نشریه مدل سازی و مدیریت آب و خاک
سال سوم شماره 1 (بهار 1402)

Analysis and modeling of temperature time series are important challenges in predicting the behavior of the climate. Temperature is one of the basic elements of climate formation and the most basic factor in determining the role and distribution of other climatic elements. The purpose of this research is to statistically model the monthly temperature of the Sanandaj synoptic station and forecast the temperature in order to know in advance the change in weather conditions for environmental planning.

Sanandaj synoptic station is located in Kurdistan province. The average annual temperature is 12.8 °C and the average annual rainfall is 492 mm. The seasonal autocorrelated integrated moving average model is a time series forecasting method developed in the 1970s by Bucks and Jenkins. The two general forms of ARIMA models are: non-seasonal ARIMA (p,d,q) and multiplicative seasonal ARIMA (P,D,Q) × (p,d,q). The general form of SARIMA is (p,d,q)(P,D,Q)S ~ Zt, where P, D, q, and d are the degrees of autocorrelation (AR), moving average (MA), and degree of differentiation, and P, Q are also degrees seasonal and S are the number of differentiated seasonal.

The average temperature is 14.39, the median is 14.45, and the mean is 7.8 with a low difference, which indicates that the data is almost normal. The data showed that they are homogenous on average with statistics less than the critical limit and p_value of 0.84 and 0.87 respectively. But Van Newman's test with a statistic of 0.30, which is close to zero, showed that the data are not homogeneous in variance. For modeling, based on the minimum of differentiated variance, non-seasonal and one-season zero difference degrees were detected. The differential degree D=1 indicates that the time series oscillates around a non-horizontal line. Then, according to the significant branches of autocorrelation and partial autocorrelation diagrams and fitting different models, two significant final patterns of Sarima were extracted. According to the Akaike criterion, the M1 pattern, that is  SARIMA (0, 0, 2) (0, 1, 1) 12, was determined as the final pattern. According to the obtained coefficients, the model can be written as follows: Zt=Zt-12- 0.407at-1 - 0.1753at-2-0.95a-12+ at, where it is defined as independent normal random variables with zero mean and one variance. According to this model, the monthly temperature of Sanandaj is a function of the average temperature of one and two months before and the corresponding month of the previous year, as well as a function of random phenomena.

According to this model, the monthly temperature of Sanandaj is a function of the average temperature of one and two months before and the corresponding month of the previous year, as well as a function of random phenomena. By fitting the regression equation, a value of 0.007 was observed; but this constant value was not significant and the absence of a constant (θ0) in the fitted model indicates the lack of certainty of the trend in the average monthly temperature of Sanandaj. The trend of temperature increase in Sanandaj is 0.0002 degrees per month. it is suggested to take into account more stations with statistics and more time periods in the study area and the effect of biennial fluctuations, El Nino and Enso, on the western part of the country, which the authors will consider in future research.

The physical properties of soil are essential because of their role in supporting plant growth. These characteristics determine how the plant interacts with the soil, absorption of water and nutrients, root penetration, soil temperature, and the activity of microorganisms. Among the physical properties of soil, soil texture has a significant impact. On the other hand, with the increase in the population and the pharmaceutical industry's need for medicinal plants as the primary raw material for drug production, attention and research on the Lippia citriodora plant are essential. L. citriodora is effective in treating colds, asthma, colic, fever, diarrhea, dyspepsia, insomnia, and anxiety. L. citriodora tea is relaxing and soothing to the nerves. Its analgesic, anti-inflammatory, and antioxidant effects have been proven. Plants containing essential oil, such as L. citriodora may differ in yield and quality of oil according to the type of soil texture, so in order to achieve the highest yield of essential oil and the best quality, the soil should be adapted to the type and needs of the plant for better growth and development be provided. Therefore, the purpose of this research is to investigate the effects of different soil textures on the growth and development and the amount of essential oil of L. citriodora in greenhouse conditions.

This research has been carried out in pots in the research greenhouse of Yazd University in the crop year of 2017-2018. The experiment was conducted as a randomized complete block design in three replications for five months. Plastic pots with drains of 18 heights, opening diameter of 17 cm, and drain diameter of one cm were used for growing seedlings. The pots with different soil textures (loamy, sandy loam, sand, and silty clay loam) based on the calculation of the weight percent of soil moisture at the field capacity (FC) were irrigated. The length of the stem and roots were measured with a ruler with mm accuracy. A caliper was used to measure the thickness of the stem. The samples of shoots and roots were kept in an oven at 70 °C for 48 hr. A digital scale with an accuracy of 0.001 gr was used to measure the dry weight of the shoots and roots of the plants. In order to measure the root volume after separating and washing the roots, the roots of each replicate were placed inside a graded cylinder with a certain amount of water, and the volume of the root was measured in mm based on the change in the water volume inside the cylinder. To measure the plant leaf surface, five leaves of different sizes were randomly separated from each repetition and different parts of the plant, and using the ADC Bioscienticltd model (AM200) device, the total surface size of each of five leaves was measured in each repetition. The essential oil of L. citriodora was extracted by distillation method with water from a Cloninger machine. In this way, in order to increase the contact surface of the water with the plant in the process of extracting essential oils, first the dry leaves of the L. citriodora, were turned into powder by an electric mill and immediately transferred into a 2000 ml flask, then 500 ml of water was added to it. After boiling in water for three hr, the heat source was cut off and the resulting essential oil was poured into opaque glass containers and stored in a refrigerator at 4 °C. To check the statistical effects of each treatment on the study variables, the normality of the data was checked using the Kolmogorov-Smirnov test at the 95% confidence level. One-way analysis of variance (ANOVA) was also used and Duncan's test was applied to compare means. Pearson's correlation method was performed to check the correlation between soil texture and measured traits.

The results showed the significant effects of soil type on the amount of L. citriodora essential oil (P≤0.001). It has shown significant effects on plant height and root volume (P≤0.01) and on the thickness of the stem, the number of leaves per plant and the dry weight of the leaves (P≤0.05). While on some other traits, such as the number of plant branches, fresh weight of aerial parts, fresh weight of leaves, leaf area, root length fresh and dry weight of roots, no significant difference was observed. The results of Pearson's correlation coefficients showed that there is a positive and significant relationship between different soil textures and measured traits. The highest correlation for root length and leaf area was observed with coefficient of 0.89 and 0.80, respectively (P≤0.01). The results of the mean comparison showed that the highest length of the plant was in loamy texture and the lowest was related to silty clay loam texture, the highest number of leaves and dry matter in the aerial part was reported as well as root volume related to loamy texture. To investigate the effect of soil texture on the percentage of essential oil of L. citriodora showed that the highest percentage of essential oil is in silty clay loam texture and, the lowest percentage is related to sandy soil texture.

