نشریه آب و خاک
سال سی و پنجم شماره 5 (پیاپی 79، آذر و دی 1400)

Soil erosion and sediment transportation decrease water resources, and cause many social and economic problems. On the other hand, sediment transportation by rivers causes problems such as water quality degradation, reservoirs sedimentation, redirect of rivers, or decrease in their transportability. Therefore, finding the proper methods in sediment yield study in watersheds is essential in planning and management of land and water resources.  Climatic characteristics, physiography, geology, and hydrology of basins are the most effective factors in producing and transporting sediments according to several sources, but the role and impact of some factors are more pronounced than the others in different areas. As a result, the objective of this study was to investigate and identify the most important climatic, physiographic, geological, and hydrological factors in several watersheds of the northeastern part of Iran, by applying Gamma Test (GT) and principal component analysis (PCA) techniques. In this study, the data of discharge flow and suspended sediment concentration, and daily flow discharge recorded in 15 hydrometric stations in Mashhad and Neyshbour restricts and required maps were provided from the Regional Water Company of Khorasan Razavi, Iran. After drawing statistical bar graph period of suspended sediment, daily discharge, annual precipitation, and relatively adequate data, stations with the longest period and with the lowest deficit data were selected to determine the common statistical periods. Therefore, in this study, the time period of 1983-1984 to 2011-2012 was selected, and the run test was applied to control data quality and homogeneity. Then, the most effective factors of sediment yield were determined by principal component analysis (PCA) and Gamma Test (GT). The results of the principal component analysis showed that 90 percent of the first five components justify the changes. Among the factors, area and gross gradient of the mainstream from the first component, the average annual flow rate of mainstream, meandering waterways of the mainstream from second component, and drainage density of third component were identified as the most important influencing factors on suspended sediment production. Ninety superior combinations of 1500 proposed combinations were obtained by Gamma Test to evaluate the effects of each parameter on suspended sediment yield. To determine the order of importance of the entered parameters, first, Gamma Test was performed on all 12 parameters. Gamma values of all cases for each proposed combination were compared. The results showed that the impact of these statistics was lowered by eliminating high gamma parameters and the removal of low values. The data analysis revealed that the low levels of gamma and high accuracy of ratio to find the desired outputs from entries. By lowering the gradient, the complexity of the model was lowered and more suitable model was provided. As a result, high levels of gradient represented the complexity of the final model. The results of the percentage values of each of the 12 variables were considered among the superior equations for estimating the suspended sediment composition. In this regard, the mean annual discharge, main channel length, area, average annual rainfall, and percentage of the outcrop of erosion sensitive rocks with a total of 63 percent of the proposed equations were the most important factors affecting the sediment yield in the study area. The average height parameter of area, the average and gross slope of the mainstream had the lowest presence among the optimized compounds. Based on the results of the principal component analysis, the two factors of basin area and gross slope of the mainstream were selected as the most important factors affecting the amount of annual suspended sediment load, respectively. Based on the results of the Gamma Test, 12 main variables affecting suspended sediment load were identified and the effect of each of them on the production and transport of suspended sediment was determined. Based on the comparison of the results of the two methods of PCA and GT, it can be concluded that if the purpose of research or study is to prepare a model with the highest accuracy in estimating suspended sediment load, the 12-variable model of GT includes factors related to physiographical, geological, climatic and hydrological factors are suggested. However, if the preparation of a model with appropriate accuracy and a limited number of input variables is considered, a 5-variable model derived from the PCA method is proposed. At the same time, if the purpose is to prepare a model with the least input variables and their easy access and calculation and initial estimation of suspended sediments, a bivariate model (based on basin area and gross slope of the mainstream factors) resulting from PCA is proposed. According to the results of the present study, it can be concluded that the study of more parameters has provided grounds for evaluating their importance in sediment yield. Finally, due to the correlation of many parameters with each other, a limited number of parameters that have a more important role in suspended sediment estimation, were selected. Another finding of this study is the increase in the accuracy of the sediment model’s preparation due to achieving more important and effective parameters in sediment yield and identifying them in order to investigate the best sediment management measures in watersheds. It is suggested that similar research should be done in other watersheds with different conditions in terms of climatic conditions, topography, geology, and so on.

