نشریه مدل سازی و مدیریت آب و خاک
سال سوم شماره 4 (زمستان 1402)

The accuracy and efficiency of the analytical and numerical models to describe water flow in soil, in unsaturated environments are affected by input data uncertainty, model structure uncertainty, and hydraulic required parameters by the model. Parameter uncertainty has an impact on the model simulation by displaying uncertainty in the simulation results. Hence, the quantitative assessment of the parameter uncertainty and its influence on the model simulation is important in reducing simulation uncertainty. The Bayesian method is a common method for uncertainty analysis that has widespread application in science and engineering to reconcile the concepts of model structure with data (assimilation of input and model outputs, and inference of the parameters). Therefore, a Markov chain Monte Carlo (MCMC) algorithm based on the Bayesian inference to improve the computational efficiency of the analysis was used. The DREAM algorithm is one of the adaptive methods, the Markov chain sampling method which is known as an effective method in used soil-water models due to searching in vast space and solving complex models with a large number of variables. In addition, one of the main problems in using Bayesian inference for hydrological models is their nonlinear relations and using them in heterogenic conditions, DREAM algorithm has been developed to use Bayesian analysis in soil-water problems. Hence, this study has taken the efficiency of the DREAM algorithm as a global optimization method and convergence parser in sampling chain paths and posterior distribution of parameters. The HYDRUS model is a hydraulic model to study the soil-water processes that include nonlinear equations. In addition, center pivot irrigation is a modern method of water management that need to study using hydraulic models under various conditions. Hence, the main purpose of this article is assessment the role of the management method and environmental prevailing conditions in the uncertainty of hydraulic parameters and model structure in estimating water flow under a center pivot irrigation system in four-year alfalfa cultivation.

The profile was dug at 120 cm depth. The soil profile was divided into three layers and two soil texture classes. The physical-chemical soil properties were studied in each layer. Assessment of soil properties stated that exists a heterogeneous layer in this soil profile. TDR was used to measure soil water content before, after, and during every irrigation period. Soil water content was measured from 10 June to 11 September 2018 consecutively. The van Genuchten-Mualem equation was used to estimate soil hydraulic parameters and describe water flow in the HYDRUS model. The HYDRUS model is coupled with the DREAM algorithm to evaluate parameter uncertainty and the model structure uncertainty based on measured soil water content data using TDR in every three categorized layers. In this article the p-factor, d-factor, and S and T indices were used to evaluate parameter uncertainty, the model structure uncertainty, and model performance.

The qualitative evaluation of soil hydraulic parameters was compiled by the posterior distributions of parameters in every three depths. The parameters had a normal distribution, the model could be recognized the value of parameters, whereas the parameters didn't have a normal distribution and had high uncertainty. The “α” parameter had high uncertainty in every three depths, in other words, in two soil texture classes, this parameter compared to other parameters had high uncertainty. Along heterogeneous soil profiles, the "α", "θs", and "n" parameters were shown high uncertainty to the Hydraulic conductivity parameter of soil saturation. The value of p-factor and d-factor were obtained equal to 83.6 and 0.13 on the soil surface and 10 and 0.14 on the subsurface soil. Reducing the p-factor index in the lower soil layers explained the overlap between measured soil water content points with estimated soil water content. So, along the soil profile could be observed high uncertainty of soil hydraulic parameters under center pivot irrigation. On the other hand, increasing the d-factor index in the sub-surface soil stated increased confidence intervals which indicate the model structure uncertainty and the poor performance of the HYDRUS model in heterogenic conditions. Also, the value of two indices of S and T were obtained 0.3 and 0.76 for the surface layer and 0.88 and 1.4 in the lower soil layers respectively. The values of S and T indices stated the ability of the DREAM algorithm to reduce parameter uncertainty and the model structure uncertainty in soil surface whereas the trend of changes in the two indices explained Asymmetry of the confidence interval with respect to the measured points and the pre-estimation of the model in the lower soil layers. Therefore, the trend of the d-factor, S and T indices showed the influence of the mathematical-physics concepts in the HYDRUS model structure in the heterogenic layer and unsaturated conditions. The research results stated the ability of the HYDRUS model in describing water flow under center pivot irrigation as a novel method of managing water sources, especially in arid and semi-arid areas. Even though, the results of the assessment indices showed decreasing model performance in the lower soil layers.

The results of soil profile indicated the effect of parameter uncertainty and the model structure uncertainty in soil moisture estimation affected by management and environmental conditions. In addition, the results showed the ability of the DREAM algorithm to simultaneously evaluate the uncertainty of the parameters and the model structure in order to increase the accuracy of the HYDRUS model under the applied conditions. Also, in this study, the DREAM algorithm indicated the role of the heterogeneous layer in parameter uncertainty and its effect on the accuracy of the model performance. The DREAM algorithm is a practical and management option to evaluate the HYDRUS model during the application of the center pivot irrigation method at the farm level. So, this is an appropriate option to study the efficiency of the HYDRUS model using modern methods in agricultural practices. Moreover, to survey the efficiency of hydraulic models under the prevailing conditions could be used the ability of the DREAM algorithm based on the Markov chain.

In forest management, it is essential to maintain soil performance. Forest soils control and support many forest ecosystem services and tree growth. So, maintaining soil health is critical to sustainable forest management. Forest management practices, such as converting degraded forests into man-made forests, lead to a wide range of adverse effects on soil performance and microbial communities, including negative impacts on organic carbon, nitrogen, bacterial biomass, fungal biomass, microbial biomass carbon, and fungi-to-bacteria ratio. It is possible to achieve the appropriate method of forest management by considering the biological stability of the soil. Therefore, in this stduy, it has tried to gain a better understanding of the relationship between soil biological conditions and forest management methods and their application in sustainable forest and soil management by evaluating the effect of different forestry methods on soil biological characteristics.

To influence forest management practices on soil biological properties, three study areas with different management histories in the Asalem forest, Guilan province were selected and soil sampling using a random systematic method was collected. The selected plots included 1) control, 2) a plot with a history of project implementation in the medium term of maximum 20 years under selective management and 3) a plot with a history of long-term plan implementation, which was under the management of shelterwood method for more than 50 years. There may be effects of the implementation of past plans in the forest for many years. The number of 15 samples in each plot was collected from the nearest tree to the center of the intersection of the sides of the sampling network, from a depth of 0-30 cm, and transported to the soil laboratory at a temperature of 4°C in the winter of 2016. Sampling from all plots in the forest area was done only in the pure type of beech, so in this study, the effect of the type is constant. In this study, micro-scale habitat conditions considered to investigate microbial activities were homogeneous. In this research, three parcels with an area of ​​about 132 ha were selected. In each sample, the biological characteristics of the soil, including basic respiration, stimulated respiration, microbial carbon dioxide, and the population of microorganisms were measured, and indicators of microbial benefit and metabolic benefit were calculated. Some soil characteristics including soil organic carbon, electrical conductivity (EC) and pH were also measured. The normality of the data was checked using the Kolmogorov-Smirnov test and the homogeneity of variances was checked using the Levene's test. Due to the fact that the data had a normal distribution, one-way analysis of variance test was used for totla mean comparisons, Tukey test was used to compare the average indicators in the studied parts, and the relationship between chemical indicators the Spearman correlation test was also used for soil biology. In some cases, due to the impossibility of normalization and other assumptions, the non-parametric test has been used. Statistical analysis was done using SPSS version 22 software. Excel was used to draw graphs.

The results showed that there was no significant difference between the three components in terms of organic carbon, microbial quotient, electrical conductivity, and acidity. But baseline respiration, exhaled breath, microorganism population, metabolic rate, and carbon microbial degradation showed a significant difference in the three parts. The highest basal respiration rate (46.8 mg of carbon dioxide in one day in one gram of dry soil), substrate-induced respiration (42.77 mg of carbon-dioxide in one gram of dry soil in six hours) and microorganism population (1.09 x 108 in one gram of dry soil) and carbon microbial degradation (16.5 mg of carbon in one gram of dry soil) were obtained with a patchy management method and the lowest in the selective portion. The highest metabolic quotient (1.2 mg of oxidized carbon in basic respiration per kg of dry soil per day) in selective fraction and the lowest in solitary incremental part were calculated. The soil texture is sandy-loamy in most of the three plots and loamy-sandy in some areas. EC was in the range of 0.629-3.83 dS m-1 and soil acidity was also in the range of 5-6.5 in all three plots. There was no significant difference in soil organic carbon in three plots. Correlation analysis results of forest soil biological indicators show that soil microorganisms have a significant positive correlation at the level of 1% probability with microbial biomass. In addition to soil micro-organisms, microbial biomass carbon showed a significant positive correlation with basal respiration at the 1% probability level. Basal respiration also has a significant positive correlation at the probability level of 1% with the amount of substrate-induced respiration, soil microorganisms, and microbial biomass carbon, and besides that, the organic carbon has a significant positive correlation at the probability level of 1% with soil microorganisms, microbial biomass carbon, basal respiration, and substrate-induced respiration.

The control plot has a higher quality soil compared to the plot under shelterwood management due to the least human interference (although illegal harvests have been observed in the forest, but compared to the other two plots, human interference was minimal). According to the results, it can be acknowledged that the plot under shelterwood's management has better conditions in terms of soil biological characteristics. Knowledge of the effectiveness of the biological characteristics of the soil from the application of different methods of forest silviculture and human interventions, provides the possibility of choosing suitable methods with the habitat conditions. At the same time, it will be very beneficial to determine the intensity of breeding interventions in forest stands based on the prediction of its effects. The stability of the forest does not depend only on increasing the biological quality of the soil. Hence, it is recommended to implement silviculture operations in sheltered plots and to increase the mixture of forest stand and support species with fast decomposition of litter such as horbean in selective plots.

Snow monitoring and estimation of runoff from snow melting play an important role in controlling and managing watersheds and reservoirs and flood warning systems more efficiently. Given that the Koohrang area is found to be one of the snowy mountains in the country, and thanks to the high volume of runoff from precipitation, and snow melt, it plays a vital role in water supply for drinking, industry, and agriculture, neighboring provinces and even Iran as a whole so that it meets 10 % of total water demands in Iran. Accurate estimation of runoff from snowmelt entails spatial distribution of snow so that spatial variability of snow depth is measured via measuring snow depth in close resolution. On the other hand, the non-availability of gauge stations and extreme sampling conditions in snowy watersheds have caused researchers to think of simple and indirect strategies including regression techniques, interpolation methods, artificial intelligence, data mining, and also the use of satellite images, especially the use of radar interferometric method. Given the importance of snow depth variations and accurate estimation, although many methods have been used, there is an urgent need formore precise calculation and strategic position in this area requires procedures that are more accurate and more effective variables that are used in snow depth estimation. Study of artificial intelligence techniques and linear regression analysis and principal component analysis (PCA) along with geomorphometry parameters and inputs as well as satellite images were used to estimate the snow depth he and the results were compared. Therefore, in this study, unlike previous studies used much more variables to model snow depth, and also, the digital elevation model with the higher spatial resolution was used to model snow depth in a more accurate manner..