The results of investigating the effect of different soil textures on the morphological characteristics of L. citriodora medicinal plant showed that the maximum amount of stem thickness, number of leaves per plant, dry leaf weight, plant height, shoot dry weight, and root volume in L. citriodora was in sandy loam soil texture. Moreover, the highest percentage of essential oil is in silty clay loam texture and the lowest percentage is related to sandy soil texture. According to the obtained results, the L. citriodora plant needs a medium amount of nutrients for growth and development. Since the number of leaves produced by the L. citriodora plant in loam and sandy loam soil texture was more than in other textures. Therefore, it can be concluded that the performance of L. citriodora medicinal plant for producing essential oil in each plant is higher in loamy and sandy loam soils.

The use of plant models is one of the methods that help researchers to understand the response of plants to different agricultural managements without conducting numerous experiments that require spending a lot of time and money. The use of plant models for simulating the reaction of plants to water deficit has a relatively long history and has gone through four stages: infancy, adolescence, youth and maturity. These models were introduced to researchers about fifty years ago by developing computer programs and taking into account the biochemical and biophysical mechanisms of solar energy available for the production of chemical energy and plant biomass. Then, from the late 60s and with the advent of computers, the adolescent stage of plant modeling began. In the youth period of plant modeling, some beliefs of the previous years were lost and detailing and validation of models were considered. Among the first measures taken to provide an acceptable model can be the results of research (1998) by Gerik et al. Cited. The results of these researchers' studies led to the presentation of a model called SORKAM, which was able to simulate the dynamic growth of sorghum (Sorghum bicolor). The maturation period of plant modeling started in the 90s and has continued until now. During this period, more comprehensive software for the simulation of crop plants was developed by research centers around the world, including WOFOST, SWAP and MARS and modeling the plant was widely used in different countries.The AquaCrop plant model, which was developed by the Food and Agriculture Organization since 2009, is one of the user-friendly, flexible and widely used models that is widely used by researchers due to the closeness of the simulation results to real conditions. The AquaCrop model is used to simulate crops under various stresses, including fertilizer stress. In the developed version of the model, a semi-quantitative method is used to simulate nitrogen fertilizer stress. However, this model has not been evaluated to simulate the effect of different amounts of nitrogen fertilizer under different application methods. For this reason, this issue is addressed in the present study.

To achieve the research objective, the data was collected from the research project carried out in the 500-hectare research farm of the Seed and Plant Breeding Research Institute, located at 50.58° East longitude and 35.56° N latitude and 1312 m altitude. In this study, nitrogen fertilizer at three levels (N1: 100, N2: 80, and N3: 60) and application time in two methods (T1: three equal usage including 4-6 leaf stage, 10 leaf stage and reproduction stage, and T2: Four equal usage including 4-6 leaf stage, 10 leaf stage, reproduction stage, and inoculation stage) were considered. Then, all treatments were compared with the control, which included traditional fertilization applications in the region. The input data of the AquaCrop model includes four groups of climatic, plant, soil and farm management data. Climatic data includes maximum and minimum daily temperature, rainfall, reference plant evapotranspiration (ET0) and annual average CO2 concentration. Soil data includes saturated hydraulic conductivity, soil texture and volumetric soil moisture at the points of crop capacity and permanent wilting. Farm management data also includes farm management and fertility and irrigation. To evaluate the plant model, calibration was done first. For this purpose, the AquaCrop model was evaluated based on the conditions without fertilizer stress and using the data collected from the farm in the first year. Then, in order to calibrate this model under fertilizer stress conditions, it was necessary to determine the reduction coefficients of coverage development, maximum coverage, average reduction and normalized water productivity reduction percentage.

The difference between the simulated and observational yield for the control was about 5.7%. The AquaCrop model had an overestimation error to simulate control yield. The mean difference between the simulated and observed yield for T1 was about 4.4%. The highest and lowest yield differences were observed for N3T1 (5.9%) and N1T1 (1.8%) treatments, respectively. The average difference between the simulated and observed yield in T2 was about 5.7%, which is equal to T1. The highest and lowest yield differences in T2 treatment were obtained in N3T2 (4%) and N1T2 (1%), respectively. Therefore, with increasing fertilizer stress, the difference between simulated and observational yield increased. The results showed that the AquaCrop model had an overestimation error (MBE <0) to simulate corn grain yield and an underestimation error (0<MBE) to simulate water productivity. The error of the AquaCrop model for yield simulation was about 0.36 t ha-1 in the T1 method and about 0.24 t ha-1in the T2 method. According to NRMSE statistics values, the accuracy of this model for simulating yield in both fertilization methods was in the excellent category (NRMSE <0.1). The error of the AquaCrop model for simulating water productivity in the T1 method was about 0.29 kg.m-3 and in the T2 method was about 0.30 kg.m-3. However, the accuracy of this crop model to simulate water productivity in both fertilization methods was in the middle category (NRMSE <0.3). According to all the results, the accuracy of this crop model to simulate the yield was better than water productivity and its use for similar conditions is recommended. 

The results showed that the accuracy and efficiency of the AquaCrop model for simulating corn yield were acceptable in both calibration and validation stages. No difference was observed in the accuracy of the AquaCrop model for simulating corn yield under both fertilization methods. The error of this plant model to simulate water productivity increased slightly but its efficiency was acceptable. As the fertilizer stress increased, the accuracy of the AquaCrop model decreased. The reason was the increase in the error of this plant model to simulate the development of vegetation under fertilizer stress conditions. Based on all the results, since the fertilizer division methods have not been simulated with this plant model so far, it is possible to rely on the accuracy of the output of this plant model in the mentioned conditions, and its use for similar conditions is recommended.

