes. The model’s performance in simulating soil water content and soil water electrical conductivity (ECsw) was good when tested with the 50% quantile of the posterior distribution of the parameters. Uncertainty assessment in this study revealed the effectiveness of the Metropolis-Hastings algorithm in exploring uncertainty aspects of the HYDRUS-1D model for reproducing soil salinity dynamics under saline water irrigation at a field scale.

In this study, we developed a general mathematical relationship to determine hydraulic tortuosity. An optimization code was run in MATLAB R2014a software, using Monte Carlo algorithm, to determine tortuosity at different water contents for 69 soil samples of UNSODA. Considering fractal concepts, a linear equation was developed empirically to determine hydraulic tortuosity as a function of effective saturation, pore fractal dimension, porosity, inverse of air entry pressure, and soil water content. Based on the results, estimated values of tortuosity using the proposed relationship were greater than the values proposed by Shepard by about 30%. To evaluate developed equation, statistical parameters of Root Mean Square of Logarithmic Deviation (RMSLD) and Akaike’s Information Criterion (AIC_c) were used for 17 soil samples. According to the calculated statistical parameters, the developed equation to estimate tortuosity has improved the results of Shepard’s method. Though the developed equation has a relatively complicated structure, it displays acceptable performance in terms of the compromise between accuracy and complexity. Furthermore, based on calculated tortuosity values using developed equation, we determined pore continuity according to Burdine’s model (1953). Considering the results, calculated pore continuity is much less than the value proposed by Burdine (1953) and is approximately close to the values proposed by Mualem (1976).

The effects of bulk and Nanosized SiO₂ on seed germination and seedling growth indices of fenugreek under salinity stress were studied in the College of Agriculture, Ferdowsi University of Mashhad, Iran, in 2013. The experimental treatments included 4 levels of salinity stress (0, 50, 100 and 150 mM), 2 concentrations of bulk (50 and 100 ppm), 2 concentrations of nanosized SiO₂ (50 and 100 ppm), and control (without any SiO₂ types). Seedling growth attributes significantly improved when bulk and nanosized SiO₂ concentrations applied singly or with different levels of salt stress. However, they significantly declined with salt application. The adverse effects of salt on shoot, root and seedling lengths were alleviated by application of 50 ppm nanosized SiO₂ treatment. Under salt stress condition, addition of 50 and 100 ppm nanosized SiO₂ to fenugreek seeds increased shoot, root and seedling dry weights as compared to bulk SiO₂ concentrations and control treatments, though 50 ppm nanosized SiO₂ was more effective than 100 ppm nanosized SiO₂ application. It was concluded that nanosized SiO₂ improves growth attributes of fenugreek and mitigate adverse effects of salt stress.

A case study examining Bayesian techniques for assessing parameter and predictive uncertainty of semiarid flash flood events is presented here. The focus is on testing a fully distributed rainfall-runoff model (i.e., AFFDEF) linked with Markov chain Monte Carlo (MCMC) samplers to simulate four semiarid flash flood events with varying rainfall durations (20 mm). MCMC samplers showed consistent behaviors with the a priori assumption and successfully improved performances on complex and multivariate search problems of semiarid flood simulation over the Abol-Abbas watershed, Iran. Analysis suggests that parameters associated with infiltration and interception capacity along with the contributing area threshold for the digital river network were the key model parameters and were more influential on the shape and volume of the flood hydrograph. Model predictive uncertainty was heavily dominated by error and bias in the soil water storage capacity, which reflects inadequate represe...

The Miller‐Miller similar media theory is widely applied to characterize the spatial variability of soil hydraulic properties. For a group of soils, a distinct scaling factor is commonly assigned to each individual soil to coalesce the soil water characteristic and hydraulic conductivity functions to single curves. It is generally assumed that the Miller‐Miller theory is valid as long as soils are “similar” either with regard to their microscopic pore space geometry or the closely related macroscopic soil hydraulic functions. In this paper, it is illustrated that similarity is not the sole required condition for validity of the Miller‐Miller theory. In addition, the interrelation between the soil water characteristic and the hydraulic conductivity functions considered for scaling need to be comparable. The interrelation is dependent not only on the pore space geometry, but also on solid‐liquid interactions. Hence similar interrelation cannot be concluded from similarity of microscopic pore space geometry. A dimensionless parameter termed the “joint scaling factor” was defined and applied to evaluate the soundness of the interrelation condition for 26 soils from the UNSODA database that were grouped into six classes of similar soils. Obtained results clearly reveal the crucial importance of the interrelation condition for the Miller‐Miller scaling theory, which has been hidden behind the “similarity” requirement, and contradict the general belief that Miller‐Miller scaling is valid as long as soils are “similar.”