Methods and Materials:

Koohrang region is located in the west and Chaharmahal and Bakhtiari Province with an area of over 3700 km2. It is characterized by unique climatology, hydrology, and topography. Climatic characteristics of the region include an average annual temperature of 8.5 C, rainfall of 1430 mm, a frost period of 130 days, and a winter rainfall regime. In this research, using the hypercube technique, first, 100 points were selected for sampling in the Chalgerd area. In addition to these points, 195 other points were randomly collected from the study area. To obtain the data required for this research in field work, sampling was done over three days by the Monte-Rose model sampler. After the collection of snow samples, auxiliary data required for zoning, which includes data related to satellite images and variables derived from the digital elevation model, was extracted in the Saga software environment. The artificial neural network (ANN) was chosen as a new computing system and method to estimate snow depth using morphometric and climatic information related to snow depth. After extraction of the auxiliary variables in the study, between 32 input variables and snow depth, multiple linear regression analysis was conducted to test this model is 295 points. In order to fit the multiple regression equations, snow depth data as the dependent variable and physical variables as independent variables were considered. After obtaining an equation relating to the model was tested on regression test data (20% of data) to determine the accuracy of the model to predict the snow depth. In this study, in order to reduce the number of input data to the ANN and linear regression models, the PCA method was used, and finally, the number of components was chosen to be eight. For model evaluation, the predicted snow depth was evaluated using a linear regression model and ANN followed by calculating RMSE and R2.

By trial and error, we found that a multi-layer neural network with a sigmoid activation function and a hidden layer of snow 1-6-32 for the optimal structure for the network as well as the number of repetitions and the coefficient of torque and 0.7 and 1000 was found. To evaluate and compare the performance of ANN, test data (20%) were used. ANN output values were compared with the corresponding observational values and details on the correlation coefficient were extracted. So as can be seen in the results, ANN and regression accounted for snow depth variation of 62 and 46% respectively and this regression model was significant at a probability level of 5%. The results of the PCA are to reduce the number of entries after the model of 32 to 8, the values in the model ANN and linear regression coefficient was reduced and root mean square error (RMSE) increases, and the 55 and 45 % variations in snow depth have been able to properly modeled. The less R2 and RMSE, the more accurate model is. Thus, according to the error criteria value, the ANN model outperforms other ones. According to the results obtained of all variables used in ANN, the most important variables affecting the spatial variability of snow depth in the study area in order of importance, include profile longitudinal curvature, general curvature, gradient, transverse profile curvature, watershed gradient, slope middle position, wind, normalized elevation, geographical directions, and snow normalized difference index. It is worth noting that additional variables with negligible contributions were neglected. Given that prevalent winds blow in west and southwest directions and most of the highlands are nestled in these directions, much more snow accumulation can be found in this direction than those north, east and southward directions.

In the present research, to estimate the spatial distribution of the snow, the four models of ANNs, linear regression, PCA, and neural network were considered. After reviewing the methods according to the statistical criteria, the lowest error rate was attributed to ANN (RMSE, 19.57), followed by PCA using ANN (RMSE, 20.86), then linear regression (RMSE, 21.09), and the highest error rate on PCA using linear regression (RMSE, 21.59). Of all variables used in ANN, the most important variables affecting the spatial variability of snow depth in the study area in order of importance, include profile longitudinal curvature, general curvature, gradient, transverse profile curvature, watershed gradient, slope middle position, wind, normalized elevation, geographical directions, and snow normalized difference index. Therefore, digital elevation models with different resolutions in modeling can be used. However, here, variables such as vegetation, geology, solar radiation were not used and therefore it is recommended to use these variables in similar studies and different time resolutions. However, in future research, the most effective variables mentioned here can be promising for accurate zonation of snow depth in snowy watersheds.

One of the most important challenges of exploiting water resource management systems and specifically surface reservoirs in facing hydrological changes is to consider the occurrence of drought in the way of exploiting water resources. In addition to the changes in discharge compared to the average, another important factor is the continuation of the drought phenomenon. One of the ways to deal with drought is the proper use of water resources in drought conditions. Various models such as WEAP have been developed to exploit water resources. But such a model does not have the ability to model the exploitation of reservoirs in drought conditions. Therefore, developing such a possibility in the WEAP model is very important for water resources engineers. Considering the development of the WEAP model as a tool for flexible, comprehensive, and transparent planning in evaluating the various long-term conditions of the basin, this model has been used to simulate the water resources and develop different scenarios for the exploitation of water resources.

The studied area is located in the southwestern part of Iran, in Lorestan province, and in the range of Zagros slopes, which is called the catchment area of the Chaghlund reservoir dam. The area of this watershed is about 1187.8 square kilometers, which is surrounded by the Chaglundi watershed. Working with the WEAP model is done in several steps including problem definition, time frame, spatial boundaries and system components, and problem settings. The scenarios are made in the existing conditions and by using them, the effect of different assumptions or policies on the availability and consumption of water in the future can be checked. Finally, the scenarios are evaluated according to the amount of water, costs, and benefits, compatibility with environmental goals, and sensitivity to uncertainty in key variables. In the WEAP model, system components (including nodes of water resources and uses, how they are related to each other, and allocation priorities) and the introduction of time characteristics (base year, time period, time steps of calculations, etc.) are defined. In the base year, water needs, capacity, and characteristics of resources, pollution loads, system costs in the current state are entered. By initially running the model in the base year and comparing the results with the available information, this step can be considered a calibration step. To prepare the main framework of the model in the WEAP software, first, the basic maps that include the borders of the studied basin, the routes of the rivers in the basin, the location of dams and diversion dams for water intake and hydrometric stations, all the points of need and available water resources and in general all the tolls which are needed to determine the main framework of the model was prepared in the GIS environment. After this work, the prepared maps were called in the WEAP software environment.

To calibrate the model, the monthly discharge data of the Ayushan dam before the intersection of the Herod river and the observation storage volume of the simulated reservoir were considered as observation values from the Ayushan hydrometric station at the site of the Ayushan dam. In order to ensure the performance of the simulation model, the observed and simulated values of the Mokhren storage volume in Herod River were drawn. A correlation of 0.57 was obtained between the observed and calculated values ​of the model, and the maximum values simulated by the model are close to the observed values. Also, at this stage, the storage volume of the Ayushan dam reservoir was simulated in the existing conditions, and it was observed that the correlation between the storage volume of the reservoir dam and the computational storage volume of the model was 0.49, and contrary to the low The degree of simulation correlation is simulated in maximum values ​with a small time difference and in some places the values ​are lower than the observed values. Based on the existing conditions, the monthly amounts of agricultural needs were simulated for each network. Based on this diagram and the reliability percentage diagram, it can be seen that the needs are 100 % provided except for the Dolisan range, every month. Reliability or reliability, in fact, means the probability that the system will perform the assigned tasks without failure, this value has been obtained. For this scenario, it can be seen that the amount of water withdrawal for drinking and industry and the irrigation networks downstream of the Ayushan dam is 100%, and the irrigation networks of the two aquifers of Chaglundi and Yesian are 100%, and for Dolisan agriculture is less than 80% was achieved by first providing surface water sources (Herod River) and then underground water. From the results of the first scenario for the environmental needs of the region, it can be seen that in the months of April and May, during the simulation period, for the months of spring, which should be around 60%, around 10% less than the allowed limit is provided, and this is undesirable. In a study conducted in the Lifan Basin in South Africa using the WEAP model to simulate and analyze its allocation scenarios, it was observed that 85% of the water demand was provided under the existing conditions.

The results showed that the simulation of water resources exploitation of the study basin using the WEAP model was done with appropriate accuracy and the performance had a good. The results of the evaluation of the scenarios showed that in the scenario of the existing conditions, due to the limitation created by the relevant bodies in allocating water to a limited area of the covered plain, the reliability coefficient for drinking and irrigation and drainage networks is 100% and the environmental needs of the region under these conditions in the months of April and May during the simulation period were provided about 10% less than the allowed limit and this is undesirable for the spring months which should be around 60%. In the reference scenario, despite the population increase in the coming years, due to setting the first priority in the allocation to drinking consumption in the simulation model, the need for drinking and industry will be fully provided. The agricultural needs in the irrigation networks downstream of the studied dam are 100% provided in all months, but the agricultural needs supplied from the Dolisan Plain aquifer in all months were less than 60% of the water needs. The minimum environmental requirement of the lower reaches of Herod River, which is considered for the survival of the region's ecosystem, is less than 50% in the spring months and is significant.

Corn is one of the most widely consumed cereals in the world, which is highly compatible with many climates. For this reason, corn has been cultivated in most regions of the world since ancient times. Therefore, it is also considered a part of people's food all over the world. The effect of nitrogen fertilizer, as an agricultural solution, on the growth and yield of corn has caused it to be split to increase the plant's access time to this nitrogen source. In fact, due to the leaching of nitrogen fertilizer, it is usually not applied in one step. For this reason, based on the prevailing conditions of the field, the operators divide it into two or more divisions and perform nitrogen fertilization during the growth period. In each division, it is necessary to determine and apply the optimal amount of nitrogen fertilizer in order to minimize environmental pollution in addition to being economical. It requires many field experiments, which require a lot of time and money. To solve this problem, the use of simulation and optimization models, such as response-surface modeling, is suggested. The response-surface method is one of the suitable optimization tools that has been considered in various sciences for many years. The statistical basis of this method is very complex and uses a multi-objective nonlinear model for optimization and modeling. The response-surface method first provides a suitable combination of treatments, and by considering them, a statistical model is created that has the best fit compared to other models. Next, the most optimal value is determined for the independent variables so that the value of the dependent variables reached their maximum or minimum.

For this purpose, the data collected from a research project, which was carried out in the 500-hectare farm of the Seedling and Seed Research Institute in two years (2011-2012), were used. Two factors consisted of fertilizer in three levels (N1: 100 and N2: 60% and N3: 50% of fertilizer requirement) and the time of splitting into three methods (T1: the farmer's application with two splittings; T2: three equal divisions and T3: four equal divisions) was considered. The response surface method was used to optimize yield and yield components. In the response-surface method, the code of -1, 0, and +1 for nitrogen indicates 50, 60, and 100 kg/ha of nitrogen fertilizer, respectively. The code of -1, 0, and +1 for fertilizer splitting indicates the number of 2, 3, and 4 nitrogen fertilizer splitting during the growing season, respectively. In this method, to fit the data, multivariate regression was used by adding linear terms, quadratic, and interaction between factors. Then, regression was evaluated based on the analysis of variance. The statistical criteria used included root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), model efficiency (EF), index of agreement (d), and coefficient of explanation (R2).

The results of ANOVA showed that the linear and quadratic regression model for seed yield and the linear regression model for fertilizer efficiency was significant at the 5 % probability level (P-value ≤ 0.05). For water productivity, the splitting factor had a greater effect on the regression than the amount of fertilizer, although both factors did not show a significant effect. The regression model had a significant effect on the 1000 seed weight, number of seeds in a row, number of rows in a cob, cob length, and seed size. The regression of other variables was not statistically significant. Therefore, the response-surface method can be used to predict and optimize variables with significant regression. The results showed that the regression model was capable of predicting variables including 1000 seed weight, number of seeds in a row, number of rows in a cob, corn length, and seed zinc content. But this model had an underestimation error (MBE ≤ 0.0) for all variables. The accuracy of the regression model for grain zinc content was in a good category (0.1 < NRMSE < 0.2) and for other variables in the excellent category (0.0 <NRMSE< 0.1). By increasing the amount of fertilizer (changing from code -1 to + 1), the yield initially decreased and then increased. With the increase of fertilizer splitting, corn yield decreased first and then increased. The effect of the amount and splitting of fertilizer on changes in the 1000 seed weight was linear and with the increase of these two factors, the 1000 seed weight also increased. This result was also observed for the number of seeds in the cob. In terms of cob length and grain zinc percentage, the two factors of fertilizer amount and splitting had similar effects on the increase of these two variables, but at low values of both factors, the mentioned variables decreased slightly. Increasing the amount and distribution of fertilizer caused an increase in the number of rows in the cob, but high amounts of these two factors had no effect on the increase in the number of rows in the cob. Except for the number of rows, other variables increased along with increasing the amount of fertilizer and its splitting. Providing 100% fertilizer requirement and increasing the number of divisions to 5 times, can increase maize yield by up to 1.5 tons per hectare. This was about 28% of the average yield and 6 % of the maximum corn yield in this study. The weight of the thousand seeds increased to 3.5 grams under optimal conditions, which increased by 32 and 9 % compared to the average and maximum values in this study, respectively. The variable of the row was not much of a change in the average variable (1.5 cm) and increased by only 1 %. The optimal length increased to 3.5 cm and the optimal rate increased to 62%.