Soil erosion by water is one of the most common environmental problems worldwide and is considered a serious risk for sustainability in developing countries. Water erosion on a global scale is one of the most critical types of soil and environmental degradation due to its geographical extent and ecological effects. In this regard, effectively controlling sediment load is an important component in watershed management. In the formulation of a watershed management strategy, the estimation of sediment delivery ratio (SDR) plays a significant role. SDR is defined as the sediment yield from an area divided by the gross erosion of that same area. SDR is expressed as a percentage and represents the efficiency of the watershed in moving soil particles from areas of erosion to the point where sediment yield is measured. One of the problems in estimating the SDR in watersheds is the lack of proper information on the amount of soil erosion and sediment yield. In this context, the Sanganeh soil conservation research station, having measured soil erosion and sediment yield of small watersheds, is a suitable place to evaluate the accuracy of the RUSLE model and estimate the ratio of sediment delivery on the scale of small watersheds. The current research aims to achieve two goals: a) determining the accuracy of the RUSLE model in estimating soil erosion based on the measurements in the erosion plots, and b) estimating the SDR using the estimated soil erosion values as well as the sediment yield measured at the outlet small watershed are planned.

Considering the importance of soil erosion and the study of sediment processes in semi-arid rangeland ecosystems, the Khorasan Razavi Agricultural and Natural Resources Research Center (KANRRC) assessed some micro-watersheds for the collection of storm-wise runoff and associated sediment. The Sanganeh research micro-watershed, located 100 km from Mashhad City (northeast Iran), is one of the watersheds selected for this study. The watershed area, the longest waterways, and the mean slope of the watershed are 1.2 ha, 145.0 m, and 31.2%, respectively. The study watershed consists of semi-arid rangeland dominated by Bromus tectorum and Artemisia diffusa, with a coverage of 50%. The soil is Entisol and Aridosol, young, with a maximum depth of 30 cm. The mean electrical soil conductivity (EC), soil organic matter (OM), clay, sand, silt, and surface rock fragments of soils are 1.81, 1.57, 10.6, 54.7, 34.7, and 5%, respectively. In this research, three experimental small watersheds with areas between 4300-12000 m2 were selected along with the erosion plots in them. Then, 24 rainfall events related to two periods of 2006-2009 and 2016-2018 were recorded along with the corresponding data of runoff and sediment in watersheds and plots. In this study, water flow and sediment yield were monitored at the main outlet of the micro-watersheds and plots. The runoff volume was calculated after each storm event by multiplying the depth of collected water, measured using an iron ruler at five points in the tank (corners and central), by the surface area of the collector. The collected runoff and sediment were then mixed thoroughly and one sample was taken to determine sediment concentration and sediment yield. Then, by collecting the required information (includeing rainfall erosivity, topography, conservation practice, soil erodibility, and cover-crop management factors), the RUSLE model was run and compared with the observation data of the plots. The storm-wise soil erosion predictions were compared with observed data based on the criteria of the coefficient of determination (R2) and relative estimation error (RE). In the following, by modifying the RUSLE model and observing the sedimentation data of the studied watersheds, the value of the SDR was estimated.

After collecting the required information, the RUSLE model was implemented at the plot scale. The accuracy of the model was evaluated using erosion plot data, which was not confirmed due to huge overestimations of RUSLE. Next, to achieve more accurate results, regression types (linear, exponential, power, etc.) were used between the observed and estimated values of soil erosion (RUSLE). After applying the correction coefficient, this model was able to estimate the average erosion rate of the whole period are 12, 17, and 2% for E1, E4, and E6 watersheds, respectively, which is within the acceptable range of soil erosion modeling. Therefore, it can be said that the accuracy of the modified RUSLE model (by regression model) in estimating the average soil erosion during the period is higher than the event-based scale. Also, the prediction of maximum event estimation error for E1, E4, and E6 watersheds was 25.7, 35.8, and 21.6%, respectively. After evaluating the accuracy of the RUSLE model at the plot scale and in order to know the amount of soil erosion at the watershed scale, the values ​​of L, S, K, and C factors for the watersheds were calculated based on a weighted average and entered into the modified model. Therefore, the results of the RUSLE model were generalized to the watershed scale. In the final stage, by dividing the amount of erosion by the corresponding amounts of sediment yield measured at the outlet of watersheds, the ratio of sediment delivery was calculated. The average SDR of the entire period in the E1, E4, and E6 watersheds are 42.2, 41.5, and 39.7%, respectively, and in the maximum events, it is one or two percent higher.

Overall, the results of this research showed that using the modified RUSLE model, it is possible to estimate the average soil erosion in the Sangane soil conservation station and also estimate the SDR. Therefore, this approach can be used in executive programs in similar areas. According to the obtained results, the classification of rainfall data based on the rain erosive factor and then the evaluation of the RUSLE model can provide more accurate results. In addition, in this research, due to the small area of the watersheds, waterway processes did not play a role in the deposition and transfer of eroded soils. It is also suggested that similar research could be done in larger watersheds. Finally, considering the determination of the SDR in this area, it is recommended to evaluate the accuracy of the experimental methods for determining the SDR.

The increase in demand for water resources due to population growth and economic development along with water wastage and a decrease in rainfall, on the other hand, has made it significant to pay attention to water demand and make sound policies. Our country is facing the risk of a water crisis in the coming years, mainly due to its location in a dry and semi-arid climate, as well as the ever-increasing growth of water consumption. To alleviate the water crisis, international trade in agricultural products can play a significant role in redistributing water resources because traded goods contain a large amount of virtual water. Water restriction in Iran is an undeniable fact, for this purpose, trading based on virtual water can be a solution to reduce the effects of water restriction. Due to being located in a dry and semi-arid climate, Iran is facing the risk of a water crisis in the coming years. Therefore, in order to deal with it, it is necessary to be more sensitive to the types of water consumption. Among these uses is virtual water. The water used in the production process of goods is called virtual water, a part of which is kept in the product. Virtual water trade occurs when goods are imported into global markets. Virtual water trading is expected to reduce water consumption at the national and international levels due to more efficient and specialized use of water. Today, the concept of virtual water is one of the most critical issues in water resources management. Today, the problem of water shortage has become a serious concern due to climate changes and uneven distribution of rainfall in most regions and countries, including Iran, and is considered the most important obstacle to the economic development of these countries. Trade as a tool to prevent the unnecessary withdrawal of water resources, focusing on the strategy of virtual water trade, can play an essential role in achieving the economic development of countries.

