JWSS - Journal of Water and Soil Science

Biochar as an efficient strategy for the improvement of soil properties and organic waste management may reduce the potential effects of abiotic stresses and increase soil fertility. However, the effects of this organic amendment on soil microbial indicators under combined salinity and pollution have not been studied yet. Therefore, the objective of this study was to evaluate the influence of sugarcane bagasse biochar on some soil bioindicators in a Cd-polluted soil under saline and non-saline conditions. A factorial experiment was carried out with two factors, including NaCl salinity (control, 20 and 40 mM NaCl) and sugarcane bagasse biochar (soils unamended with biochar, amended with uncharred bagasse, 400 ^oC biochar, and 600 ^oC) at 1% (w/w) using a completely randomized design. Results showed that salinity increased the mobility of Cd (12-17%), and subsequently augmented its toxicity to soil microorganisms as indicated by significant decreases in the abundance and activities of the soil microbial community. Conversely, sugarcane bagasse biochar application reduced the concentration of soil available Cd (14-18%), increased the contents of soil organic carbon (89-127%), and dissolved organic carbon (4-70%), and consequently alleviated the effect of both abiotic stresses on soil microbial community and enzyme activity. In conclusion, this experiment demonstrated that the application of sugarcane bagasse biochar could reduce the salinity-induced increases in available Cd and mitigate the interaction between salinity and Cd pollution on the measured soil bioindicators.

In the present study, a model was developed using a system dynamics approach to simulate and optimize the profitability of crops of the Jofeyr (Isargaran) Irrigation and Drainage Network located in Khuzestan Province. To validate the results, the statistical indicators of root mean square error (RMSE), standard error (SE), mean biased error (MBE), and determination coefficient (R²) were used. To validate the simulation results of the benefit-cost ratio, the values of these indicators were obtained 0.25, 0.19, 0.005, and 0.96, respectively. Then, to determine the optimal cultivated area of the network and increase the profitability, the cropping pattern was determined both non-stepwise and stepwise in 2013 to 2017 cropping years. In the non-stepwise, the cultivated area of each crop changed from zero to 2 times of current situation. In stepwise, due to social and cultural conditions of inhabitants, this change was slow and 10% of the current situation every year. The analysis of the results showed the success of the model in optimizing and achieving the desired goals and the total benefit-cost ratio increased in all years both non-stepwise and stepwise. For example, in 2017 compared to 2016, production costs decreased by 7.1 percent and sales prices increased by 5.8 percent, and increased the benefit-cost in 2017 compared to the previous year. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, its cropping pattern, and defining other scenarios.

In this study, the Drainmod-S model was used to vary soil salt concentration and the effect of underground drainage on the amount of leaching in a physical model (large lysimeter). A soil extractor was installed at depths of 40, 50, and 70 cm at a distance of 35 cm from the drainage to measure the salinity of the soil solution. In this study, three scenarios were applied including salinity profiles under conventional conditions (mid-season and end-season drainage), soil salinity profiles under different drainage conditions, and prior scenarios with saline irrigation. The second and third scenarios were applied in four drainage stages, respectively. These stages include transplanting and mid-season drainage (days 15 to 20), mid-season drainage (days 35 to 40), mid-season and end-season drainage (days 55 to 60), and end-season drainage (days 75 to 80). The results showed that after simulating the total solute concentration overtime at a depth of 40 cm and comparing it with the measured values, the coefficient of determination (R²) was 0.77 indicating an acceptable Drainmod-S model simulation. This parameter for simulating solute concentration at 50 and 70 cm depth was 0.76 and 0.75, respectively. The mean absolute error parameter (MAE) value was also negligible.

Bed shear stress is one of the most important hydraulic parameters to determine the amount of bed and suspended load and the bed and bank scouring in rivers. Bed shear stress depends on bedforms (ripples, dunes, and anti-dunes) in alluvial rivers. In this study, the effect of artificial ripple bedforms on bed shear stress has been investigated. Two types of uniform granulation with average sizes (d50) of 0.51 and 2.18 mm were used to roughen the surface of the artificial ripples. The bedform length and height were 20 and 4 cm, respectively. The angles of its upstream and downstream to the horizon were selected equal to 16.4 and 32 degrees, respectively. Different flow rates (Q= 10, 15, 20, 25, and 30 l/s) and different bed slopes (S= 0, 0.0001, 0.0005, 0.001, and 0.0015) were examined. The results showed that by increasing the particle size on the bed surface, total shear stress (t_b ), grain-related bed-shear stress (t^¢_b ), and form-related bed-shear stress ( t^²_b )  increase. The value of t_b , t^¢_b , and t^²_b in bed form roughened by sediment size of 2.18 mm were, on average, 22.38, 30.86, and 22.3% more than the bed form roughened by sediment size of 0.51 mm, respectively.