In general, the optimization results of all variables showed that if the fertilizer requirement is applied as N1 and in five splittings; the amount of yield, 1000 seed weight, the number of seeds in a row, the length of the cob and the amount of seed will increase by 6, 9, 12, 18.5, and 19.6% respectively compared to the maximum values of these variables. Therefore, it is suggested to apply this scenario in the field to improve yield and yield criteria such as zinc concentration in corn seeds.

Global climate change has led to change in the ecological amplitude of plant growth, expand plant adaptation to hot climates, and decrease plant adaptation to cold climates. Climate change resulting from human activities occurs at such a speed that many species will not be able to adapt to it. These changes have led to a change in the range of plants growth. Such high-speed changes have caused subsequent changes in the structure and entire ecosystems of the earth, therefore predicting the effect of climate change on the distribution of plant species has become a major field of research for its conservation measures and programs. Changes in the range of distribution of plants are mostly predicted by species distribution models. In this sense, every environmental factor affecting the distribution of plant species has a minimum, maximum and optimal value, which, in combination with other factors, separates the territory of the species and forms an ecological niche. These models are used to investigate species distribution and are based on ecological niche theory. This research was conducted with the aim of determining the potential habitats of Pistacia vera L. species and the factors affecting it in the present and future in Razavi Khorasan province.

For this purpose, 28 bioclimatic variables including topographic (4 cases), climatic (19 cases), soil (4 cases), and geological (1 case) factors as prediction variables have been analyzed for the correlation coefficient. The variables with high correlation (more than 80 %) have been removed. Environmental variables in ASCII format along with presence points were added for modeling in R software of the desired species. According to the size of the study area, sampling of data points was done based on the field visit during the period 2021-2022 from the introduced areas. through using the Global Positioning System (GPS) of 129 points from 8 regions (as points of presence) were recorded. Then, in order to prevent spatial autocorrelation and reduce the sampling error, the useful areas were converted into 1000×1000 meters grids in ArcGIS 10.5 software, and one presence point was obtained from each cell. In the modeling process, 70 % of the presence points (Pistacia vera L.) were used to generate models and 30 % of the presence points were used to evaluate the performance of the models. Also, to increase the modeling accuracy, the number of repetitions was considered 10. Then all data and points through R software and using Biomed 2 package models including GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt Phillips models, in determining the relationship between vegetation and environmental factors in rangelands of Khorasan Razavi province at current and future distribution of this species in 2080-2100 were predicted under climate scenarios ssp1-2.6 and ssp5-8.5 model. The accuracy of the models was evaluated using the values of KAPPA, TSS and ROC indices, which are prominent and widely used indices for determining and identifying the areas of equal potential.

The variables of climatic factors were removed from the modeling due to the high correlation of 80 %, and the analysis was done using four topographic factors, eight climatic factors, four soil factors and one geological factor. The results of this research showed that according to the accuracy evaluation index, the best modeling for the present time is done by the random forest (RF) model with the ROC, KAPPA, and TSS equal to 100. In the future, the 2.6 and 8.5 scenarios of the random forest model for the ROC, KAPPA, and TSS indicators, with the accuracy of 0.999, 0..982, and 0.989 respectively, have the highest level of accuracy; Also, in the random forest model, the factors that had the greatest impact included: Bio12 (annual precipitation) and Bio15 (seasonal precipitation changes) and land unit at the present time, in the future time under the scenario 2.6 Bio12 (annual precipitation) and Bio15 (seasonal precipitation changes) and DEM and in the scenario 8.5 Bio15 (seasonal precipitation changes) and Bio12 (annual precipitation) and aspect. The results of the relative importance show the great influence of climatic factors on the distribution of this species. It is most present in the habitat with an annual rainfall of 200-285 mm, and more than this amount of rainfall was associated with a decrease in suitability for the establishment of the species. Besides, the height of 800-1300 meters above sea level and rainfall changes up to 7.8 mm in seasonal rainfall also had a positive effect on the suitability of the habitat for the presence of wild pistachio. Also, the most desirable habitat is in low to relatively high hills with a rounded and sometimes flat top consisting of limestone, metamorphic, conglomerate, and shale sandstones and a slope of 40 to 50 % and with shallow to relatively deep gravelly soils. The highest distribution of Pistacia vera L. species is in the northeastern region to the east of Khorasan province. In general, by examining the outputs of the random forest model and comparing the areas prone to the growth of Pistacia vera L. species in the present and future climate scenarios, it can be stated that the trend of stable habitat in the province can be expected.

The results of this research can be used to identify areas prone to growth, improvement, development, protection, economic exploitation, and expansion of the habitat of Pistacia vera L. species. From the ecological point of view, the wild pistachio species is considered as one of the most important factors preventing and destroying land in the high mountains of arid and semi-arid regions in many geographical and ecological regions. On the other hand, the economic importance and the income-generating aspect of wild pistachios are also important for local operators. In general, it can be stated that vector machine models provide very good performance for identifying such prone areas. In this research, an attempt was made to evaluate different species distribution vector machine models, and then the most suitable model, which was random forest, was selected.

Arid and semi-arid areas often have sparse and scattered vegetation cover. In many arid regions of the world, open spaces between plants are occupied by particular living organisms called biological soil crusts (BSCs) or biocrusts. BSCs are the dense population of living organisms such as cyanobacteria, algae, fungi, lichens, and mosses in different proportions that live on the soil surface or within the upper few millimeters of soil. The aim of the present study was to investigate the effect of biological soil crusts on surface runoff quality in hillslopes around Ajigol Wetland in the Golestan Province of Iran.

The study area is located in the northern part of Golestan Province. Elevation in the study area ranges from 7 to 32 meters above sea level. The topography of the area is gentle and the land surface is composed of loess deposits. According to the climatic conditions, the research area is classified as arid and semi-arid regions. Due to a lack of rainfall, high evaporation, and uneven distribution of rainfall throughout the year, as well as high soil salinity, it has low-growing and weak rangeland plants. The vegetation in the area is composed mainly of annual grass species scattered heterogeneously. They often appear after rain events and have a short growth period finishing the life cycle in one season. In this research a field rainfall simulator was used. First, field visits were conducted to select places for positioning rainfall-runoff simulation plots in different types of biocrusts. To eliminate the effect of slope on runoff processes, locations were selected whose slope was around the dominant slope of the region (around 20 %). Rainfall-runoff simulations were carried out using a rainfall simulator over 1 x 2-m plots with and without biological soil crusts. The intensity of the simulated rainfalls was about 80 mm h-1 and the duration of each simulation was 30 min. The plots were positioned over five different types of surface cover including 1) dominant moss cover, 2) dominant lichen cover, 3) mixed (moss + lichen) cover, 4) dominant shrub (Artemisia spp.) cover, and 5) Bare land. Sampling and measurement of some runoff quality variables (sediment, electrical conductivity (EC), acidity (pH), color, and Total Dissolved Solids (TDS)) were conducted at 15-minute intervals during the simulation, plus one more sample from a mixture of runoff of the whole simulation. For some other water quality variables (organic carbon, nitrate, phosphorus, and potassium) measurements were made only at the end of the simulation from the total runoff mixture. The data were analyzed using graphical methods (plots) and statistical tests: analysis of variance (ANOVA), Kruskal-Wallis, and Tukey’s test.

The results showed that sediment concentrations were significantly (P ˂ 0.05) lower for biocrust-covered plots compared to the plots without biocrusts. Extreme differences were observed for the bare soil. EC, pH, color and TDS values also had significant differences between different covers. For organic carbon, phosphorus, nitrate, and potassium, no significant differences (P ˂ 0.05) between covers were detected by statistical tests though some notable differences were discernible on plots. The origin of runoff EC is mostly inorganic substances and it is caused by natural and human-induced pollution. There was a significant difference (P ˂ 0.05) between shrub-covered plots and plots with a combination of moss and lichen. EC for the shrub cover (Artemisia spp.) was found to be significantly higher than the mixed moss and lichen cover. The reason can be attributed to the increase in permeability and soil moisture in BSC dominated areas. Increased infiltration of water by biocrusts causes salts and ions to move deeper into the soil and this reduces the salinity of upper soil layer and surface runoff. With regard to runoff color, a significant difference (P˂ 0.05) was observed between bare soil and the other cover types. By producing polysaccharides and viscous materials, BSCs preserve and stabilize the soil surface materials and reduces the transport of metal ions (such as iron and manganese), decayed plant materials, organic matter, and animal waste as the main factors for the coloration of runoff. In contrast, more detachment and transfer of materials from bare soil have caused the runoff to become thicker and darker. The amount of sediment concentration from bare land was higher than shrubland and biocrust covers. For example, the average sediment concentration in the runoff from plots of bare land was about three times that of Artemisia plots. Another notable point was the large difference in sediment concentration between bare soil plots themselves. The reason for this was attributed to the presence of remaining roots of annual plants in the bare soil plots, which influence runoff and soil loss.

Overall, the results indicate the major effect of BSCs on runoff quality. So, taking proper measures to protect them and prevent their destruction is of great importance for soil and water conservation as well as water quality preservation in downstream wetlands. Therefore, it is necessary for government agencies to pay more attention to BSC-covered hillslopes around the Ajigol Wetland so no more damages are imposed on these fragile unique resources. As no comprehensive map of BSC covers the study area is present, it is recommended that such a map be prepared using satellite and drone imagery. Then, by combining the results of this study with the information obtained from mapping and generalizing it to the entire region, it is possible to make an overall estimate of the effect of BSCs on the water quality of downstream wetlands which is necessary for better-informed planning and decision-making. Exclosure and cover protection measures to prevent physical damages caused by human activities need to be implemented for the BSC-covered areas so they can continue their function as living mulch and protect soil from water and wind erosion.

Ecosystem health is the ability of ecosystems to maintain structure and function in the face of external pressures over time. The knowledge of watershed health with a systemic approach seeks to conserve the natural ecosystem by protecting healthy watersheds and preventing changes in them. Assessing watershed health and prioritizing sub-watersheds is essential for effective watershed management and will help in proper management and optimal allocation of resources. Considering that watersheds are dynamic systems, the hydrological function and health of watersheds are constantly changing under the influence of land use changes, climate change, and human interventions. The emission of greenhouse gases in recent decades has caused global warming, followed by changes in the hydrological regime and function of watersheds, which can threaten the health of the watersheds. In order to evaluate the health status of the ecosystem, various methods such as pressure-state-response (PSR), vigor-organization-resilience (VOR), reliability-resilience-vulnerability (RRV), and watershed health index (WHI) have been presented which determine the watershed health using several indicators. The aim of this research is to evaluate the health of the Fariman dam watershed in Khorasan Razavi province under current and future climate using the VOR model and hydrological simulation.

