نشریه علوم آب و خاک (علوم و فنون کشاورزی و منابع طبیعی)
سال بیست و ششم شماره 2 (پیاپی 100، تابستان 1401)

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.

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.

Low organic matter content and alkaline pH of calcareous soils in arid and semi-arid regions are the main reasons for the low nutrient availabilities for plants in these soils. One way to improve the chemical properties and fertility of calcareous soils is the application of organic substances such as biochar produced from pyrolysis of organic wastes. However, biochars have an almost predominant alkaline pH, which exacerbates plant nutrient deficiencies in calcareous soils when used for a long time. Pyrolysis of some organic wastes under controlled temperature conditions can lead to the production of acidic biochar. The effect of acidic biochars on several chemical properties of two calcareous soils in Isfahan province was investigated in the present study. Treatments included two types of biochar (pine cone and rice husk), three levels of biochar addition (one, three, and six percent), two types of soil (a sandy loam (Tiran) and a clay loam (Lavark)), and two incubation periods (one and six months). The results showed that applying biochar could slightly decrease soil pH but raised soil electrical conductivity. In addition, the amount of organic carbon, total nitrogen, and available concentration of manganese in all treatments and the concentrations of available phosphorus, potassium, iron, zinc, and copper in the most of treatments showed a significant increase compared to the control. Amending soil with biochar at a 6% rate caused the most significant changes in the measured parameters in both soil types. In general, the results of this study indicated that acidic biochar produced from pine cones and rice husk can be used as a suitable conditioner to improve the chemical properties and fertility of calcareous soils.

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.

In the last decade, the use of gabion structures in hydraulic engineering for stabilizing the structure due to its high density and weight has become widespread. Also, the material's roughness and porosity cause it to be used in energy dissipation and drainage projects. This study evaluates the relative energy dissipation of gabion structures downstream of the ogee spillway in the conditions of a submerged hydraulic jump. The evaluated parameters in this study were Froude number, gabion height, gabion thickness, and material diameter. The experiments were performed with three average diameters of 1.5, 2.2, and 3 cm for rock material, three gabion heights of 10 and 20 cm, and Max. The end sill heights were 10, 20, and 30 cm. The operated discharges were regulated from 20 to 40 l/s. The results showed that by decreasing the average diameter of gabion aggregates, the amount of relative energy dissipation increases in all tested models, so that in gabion with a 1.5 cm average diameter of aggregates, the amount of energy dissipation increased by 3.6% in comparison with using the diameter of 3cm for the average diameter of the material. Increasing the height of the gabion to the extent that the flow is entirely inward can have up to 33% more relative energy dissipation than the gabion with a height of 10 cm. Also, by increasing the diameter of the gabion from 10 cm to 30 cm, relative energy dissipation increases up to 15%.

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.

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.

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.

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.

Water scarcity and lack of soil fertility are two major problems in the agriculture sector. This study aimed to use Azolla anzali and Lemna minor as a cover for a free surface of the water since not only do they have the potential to reduce evaporation, but they can also produce green fertilizer. Therefore, a completely randomized design experiment with 4 treatments (Azolla anzali, Lemna minor, combination of Azolla anzali+ Lemna minor and control) was performed with three replications. The surface of the reservoirs was covered with the mentioned plants and the changes in water height were measured every other day and the amount of nutrients (nitrogen and phosphorus) of the plant tissue was measured three times at the beginning, middle, and end of the period. Eventually, water loss in tanks containing Lemna, Azolla, and Lemna+ Azolla, was 39, 33.2, and 28.7% less than the control tank. The highest amount of nutrients in plant tissue was observed in Lemna, Azolla+ Lemna, and Azolla treatments, respectively. Although the amount of nutrients in the combined treatment was not higher than that of Lemna more biomass was produced, which means it can provide more fertilizer. Finally, the combined treatment of the two plants is a more suitable option to be used.

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.

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.

The current study aims to find the best methods of using remote sensing and supervised classification algorithms in long-term salinity monitoring of salinity changes in the Atabieh area with an area of 5000 hectares in the west of Khuzestan province. The procedure is based on the separation of different levels of saline soils utilizing information obtained from Landsat 7 and 8 satellite images (2001 to 2015) along with salinity data taken from the study area, and salinity indices including SI1, SI2, SI3, NDSI, IPVI, and VSSI. The results show the expansion of the saline zone trend in the soils of the study area, among which, soils with EC of more than 16 dS m-1 (very saline) have the highest frequency. The area of saline soils has increased significantly over the past 15 years, with a saline land area increasing by more than 90%. The percentage of salinity class is low (S1). According to this study, the only significant index in soil salinity at a 95% confidence level is the SI3 index, which has been able to have a good estimate of the increasing changes in soils in the region. The results of the supervised classification showed that the support vector machine (with an overall accuracy of 95.78 and a kappa coefficient of 0.89) is more accurate. After the vector machine method, the methods of minimum distance, maximum likelihood, and distance of Mahalanobis have the highest accuracy, respectively. Based on salinity maps obtained in years in 2001, 2005, 2010, and 2015, it can be said that the salinity rate in the whole of the study area was progressing and at the same time the salinity area in the middle and high classes increased decreased and on the other hand, the salinity area in the high class in 2001 gradually increased and distributed in 2015 throughout the region.