A compound sharp-crested weir is often used to measure a wide range of flows with appropriate accuracy in open channels. In this study, experiments were performed to investigate the hydraulic flow through a compound weir of circular-rectangular with changes in hydraulic and geometric parameters in free and submerged flow conditions. The characteristics of the weirs include rectangular spans width of 39 cm, a circular radius of 5, 7.5, and 12.5 cm, and heights of 10 and 15 cm. The results showed that by increasing the radius and height of the Weir, upstream water depth increases around 28.4%. At a constant h/p, the discharge coefficient increases with the increasing radius of the circular arc. Also, in the submerged conditions, the discharge coefficient is less (around 40%) than in the free flow condition, which is due to the resistance of the depth of the created stream against the passage of the flow.

Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.

One of the significant factors affecting biochar properties is the pyrolysis temperature. This study aimed to investigate the effect of pyrolysis temperature on the properties and fractionation of Zn and Pb in biochars produced by sewage sludge. Biochars were prepared at temperatures of 300 to 700 °C and the physicochemical properties, availability, and fractionation of Zn and Pb were investigated. The results showed that pH, pHzpc, percentage of calcium carbonate, cation exchange capacity, specific surface area, and porosity in biochars were higher than in the feedstock. By increasing biochar production temperature, the mentioned properties increased. FT-IR revealed that the OH functional group is present in free form, either in the structure of C-OH and -COOH and aliphatic-CH2 groups in the sewage sludge. By converting sewage sludge to biochar, the intensity of these peaks decreased. In contrast, peaks representing complex aromatic structures appeared. The availability of Zn and Pb in biochar was reduced as compared to sewage sludge. The results of fractionation indicated that sewage sludge has bio-availability and toxicity of Pb and Zn. the amount of oxide and residual fractions of these two metals increased by converting sewage sludge to biochar at different temperatures. Therefore, it seems that biochar production from sewage sludge reduces the toxicity and bio-availability of Zn and Pb. Also, by incrementing the temperature of production, the bio-availability potential (bonding with organic matter), and non-toxicity (residual) of these metals increased.

Increasing crop production per unit volume of water consumption requires recognizing the most dependent variable in drip irrigation to the volume of water consumption and also identifying the most important variables independent of water productivity in surface and subsurface drip irrigation for optimal use of available water resources. The present research was carried out in Behbahan Agricultural Research Station during four cropping seasons (2013-2017) on a Kabkab date variety. Experimental treatments include the amount of water in the subsurface drip irrigation method based on two levels of 75% and 100% water requirement and in surface drip irrigation based on 100% water demand. Data were analyzed using a randomized complete block design with three replications. The results of the analysis of variance of the mean of different irrigation treatments in quantitative traits showed that the effect of irrigation was significant at the level of 1% in terms of cluster weight index, fruit weight, and fruit flesh to kernel weight ratio. The results of regression analysis of variance showed that in the dependent variable of cluster weight, the consumption water volume explained 19.1% (R2 = 0.191) of the fluctuations of the dependent variable (cluster weight). Among all the studied variables, the volume of water consumption explained the most significant changes in date cluster drying. Fruit moisture with t (2.096) and equivalent beta coefficient (0.046) had a significant positive effect on water productivity at the level of 5%. The results of the Pearson correlation coefficient showed that the effect of yield on changes in water productivity was much greater than the volume of water consumed so the yield caused significant changes in water productivity. While the effect of water consumption on water productivity was not significant.

An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.

To study the soil-landscape change in the Chaldoran region, 9 representative soil profiles were studied in 5 dominant geomorphic units of the study area including piedmont plain, mantled pediment, alluvial fan, plain, and flood plain. The results showed that the accumulation of pedogenic carbonate in some soils was concretion and light in color. In control soils in the piedmont plain (profile 5 and 7), mantled pediment (profile 6), and flood plain (profile 8) clay transferred from the surface horizons and accumulated in the lower horizon, due to relatively good rainfall in the region and distinct dry and wet seasons has led to the formation of argillic horizons along with the formation of crust on the surfaces of aggregates and building units and has formed the Alfisoils order. Mineralogical results showed the presence of chlorite, illite, kaolinite, and smectite minerals. According to the evidence, illite, chlorite, and kaolinite minerals were inherited and smectite minerals were formed due to weathering and evolution of illite, chlorite, or palygorskite minerals. Also, the results of the CIA index in the region indicated that the soils of the region are in the stage of weak to moderate weathering. In general, the results indicated the critical role of drainage, land use, and parent materials in the soils of the study area.