In order to achieve the research objectives, the hydrology of the watershed was simulated using the SWAT model. For this purpose, parameters sensitivity analysis, calibration, and validation of the model were performed using the SUFI-2 algorithm in SWAT-CUP software using daily discharge and suspended sediment yield data for the period of 2008-2014 and 2016-2019. Then, using the VOR model, the health of the watershed was calculated for the historical period of 1985-2014. In the VOR model, the indicators of landscape, soil erosion, and water loss were used to determine the components of the vigor, organization, and resilience of the watershed. The landscape indicators were determined using the watershed land use map in FRAGSTATS 4.2.1 software and indicators related to watershed hydrology (sediment yield and runoff) achieved from the output of the SWAT model. To assess the effect of climate change on watershed hydrology, precipitation and temperature data from CMCC-ESM2, GFDL-ESM4, and MRI-ESM2-0 climate models of IPCC sixth assessment report for three SSP1-2.6, SSP2-4.5 and SSP5-8.5 emission scenarios for two future time period (2030-2059 and 2070-2099), were downloaded. Then, CMhyd software was used for bias correction and downscaling of climate data. In the end, the SWAT model was run and the health index was calculated for future periods and compared with the historical period.

Calibration results of the SWAT model showed that Nash-Sutcliffe criterion for discharge and monthly sediment in the calibration period was 0.66 and 0.65, respectively. Nash-Sutcliffe criteria values ​​for the validation period were 0.57 and 0.56 respectively for discharge and sediment. The results of watershed health by VOR model in the historical period showed that the average health index of the sub-watersheds for MRI-ESM2-0, GFDL-ESM4, and CMCC-ESM2 models is 0.545, 0.533, and 0.665, respectively. The average index of all three models is 0.581 which means the watershed health status is "Moderate". The presented results show that in the SSP1-2.6 scenario in the period of 2030-2059, the health index in three sub-watersheds 2, 8 and 9 has decreased by 16.1, 3.6, and 0.6% (average 7.6%) compared to the historical period (1985-2014). The health index has decreased in 4 sub-watersheds in the SSP2-4.5 scenario and in 6 sub-watersheds in the SSP5-8.5 scenario. The average reduction in the SSP2-4.5 scenario is 9.3 percent and in the SSP5-8.5 scenario, it is 10.6%. The health index of sub-watersheds 2 and 9 has decreased in all emission scenarios and the health index of sub-watersheds 5 has decreased only in the SSP5-8.5 by 10.7 %. As a result, watershed health in the future and under climate change indicated that in the period of 2030- 2059 with the increase of greenhouse gas emissions, the number of sub-watersheds with a decrease in watershed health index will increase from three sub-watersheds in the SSP1-2.6 to 4 and 6 sub-watersheds in the SSP2 -4.5 and SSP5-8.5. In other words, the watershed health index has decreased in 34.6 % of the watershed area in the SSP1-2.6, while in the SSP2-4.5, 51 % and in the SSP5-8.5, 5.65 % of the watershed area will experience a decrease in health. Also, The results for the period 2070-2099 show that in the SSP1-2.6, the health index has decreased in sub-watersheds 2, 3, 5, 6, and 9 with an average of 11.2%, in the SSP2-4.5 scenario, sub-watersheds 2, 5, 7, 8, and 9 with an average of 5.1% and in the SSP5-8.5 scenario, sub-watersheds 2, 4, 5, 6, 8 and 9 with an average of 7.5% had a more decreasing trend. Sub-watersheds 2, 5, and 9 had a decreasing trend in all three scenarios, and sub-watersheds 3, 4, and 7 only had a decrease only in SSP1-2.6, SSP5-8.5, and SSP2-4.5 scenarios. The results in the period of 2070-2099 indicate that the watershed health index in the SSP1-2.6 has decreased in 50.1% of the watershed area, while in the SSP2-4.5, it was 56.3%, and in the SSP5-8.5, 65.5% of the watershed area.

The results showed that the overall watershed health index in the study area based on the VOR model is “moderate”, but with the increase in the amount of greenhouse gas emissions and the increase in temperature, the watershed health index decreases in a larger number of sub-watersheds, as in the SSP1 -2.6, the watershed health index has decreased in 34.6 % of the watershed, while in the SSP2-4.5, 51 % and in the SSP5-8.5 scenario, 65.5 % of the watershed area has been associated with a decrease in health. Overall, the results of the research showed that climate change can affect the watershed health index, and these effects are different in various sub-watersheds.

Widespread entry of effluents, which are toxic and non-biodegradable, from factories and various industries into the environment, and accordingly, pollution of water and soil resources lead to many dangers for humans and other organisms. Therefore, modification of these resources is important. Currently, multiple technologies from the physical, chemical, and biological perspectives have been established for the remediation of contaminated water. However, most of them involve energy consumption, high-cost instruments, low efficiency, complicated implementation, or secondary pollution. Therefore, it is critical to develop more convenient, economic, and environmentally harmonious strategies for the decontamination of polluted water. Biochar is an emerging material that is manufactured by the decomposition of carbon-rich biomass under oxygen-limited pyrolysis. Remarkable progress has been made in the understanding of biochar as an environmentally friendly and low-cost material for carbon sequestration, energy recovery, contamination relief, and nutrient supplementation. In recent decades, biochar has gained significant attention in the remediation of contamination in terrestrial and aquatic environments. However, biochar only has limited adsorption ability to anionic contaminants in water. This is because biochar often has a negative surface charge, hindering it to absorb negatively charged compounds such as Cr(VI) and Direct Blue71. Various modification methods thus have been developed to improve its affiliation to anionic contaminants by introducing metal oxides onto the carbon surface within its pore networks. Studies showed that the application of biochar and metal-coated biochar, effectively leads to the removal of significant amounts of contaminants from water and soil, but so far the impact of metal-carbon composite on the removal of contaminants, especially anionic contaminants has not been reviewed. Therefore, the purpose of this research is to produce different types of biochar-metal composites, to investigate the effectiveness of different types of composites in removing Direct Blue 71 and chromium from aqueous solution, and also to compare the composites with: plant biomass, metal-coated biomass, biochar, and metal coated biochar.

In this study, the effect of plant biomass, metal-coated biomass, biochar, metal-coated biochar, and metal-biochar composite at two temperatures (300 and 600  ̊C) on the removal of Direct Blue 71 and Chromium contaminants from the water was investigated. The biochar used in this experiment was produced from the thermal decomposition of rice straw, which is abundant in the region. At first, the sample was passed through a 2 mm sieve, then they pyrolyzed at 300 and 600  ̊C for three hours. Metal-coated biochars and metal-biochar composites were prepared from the combination of metals (manganese, zinc, copper, iron, and aluminum) with a concentration of 10,000 mg L-1 with agricultural residues (rice straw) as a raw material or biochar. The samples were mixed with metals with a ratio of 1:50 (1 gram of sample, 50 mL of metal solution) and shaken for 24 hours. Then the samples were filtered and oven dried at 70  ̊C. After the preparation of adsorbents, a specific amount of adsorbents and pollutants with a concentration of 20 mg L-1 were combined and shaken for three hours until they reached equilibrium. All the samples were centrifuged for 5 minutes at 6000 rpm. After filtration, the final concentration of pollutants was determined and the removal percentage of Direct Blue 71 and chromium was calculated.

Based on our results, the application of different adsorbents has a significant effect on the removal percentage of Direct Blue 71 and chromium from the water. Our data showed that high-temperature adsorbents were more efficient in removing Direct Blue 71 and chromium. For example, by increasing the biochar pyrolysis temperature from 300 to 600 °C, the Direct Blue 71 removal percentage has increased from 10.733 to 63.695 %. According to the results of the current study, it can be observed that covering the agricultural residues and biochars with metals has been able to increase the efficiency of the adsorbents in pollutant removal due to the creation of a cationic bridge. In general, the results of this study showed that the application of aluminum and iron composite and aluminum and iron coated biochar produced at 600°C was beneficial in the remediation of contaminated water and these adsorbents could remove 98.303, 88.847, 98.302 and 96.777 % of Direct Blue 71 and 97.983, 78.733, 96.75 and 92.167 % of chromium pollutant from the aqueous solution, respectively. Therefore, the application of these adsorbents can be useful to modify water polluted with these contaminants.

According to the results of this research, it can be observed that the addition of metal-coated biochar and biochar-metal composite to water has reduced the amount of direct blue 71 and chromium pollutants, so their use in water contaminated with these pollutants can be beneficial. Organic materials and biochar are among the adsorbents that are widely used to reduce pollutants from water and soil, but anionic pollutants are not well absorbed due to the dominant negative surface charge of these adsorbents. Therefore, it seems that for more effective use of organic adsorbents, it is necessary to combine these materials with metals or other materials so that their absorption capacity increases and they can be effective in removing anionic pollutants. The composite has a new physical and chemical nature compared to Biomass which is pyrolyzed alone (biochar). Even composite can be significantly different from metal-coated biochar.

The climate of a region is influenced by many factors, some of which are planetary and some are regional and local. Teleconnection patterns are the origin of anomalies. Therefore, revealing the relationships between climatic parameters and teleconnection patterns is important for further understanding of climatic fluctuations and variability in each region. The purpose of this research is to investigate climate anomalies using Teleconnection patterns are the origin of anomalies that are seen Therefore, revealing the relationships between climatic parapatterns. For this purpose, the rainfall data of two observation stations (Khorram-Abad and Kermanshah) were collected during a period of 68 years (1951-2018). In this research, two types of data were used. 1- The monthly rainfall data of two synoptic stations of Khorramabad and Kermanshah were obtained from the National Meteorological Organization. 2- Data related to remote connection patterns including: Polar Oscillation (AO), North Atlantic Oscillation (NAO), Scandinavian Pattern (SCA), East Atlantic Pattern (EA), East Atlantic-West Russia Pattern (EA/WR) ), the North Tropical Atlas pattern (TNA), the Polar-Eurasia pattern (POL) and the Meridian Wind pattern (AMM), the Southern Oscillation (SOI), the combined pattern (Best) of the East Pacific-North Pacific Oscillation (EP/NP), Water surface temperature in Niño 1 and 2 (Niño 1+2) and water surface temperature in Niño 3 and 4 (Niño 3.4)) were obtained from the NOAA website.

Analytical statistics and inferential statistics methods were used to determine the effect of Teleconnection indicators on rainfall in the region. First, the rainfall time series of each station was tested for significance using the Anderson-Darling test at the 95% confidence level. Also, to evaluate the condition of independence of time series, the sequence test was used. Then, the Teleconnection indices were divided into two groups. Atlantic Ocean-based indices (AO, NAO, SCA, EA, EA/WR, TNA, POL, AMM) and Pacific Ocean-based indices (SOI, Best, EP/NP, Nino 1+2, Nino 3.4) And their interaction. In most statistical materials, parametric tests such as variance analysis, correlation analysis, regression analysis, etc., are based on the assumption that the measurements within each statistical population have a normal distribution and an equal variance-covariance structure.The hypothesis of establishing a normal distribution is related to the distribution of the studied population and not the samples.In order to be able to accept this hypothesis, that hypothesis must be substantiated in theoretical fields, that is, the values must be symmetrically centered around the average number. In this regard, data that have a skewness (lack of symmetry) or are strongly integrated in a part of the measurement scale, affect the variance-covariance between the variables. Analysis of variance is one of the parametric methods that evaluates the relationship between a dependent variable and an independent variable.In this approach, the independent variable is considered as the agent variable and the dependent variable as the response variable. In order for the results of the analysis of variance to be valid, several assumptions must be considered when applying its formulas. The first assumption is that the observations are independent. It means that each observation is uncorrelated with another observation. The second assumption is that the observations are normally distributed. That is, all observed measures of central tendency, including mean, mode, and median, should be the same. The third assumption is that the variance is homogeneous. That is, the sizes of the distribution of scores should be determined. This assumption is called homogeneity of variance. Therefore, before applying the statistical tests, first, the time series of monthly rainfall of Khorramabad and Kermanshah stations were tested for significance using the Anderson-Darling test at the 95% confidence level. If any of the precipitation time series is not normal, we tried to normalize that time series by using Johnson transformation functions. If these functions were not able to place the precipitation time series in the normal range. Kruskal-Wallis method, which is equivalent to non-parametric analysis of variance, was used to test the precipitation of these non-normal time series. In the following, in order to find out whether the average monthly rainfall of Khorramabad and Kermanshah synoptic stations has changed during the different phases of the teleconnection patterns, or in other words, whether the average monthly rainfall in the west has undergone changes due to the positive or negative phase of these patterns, first the values The standardized index of these patterns was divided into three levels: neutral, positive and negative. Then one-factor analysis of variance and Kruskal-Wallis test were implemented on these three levels as factors and the time series of monthly rainfall as the response variable.