The study area of the research is the Shiblo-Poldasht plain in the northwest of Iran. This area is located in the east of the Poldasht study area and in the north of the Qara Ziauddin study area. The aim of this research was to investigate the statistical status of the cultivated area, the production performance, and the evaluation of the productivity and virtual water of agricultural crops in the Poldasht plain. The time frame of the research is from 2011 to 2021 in an 11-years period. Accurate calculation and determination of water requirement (m3 ha-1). The amount of water required by a plant for its proper growth, taking into account the loss of evaporation and transpiration of the plant, is called the water requirement of the plant. Therefore, the water requirement of the plant depends on the amount of evaporation and transpiration of the plant. It is worth noting that due to different climates and weather conditions, plant growth conditions and as a result, the amount of water needed by plants are also different. In the present research, the various productivity indicators and virtual water of the crops of Dasht-Poldasht have been examined. Moreover, according to the objectives of the research, the physical and financial indicators of water productivity, including the performance index per unit of water volume (CDP), income per unit of water volume (BPD), and net return per unit of water volume (NBPD) have been calculated.

In this research, the amount of virtual water and the productivity index as well as the net and gross economic value of the major crops grown in Poldasht city in West Azarbaijan province were investigated. In this regard, first, data and information related to crops were collected through relevant organizations and institutions, and NETWAT, CROPWAT, and CLIMWAT programs and Excel programs were used to draw graphs and graphical results. Then the yield of crops was calculated by dividing the amount of crops produced by the area of ​​planting crops and the productivity index and virtual water. The results of this research show that the watermelon crop with a harvesting area of ​​5789 ha and a production rate of 237951000 kg and a production yield of 41103.99 kg ha-1 with a water requirement of 2760 m3 ha-1 has a productivity of 14.89 kg m-3 and has The highest level of productivity is also the results show that the alfalfa product is the lowest level of productivity. It is worth noting that despite the fact that the watermelon product has high production and productivity at a very low harvest level, it is also a very water-loving product that has a relatively high water requirement, and generally experts are looking for an alternative product due to the lack of water resources. Finally, it is suggested that traditional (submerged) irrigation methods should be replaced by modern pressurized irrigation methods so that in addition to increasing efficiency and productivity, we can see a reduction in water consumption and its wastage. It is also suggested that the water requirements of agricultural crops be compared with each other using the data of the Agricultural Jihad Organization and the aforementioned programs, and its effect on the amount of water consumed and its savings, as well as the net and gross values ​​of the production of crops, and the final results It is compared with the national water document to fully verify the amount of water needed.

Despite the fact that the watermelon product has high production and productivity at a very low harvest level, it is also a very water consuming product possessing a relatively high water requirement, and generally experts are looking for an alternative product, due to the lack of water resources. Finally, it is suggested that traditional flood irrigation methods should be replaced by modern pressurized irrigation methods, so that in addition to increasing efficiency and productivity, we can encounter with a reduction in water consumption and its wastage. It is also suggested that the water requirement of agricultural crops should be compared with each other using the data of the Agricultural Jihad Organization and the aforementioned programs, and its effect on the amount of water consumed and its saving, as well as the net and gross values of crop production, should be evaluated. Finally, the results have been compared with the national water document so that the amount of water needed can be fully verified.

Increasing agricultural production is necessary due to the growing population in order to ensure food security. Also, a large part of agricultural lands in arid and semi-arid climates face the limitation of carbon storage and nutrients. This limitation is more visible in coarse textured soils with low clay content, due to the inability to supply elements required for plant growth and the ability to retain water in the soil. Therefore, modifying the physical, chemical and biological characteristics of the soil is inevitable to achieve sustainable agriculture. In the meantime, with the high use of chemical fertilizers along with the excessive tillage activities while destroying the soil, management costs increase. Soil degradation is an increasing worldwide threat to the sustainability of agriculture. The use of organic amendments like biochar may prove a key for sustainable agriculture, as it could keep the carbon pool in soil over the long term, thus improving soil fertility and crop productivity, mitigating global climate change, and finally enhancing soil physicochemical quality. The ability of biochar to enhance the physical and mechanical properties of soils is dependent on the characteristics of biochar including its particle size, application rate, feedstock type, and pyrolysis conditions. This study aimed to assess the effect of applying different application rates, feedstock, and particle sizes of biochar on the soil’s physical and mechanical properties in sandy loam soil.

The study was conducted in a research greenhouse of Shiraz University in 2019. To investigate the effect of biochar feedstock, application rate, and particle size on soil bulk density, aggregate stability (mean weight diameter), penetration resistance, and shear strength in sandy loam soil, using two feedstock types (palm leaf biochar and lemon peel biochar) were pyrolyzed at a temperature of 500 °C for 3 h. Each biochar was fractioned by dry sieving into three sizes: 2-4, 0.8-2, and < 0.8 mm, and mixed with sandy loam soil at four application rates of 0.5, 1, 2, and 4 % (v/v) with zero application rates (control). The pots were incubated in standard conditions and water content was kept at near field capacity throughout the experiment for 15 months. In the production of biochar, plant residues of palm leaves and lemon peel were used, which are surplus plant wastes available in Fars province. The apparent specific gravity of the soil was measured by the conventional method of paraffin blocks. The statistical analysis of the results was done in order to check the influence of the studied treatments on the soil characteristics using SAS statistical software. The mean of the effect of each treatment separately and also the interaction of the effects of the investigated treatments (if significant) were compared using Duncan's multiple range test at the five percent probability level (p < 0.05).

This result indicated that applying biochar improved the physical properties of soil, including a significant decrease in soil bulk density from 5.4 to 19.8% by using application rates of 0.5 to 4% biochar, increase aggregate stability from 37.6 to 73.6% and increase shear strength from 3.2 to 15 by using application rates of 1 to 4% of biochar as compared to control. For a 4% biochar rate, penetration resistance increased by 5% as compared to control. The results show that lemon peel is more efficient in soil bulk density whereas palm leaf biochar was efficient in aggregate stability and shear strength (no significant difference in penetration resistance was observed with the application of different biochar sources). Moreover, irrespective of biochar sources, biochar with finer particle sizes (< 0.8 mm) improved aggregate stability and shear strength, when the biochar application rate was the same, but the most notable improvement in soil bulk density was observed at the coarse fractions (2-4 mm).