Predicting yield is increasingly important to optimize irrigation under limited available water to enhance sustainable production. Calibrated crop simulation models therefore increasingly are being used an alternative means for rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly-intuitive parameters to be defined compared to other crop models, and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic setting. This study was conducted as a two-year field experiment with the aim of the simulation of water productivity, above ground biomass and fresh and dry yield of tomato using AquaCrop model under different irrigation regimes applied at two growth stages in Mashhad climate conditions. A two-year field experiment was conducted during 2016-2017 growing seasons in the experimental field of Ferdowsi University of Mashhad located in Khorasan Razavi province, North East of Iran. The water-driven AquaCrop model developed by FAO was calibrated and validated to simulate water productivity, above-ground biomass and yield of tomato crop under varying irrigation regimes. AquaCrop was calibrated and validated for tomato under full (100% of water requirements) and deficit (75 and 50% of water requirements) irrigation regimes at vegetative (100V, 75V, and 50V) and reproductive stages (100R, 75R, and 50R). Model performance was evaluated in terms of the normalized root mean squared error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF), Willmott’s index of agreement (d) and coefficient of determination (R2). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. The 2016 and 2017 measured data sets were used for calibration and validation of AquaCrop model, respectively. Calibration results showed good agreement between simulated and observed data for water productivity in all treatments with high R2 value (0.93), good ME (0.23), low estimation errors (RMSE=0.09 kgm3) and high d value (0.85). The goodness of fit results showed that measured WP values were closer to simulated WP values for the validation season (2017) than for the calibration season (2016). During calibration, (2016), the model simulated the biomass with good accuracy. The simulated above ground biomass values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.92 to 0.99, NRMSE in range of 7.4 to 23%, d varying from 0.94 to 1, and ME ranging from 0.71 to 0.98. Validation results indicated good performance of model in simulating above ground biomass for most of the treatments (0.92 < R2 < 0.98, 6.5% < NRMSE < 21.3%, 0.76 < ME < 0.99). During validation (2017 growing season), overall, the trend of biomass growth (or accumulation) was captured well by model. However, the range of biomass of simulation errors was high, especially in treatments with higher stress. Accurate simulation of the response of yield to water is important for agricultural production, especially in an arid region where agriculture depends closely heavily on irrigation. During validation, the model predicted dry and fresh yield satisfactorily (NRMSE = 15.64% and 11.80% for dry and fresh yield, respectively). In general, the AquaCrop model was able to simulate the observed water productivity, above ground biomass and yield of tomato satisfactorily in both calibration and validation stage. However, the model performance was more accurate in non- and/or moderate stress conditions than in sever water-stress environments. In conclusion, the AquaCrop model could be calibrated to simulate growth and yield of tomato under temperate condition, reasonably well, and become a very useful tool to support decision on when and how much irrigate. This study provides the first estimate of the soil and plant parameter values of AquaCrop for simulation of tomato growth in Iran. Model parameterization is site specific, and thus the applicability of key calibrated parameters must be tested under different climate, soil, variety, irrigation methods, and field management.