The distribution values of Kruskal-Wallis statistics for the months of October and September and the values of the analysis of variance statistics for other months of the year revealed that the influence of the indices based on the Atlantic Ocean has less homogeneity and order than the indices based on the Pacific Ocean. In a way that the phase change of the Pacific Ocean indicators has caused a significant change in the rainfall of October and November in Khorramabad and October in Kermanshah. If the influence of the patterns based on the Atlantic Ocean does not have such an arrangement. In general, the patterns of the Atlantic Ocean have caused a significant change in precipitation mainly in the winter season, while the patterns based on the Pacific Ocean have had a significant effect on the precipitation in the autumn season. In this regard, the East Atlas-West Russia pattern had the most significant effect on the precipitation of these two stations, while the polar oscillation pattern, the Eurasia polar pattern, and the meridian temperature pattern caused a significant change in precipitation in only one month and one station. Also, the Scandinavian pattern has a significant effect on the October rainfall in Khorramabad, January, March and December in Kermanshah. On the other hand, the East Atlas pattern and the North Tropical Atlas pattern have had a significant effect on February rainfall in Kermanshah and October in Khorramabad. On the other hand, the East Atlas pattern and the North Tropical Atlas pattern have had a significant effect on February rainfall in Kermanshah and October in Khorramabad. The East Atlas and Scandinavian patterns have a significant effect on the October rainfall in Khorramabad and Kermanshah. In general, the patterns of the Atlantic Ocean have caused a significant change in precipitation mainly in the winter season, while the patterns based on the Pacific Ocean have had a significant effect on the precipitation in the autumn season.

Watershed degradation had negative effects on ecological and anthropologic functions at different scales. Therefore, strategic planning and conserving watershed resources is the main goal for managers and policy-makers. To achieve this goal, it is essential to provide a scientific roadmap concerning the health degree of the watershed in terms of its multi-functions. A healthy watershed improves the resilience of local ecology to climate change and provides essential services for human and ecological functions. Identifying healthy watersheds could be an effective managerial tool for monitoring natural and human phenomena and impacts. Although, in recent decades, there have been numerous types of research on watershed health and its assessment methods in different water and soil environments and in relation to environmental and social processes with economic models for decision-making in different fields. But regarding to the interpretation of different watershed health assessment models with fuzzy logic, limited studies have been carried out. This is the fact that fuzzy science has been well-considered in various sciences. In recent years, fuzzy logic has been mentioned as a powerful technique in hydrological component analysis and resource decision-making. Hydrological problems are associated with uncertainty, which is managed by fuzzy logic-based models. Fuzzy logic is based on the language of nature.  To this end, the present study was planned to accomplish our previous information on the KoozehTopraghi Watershed health and develop a new PSR-Fuzzy-based framework.

To do this research, firstly the pressure, state-response (PSR) model was conceptualized and customized for the study watershed. Secondly, the main criteria of road density, watershed slope, runoff coefficient, agriculture area with a slope of more than 25%, precipitation, and temperature were computed for building the pressure indicator. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) also were computed for building the state indicator. Then, the specific erosion (m3 y-1), erosion intensity coefficient, river density, and rangeland area were computed for building the response indicator. Thirdly, these criteria are converted to fuzzy bases using Fuzzy Linear membership functions in the ArcGIS 10.8 environment. Fuzzification is a method in which each pixel in the map is given a value between zero and one. This amount expresses its value according to the goal it pursues, and the higher it is in terms of value, the higher it is awarded to it as a result. Six operators including AND, OR, SUM, PRODUCT, Gamma 0.9, and Gamma 0.5 were used for incorporating three indicators of PSR and watershed health zoning. Fourthly, to evaluate and classify the output results of the operators used in the estimation of watershed health, the Quality Sum (QS) was used.

The results proved the better performance of two operators of Gamma 0.9 and PRODUCT. The Qs was 0.46 for PRODUCT as the first priority, followed by Gamma 0.9 operators with a Qs of 0.37 in the second priority as the most efficient operators in mapping watershed health. The pressure indicator results showed that 33.84, 0.16, 9.45, 50.51, and 6.04% of the total area of the KoozehTopraghi watershed were classified as very high, high, medium, low, and very low, respectively. The results of the state indicator, 7.55, 52.71, 39.67, and 0.07% of the total area of the study watershed were classified as very high, high, medium, and very low, respectively. The response indicator results indicated that 15.16, 13.30, 29.99, 34.80, and 6.76% of the total area of the KoozehTopraghi watershed were classified as very high, high, medium, low, and very low, respectively. According to the results of the PRODUCT operator, 67, 23, 9, and 1 % of the study watershed were classified as unhealthy, relatively unhealthy, medium, and relatively healthy, respectively. For Gamma 0.9 operator 0.9, 46, 1, 17, and 36% of watersheds were classified in unhealthy, medium, relatively healthy, and healthy classes. Based on this, it is a priority to provide suitable solutions for basic land management. Because it may be intensified the continuation of the irreparable process at the watershed level.

The results confirmed the spatial changes in health status throughout the KoozehTopraghi Watershed. Therefore, different scientific and rational programs need to be adapted to improve health to various degrees. It is highly suggested to prioritize nature-based solutions, integrated participatory management, and adaptive co-management for improving the KoozehTopraghi watershed health. Acquaintance with modern management patterns in the world, of course, with the different climatic and social conditions of our country, we can open up in the field of comprehensive watershed management compared to the past.  The watershed health index as a practical tool in watershed management can be used to determine priorities and monitor watershed status changes. In addition, since the factors affecting the management of ecosystems are considered in the health index, it can be considered as a tool for analyzing the vegetation, water, and soil resources for use with the needs of the living organism.

Investigating runoff changes in a watershed can help to understand better understand the factors affecting it. Climate changes and human activities in recent years have caused a decrease in runoff in different parts of the globe and have created social and economic problems. In general, the influencing factors on runoff changes can be physical factors (vegetation cover, initial soil moisture, land topography, etc.), climatic factors (precipitation amount, air temperature, earth warming, etc.), and changes caused by human activities (building a dam, building a reservoir, expanding urbanization, illegal withdrawals, etc.). The increase in greenhouse gases and climate change has caused changes in the hydrological cycle and the amount of runoff in watersheds and has increased the frequency of climate extreme events. Also, observations in most regions around the world show, that the hydrological cycle has also been affected by human activities. Human activities, such as agricultural development, urban development, dam construction, and exploitation of reservoirs, have direct and indirect effects on the hydrological cycle, and as a result, the temporal-spatial distribution of water resources has changed. The primary purpose of this study is to determine the contribution of each of these factors to the discharge changes of the GharehSoo River, one of the most important rivers of Ardabil province, using different classical and intelligent methods.

In this research, some classical and intelligent methods namely, linear regression, bivariate linear regression, double mass curve, and artificial neural network methods are used to determine the contribution of climate changes and human activities on the discharge change of GharehSoo River. First, by using Pettitt's test, the change point of the discharge time series is detected and divided into two natural and changes periods. Then, the contribution of each of these factors is determined using the mentioned methods.

In general, it can be said that the amount of runoff calculation error is almost the same for all the applied methods, and therefore the methods have relatively similar performance. However, in Samyan station, the two-variable linear regression model shows less error and the single-variable linear regression model shows more error than the other methods. For the Dost-Beiglo station, the two-variable linear regression model shows less error and the artificial neural network model shows more error than the other methods. The reason for the not so small error of the artificial neural network in predicting the runoff can be related to the error in the data used and the relatively short length of the data. In general, the results of different methods in both stations showed that based on the calculation error, the bivariate linear regression method provided better results in modeling the river discharge in both hydrometric stations. The results of this research showed that for the Samyan station, the contribution of the climate change using the linear regression, bivariate regression, double mass curve and artificial neural network is 6.45%, 14.42%, 14.86% and 8.61%; and the contribution of human activities is 93.55%, 85.58%, 85.14% and 91.38%, respectively. For the Dost-Beiglo station, the contribution of climate change for the mentioned methods is 2.1%, 3%, 27% and 0.14%; and the contribution of human activities is 97.9%, 97%, 73% and 99.86% respectively. By comparing the results of Samyan and Dost-Beiglo stations, it can be concluded that the effect of climate change on the discharge of Gharehsoo River at the Samyan station (11.08%) is more than the Dost-Beiglo station (8.06%) and on the contrary, the impact of human activities on the river flow at the Dost-Beiglo station (91.94%) is more than the Samyan station (88.91%), which can be due to the simultaneous effect of the construction of two dams including Yamchi and Sabalan in spstream of the Dost-Beiglo station. Also, as expected, the contribution of climate change (less than 27%) is less than the contribution of human activities (more than 73%) in reducing the flows of Gharehsoo River in both studied stations.

In this research, different hydrometeorological data such as precipitation, evaporation and transpiration and monthly discharge from the Samyan and Dost Beiglo stations were used for the statistical period of 1982-2019. First, by using Pettitt's test, it was determined that the river flow rate has changed abruptly since 2016. Therefore, the entire statistical period was divided into two natural and change periods, and then, using the mentioned methods, the contribution of human activities and the contribution of climate change were determined.
Two climatic factors, i.e. decrease in rainfall and increase in evapotranspiration in climate change and carrying out activities such as the construction of Yamchi and Sabalan Dams, development of orchards and agricultural lands as human activities have been effective in reducing the flow of the Gharehsoo River. However, human activities have had a greater impact (over 73 %) than the climate change factor (less than 27 %) in reducing the flow of this river.Finally, in future studies, it is suggested to use other intelligent and hydrological models of runoff estimation for rivers in the country and to evaluate their efficiency in determining the contribution of climatic and human effects on river flow. Also, other climate variables such as temperature, wind, etc. should be used in determining the contribution of climate change effects.

Today, water security is one of the important limitations of development, especially in dry and desert areas. Because these areas not only have low rainfall, but also this low rainfall is not properly distributed. Despite the increase in irrigation efficiency in some agricultural methods, the limitation of freshwater resources in some areas makes it necessary to use salt water in agriculture. However, the use of these water sources has negative effects on the soil and the environment. So the salinity of soil and irrigation water reduces crop yield and puts soil resources at serious risk. It is possible to increase the crop yield and control soil erosion by using the appropriate irrigation method. The problem of salinity in plants is due to the accumulation of excessive amounts of sodium chloride, which is widely spread in coastal areas, soils of dry areas, and fertile lands. Studies have shown that the use of saline water, especially in conditions of equal fertilization between treatments, often reduces the absorption of important nutrients such as nitrogen due to the existence of a significant relationship between the absorption of water and nutrients. Research in the north of Golestan province showed that salinity causes a significant decrease in plant biomass. The effect of salinity stress on the accumulation of sodium, potassium, and chlorine in the plant was significant and the highest amount of ions was accumulated in the leaves. The plant's root system is selective in absorbing and transferring sodium to its aerial parts.