Biochar as a type of organic compound that has great compatibility with the environment, is of interest in advanced agriculture due to its stable carbon storage and its positive effects on the biochemical and physical characteristics of the soil. According to the results of this research, the use of biochar in soil had a significant effect on reducing the apparent specific mass of the soil and also a significant increase in the stability of soil grains and shear resistance, which improves the physical and mechanical properties of the soil can have an effect on protecting the soil against water and wind erosion. On the other hand, although at the application level of 4% biochar, the resistance to subsidence increased by a significant amount of 5%, but no significant changes were observed in this feature. Palm leaf biochar had a stronger role in increasing the stability of soil grains and shear resistance, on the other hand, lemon pomace biochar had a greater effect in reducing specific mass due to its structure similar to sand particles. Also, by increasing the levels of biochar to four percent as the most effective level of biochar application, the apparent specific mass values ​​decreased by 19.8% and the stability of soil grains and shear strength increased by 73.6% and 15% respectively. Also, the results showed that in general, by reducing the size of biochar particles from 4 mm to less than 0.8 mm, the weight average of soil grain diameter and shear strength increased significantly, but the average apparent specific mass increased with the increase of particle size (2-4 mm particles) reduced. Based on the above results, it can be concluded that choosing an optimal mode of sources, levels and size of biochar particles as an organic soil conditioner can lead to maximum productivity in agricultural production while reducing agricultural management costs. In this research, it was found that under the same conditions, the use of two percent palm leaf biochar with a particle size of less than 0.8 mm in sandy loam soil is useful. It should be kept in mind that the behavior of biochar is not only limited to the characteristics investigated in this research, and the change in the conditions of biochar production (thermal heating) causes a change in the characteristics of biochar. Therefore, it is suggested that in addition to the studied effects, the effects of characteristics based on the biochar production process, such as temperature and duration of thermodilution, should be investigated at different times on the physical, chemical and biological characteristics of different soils and in field conditions in order to obtain more comprehensive information on the optimal amounts of charcoal. Biologically, especially at the farm scale, to obtain soil amendment.

Nowadays, with the increasing urban population on the one hand and growing water consumption per capita on the other, the use of treated wastewater has been the subject of much attention, especially in arid and semi-arid areas. The city of Yazd, in central Iran, with its hot and dry climate, is the driest major city in Iran, with annual precipitation of 50–60 mm. Since there is no surface water, the city has relied on its groundwater system. In past decades, underground aqueducts, called Qanats (a series of well-like vertical shafts, connected by gently sloping tunnels) were used to irrigate farmland in this area. Over the past few decades, the qanats have experienced decline and deletion due to low rainfall and the excessive use of groundwater resources. Considering that the qanats are failing, water shortage is a critical challenge in this area. Therefore, treated wastewater is a good alternative, especially in agricultural applications. In the Yazd municipal wastewater treatment plant (YMWTP), the stabilization ponds had been used for sewage treatment until 2013, but after that, the advanced sequencing batch reactors (SBRs) became operational, and treated wastewater and sludge are used in agricultural applications. The most important aspect of wastewater application is a concern for public health.  Therefore, the main objectives of the present study are: 1) risk assessment of the SBRs effluent in agricultural irrigation based on the comprehensive pollution index (CPI) and 2) evaluation of SBRs performance and comparison with the efficiency of stabilization ponds and artificial wetlands.

The YMWTP is located in northern Yazd, close to the main road of Yazd airport. In the YMWTP, wastewater passes two initial treatment units including a screening and grit chamber and is then discharged into the SBRs. In the YMWTP, six SBRs are employed. The dimension of each reactor in meters is as follow length 40, width 23.7, and depth 6.6. In the SBR process, five stages including filling, reaction (mixing and aeration), settling, effluent, and idle are conducted in each reactor. The treated WW is decanted from SBR units to the disinfection section and then transferred to irrigate the green space. It should be noted that extra sludge is also discharged from the system in other treatment processes including digestion, dewatering, and drying to use in farmlands. All of these phases plus the total retention time is 4.9 h. For sample collection, composite sampling was carried out daily (every four hours) from October (2018) to September (2019). In this regard, special polyethylene bottles (1 L) for wastewater sampling were utilized to collect samples from the influent and effluent of the SBR system in the YMWTP. The American Public Health Association (APHA) method was applied To the measurement of turbidity, biological oxygen demand (BOD), total suspended solids (TSS), chemical oxygen demand (COD), ammonia (NH3), total phosphorus (TP), electrical conductivity (EC), total dissolved solids (TDS), and power of hydrogen (pH). The Statistical Package for the Social Sciences (SPSS) was also used for data analysis. This study adopted a simplified approach to risk assessment named CPI. The CPI was evaluated by using the measured concentration of parameters concerning their permissible limit in irrigation wastewater quality prescribed by the department of environment of Iran (DOE) and the United States environmental protection agency (USEPA). 

Based on our results, the annual average of the studied parameters in the influent, effluent, and also the percent of removal efficiency were; turbidity 173.5, 7.59, 96%, BOD 325.6, 14.9, 95%, TSS 293.7. 17.4, 94%, COD 650.2, 43.5, 93%, NH3 44.6, 5, 88%, TP 5.6, 2.6, 52%, EC 1881.1, 1463.1, 22%, TDS 981.5, 825.5, 16%, and pH 7.65, 7.27 and 5%. The results also indicated that the efficiency of SBR is higher than the stabilization ponds and artificial wetlands. Considering the DOE limits, the results of the calculation of CPI showed that the effluent from the SBR units of the YMWTP is suitable for agricultural purposes and irrigation of green spaces because the CPI rate was less than 0.5 in all months; and according to the annual average CPI (0.17), the effluent quality is in the clean category. But considering the limits set by the USEPA and the obtained average annual CPI (1.63), the quality of effluent from the YMWTP SBR units is placed in the category of medium pollution. It should be mentioned that in some months, such as Bahman and Farvardin, the monthly average of the CPI index exceeded the number of 2, and the quality of effluent was placed in the category of severe pollution. It was also found that the most effective parameters in increasing CPI are turbidity and TDS parameters. Our results revealed that the annual average of EC in the YMWTP SBRS was 1601 ± 196 µS/cm, which according to the Wilcox classification, the quality of the effluent is placed in the high salinity category; therefore, it is at the medium level for agricultural uses. According to the correlation analysis, a positive significant relationship was found between the EC and TDS and also turbidity and TSS in the influent. There was a negative significant correlation between the TSS, turbidity, and NH3 in the influent, while it was positive in the effluent. Our data showed a negative significant correlation between the TP, TDS, and NH3 in the effluent.