Sulfate is one of the important groundwater pollutant sources in many parts of the world and it can enter into groundwater from various sources, such as lithology (dissolution of evaporative and pyrite oxidation), atmosphere (sea water spray), industrial (combustion of fossil fuels, sulfide- minerals production, and agricultural fertilizers), and etc. Identifying sources of soluble sulfate in water sources is important.  The sulfate in groundwater can be investigated using isotope and geochemistry techniques. Using isotope (34S and 18O) and hydro geochemical techniques, it can be possible to find out: sulfate origins and the effective processes/reactions on sulfate concentrations and hydrogen sulfide gas (H2S) production. In this paper, for the first time, the sulfate source in groundwater of Sarpol Zahab and the parameters affecting sulfate concentration and its isotopic compositions (d34S and d18O) in groundwater were studied. Sarpol-e Zahab is located in the catchment area of Alvand river in the west of Kermanshah province, west of Iran. The formations of the region, based on age from old to new, include the Ilam, Gurpi, Amiran, Telezang, Pabdeh, Asmari, Gachsaran, Aghajari, Bakhtiari and Quaternary alluvium formations. Asmari formation and Quaternary alluvial sediments form the largest area of the region. Gachsaran formation contains evaporative materials which is reducing the quality of groundwater in the region. Ilam formation is effective in providing the organic matter required for the bacterial sulfate reduction process.
13 water samples were taken from the water resources (wells, springs and river) of Sarpol-e Zahab region in two steps (December 2014 and September 2015). Measuring field parameters (T, pH, Eh, Ec, and TDS) and sampling of water resources were performed according to the instructions of Groundwater and Geothermal Research Center (GRC), Ferdowsi University of Mashhad. Field parameters were measured by VWR Handheld Multi parameter Research meter at the location of each water source. The concentration of cations and the anions were determined by the devices inductively coupled plasma elemental analyzer (ICP-EA) and ion chromatography (IC), respectively. Chemical and isotopic analyses of all water samples were performed in Ottawa university geochemistry laboratory and Waterloo university isotope laboratory, respectively. The measurement reference for isotopic sulfate and oxygen were VCDT and VSMOW, respectively, and the value isotopic are expressed as permil ((‰.
The sulfate concentrations in different water resources of the region varied from 5 to 950 mg L, however, it is very high in Gandab spring’ water, due to the association with hydrocarbons, and in Patagh Tunnel water, due to discharging of water from Gachsaran Formation (339.6 mg L and 950.1mg L, respectively). Chenarpiran spring has the lowest sulfate concentration because it is located in highlands and is discharged from Asmari formation with good water quality. The amount of  d18O varied between 5.8 to 13.1 VCDT ‰ and the amount of  d34S ranges from 9.5 to 31.8 VSMOW ‰. In Gandab spring’ water, due to sulfate reduction by microorganisms, in addition to the unpleasant smell of sulfide hydrogen (H2S), the d34S and d18O values were enriched than the isotopic composition of other water sources ( about 31.8 ‰ and 10.3 ‰, respectively). In other hand, the Gel va Darreh spring's water, due to the effect of upstream bath waste water (soap and detergent with a value of about 1 ‰ for d34S), indicated depleted isotope values.
 The concentration and isotopic composition of sulfate in the water resources of the Sarpol-e Zahab region are affected by the geological, atmospheric, human and hydrocarbon materials. The impact of human and hydrocarbon factors on the concentration and isotopes of sulfate is local, however, the lithology effects are on all water resources of the region. Since, Gachsaran formation forms the alluvial aquifer bedrock of Ghaleshahin plain, the dissolution of gypsum has an effective role in the hydro chemical evolution of the alluvial aquifer of this plain. The lithology of the area is often limestone and evaporation. Due to the high solubility of these materials in water, they have been able to have a significant effect on sulfate isotopic in water sources. The amount of measured isotopic values indicated that the origin of evaporators and atmospheric sulfate is in water. The dissolution of the evaporation formation has reduced the water quality. The association of bedrock in the Gandab spring has led to have a reduction environment and the occurrence of sulfate reduction and production of hydrogen sulfide gas (H2S). The human activities (by making bath upstream) caused oxidizing conditions, isotopic depletion and contamination of the Glodarreh spring.

Correct assessment of evapotranspiration fluctuations in different meteorological scenarios plays an important role in the optimal management of water resources. Probability analyzes with different probabilities of occurrence can increase flexibility in decision making and increase the reliability of decisions. Rice (Oryza sativa L.) is one of the most important agricultural products in the world. Although rice is cultivated in a wide range of climatic and geographical conditions, it is vulnerable to changes in environmental conditions. Planting management, design of irrigation systems, and suitable irrigation cycle for optimal production are important issues for sustainable production.

The study area includes the northern region of Iran, i.e. the provinces of Gilan, Mazandaran and Golestan, which is the main rice-growing area in Iran. Changes in rice evapotranspiration in three different cultivation dates with four different occurrence probabilities of 75, 50, 25 and 10%, was calculated using the FAO Penman-Monteith equation and meteorological data with a statistical period of 30 years (2020- 1990). Also, the average rice crop coefficient at different stages of growth in 10-day periods was estimated based on the Weibull model. These probabilities represent the probable limits of the expected values of evapotranspiration in different scenarios of low, normal, high, and very high evapotranspiration years.

The results showed a relatively constant difference of 1 to 2 mm between different rice cultivation histories in the major rice cultivation areas of Gilan and Mazandaran in normal to very high evapotranspiration years. In the years of low evapotranspiration, the water requirement was significantly different from the normal, high and very high evapotranspiration years, which decreased from east to west. This difference was approximately 30% higher in Golestan province as compared with other areas. In the early planting situation relative to the late planting situation in the major western and central coastal areas, there was a 10% decrease in water consumption. At the scale of the whole growing season in Gorgan, evapotranspiration in different conditions of planting date was on average 20% (1300 cubic meters) more than the main regions of Gilan and Mazandaran. In case of timely planting, the net irrigation requirement in very high evapotranspiration years was about 2000 cubic meters per hectare more than the normal years. In years with high evapotranspiration, late planting increased the net irrigation requirement by more than 210 mm compared to different planting dates in Gorgan. According to the obtained results, the largest difference between evapotranspiration values during normal and very high evapotranspiration years was in the late planting situation. Therefore, it seems that late planting causes a significant increase in water consumption in the high evapotranspiration years. Consequently, it is better to avoid rice cultivation when the rice growing season is anticipated to be warm.