The optimal strategy selection is an influential factor to enhance the efficiency of land reclamation projects. On the other hand, the process of desertification of the land during environmental degradation is the consequence of the interaction between various factors that make it challenging to select appropriate solutions. Therefore, this study aimed to provide systematic optimization strategies in the form of a group decision-making model in the Yazd-Khezrabad plain. The important strategies were identified using the Delphi model. Then, the alternative initial ranking was formed by Cook and Seiford method framework using the vote taken from the decision makers on strategies. Finally, by estimating the linear distance of each option and including the matrix of the intervals, the last priority of the strategies was obtained from solving the assignment problem. The results indicated that the strategies of "prevent land use inappropriate change" (A18) and "the regeneration of vegetation cover" (A23) with Value=1 and Reduced Cost=0, were identified as the most important combating-desertification strategies in the region, respectively. The results of this study help desert managers to utilize limited facilities and capital dedicated to controlling the desertification process efficiently and effectively.

Unpredictable settlement of earth dams has led researchers to develop new methods such as artificial neural networks, wavelet theory, fuzzy logic, and a combination of them. These methods do not require time-consuming analyses for estimation. In this research, the amount of settlement in rockfill dams with a central core has been estimated using artificial intelligence methods. The data of 35 rockfill dams with a central core were used to train and validate the models. The artificial neural network, wavelet transform model, and fuzzy-neural adaptive inference system are the proposed models which were used in the present study. According to the results, the best model for an artificial neural network had two hidden layers, the first layer of 18 neurons and the second layer of 7 neurons, with the Tansig-Tansig activation function, with a coefficient of determination R2=0.4969. The best model for the fuzzy-neural inference system had the ring function (Dsigmoid) as a membership function, with three membership functions and 142 repetitions with a coefficient of determination R2=0.2860. Also, combining wavelet-neural network conversion with the coif2 wavelet function due to the more adaptation this function has to the input variables, the better the performance, and this function, with a coefficient of determination R2=0.9447, had the highest accuracy compared to other models.

The study of annual damage statistics due to floods in Iran and the world shows the extent of flood damage to natural and human resources in different regions. Determining the flood zone of rivers in order to protect national resources and reduce flood damage provides the possibility of protecting the river from encroachment and the construction of any unauthorized facilities in it. Therefore, in the present study, the capability of numerical models in simulating the flood zone of rivers was evaluated in the range of Azarshahr Qushqura river and the two-dimensional hydraulic model HEC-RAS 5.0.7 and one-dimensional HEC-RAS model were compared. Changes in the hydraulic characteristics of the flood flow including depth and velocity of the flow at different cross sections of the models were evaluated. The results showed that the water surface level (flow depth) of the two-dimensional model HEC-RAS compared to the one-dimensional model had the lowest error as compared to other hydraulic parameters of flood flow. The two-dimensional HEC-RAS model showed the highest error rate in the flow velocity parameter in comparison to the one-dimensional model. The results indicated that two-dimensional HEC-RAS model V5.0.7 determined the surface of the flood zone 12.46 % more than the one-dimensional HEC-RAS model. The confirmation of the resulting zones on the current state of the river and comparison with the river aerial photo of 1346 indicated the higher accuracy of the two-dimensional HEC-RAS model in estimating the flood zone of the river.

This study was conducted to investigate the effects of climate change on temperature and precipitation changes in important synoptic weather stations in Yazd province (including Yazd, Bafgh, Marvast, and Robat-e-Poshtebadam). Accordingly, a combination of the outputs of the latest AOGCM models presented in the IPCC sixth assessment report (CMIP6) were used to increase the accuracy of temperature and precipitation forecasts. A weighting method was used based on the Kling-Gupta combined index (KGE) to combine these models. After weighting the models, the monthly temperature and precipitation changes were calculated based on SSP126, SSP245, and SSP585 emission scenarios. Then, daily temperature and precipitation time series were extracted for different weather stations using the LARS-WG downscaling model. The results showed that in all the weather stations, CanESM5 and BCC-CSM2-MR models have the best ability to simulate the temperature and precipitation of the historical period, respectively. Results also showed that in all emission scenarios, the annual temperature will increase and the annual precipitation will decrease. The annual temperature of this region will increase between 0.2 to 0.6 °C, and the annual precipitation will decrease between 2.9 and 13.7% in different weather stations. Also, the maximum temperature increase and precipitation decrease in this region, will occur in spring and autumn, respectively.