The irrigation schedule of crops is the most effective way to increase agricultural water use efficiency. In irrigation planning, determining the irrigation time is more important and difficult than determining the depth of irrigation water. Among all methods of determining the irrigation time of crops, the methods which used plants are more accurate than other methods. In this study, the wheat water stress index has been used which is based on the air vapor pressure deficit and the difference between vegetation and air temperature (Tc-Ta). First of all, the diagram and the relationship between the top and bottom baselines were extracted, then the water stress index of wheat was drawn in the Karaj region. Secondly, to determine the optimal water stress index of wheat, four treatments including I1: 30% of maximum allowable depletion of moisture, I2: 45% of maximum allowable depletion of moisture, I3: 60% of maximum allowable depletion of moisture, I4: 75% of maximum allowable depletion of moisture were performed in four replications. The amount of water stress index of each treatment was calculated during the season separately, and the CWSI of the treatment with the highest water use efficiency was used to determine the irrigation time of wheat. The results showed that the relationship between the upper and lower baseline for wheat in the Karaj region is Tc-Ta = 3.6 0c and  Tc-Ta = -0.27VPD - 2.64, respectively. The treatment of 45% of maximum allowable depletion of moisture had the highest water use efficiency and the optimal water stress index for wheat was obtained at 0.36 in the Karaj region.

The runoff curve number method is widely used to predict runoff and exists in many popular software packs for modeling. The curve number is an empirical parameter important but depends largely on the characteristics of soil hydrologic groups. Therefore, efforts to reduce this effect and extract more accurate soil information are necessary. The present study was conducted to integrate fuzzy logic for extraction runoff curve numbers. A new distribution model called CNS2 has been developed. In the first part of this research, the formulation and programming of the CNS2 model were done using the Python programming language environment, then the model was implemented in the Beheshtabad watershed. This model simulates the amount of runoff production in a watershed in the monthly time step with the fuzzy curve number and takes into account the factor of rainy days, the coefficient of management of the RUSLE-3D equation, and the soils theta coefficient. The results indicated that the model with Nash-Sutcliff 0.6 and the R2 coefficient 0.63 in the calibration set and Nash index 0.53 and R2 coefficient 0.56 in the validation set had appropriate efficiency in runoff simulation. The advantage of the model is that distributive and allows for the identification of areas with higher runoff production.

In the past years, by increasing population and water consumption, as well as the high cost of developing surface water resources, the exploitation of groundwater resources has increased significantly. In the current situation, a significant part of the country's water consumption in all sectors of consumption is provided by groundwater sources. On the other hand, the development of industry and the entry of pollutants, including heavy metals, into the groundwater endanger the health of humans. The present research has investigated the non-cancerous risk caused by heavy metals in the groundwater of Urmia plain for both children and adults. This research is based on a descriptive-analytical method based on the available data, in which the concentration of polluting metals obtained from the studies conducted in the fall and winter of 2016 from the number of 12 wells supplying rural drinking water in the Urmia plain has been analyzed. Also, human health risk assessment was measured using the United States Environmental Protection Agency index. The results showed that there are six heavy metals including cadmium, copper, iron, manganese, nickel, and lead in the region's groundwater, among which two of the wells have cadmium and lead values higher than national and international standards. Also, the total non-cancer risk index through ingestion and skin absorption for both children and adults groups was found to be 0.23 and 0.096, respectively, which is less than one, and this indicates that the water quality of the region is suitable for drinking.

Surface runoff is one of the most significant components of the water cycle, which increases soil erosion and sediment transportation in rivers and decreases the water quality of rivers. Therefore, accurate prediction of hydrological response of watersheds is one of the important steps in regional planning and management plans. In this regard, the rainfall-runoff modeling helps hydrological researchers, especially in water engineering sciences.  The present study was conducted to analyze the rainfall-runoff simulation in the Gorganrood watershed located in northeastern Iran using AWBM, Sacramento, SimHyd, SMAR, and Tank models. Daily rainfall, daily evapotranspiration, and daily runoff of seven hydrometric stations in the period of 1970-2010 and 2011-2015 were used for calibration and validation, respectively. The automated calibration process was performed using genetic evolutionary search algorithms and SCE-UA methods, using Nash Sutcliffe Efficiency (NSE) and root mean of square error (RMSE) evaluation criteria. The results indicated that the SimHyd model with NSE of 0.66, TANK model using Genetic Algorithm and SCE-UA methods with NSE of 0.67 and 0.66, and Sacramento model using genetic algorithm and SCE-UA methods with NSE of 0.52 and 0.55 have the best performance in the validation period.

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 (tb ), grain-related bed-shear stress (t¢b ), and form-related bed-shear stress ( t²b )  increase. The value of tb , t¢b , and t²b 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.

The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.

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.