Evapotranspiration, as one of the main components of the hydrological cycle, had a significant role in proper irrigation planning and water resources management. The results underline the importance of estimating the rice evapotranspiration to avoid appreciable yield loss under extreme conditions.

Nowadays, the use of effluent in irrigation and especially drip irrigation systems has increased. The findings uncovered that drip irrigation is assumed as the only method which is capable of overcoming specific problems caused by wastewater usage. In this study, the efficiency of sand and geotextile filters with zeolite on wastewater properties and their application in the filtration of the drip irrigation system was investigated.
This study was conducted to investigate the changes in important chemical properties in the municipal wastewater of a university after passing through sand filters, geotextiles, and zeolites in the drip irrigation filtration system. A factorial experiment was performed in a completely randomized design with three replications. Treatments included sand filter (control-CTRL treatment), geotextile filter (G), sand filter with zeolite (SZ), and geotextile filter with zeolite (GZ). The sand used in this study was the usual silica sand which was in three sizes of 3-5, 5-8, and 8-12 mm. In order to remove any contamination and soil particles and increase the accuracy of the measurement of suspended solids in the effluent, the sands were washed several times with water before usage. The geotextiles used in this study had three types with weights of 300, 500, and 1000 grams per cubic meter. The zeolite used in this study was clinoptilolite modified with hydrochloric acid. The wastewater used in this study was obtained from the effluent of Shahrekord University. System flow rate, Pressure, nitrate, suspended solids, sodium, calcium, magnesium, electrical conductivity, effluent pH were measured before and after entering the filters at different hours. Statistical analysis was done by SAS software and the Duncan test was used to compare the means of the data.
The results showed that the sand-zeolite treatment had a good performance in terms of pressure and the geotextile-zeolite treatment was able to provide pressure for a short time. The amount of nitrate in the wastewater of the sand filter was not significantly different from the amount of inlet, but in other filters was significantly reduced. The amount of nitrate input of all treatments was 26 mg/l.The average output nitrate in the sand filter was about 25 and in the other filters was less than 20 mg/l. The average amount of suspended solids in the effluent was about 110 mg/l, while the average amount of suspended solids in the wastewater was reduced to less than 72 mg/l. The sand and sand-zeolite treatments increased the total amount of calcium and magnesium in the wastewater more than geotextile and geotextile-zeolite treatments, respectively. The average Ca + Mg of effluent in the total operating hours of the system was higher than the average input in all filters. The percentage of EC changes in total hours increased about 4% in sand and geotextile treatments and 14% in geotextile- zeolite and sand -zeolite filters. The highest percentage of pH changes was related to sand-zeolite filter, which reduced the pH of incoming wastewater by about 4.5%. After that, geotextile- zeolite filter reduced the pH of the incoming wastewater by 4%. The average pH of the effluent of all filters is lower than the average of their inlet.
Sand and geotextile filters alone cause pressure drop and dropper clogging. However, the sand-zeolite treatment has performed well in this regard. The geotextile-zeolite treatment has the potential to be used in terms of supplying the necessary pressure for a short period by applying special treatment measures before this filter. These conclusions are only in terms of pressure drop due to the ability of filters in practical use and do not refer to their ability to filter the parameters and provide the desired flow. All treatments, especially zeolite treatments, significantly reduced nitrate, and these filters can be used to reduce effluent nitrate in cases where the amount of nitrate is more than allowed. However, since the sand filter had no effect on nitrate reduction, the effluent must be treated for nitrate before using sand filters. The geotextile filter had a higher percentage of suspended solids removal at all hours. The addition of zeolites to both geotextile and sand base filters reduced their ability to treat suspended solids. Therefore, geotextile filters can be a good alternative to ordinary sand filters in terms of this parameter. All treatments increased Ca + Mg relative to the input. The sand- zeolite treatment reduced the pH of the incoming wastewater more than other treatments (about 4.5%). Also, desalination of salts from zeolite treatments increased the EC of effluent in the sand-zeolite and Geotextile- zeolite treatments. According to this study, the use of sand-zeolite in terms of reducing nitrate and suspended solid, increasing calcium and magnesium, and reducing pH and no pressure drop is recommended.