To do this research, first, by selecting 36 experimental units, holes with a diameter of 50 cm and a depth of 60 cm were dug in the center of each one, and then the treatments were prepared. This research is in the form of treatments consisting of irrigation factor (clay and drip irrigation method), salinity level (salinity up to 1200, salinity up to 2200, and salinity up to 3200 µmhos cm-2), and plant (Rose and Hibiscus sabdariffa) in three repetitions and it was done factorial randomized complete blocks design. Three water sources each with a capacity of 200 liters were placed at a height of less than two meters from the ground. Rose plant was prepared in the form of potted seedlings and Hibiscus sabdariffa seeds were planted in the greenhouse and after about two weeks in March, it was transferred to the field. The growth height of the plants, as well as the crown, the diameter of the plant stem, and the number of their branches in the growing season were measured. Also, three soil samples were collected and their characteristics of salinity, acidity, and texture were measured. In order to analyze the data, the statistical method of analysis of variance (ANOVA) and the least significant difference (LSD) test were used to compare the average of the studied indicators using MSTAT-C software and SPSS software.

The results of variance analysis of some growth traits of the studied species showed that seedling height and stem diameter were affected by different levels of salinity and the values ​​of this plant characteristic showed a statistically significant difference. The reason for the decrease in plant growth in a plant that is irrigated with more salinity is that the presence of salt in the soil exceeds the tolerance threshold of the plant, and as a result, the accumulation of excess salt in the root zone is a limiting factor for plant growth. According to the results of the effect of irrigation methods, as well as the interaction effect of salinity and irrigation method on the aforementioned indicators, there was no statistically significant difference. The interaction effect of plant and water salinity levels on the values ​​of these variables was significant. The comparison of the average data showed that the height of the studied species was significantly increased by using the clay irrigation method. The maximum diameter of the stem was also measured in the clay irrigation method, which was associated with an increase of 1.7\% compared to the drip irrigation method. Also, the results show that the highest values ​​of the studied variables are related to the rose flower plant, which is 1.7 and 3 times more than the sour tea plant, respectively. Clay irrigation causes water to be transferred to the root area of ​​the plant, which improves the performance and growth of the plant by providing the required moisture around the root. In other words, the way to distribution soil moisture in clay irrigation takes place in the form of percolation and uniformly around the root of the plant, which causes the moisture to be placed directly around the root area and thus affects the growth of the plant. In addition, the canopy data and the number of branches showed that there is no significant difference between them.

This research tested the effect of different levels of water salinity and clay and drip irrigation on the establishment of plants in the Sistan plain, considering the existence of a water shortage crisis in the region, in order to use saline water on two plants, rose and Hibiscus sabdariffa. The results showed that clay irrigation performance was better than drip irrigation at all salinity levels. Because in the drip irrigation method, with the occurrence of drought stress, it reduces plant growth compared to the clay irrigation method. In addition, in the drip irrigation method, water is placed on the soil surface and deep penetration is limited, and as a result, the increase in humidity in the subsurface layers is less. In the clay irrigation method, due to deep penetration and uniform distribution of moisture in the soil profile, the amount of moisture stored in the soil increases.

Soil erosion is a natural process that is intensified by human activities. Experts of natural resources consider soil erosion as a phenomenon that has caused the destruction of civilizations. Preventing erosion and actually reducing its damages to the natural level of soil losses depends on choosing appropriate strategies for soil protection. Preventing erosion and actually reducing its damages to the natural level of soil losses depends on choosing appropriate strategies for soil protection. Among the many methods of preventing soil erosion and increasing the stability of slopes, bio-engineering methods of protected areas have received a lot of attention due to environmental and economic issues in engineering today.

In this research, the effect of the root of Celtis caucasica and Pistacia atlantica in soil reinforcement around the Kalan Dam, 34 km southwest of Malair City, was investigated on different slopes. To carry out this study, two species of Celtis caucasica and Pistacia atlantica were investigated in three different populations in three areas with low (0-10 %), medium (10-25 %), and high slopes (25-40 %). The root samples were collected on December 15, 2021, from a depth of about 30 cm. In general, 6 individual trees were randomly selected in each area and all the characteristics of the soil were examined in 6 repetitions and the root in 10 repetitions (to test the tensile strength of the roots). The treatment used to preserve and prepare the roots includes washing and placing them in plastic bags containing a 15 % alcohol solution. Then samples with a length of about 10 cm were randomly selected and the speed of the tensile strength test was 10 mm min-1. The root was measured using a standard Instron device manufactured by the Santam factory. Considering the normality of the data, the t-test was used to compare the two species. Three harvesting areas with different slope classes, including low (Region 1), medium (Region 2), and high (Region 3) slopes. The treatment used to preserve and prepare the roots includes washing and placing them in plastic bags containing a 15 % alcohol solution. Then samples with a length of about 10 cm were randomly selected and the speed of the tensile strength test was 10 mm min-1. The root was measured using a standard Instron device manufactured by the Santam factory. Considering the normality of the data, the t-test was used to compare the two species. In three harvesting areas with different slope classes, including low (Region 1), medium (Region 2), and high (Region 3) slopes, 18 stems of Callaghan and 18 stems of P. atlantica were harvested for root Callaghan, and 18 stems of P. atlantica were harvested for root sampling.

The results showed that the root elasticity of C. caucasica is higher than P. atlantica. The relationship between the diameter and root reinforcement was different, and about C. caucasica it was negative. the highest root reinforcement is related to fine roots. The RAR (Root Area Ratio) in C. caucasica was higher on a high slope rather than a low slope. In steep slopes (Region 3) root tension of C. caucasica is higher than the area with the average slope. There was a positive correlation between PL and LL of soil in C. caucasica stand in Region 3. Finally, strengthen the soil and reduce erosion in the upper slopes. The noteworthy point is that the percentage of carbon in the soil in Region 3 is higher than in the other two regions, which is due to the negative correlation between carbon and sand in the soil. C. caucasica in high-slope lands with a lower percentage of sand causes an increase in carbon deposition and parameters of the dough limit and liquid limit of the soil. According to the results of the data, the amount of root elasticity of C. caucasica is higher than P. atlantica species. The relationship between diameter and root tension is different in the case of species. About C. caucasica it was negative and the highest root tension is related to the roots of the fine roots, but in P. atlantica, the relationship between diameter and tension is a positive power function and As the diameter increases, the amount of tension increases. The amount of root area ratio (RAR) in C. caucasica is higher on the higher slope than on the lower slope. The percentage of clay in the soil texture has a negative correlation with the amount of RAR, and root growth and distribution are less in clay soils. C. caucasica in high-slope lands with lower sand percentage increases carbon deposition and parameters of the plastic limit and liquid limit of the soil.

It is suggested to use species that increase soil reinforcement and reduce the amount of erosion in the area of the dam in order to reduce the amount of erosion and increase the useful life. according to the results, it can be recommended that it is better to use C. caucasica in afforestation around the Kalan Dam because of its greater effect in increasing soil improvement and reducing erosion.

Estimating the amount and intensity of precipitation and its spatial distribution in various return periods is necessary for flood estimation hydrological models. This information is obtained based on traditional methods through intensity-duration-frequency curves with many assumptions, such as the choice of suitable distribution for each period, and the requirement of many parameters in different return periods. If a study area has incomplete data or a lack of data, traditional methods are limited. For this reason, the fractal method is used to transform the precipitation hyetograph in different durations and transfer the precipitation data from one place to another. The fractal method is a self-similar method; It means that every part of it is similar to the whole, like a pine tree, where every branch is like a whole tree. This method has high reliability, convenient access, and less number of parameters, which can be used to create daily precipitation data over long and short periods. It is noteworthy, that in the past valuable research has been done in the field of using the fractal theory to extract IDF curves. Nevertheless, in this research, in addition to extracting the characteristics of precipitation based on the above method in all the stations inside and outside the selected watershed, the spatial distribution of the rainfall intensity mapped based on the Co-Kriging method, and has been compared with the Ghahraman method.

The study area is the Tireh watershed in the Borujerd-Dorud region, which is located between east longitudes from ̍28˚48 to ̍17˚49 and north latitudes from 51˚33 to ̍35˚33. This watershed, with an area of 2127.28 km2, is in the northernmost part of the large Karun River watershed and in the south of Oshtorinan town. The average rainfall in the mountains and plain areas has been estimated as 611.4 and 410.6 mm, respectively. The average annual temperature of the plain with an average elevation of 1493.3 m is 13.4 ̊C and in the highlands with an average elevation of 2025 m, it is 8.5 ̊C. In addition, the amount of evaporation in the highlands and plains is 1852.2 and 2148.8 mm per year respectively. In this research, the maximum intensity and amount of precipitation were estimated based on the fractal theory using the daily precipitation data for 12 stations with a statistical period of 31 years recorded from 1990 to 2021. The research method was conducted based on studies of Azhdary Moghaddam and Heravi, (2018) in the following steps. A) data extraction of the maximum amount of precipitation in different durations of 1, 2 and … days B) determining the maximum intensity of annual precipitation by dividing the maximum precipitation values by their durations C) calculating the weighted moment of the data ( ) in different orders (r) and durations (d) and then drawing linear graphs on a logarithmic scale, D) and then, using the related relationship, the maximum rainfall was calculated in the specified duration and return period. Since hourly precipitation data are not available in most of the stations, therefore, at this step, the IDF curves were extracted using the fractal theory and were compared with the experimental method of Ghahraman (which is based on the maximum daily precipitation) by the Pearson correlation coefficient. The Co-kriging method was used to create spatial distribution maps of precipitation intensity and amount based on fractal theory. The geostatistical Co-kriging interpolation method is similar to kriging and auxiliary variables can be used for better spatial analysis. Optimal spatial distribution maps of rainfall intensity and amount are provided by the existing point data extracted from the fractal method, and introducing different auxiliary layers such as maximum daily rainfall in the 24-hour continuity period.

Fractal analysis of precipitation data showed that there is a linear relationship between scale power and moment order in all stations. Therefore, the maximum precipitation data in the study area have a mono-fractal nature, which means that by using the fractal theory, the precipitation data can be converted from one duration to another. The results of the density of precipitation zoning based on fractal theory using the Co-kriging method showed that the accuracy of interpolation increases with the increase of the return period. Indeed the calculated values have a suitable fitting with the observed values and are close to the fitted line. Contrary to this, the results of precipitation zoning based on the Ghahraman method using the Co-kriging method showed the most scattered points around the fitting line; which actually shows the low accuracy of this method in estimating and zoning the area precipitation. The results of the 24-hour rainfall interpolation error using the fractal method showed an increase in the RMSE value with the increase of the return period based on only the auxiliary variable of the rainfall intensity data produced by the fractal method. The RMSE was calculated based on adding auxiliary data such as the amount and annual average of precipitation and the value of the maximum one-day precipitation intensity of the original data to precipitation intensity prepared by the fractal theory. According to this, the RMSE in the return periods of 2, 5, 25, 50, 100, 200, and 300 years equals 0.09, 0.27, 0.74, 0.18, 0.25, 0.059, and 0.13, respectively, have a decreasing trend compared to the use of only auxiliary variable of precipitation magnitude. This composition has reduced the error criterion values to less than a fifth compared to the initial state (only by precipitation intensity covariate) in the return period of over 50 years.