According to our results, the highest efficiency of the SBR units is in removing turbidity, BOD, TSS, and COD, respectively. Although according to the USEPA limits, the quality of the effluent from the SBR units of the YMWTP is in the medium pollution category and it is risky to use YMWTP effluent for agricultural purposes, according to the DOE limits, the quality of the effluent is in the clean category and it is suitable for agricultural use. Considering the characteristics of the treated wastewater of this city and considering the presence of many industries in the Yazd-Ardakan plain and the lack of water in this area, it is suggested that in future research, the feasibility of using treated wastewater for industrial purposes such as cooling towers, steam boilers, product production process, fire extinguishing, dust control, construction industry and artificial feeding of underground water should also be investigated.

Mechanical properties of most soils alter upon the increase of moisture and saturation. In some soils, certain phenomena appear due to increased moisture. Some of these phenomena lead to major damage in development projects. These soils are called Sensitive soils to water”. The most significant types of these soils are swelled soils, dispersive soils, and collapsible soils. Dispersive soils refer to clay soils that are easily washed up in waters with a low concentration of salt. Dispersion is a progressive phenomenon that starts from one point and is gradually extended. The start point of the dispersive phenomenon may refer to cracks resulting from shrinkage, soil deposition, or cracks made due to the roots of plants. This phenomenon is of great importance in such plans as soil dams and water supply channels where there is a water concentration inside the soil too. One of the essential reasons for soil erosion and sediment production in irrigation and drainage networks is the instability of canal soil, so it is necessary to use erosion control methods in parts of the canal route, especially at the intersection with other structures. To this end, the solution investigated and tested in this article is the use of biological mulches to reduce the erosion of the side slopes of earthen channels.

This research has been conducted on soil with a Loam sandy texture of irrigation and drainage channels network of the Arayez Plain of Khuzestan that lies on the west side of the Karkheh River. Using a simulated system, two-nozzle rainfall of the K18 feature was performed. To determine the effect of mulches on parameters of side wall erosive resistance of the soil structure, after the soil of the region passes through a sieve of 4.76 mm was put in the basin designed for 1 x 0.33 x 0.1 m for about 25 kg. After filtration, leveling, and pressing the soil to the edge of the Flume basin proportionate to the physical special weight of the soil, the stabilizing materials, and different bio mulches were sprayed in their different concentrations. Then, the basins inside the Flume with a side slope of 80% were put into a depth of 10 cm. In this research, with the objectives of determining the type and most appropriate level of biological mulches and their effect on the soil erosion resistance parameters of the lateral wall of the drainage channels of the Araiz plain of Khuzestan, the necessary experiments were conducted in the rain simulator laboratory of the Soil Conservation and Watershed Research Institute. So that using four types of water-based biological mulch, which were named with numbers 1 to 4 and at three levels of minimum (C1), medium (C2), and maximum (C3), experiments were conducted on the soil sample of the researched area. Each of the experimental treatments on a flume with a side slope of 1 to 1.25, similar to the side slope of canals in nature, was repeated and simulated three times in two rainfall intensities of 30 and 80 mm h-1. In each experiment, primary and secondary soil moisture, runoff volume, sediment weight, the intensity of water penetration in the treatments, and the shear strength of the treatments were measured. The experiments were conducted in a randomized complete block design in the form of split plots and the data were analyzed using SAS software and the averages were compared using the Student–Newman–Keuls (SNK) multi-domain test. Finally, by using mathematical models with optimization and minimization of sediment amounts and economic costs, the most suitable treatment for stabilizing the lateral walls of drains was determined. SPSS software was used for the statistical analysis of data and ANOVA and the Duncan test were used for the statistical comparison of data. Considering the tables obtained from data variance analysis and considering the statistic F and significance level, it can be said that all treatments are different in a significance level of 5% concerning the extent of sediment and there is a significant difference in the sediment amount.

The tests conducted on the soil of drainage channels of Arayez plain with a loamy – sand texture indicated that the presence of mulch coverage results in reduced sediment arising from rainfall in the manner that by the increase of density in any of the mulches, sedimentation reduces accordingly. The results obtained from the statistical analysis of this research confirmed that there is a significant difference in a level of 5% between sediment amounts of test control and mulch treatments for the sample of the soil under study. Therefore, bio mulches have an effective role in erosion control and the decrease of sediment from the walls of soil channels. The results indicated that only two types of biological mulches used in this study had a positive effect on shear resistance. General Linear Model (GLM) results showed that the interaction of two parameters of rainfall intensity with the type of treatment on shear strength is significant, so the interaction of rainfall intensity parameters with the level of materials used was not significant at the 5% level. In addition, the interaction of the type of treatment with the level of the materials used was also significant. According to the results of the analysis of variance in investigating the role of rainfall intensity on biological mulch treatments, it was found that rainfall intensity affected shear strength.

According to the above photos, all mulches compared to the control treatment have had a remarkable effect on the decrease of sediment. Moreover, it was found that the increase in the density of mulches used in all densities has had a remarkable effect on the decrease of outlet sediment. Furthermore, it was realized that the rainfall factor affects the increase of sediment. Thus, this effect in mulch 2 is the least effective in such a manner that upon increased rainfall in mulch 2m we see the minimum increase in the amount of sediment. The results after conducting system engineering and analyses related to the optimization of sediment amounts and economic costs indicated that mulch 2 at the C3 level has the best efficiency in increasing shear resistance. This conclusion could help the decision makers to allocate their soil conservation budget for the best performance activities.

LST (LST) is one of the important parameters that affect the physical, chemical, and biological processes of the earth as well as environmental science and urban planning. Human activities such as land use changes and the development of urban areas led to an increase in the LST and the appearance of thermal islands. The main source of climate data such as temperature are synoptic stations, however, it is impossible and time-consuming to use traditional methods to estimate the LST for all types of earth conditions, on the other hand, synoptic stations only measure temperature information for specific points, and the obtained values are only related to that specific point; while according to the land cover and other conditions, the temperature in different parts of a region is different compared to the temperature recorded for a specific point and can be several degrees celsius lower or higher, therefore, it is necessary to use scientific methods that provide the possibility of calculating the temperature of any point on the earth's surface. At present, remote sensing images due to features such as wide and continuous coverage, low cost, timeliness, and the ability to obtain information in reflective and thermal ranges, are suitable tools for extracting LST and land use maps. Spatial analysis is one of the important subjects in the temporal and spatial evaluation of land surface data, which can be used to examine the spatial and temporal changes of spatial data in a region. Given that the data that are examined in environmental studies are not independent of each other in most cases and their dependence is due to the location of the observations in the studied space, which are called spatial data; Due to the existence of a spatial correlation between the data, the usual statistical methods are not a suitable method for examining these data, and spatial statistics can be used as a suitable option for analyzing these data. The aim of this research is to extract LST and land use map of Yazd county using a remote sensing technique. In this study, the spatial autocorrelation of LST in Yazd city and the identification of hot thermal clusters have been investigated using the global Moran statistic and the Getis-Ord GI statistic.