The excessive use of chemical fertilizers devastates soil fertility and causes different types of environmental pollution. Therefore, using adequate eco-friendly fertilizers in agriculture enhances productivity but has no adverse effect on nature. Recently, there has been reported that beneficial soil microbes produce some volatile organic compounds, which are beneficial to plants. The amendment of these microbes with locally available organic materials and nanoparticles is currently used to formulate biofertilizers for increasing plant productivity. These bacteria are naturally present in soils, but their population decreases for a long time because of long-term environmental stress, improper use of chemical agents, and the absence of a suitable host plant. Adding these bacteria to the soil, before or during the growing season, increases the growth and production of agricultural products. Since available water is the main growth limiting factor in chickpea cultivation, it is useful to improve nutrition, especially using plant growth-promoting rhizobacteria, for accelerating the growth and development of plants at the end of the season.
In order to evaluate the effect of bio-nutrition and seed priming on growth and yield of chickpea genotypes (MCC463, MCC741, ILC8617, ILC72, FLIP02-51C) an experiment was carried in split plots based on Randomized Complete Block Design with three replications in 2019. Experimental factors included nutritional treatments as the main plots and chickpea genotypes as the subplots. Nutritional treatments were 1- seed priming with the use of free-living nitrogen fixing bacteria, phosphorus solubilizing bacteria and potassium solubilizing bacteria (P + BF), 2- free-living nitrogen fixing bacteria, phosphorus solubilizing bacteria and potassium solubilizing bacteria before sowing (BF), 3- seed priming with the application of free-living nitrogen fixing bacteria, phosphorus solubilizing bacteria and potassium solubilizing bacteria with foliar application of amino acid, potassium and silicon during growth stages (P + BF + F), 4- application of free-living nitrogen fixing bacteria, phosphorus solubilizing bacteria and potassium solubilizing bacteria before planting with foliar application of amino acid, potassium and silicon during growth stages (BF + F), and 5- control (without biological and chemical fertilizers). Free-living nitrogen fixing bacteria, phosphorus solubilizing bacteria and potassium solubilizing bacteria were sprayed five liters per hectare on the soil surface before planting with 107 CFU per ml and mixed with soil. Foliar application with amino acid (1:1000) was done in two stages (before flowering and 50% flowering stage), and foliar application with potassium (1:1000) and silicon (1.5:1000) was carried out in the 50% flowering stage.
Results showed that the highest concentration of chlorophyll a was obtained for BF and MCC463 with an increase of 3.1 times greater than control. The highest concentration of chlorophyll b was obtained for BF + F and FLIP02-51. The highest green area index was recorded for MCC741 in P + BF. The highest number of pods per plant in MCC463 and FLIP02-51 was observed in BF + F, with 88 and 30% more than the control, respectively. The highest biomass produced was obtained for ILC8617 and BF + F, by 24% higher than the control. ILC72 and MCC463 showed the highest grain yield in P + BF + F treatment, which increased grain yield by 35% and 4% (320 and 50 kg/ha), respectively, with respect to control. MCC741under BF treatment showed a doubled (810 kg/ha) grain yield relative to control. The highest grain yield for P + BF was found in ILC8617 and increased by 28% (340 kg/ha) as compared to control. In this genotype, grain yield in BF + F was also significantly greater than that in the control by 22%, (270 kg/ha). FLIP02-51 grain yield in BF increased by 12% (170 kg/ha) as compared with the control.
In terms of seed yield, ILC72 and MCC463 were more responsive to P + BF + F and ILC8617 and FLIP02-51 in the BF and ILC8617 in P + BF with respect to other treatments. It seems that despite the positive effect of biofertilizer, genetic characteristics of genotypes are influential in plant growth and yield; therefore, it is necessary to select the appropriate genotype for each region so as to make the most utilization of the nutrients and achieve high yield.

FAO agro-ecological model determines the production capacity, creating a logical relationship between the natural potential of the environment, the needs of communities, human activities, and sustainable adaptation. With the development of plant growth simulation models, researchers have begun a large-scale effort to agroecological zoning of various crops on a regional scale. In this method, an area was divided into homogeneous units with maximum similarity in terms of climate and land characteristics. Then, the potential yield map predicted by a simulation model is used for zoning. Pistachio is a subtropical plant that has long been cultivated in the central areas of Iran. With the occurrence of drought in the last two decades, farmers cultivated Pistachio in East Azerbaijan province without considering this crop requirement. This study aimed to use the AHP model to evaluate the suitability of East Azerbaijan lands for cultivating pistachio.