The analysis of statistical moments showed that the precipitation maximum intensity data has a mono-fractal nature. In other words, the changes in the power of the scale are completely linear with respect to moment order, and it can be used to produce the data in different durations. The statistical analysis of the results of estimating the intensity of precipitation in the different return periods and durations with this method compared to the Ghahraman method showed that in most stations there is a significant relationship with a correlation coefficient of over 99 % at a confidence level of 99 %. Generally, the results of the spatial distribution error of precipitation intensity using the Co-kriging method based on fractal in the return periods of 2, 25, 100, and 200 years showed acceptably reduced interpolation error by adding different auxiliary data.

In the last 30 years, many forests have been destroyed in the Zagros region of Iran. Afforestation is necessary to reduce the pressure on the natural forests. Forest plantation and protection should be at the center of conservation efforts, significantly since plantations can offset the negative impact of climate change and be effective in absorbing atmospheric pollutants and helping to improve air quality. This research was conducted with the aim of evaluating the success of afforestation with the mountain almond (Amygdalus scoparia Spach.) in Arjan (Amygdalus elaeagnifolia Spach.) habitats of Jamal Beyg, Fars province and its effect on the understory vegetation, especially herbal species and soil. Comparing Arjan with mountain almond can provide additional information, to know the conditions of the native species in the region and the planted species, and it is a kind of comparative mode. In fact, evaluating the success and quantifying the ecological effects of the afforestation carried out by the executive organizations, which has been done at great expense, can guide managers for better decision-making. As no study has been done in this area yet, this research is the first one that quantifies the results of almond plantations in the Jamal Beyg region.

In order to check the percentage of survival and according to the budget and facilities, since the plantation rows were very long, three rows of planted shrubs in the Jamal Beyg region of Euclid in Fars province were randomly selected and the number of empty planting holes was counted. There are naturally but rarely shrubs such as Arjan (Amygdalus elaeagnifolia Spach.). In order to evaluate the existing vegetation, after initial sampling and based on the adequacy of the sample, 30 circular sample plots of 1000 square meters were taken in the form of a random-regular grid with dimensions of 100×100 meters. In the sample plots, the density of shrubs, their crow width, and survival, as well as the frequency of regeneration were measured. In order to check the number of species present in the plantation and control areas, at the end of May and the beginning of June 2021, all the plant species in the sample plots were identified or after they were collected and transferred to the herbarium, with the help of photos taken in the field, were identified. The life form and biological form of plant species were determined using the Raunkiaer system. Some physical and chemical characteristics of the soil such as texture, percentage of organic matter, phosphorus, EC, and pH amount were also measured and compared with the control area in the vicinity of the range which has similar topographical characteristics without afforestation operation. Due to the non-normality of the data distribution and the failure of various transformations, the Mann-Whitney test was used to compare two species of Arjan and mountain almond in terms of density, regeneration frequency, and crown area.

The survival rate of the plantation was 95 %, and the plantation area had a density of 342 trees per hectare, which created a canopy cover of five percent. The regeneration density of mountain almond and Arjan species was estimated to be 90 individuals and 4.5 individuals per ha, respectively. Afforestation with the mountain almond species in this area has increased the number of herbaceous species in such a way that there were 18 plant species belonging to 13 families in the afforestation area while 12 plant species belonging to eight families in the control area (without plantation area). In terms of canopy area, there was no significant difference between the two species of mountain almond and Arjan. In terms of regeneration density, there was a significant difference between the mountain almond and Arjan, and the regeneration density of the mountain almond was significantly higher than Arjan. The amount of organic matter (1.62), nitrogen (3.89), and phosphorus (11.02) of the soil in the afforestation area was higher than control area, and the ratio of carbon to nitrogen (C/N) in the afforestation area (0.46) was lower than control area.

The results of this research indicate the significant success of mountain almond afforestation in the Jamal Beyg region, Fars province. Afforestation in this area has increased organic matter and also the number of herbal species. The existence of a significant natural regeneration of the mountain almond indicates that the stand is on the true way of its succession. In order to control the grass cover and prevent fires, it is better to do light grazing in the spring season in the stands. Also, if there is a history of the presence of Pistacia atlantica in the area, planting its seeds or seedlings under the shelter of existing shrubs will help the stability of the stand. It is suggested that sufficient research be carried out to determine the appropriate method for determining the age of shrubs in such a way that natural regeneration can be separated from planted shrubs. The main goal of initializing a seed garden is to produce the modified seeds of the desired forest species in abundance, cheap, continuous, and easily accessible, far from the reach of unwanted pollen and with better genetic quality and quantity. To select suitable genotypes of a species in terms of traits such as resistance to drought stress, the genomic selection method can be used. Jamal Beyg aforestation is a valuable seed garden for future plantation. Considering the significant survival of mountain almonds and the possibility of natural reproduction, it is suggested to use this nurse species in the restoration of similar fields in the study area. Considering the effect of mountain almond afforestation in improving the soil properties of the region, it is suggested to give more importance to bioengineering operations and stabilization of slopes with this shrub in the Fars Province watersheds. As this afforestation has supported herbal species richness, it is suggested that the results of afforestation be explained to rural communities so that they are encouraged to preserve and protect forest plantations.

Estimating the amount and intensity of precipitation and its spatial distribution in various return periods is necessary for flood estimation hydrological models. This information is obtained based on traditional methods through intensity-duration-frequency curves with many assumptions, such as the choice of suitable distribution for each period, and the requirement of many parameters in different return periods. If a study area has incomplete data or a lack of data, traditional methods are limited. For this reason, the fractal method is used to transform the precipitation hyetograph in different durations and transfer the precipitation data from one place to another. The fractal method is a self-similar method; It means that every part of it is similar to the whole, like a pine tree, where every branch is like a whole tree. This method has high reliability, convenient access, and less number of parameters, which can be used to create daily precipitation data over long and short periods. It is noteworthy, that in the past valuable research has been done in the field of using the fractal theory to extract IDF curves. Nevertheless, in this research, in addition to extracting the characteristics of precipitation based on the above method in all the stations inside and outside the selected watershed, the spatial distribution of the rainfall intensity mapped based on the Co-Kriging method, and has been compared with the Ghahraman method.

The study area is the Tireh watershed in the Borujerd-Dorud region, which is located between east longitudes from ̍28˚48 to ̍17˚49 and north latitudes from 51˚33 to ̍35˚33. This watershed, with an area of 2127.28 km2, is in the northernmost part of the large Karun River watershed and in the south of Oshtorinan town. The average rainfall in the mountains and plain areas has been estimated as 611.4 and 410.6 mm, respectively. The average annual temperature of the plain with an average elevation of 1493.3 m is 13.4 ̊C and in the highlands with an average elevation of 2025 m, it is 8.5 ̊C. In addition, the amount of evaporation in the highlands and plains is 1852.2 and 2148.8 mm per year respectively. In this research, the maximum intensity and amount of precipitation were estimated based on the fractal theory using the daily precipitation data for 12 stations with a statistical period of 31 years recorded from 1990 to 2021. The research method was conducted based on studies of Azhdary Moghaddam and Heravi, (2018) in the following steps. A) data extraction of the maximum amount of precipitation in different durations of 1, 2 and … days B) determining the maximum intensity of annual precipitation by dividing the maximum precipitation values by their durations C) calculating the weighted moment of the data ( ) in different orders (r) and durations (d) and then drawing linear graphs on a logarithmic scale, D) and then, using the related relationship, the maximum rainfall was calculated in the specified duration and return period. Since hourly precipitation data are not available in most of the stations, therefore, at this step, the IDF curves were extracted using the fractal theory and were compared with the experimental method of Ghahraman (which is based on the maximum daily precipitation) by the Pearson correlation coefficient. The Co-kriging method was used to create spatial distribution maps of precipitation intensity and amount based on fractal theory. The geostatistical Co-kriging interpolation method is similar to kriging and auxiliary variables can be used for better spatial analysis. Optimal spatial distribution maps of rainfall intensity and amount are provided by the existing point data extracted from the fractal method, and introducing different auxiliary layers such as maximum daily rainfall in the 24-hour continuity period.

Fractal analysis of precipitation data showed that there is a linear relationship between scale power and moment order in all stations. Therefore, the maximum precipitation data in the study area have a mono-fractal nature, which means that by using the fractal theory, the precipitation data can be converted from one duration to another. The results of the density of precipitation zoning based on fractal theory using the Co-kriging method showed that the accuracy of interpolation increases with the increase of the return period. Indeed the calculated values have a suitable fitting with the observed values and are close to the fitted line. Contrary to this, the results of precipitation zoning based on the Ghahraman method using the Co-kriging method showed the most scattered points around the fitting line; which actually shows the low accuracy of this method in estimating and zoning the area precipitation. The results of the 24-hour rainfall interpolation error using the fractal method showed an increase in the RMSE value with the increase of the return period based on only the auxiliary variable of the rainfall intensity data produced by the fractal method. The RMSE was calculated based on adding auxiliary data such as the amount and annual average of precipitation and the value of the maximum one-day precipitation intensity of the original data to precipitation intensity prepared by the fractal theory. According to this, the RMSE in the return periods of 2, 5, 25, 50, 100, 200, and 300 years equals 0.09, 0.27, 0.74, 0.18, 0.25, 0.059, and 0.13, respectively, have a decreasing trend compared to the use of only auxiliary variable of precipitation magnitude. This composition has reduced the error criterion values to less than a fifth compared to the initial state (only by precipitation intensity covariate) in the return period of over 50 years.

The analysis of statistical moments showed that the precipitation maximum intensity data has a mono-fractal nature. In other words, the changes in the power of the scale are completely linear with respect to moment order, and it can be used to produce the data in different durations. The statistical analysis of the results of estimating the intensity of precipitation in the different return periods and durations with this method compared to the Ghahraman method showed that in most stations there is a significant relationship with a correlation coefficient of over 99 % at a confidence level of 99 %. Generally, the results of the spatial distribution error of precipitation intensity using the Co-kriging method based on fractal in the return periods of 2, 25, 100, and 200 years showed acceptably reduced interpolation error by adding different auxiliary data.

But by the end of the 20th century, most of the water resources have been exploited, and increasing the use of resources increases financial costs and environmental costs. Currently, water demand management is becoming important. The task of demand management is the physical storage of water and economic savings by increasing each product unit with less water and less water pollution. This management is possible through various policy measures. For example, we can refer to economic incentives to preserve water resources, price reform, and reduction of subsidies. Water prices transfer production costs to consumers, and setting appropriate tariffs is a powerful tool to manage consumption, improve allocation, and encourage the conservation of water resources. Pricing is recognized as an important tool to solve water shortage problems. It necessary to modify the pricing of agricultural water for developing countries and move towards sustainable agriculture. Pricing was considered to be one of the most important tools for demand management and it was suggested that the effects of implementing this policy should be investigated by authorities and policymakers in different regions. The results show that before implementing water policies, there is a need for a technical, economic, social, and environmental study based on sustainable development. As studies show, concerns about water scarcity are global and water price reform is essential. Because water is a basic input for production, modifying the price of water affects production costs and, as a result, the amount of production and economic variables. According to global concerns about water shortage and the geographical location of Iran, in this article, with the help of dynamic calculable general equilibrium models, the effect of water pricing in agriculture and industry sectors on macroeconomic variables has been seen.