In this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to extract the land use and LST in the study area, After performing the necessary corrections in the preprocessing stage, the land use map of the study area was prepared in 5 classes (built-up, vegetation cover, water bodies, bare land, and rock) using the support vector machine method and the overall accuracy and kappa coefficient were used to evaluate the classification result. In the next step, LST was extracted by the split window method. The relationship between LST and Soil adjusted vegetation index (SaVI) was investigated using regression analysis. In order to identify the spatial pattern of the LST, the global Moran index was used and hot spots were identified by Getis-Ord GI statistics.

Our findings show that the kappa coefficient and overall accuracy were equal to 0.96% and 98.99%, respectively, bare lands are the most, and water bodies have the least area, equal to 76.16 and 0.09%, respectively. The average LST was 50.83°C. The result showed that the type of land use had an effect on LST, the water bodies had the lowest, and barren lands had the highest mean LST, equal to 36.91 and 52.13 °C, respectively. Vegetation is one of the factors that regulate the LST, areas without vegetation have a maximum LST and areas with high density vegetation have minimum LST .Based on the results, the vegetation quality of the study area was poor and its average temperature was 45.61°C. The mean of SAVI index was equal to 0.09 and correlation analysis showed a negative correlation between SAVI index and LST (r = -051). The analysis of spatial correlation with global Moron indexes showed that the LST of Yazd has a spatial structure, in other words, LST is distributed in a cluster form, Based on the results of the Getis-Ord GI statistic, the area of hot and cold spots was equal to 66.86% and 27.4%, respectively. In general, parks, cultivated lands, tree and forest cover and water areas, formed the cold spot areas of yazd city, and the hot spot areas of yazd city were located in the industrial areas and surrounding urban lands, hospitals, passenger terminals, gas stations, places near busy roads and bare and uncovered lands.

The results showed a strong relationship between land use and LST. Based on the results, the LST data of Yazd has a spatial structure pattern, barren lands and industrial areas formed hot thermal islands, and vegetation and water bodies formed cold thermal islands in the study area; the wide area of barren lands, the lack and poor vegetation cover due to the lack of rainfall and drought are factors affecting the LST and the creation of hot thermal islands in the study area. The result showed a negative relationship between LST and SAVI, the vegetation of the study area is weak and its temperature is high. Considering the role of vegetation in adjusting LST, it is recommended to take necessary management measures in order to improve the quality of vegetation and reduce bare land in the study area, and also prevent the conversion of natural land uses into built-up land. The results of this research can be used by managers and planners for better urban management. The results of this research confirm the capability of remote sensing in environmental studies, it is suggested to identify thermal islands in other seasons and at night and compare the results with the results of this research.

Agriculture is the main consumer of water and also the most vulnerable economic sector in the face of water shortage. This issue has become one of the main challenges in water management in the dry regions of the world where the socio-economic risks of water shortage are unavoidable; One of the most important solutions is to pay attention to the problem of changing the current behavior of water consumption and achieving a sustainable behavior of agricultural water consumption. Changing human behavior is complex and usually requires a combination of components to induce a person to try, adopt, and maintain a new behavior. There are different models for investigating behavior change and identifying the factors affecting behavioral intention. One of the models that has received the most attention from researchers is the theory of planned behavior. This theory was proposed by Ajzen in 1991. This theory has good power in predicting behavioral intentions due to considering individual, social and environmental factors; In fact, this model considers the complexity of relationships between human behavior and its determining factors, and ultimately considers human behavior as a result of his intentions. The constituent components of this theory include attitude, subjective norm and perceived behavior control. In addition to the theory of planned behavior, another model that is considered as one of the best methods to know the determinants of behavioral components and can be used to examine the key factors of behavior change is the Risk, Attitude, Norm, Ability and Self-regulation (RANAS) approach. The effectiveness of the RANAS for predicting behavior has been confirmed in comparison with some behavior change models; Because it creates a strong foundation for designing behavior change interventions. Therefore, The purpose of the research is to compare the predictive power of the theory of planned behavior with the RANAS approach to measure the intention to use sustainable agricultural water consumption measures in Minoodasht county.

The statistical population of the study was made up of 2358 farmers of Minoodasht county, Golestan province, of which 331 people were selected as a sample using Cochran's formula. Considering the distribution and dispersion of farmers in different districts and in order to obtain a representative sample of the studied statistical population, multi-stage random sampling was used. The data collection tool was a researcher-made questionnaire, which was used to measure the main components of behavioral intention from the planned behavior theory and the RANAS approach. The face validity of the questionnaire was used by a panel of PNU university experts and ministry of agriculture-jihad experts, and its reliability was confirmed by conducting a pre-test study by calculating Cronbach's alpha coefficient (0.603 ≤ α ≤ 0.971). It should be noted that in the descriptive statistics section, Interval of Standard Deviation from the Mean (ISDM) method was used to describe the frequency of the respondents' responses to each of the research variables. According to this formula, individuals' responses were categorized as low, moderate, and high according to Likert type scale used: A: Low= A≤ Mean-1/2 Sd; B: Moderate= Mean–1.2 Sd≤ B≤ Mean+1.2 Sd; C: High= Mean+1/2 Sd≤ C. In order to analyze the data SPSS22 software was used.