East Azerbaijan province is located in the northwest of Iran, between the latitudes of 36˚ and 45' to 39˚ and 26' N and the longitudes of 45˚ and 5' to 48˚ and 22' E based on the geographic coordinate system. The area of the province is 45800 square kilometers. The climate is generally cold and semi-arid, but it has different climates due to its diverse and extensive topography. The area of agricultural lands is estimated to be 18,000 square kilometers, which is about 39% of the total area. In this research, climatic data were collected for 30 years from Tabriz, Jolfa, Mianeh, Sarab, Maraghe, and Malekan synoptic stations, and from four neighboring stations of Orumieh, Khoy, Miandoab, and Parsabad. Three criteria (i.e. climate, land, and soil) and 11 sub-criteria were studied. The sub-climatic criteria included the average temperature of the growing season, average temperature in the pollination stage, absolute minimum temperature in the coldest month of the year, and average percentage of relative humidity in the flowering stage. Land criteria were land use sub-criteria, land slope, and slope directions, and soil criteria were salinity (electrical conductivity of saturated extract), pH, soil texture, and soil lime content (CaCO3). The results of the analysis of about 9000 soil samples were prepared for zoning of soil factors from East Azerbaijan Agricultural and Natural Resources Research Center. Land characteristics of slope map and aspects were prepared from the digital elevation map of the study area and land use map was obtained base on the map provided by the Forests and Rangelands Research Institute of Iran. The parameters were then weighted upon AHP by the parameter importance for each region. Data were transferred to Expert Choice software and clustered, rated, integrated for producing the final layer.

According to the AHP model, there are no entirely suitable class areas for pistachio cultivation in East Azerbaijan province. Because one or more factors or sub-criteria created low restrictions for the cultivation of this crop. The results showed that 3887 square kilometers or 8.5% of the area was classified as a relatively suitable class. Although this area has low restrictions for pistachio planting, the profitability of this complex has increased the area of pistachio orchards rapidly. The suitable lands are mainly located by the agricultural lands and if water requirement could be met, they can be allocated for planting. The low water requirement and tolerance to salinity compared to other crops can be considered as the advantages of cultivating pistachio. Since 1998, droughts have occurred in different areas of the province. It caused a decrease in agricultural products by up to 35%. The declining water level of Lake Urmia is one of the consequences of the recent droughts, deteriorating the groundwater quantity and quality. The 6250 square kilometers (13.6%) of the province's lands was classified as the critically suitable class. Some of the sub-criteria studied in these lands such as the average temperature of pollination period, the average temperature of the growth period, amount and direction of slope, and soil texture were in the critical classes. The 35663 square kilometers (77.9%) of the studied lands were found to be unsuitable (N). The main reason for the unsuitability was the very high salinity of lands, as seen in the soil salinity map. Although it is a modifiable factor, the lack of quality for leaching, heavy soil texture, and the impossibility of draining drainage due to flatness, render the reclamation of these lands impossible. Under the current situation, East Azerbaijan province is much more capable of planting this crop. However, it is necessary to conduct more detailed studies to avoid pistachio cultivation in marginal suitable lands.

Calcium Carbonate Equivalent (CCE) is one of the key soils properties in arid and semi-arid regions. The study of spatial variability of surface and subsurface layers is important in the sustainable land management of arable soils. This study aimed to model the spatial distribution of CCE percentage by using three machine learning algorithms including Random Forest (RF), Decision Tree regression (DTr) and k-Nearest Neighbor (k-NN) at five standard depths of 0-5, 5-15, 15-30, 30-60, and 60-100 cm.

The study area with 60,000 ha includes the major part of the lands of Qazvin plain located on the border of Qazvin and Alborz provinces. Field and laboratory surveys included 278 representative profiles were excavated, described by the horizon, and determined physicochemical properties. The studied soils have a very high diversity in soil moisture (Aridic, Xeric, and Aquic) and temperature regimes (Thermic). These variations have led to the formation of eight great groups of soils in the region based in the USDA soil classification system with the three classes of Haploxerepts, Calcixerepts, and Haplocalcids were the dominant soil classes in the study area. A total of 22 environmental covariates, including 12 variables extracted from the primary and secondary derivation of digital elevation model (DEM), six remote sensing (RS) indicators, two climatic parameters, and two soil covariates were prepared, and then the most appropriate environmental covariates were selected using principal component analysis (PCA) and expert knowledge. The CCE percentage data were randomly divided into two parts, 80% for training and 20% for testing, which was then modeled by three machine learning algorithms RF, DTr, and k-NN, and were evaluated by some statistical indices as coefficient determination (R2), root mean square error (RMSE) and Bias.