In this study, dynamic general equilibrium models have been used. The data required to simulate the scenario proposed in this research is taken from the ninth version of GTAP-E. According to the research objectives, the regions are divided into Iran and the rest of the world. Economic sectors include agriculture, coal, oil, gas, industry, petrochemicals, electricity, water and services. Factors of production include skilled labor, unskilled labor, land, natural resources, and capital. In this study, two scenarios are defined. In the first scenario, an impact of 30 % on the price of water in the industrial sector is considered. In the second scenario, an effect of 30 % on the cost of water in the agricultural sector is considered. Due to the structure of the policy patterns, momentum from 2022 has been of interest for the next 10 years.

Pricing policy, like other forms of policy, seeks to achieve specific goals, the most important of which is economic welfare, which includes a number of different variables. Certainly, one of the main features of computable general equilibrium models is to specify the effect of shocks in economic models. Therefore, the achievement of the models estimated in this research is to determine the reaction rate of the targeted variables to the change in water price. Based on this, two scenarios have been defined in this research. In the first scenario, a 30 % increase in the price of water in the industrial sector, and in the second scenario, a 30 % increase in the price of water in the agricultural sector is considered. The obtained results showed that in the coming years, the effects of realizing the price of water in the industry and agriculture sectors on the economic welfare from 2022, from the numerical value of -87.11 to -1158.03 in 2032. Also, economic growth and investment also have negative effects. Changes in GDP growth in the country are almost equally affected by the price of water in two sectors. But gradually over time, the impact of the agricultural sector on the growth of GDP has increased. The change in the price of water affects all economic sectors and has caused a decrease in the production of these sectors. The production in the oil and gas sectors is such that when the production of the industry and agriculture sectors is affected due to the price of water, the oil and gas sectors will have the opportunity to produce more. Water pricing policy has an adverse effect on investment changes.

In this study, the 30 % water price shock in the agriculture and industry sectors has been considered. The results of the estimation of the model show that the effect of the increase in the price of water has strongly affected the growth of GDP and welfare and has significant negative effects on investment. The important point is that the negative effects of realizing the price of water are more in the agricultural sector than in the industrial sector. This means that by implementing the scenario of a 30 % increase in water price in the agricultural sector, economic welfare, production value and the amount of investment have had more negative effects than the increase in water price in the industry. This issue shows that in Iran's economy, the agricultural sector has a decisive role in the country's economy, regardless of its share in the total added value. Therefore, paying attention to the issue of pricing and inter-sectoral imbalances can provide a suitable basis for water policies.

Climate is variable in time and space, so detecting a significant trend is a major challenge for researchers. Calculating the trends of climatic elements such as average temperature, maximum, and minimum temperature has been the subject of many studies in recent years. It has been carried out in different regions around the world. Most studies in this field have focused on large-scale temperature trends. However, more research is needed to focus on the change that occurs at the regional level. In this way, the research conducted on the decadal trends of the average temperature in different regions provides impressive results. In order to study how global warming affects life at the regional level, climate change models and assessments often assume that the impact will be uniform. However, temperature does not increase uniformly in space or time.

In this research, the analyzed monthly data of ERA-Interim with a resolution of 0.25*0.25 degrees during the time period of 1979-2019 from the ECMWF website (European Center for Weather Forecasting) has been used. According to the area of the Abraqo-Sirjan catchment area and the resolution of the studied data, 338 points of the entire basin were covered and studied. Also, the data of the link indices from the North Atlas and South Atlas regions, which were extracted from the NOA site at the same time as the mentioned period, were used. Pearson's correlation and linear regression tests were used to investigate the relationship between the average temperature of the watershed and the remoteness indices.

Examining the average temperature trend of the Abrago-Sirjan basin area showed that except for the months of April, May, August, and December, the other months only have an increasing trend. In April, the northern areas and parts of the west and east of the basin have an increasing trend. Other parts of the basin have no trend. In this month, 37.05% of the area of the basin is covered by the increasing trend zone and 62.94% by the non-trending zone. In May, the northern half of the basin has an increasing trend and covers 48.95% of the area of the basin. The southern half with an area of 29252.85 square kilometers is without trend and covers 51.04% of the area of the basin. In August, only a very small part of the north of the basin showed an increasing trend. This area with an area of 5728.55 square kilometers includes 9.99% of the area of the basin. Other parts of the basin, which includes about 90% of the area of the basin, do not have any trend. In the month of December, the area has been expanding and has covered more parts of the area of the basin. In this month, an increasing trend has been observed in the northern half of the basin, parts of the center, and south of the basin. The area of increasing trend in this month with an area of 43,886.6 km2 has covered 76.57 km2 of the area of the basin. The no-trend area with an area of 13422.44 km2  includes 23.42% of the area of the basin. Correlation between the average temperature of the basin and teleconnection patterns also showed that only the NAO pattern in February showed an inverse correlation at a 95% significance level with the temperature of the basin and other patterns had a direct correlation with the temperature of the basin. In January, TSA and AMO patterns, in February, NAO, TNA, AMO, AMM and NTA patterns; in March TNA, TSA, AMM and NTA patterns; In April, TNA, TSA, AMO patterns; In May, TSA and AMO patterns; In June, the AMO pattern; In July, NTA and TNA patterns; In August, AMO and NTA patterns; In September, AMM and NAO patterns; In November, AMM, AMO and TNA patterns and in December, AMM and TNA patterns have shown correlation with average temperature. In the months of February, March, May, July and September, in addition to the correlation at the significance level of 95%, correlations were also observed at the 99% level, and a major part of the basin has correlations with distant teleconnection patterns.

The results of investigating the average temperature trend of the Abarqo-Sirjan basin showed that the temperature in this basin has only an increasing trend, and during the period of 1979-2019, there was no decreasing trend in the average temperature of this basin. In the months of January, February, March, June, July, September, October, and November, and on an annual scale, all basins have an increasing trend. In the months of April, May, August, and December, apart from the increasing trend zone, the no-trend zone is also observed at the basin level. Among the studied patterns, the AMO pattern has shown more correlation with the average temperature of the basin than other patterns. After that, TNA, AMM, NTA, and TSA patterns have the highest correlation. In the cold months of the year, the lowest correlation between the distance and average temperature of the basin was observed, and in the warm months of the year, a larger area of the basin showed a correlation.

As a great reservoir of nutrients and pollutants, soil plays an important role in health and socio-ecological sustainability. Soil pollution increases as a result of the entry of heavy metals from operations such as agriculture, urbanization, and industrialization. Unlike organic pollutants, heavy metals cannot be decomposed and remain in the soil for more than 150 years. The continuous increase in the concentration of heavy metals in the soil due to wrong agricultural operations has had a serious effect on human health. Long-term use of wastewater in land irrigation often increases the amount of heavy metals in the soil. The present research aims to investigate the amount of heavy metals and pollution indicators.

The study area is located in agricultural soils irrigated with raw sewage in Barzil village of Meshginshahr city (38° 23′ 34″ N and 47° 1′ 7″ E). To perform this research, a regular griding method with a 250 m dimension was done and 97 surface soil samples (0 to 30 cm) were taken. After transferring to the laboratory, the samples were dried and passed through a 2 mm sieve. The physical and chemical characteristics of the soil including pH, EC, texture, organic carbon, and Calcium Carbonate Equivalent (CCE) are measured. The concentration of heavy metals Copper (Cu), Zinc (Zn), Cadmium (Cd), Nickel (Ni), Chromium (Cr), Lead (Pb), Iron (Fe), and Manganese (Mn) was measured by Aqua Regia digestion method and using Atomic Absorption Spectrometry. The spatial distribution of heavy metals was displayed using the Kriging interpolation method. Pollution indices of Enrichment Factor (EF), Geo-accumulation Index (Igeo), Contamination Factor (CF), and Pollution Load Index (PLI) were calculated.

The maximum values ​​of pH and electrical conductivity of the soil in some places irrigated with wastewater have reached 7.70 and 4.35, respectively, and their average values ​​have reached 6.69 and 1.45, respectively. The organic carbon of the studied soil samples varies from at least 0.59% to 3.50% an average of 2.14%. The relatively high amount of organic carbon can be attributed to the land use type of garden. Four texture classes of sandy loam (65%), loamy sand (23%), loam (10%), and sand (2%) have been observed. The average concentration of the three metals Zn (85.41 mg Kg-1), Cd (2.42 mg Kg-1), and Pb (17.38 mg Kg-1) was higher than the average of their continental reference values (0.7, 0.2 and12.50 mg Kg-1, respectively). The higher values rather than continental reference values indicate human intervention and its effect on increasing the concentration of these element contents. It means that irritating sewage caused increasing heavy metal concentration in the study area. The averages of Cu, Ni, Cr, Fe, and Mn were lower than continental references. Pollution indices indicate the state of accumulation of polluting elements in a place compared to the initial values ​​in the parent materials. The EF index of Cd (75.85) is the highest value among the eight metals and 99% of the the study area is classified as a very high enrichment class. The EF of Pb (8.68), Zn (7.42), and Cu (6.14) are in lower ranks. 56.7 % of study area classified as considerable enrichment by Cu and 46.4 % by Zn. The EF clearly indicates the involvement of human activities in the accumulation of four elements Cd, Pb, Zn, and Cu in the study area. Also, moderate enrichment class is caused by Mn, Cu, Zn, and Cr in 63.9, 42.3, 42.3, and 25.8% of the study area, respectively. The lowest and highest amount of the Igeo index is related to Ni (-6.90) and Zn (3.72), respectively. The average of Igeo varies as IgeoCd> Igeopb> IgeoCU> IgeoZn> IgeoMn> IgeoCr> IgeoFe> IgeoNi that introduces Cd as the most pollutant metal. The negative values ​​of Igeo indicate the absence of heavy metal pollution and so absence of pollution. The entire study area grouped as non-polluted or clean class according to Ni, Cr, Fe, and Mn but 86.6% of the area grouped as clean considering Cu and Zn. Cd placed 38.1% of the area in the medium pollution class and 59.8% of the area in the severe pollution class. 69.1% of the area was found to be clean and only 28.9% of the area was moderately polluted with Pb. According to this index, Cd is in the extremely polluted class in the whole study area. The lowest (0.01) and highest (19.72) value of CF belongs to Ni and Zn, respectively. The average of this index varies from 0.13 for Ni to 12.09 for Cd. Except for Cd, which placed 98% of the area in a very high pollution class, the rest of the metals had low or moderate pollution classes. Meanwhile, the low pollution classes had higher contributions than the medium pollution classes. 100% of the area was grouped in the low pollution class considering Ni, Cr, Fe, and Mn but according to Zn and Cu 71.1 and 60.8% of the area was placed in the low pollution class, respectively. Medium pollution class was observed only by three metals Pb (73.2%), Cu (39.2%), and Zn (24.7%). PLI values ​​less than 1 indicate ideal conditions where no pollution has occurred. The values of the calculated PLI index were less than 1 in the whole study area indicating the absence of pollution.

Among the four indices, the Igeo index has classified a larger extent of ​​the studied area in extremely polluted classes, while the PLI index does not show any pollution in the study area. Because Igeo, like the other two indices (EF and CF), is an individual index and considers the concentration of each metal separately, the PLI index is a cumulative index and shows the cumulative effects of all metals. In other words, high concentrations of metals disappear among low concentrations and individual effects of metals are not visible. This may mislead decision makers in dealing with the type and origin of pollution and cause negligent actions. Therefore, it is recommended that considering the harmful effects of each of the metals, individual indicators should be taken seriously.