The results of harmonizing the CCE values at the genetic horizons with the standard depths showed the high efficiency of the spline depth function in providing an acceptable estimate with minimum error and maximum agreement between observed and predicted values. The PCA method showed that the first to fifth components with the explanation of more than 80% of cumulative variance were Multi-Resolution Index of Valley Bottom Flatness (MrVBF), Mean Annual Temperature (MAT), Greenness index (Greenness), Probability of Calcic horizon (Cal.hr), and Wind Effect environmental covariates which had the highest eigenvalues. Besides, Clay was selected on expert knowledge-based. The relative importance (RI) of the environmental covariates showed the spatial distribution of CCE were affected by Clay with an explanation of more than 57%, 41.8% and 45% of its variance at three surface depths of 0-5, 5-15, and 15-30 cm, while the Cal.hr covariate had the highest impact in the spatial prediction of CCE compared to other predictors as auxiliary variables with 67.8% and 52.8% justification, respectively, at two depths of 30-60 and 60-100 cm. Hence, using the calcic horizon probability Map (Cal.hr) as a derivative soil factor made it possible to produce more appropriate final maps, while preventing the reduction of the accuracy of the modeling results in the subsoils. The auxiliary variable of remote sensing, i.e., Greenness, could not show a significant impact on the expression of the variation of CCE percentage at all studied depths. Unlike remote sensing indices, the topographic attribute of the MrVBF, at two standard depths of 0-5 and 5-15 cm, the MAT at a depth of 15-30 cm, and the Wind Effect at the standard depths 30-60 and 60-100 cm, after the soil covariates, were the most effective in justifying the spatial variations of CCE%. RF algorithm with a range of R2 values of 0.83 - 0.76 and RMSE of 2.14% - 2.21% resulted in the highest accuracy and minimum error. Even though the DTr method presented R2 values (0.52-0.39) weaker than the RF in the validation dataset, in general, the results of its spatial predictions were similar to the RF model from the surface to the subsurface and more stable than the k-NN. Against RF and DTr, k-NN couldn’t display acceptable performance in the prediction of CCE% at all standardized depths.

In general, it is necessary to understand the spatial distribution of CCE due to its effect on soil moisture accessibility and plant nutrient uptake. Therefore, in the present study, we tried to introduce the RF machine learning algorithm as a superior model with environmental variables that were selected by PCA and the expert knowledge variable selection method. The maps prepared by this approach have an acceptable level of reliability for agricultural and environmental management by managers, soil experts, and farmers.

 Heat waves (HWs) are one of the most important climatic disasters that have devastating environmental consequences in nature every year). The purpose of this study is investigation of the effect of heat waves on the intensification of thermal islands in Sanandaj city from 1989 to 2018. The constant rise in temperature of the city as an urban heat island and the sudden occurrence of HW's as one of the major climatic hazards, is an important concern of urban management policy makers; because intensify heat of this city and cause a lot of environmental damage.

 In order to identify HWs in Sanandaj city, from 1989 to 2018, by using Fumiaki Index and MATLAB software, days whit temperature above +2 standard deviation or above the mean Normalized Thermal Deviation (NTD) that lasted at least two days, were identified as the day with HWs and calculated by equation 1. (1) Where, T (i, j, n) is temperature of day ith from month jth in year nth indicates the average temperature of day i from month j. To eliminate the noise in the mean, a 9-day moving average filter was performed on these data three times and calculated by the following equation. (2) Where, ∆T= (i, j, n) indicates absolute deviation of temperature from the average on day jth of the month i th, in year n th compared to the average temperature of the same day. In order to the values of temperature deviation of different times and places to be comparable at a certain time and place, it is necessary to standardize these absolute values of temperature deviation by means of temperature diffraction. Like day-to-day changes, diffuse T∆ at 31 days for each day is calculated by the following equation. (3) The value is the average temperature deviation in 31 days that is calculated by the following equation. (4) Finally, (NTD) is calculated by the following equation. (5) Where .Then in MATLAB software, days with temperatures +2 above average (NTD) and lasting at least two days, were selected as the day with the HW. (6) Then the thermal island was calculated in Sanandaj city using Equation 7. SUHI= MLSTurban –FLSTrural (7) Where, SUHI is the island surface heat index, MLSTurban and FLSTrural are the average surface temperature of urban and rural areas, respectively.

 The results showed that, during the study period (1989-2008), the highest frequency of HW hazards in this city was in September, February, March, and October 1991. The maximum duration of HWs was 6 days, which occurred in December 2017 and 2005, therefore long-term HWs have been experienced in this city. Results also showed, in both HW and NHW conditions, in the hot and cold months of the year, often a cold island is formed in the city center during the day and a heat island is formed at night. Results also showed that short-time heat waves have been effective in intensifying heat islands. Examination of the intensity of thermal islands in this city showed that during the day in both HW and NHW conditions, which in the HW conditions dominance of the cold island compared to normal day, it has been reduced and in the last months of winter (February), even during the day, a heat island has been created in the center of the city. At night time, in both HW and NHW conditions, a heat island was created in Sanandaj center, but the intensity of night- time heat islands in HW conditions is often significantly higher than normal conditions especially in the winter. Investigation of the condition of thermal islands in the warm months of the year showed that in both HWs, a cold island has been created in the city center that the intensity of cold islands during the HW conditions, especially in the summer months, was often higher than NHW conditions. At night time, there was often a heat island in the city center that was more intense than normal day. Also, in HW conditions, wind speed and especially relative humidity has decreased significantly more than the cold months of the year.

 

 According to the results the highest incidence of HW hazards occurred in the winter and early spring. Also, long-term (6-days) HW occurred in this period. The increasing trend, frequency and continuation of HW, especially in the cold months of the year, can be the effects of climate change and global warming. Severe and continuous HWs occurred in Sanandaj city, especially in late winter, can cause early germination and flowering of crops and gardens and it will negatively affect agriculture and horticulture and will lead to great economic losses. The effects of HWs on heat islands occurred in the suburbs due to having a clear sky without pollution, with minimal vegetation and lack of surface water resources and ground with low heat capacity is affected by HWs faster than the city center and as the land surface around the city becomes warmer than its center, a cold island is formed in the city center. At night, the suburbs due to low heat capacity, lose absorbed heat faster and as a result, the heat island is formed in the city center. In general, the occurrence of heat waves in the intensification of thermal islands in the Sanandaj city, especially in the warm months of the year, has a significant effect, and it is likely to intensify in the coming decades, especially at night during the hot months of the year.

Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations require more installation costs and monitoring. Some common techniques including statistical methods, spatial interpolation, information-based theory and combination are used to evaluate and design the network. Chaharmahal va Bakhtiari province is a mountainous region; hence, a denser rainfall network is expected in this mountainous environment. The aim of this study was to evaluate the condition of rain gauge stations in Chaharmahal va Bakhtiari province using two approaches, i.e. geostatistical methods and entropy theory.
The main required data set for this study is a time series of rainfall data. These data were collected on a daily scale from the Regional Water Company of Chaharmahal va Bakhtiari. After performing statistical tests, the annual data series was prepared for 46 rain gauge stations. A statistical period of 2000 to 2016 was used. The homogeneity of data was investigated by double mass test and histogram drawing methods using Excel and SPSS software, and the existence of trend in the time series of data was investigated by applying a Spearman test. Then, the adequacy of rain gauges in the gauging network was investigated. Annual rainfall interpolation maps and their standard error maps were prepared using the kriging method. Contribution of each station in reducing or increasing the error in the rain gauge network was investigated by removing each station in a cross validation procedure. The efficiency of the rain gauge network was evaluated using the concept of discrete entropy and the values of entropy indices. The value of keeping the rain gauge stations was determined using the net exchange information index.
There was no homogeneity problem and significant trend in the data series. Considering the permissible error percentage of 5%, there is a need to add 15 new rain gauge stations to the network. To apply the geostatistical method, we applied it once without deleting any station; then, the kriging interpolation error was calculated for the precipitation data. Then, only one station was removed at each stage, and both the error and the contribution of each station in increasing or decreasing the error compared to the case without Station deletion were obtained. The results indicated that Ab-Turki, Shahrekord, Borujen and Barez stations were more important than other stations. Two stations namely Chaman-Goli and Ben stations can also be considered as the influential stations in error due to the density of stations in the region and error maps. Similarly, the results of the entropy theory method were found effective in evaluating the design of the rain gauge network. The highest value of H(x) was observed in the data of Armand station (3.26) and the lowest value was observed in Abbasabad station (2.28). Since H(x) shows the uncertainty of measuring data, the maximum and minimum uncertainty were found for Armand and Abbasabad sites, respectively. Based on the Net Exchange Information Index, Bardeh, Bareh Mardeh and Dezkabad stations were ranked 1 to 3, respectively, indicating that they transmit and receive more information than other stations. On the other hand, a number of stations including Dorak anari, Abtorki and Chelo stations had the lowest values.
Due to the vast extent of the area and also considering the permissible error percentage of 5%, the number of the stations in this area was found to be insufficient. Thus, although calculating the kriging error maps showed that some stations do not have a significant share in increasing the error, removing the stations is not recommendable. Regarding the new stations, new 15 rain gauge stations are needed to check out the error maps. According to the field observations, the higher priority should be given to the northwestern area (which had the largest interpolation error) in the first place. For the regions with lower error, such as northeast, east, southeast, west and southwest that do not have rain gauge stations, additional rain gauge stations should be constructed.