The components of the planned behavior theory, i.e., attitude, subjective norm, and perceived behavioral control, were able to explain 88.5% of changes in behavioral intention, and the components of the RANAS approach, i.e., risk, attitude, subjective norm, ability, and self-regulation, were able to explain the 89.4% of behavioral intention changes. In the planned behavior theory, only the attitude variable (P=0.000, T=24.13, and B=0.935) has a direct, positive and significant effect on the behavioral intention variable. Also, the findings of this model showed that perceived behavior control has no significant relationship with behavioral intention, which were consistent with Pino et al. (2017), and Tavassoti et al. (2021). The respondents did not find it easy to use the methods and measures that lead to less water consumption in the fields, and they considered changing the irrigation method and adapting new irrigation methods to the cultivation pattern to be associated with risks. According to the farmers' understanding of the difficulty of the new behavior, they underestimated their own success in changing the new behavior. In fact, farmers' motivation to implement activities and measures to reduce water consumption in the farm due to their understanding of the poor condition of the internal environment (ability, knowledge and skills) as well as the external environment (opportunities, support, economic, financial, security and social issues) considered difficult. The results of the RANAS approach showed that based on the standardized beta coefficient, the attitude variable (0.752) had the greatest role and influence on the intention of farmers to sustainable water consumption, followed by risk (0.169) and self-regulation (0.154). The subjective norm (0.106) contributed to the prediction of the intention to accept the behavior of sustainable agricultural water consumption. The results of this approach in the field of attitude were consistent with Tajeri Moghadam et al. (2018), and Khani Filestan et al. (2020). The findings of this approach showed that sunjective norm has a significant effect on behavioral intention, which was consistent with Mohammadi et al. (2016) and Bakhshi et al. (2019). This study showed that the risk variable has a significant effect on behavioral intention, which is consistent with Tajeri Moghadam et al. (2018) and Hassani et al. (2017). The strongest predictive component in both models for the readiness to apply water-saving measures was attitude with coefficients (0.933) and (0.752); Also, the level of this variable was in a low state (64.4%). Therefore, it is suggested that the extension department, considering the high average age (69 years) and the low literacy level of the majority of farmers (84.9 %), should design and implement extension and educational programs that suit their conditions in the form of field visits, farm days, and demonstration farms of leading farmers so that they have a more positive attitude towards reducing water consumption.

Both models have a high power in explaining the prediction of behavioral intention. But the RANAS approach is better able to recognize behavioral determinants than the programmed behavior theory. Therefore, It is suggested that the extension department should use the RANAS approach to design intervention programs to change the behavior of farmers to reduce water consumption.

Climatic changes and the occurrence of long-term drought have greatly affected the vegetation pattern of watersheds, which results in the change of runoff coefficient and flood potential. The phenomenon of flooding in Iran is more than caused by the occurrence of heavy rains, it is the effect of disturbing the natural balance and geographical conditions of the region so that the occurrence of ordinary rains also causes floods.

In order to investigate the flood changes and prioritize the sub-basins of the Dehak watershed in South Khorasan province based on flood potential under the influence of drought periods, 30 years of annual rainfall statistics were used and the SPI drought index was determined. Digital elevation model maps, soil hydrological groups, and Landsat 5 and 7 satellite images for the years 1990, 2000, and 2009 were prepared and the Normalized Difference Vegetation Index (NDVI) was calculated with the help of ENVI 4.8 software and maps of vegetation and land use status were prepared. With Arc GIS 9.3 software, curve number maps from the integration of soil hydrological group maps, land use, and vegetation status, based on the Soil Conservation Service method, and with the SCS method, the participation rate of each of the sub-basins in the output flood of the entire area is determined, and by repeating the individual removal of each of the sub-areas, the sub-areas were prioritized based on flood potential.

The results of classifying Landsat images using the maximum likelihood method showed that the overall accuracy and the Kappa coefficient obtained from the estimation of accuracy in the NDVI map in 1990, obtained from Landsat images were equal to 73.2 and 0.68%, respectively, in the map obtained in 2000 equal to 75.2 and 0.71% and in the map obtained in 2009 it is 79.8 and 0.84 respectively. The results of the correlation of the percentage of the area of the NDVI for the three years 1990, 2000, and 2009 using SPSS software And the analysis of variance test (One-Way ANOVA) and Duncan's subset and also a significant level of 95% showed that between the three NDVI indices for the three years 1990, 2000 and 2009, the normalized difference index of vegetation cover had a high correlation (0.91) at the level There was a significant value of 0.05. Based on the standardized precipitation index, from 1998 to 2010, with the exception of 4 years, the rest were determined as drought periods in the region. The kappa coefficient obtained from the estimation of the accuracy of the NDVI map was 0.84, which was the most accurate in monitoring vegetation changes. The set of the slope, soil, and geological factors has led to placing 86.6% of the area in hydrological group C, which by definition has a high ability to produce runoff. The weighted average curve number (CN) of the Dehak watershed has changed from 62.35 in the wet year of 1990 to 65.04 and 63.50 in 2000 and 2009, which were dry years. Examining the significance of the simulated values of runoff height and flood peak flow using the analysis of variance test showed that there is a high correlation (0.71) at a significant level of 0.5 between the values of the runoff height corresponding to the three years 1990, 2000 and 2009 and there was a high correlation (0.68) between the values of the peak flow of the flood corresponding to the three years, the studied index. The peak flood discharge with a 5-year return period increased from 7.89 m3/s in 1990 to 13.67 m3/s in 2000, which was affected by drought, which is equivalent to 74.87%. This increase for the peak discharge of 200 years was equal to 21.64% so its amount increased from 93.68 m3/s in 1990 to 112.42 m3/s in 2000. The investigation of the runoff curve number of the Dehak watershed in 1990, i.e. before the drought period, showed that it was 62.35 in 1990 and 65.04 in 2000, and 63.50 in 2009, so it can be concluded that the period of Drought caused an increase in the number of runoff curves in Dehek watershed. The investigation of the height of the flood runoff of the Dehak watershed in 1990, that is, before the drought period, was determined to be 1.8 mm, and this ratio changed from 3.23 mm in 2000 to 2.8 mm in 2009, so it can be concluded that the occurrence of the period Drought has caused an increase in the height of the runoff in the Dehak watershed. Vegetation has less effect in the relative reduction of terrible floods with a high return period. In the study of the effect of changes in vegetation cover on the peak discharge and flood volume, it is also observed that the peak discharge of the flood is more sensitive to land use changes. The results of the flood discharge in the land use scenario (continuing the process of vegetation destruction) also showed that in case of further destruction of forests and pastures in the area and development of agricultural lands, the peak flood discharge will increase by 35 and 24% with a return period of 5 and 100 years, respectively. Found. This means that vegetation alone plays a limited role in controlling large floods with high return periods. The high weighted average of the curve number in the whole area in different years indicates the high risk of flooding in the Dehak watershed.

By prioritizing flood potential, it was found that among the 7 sub-areas, F3 sub-area (CN) has the highest value with 66.89 and is the most flood-prone sub-area, which is due to the existence of phyllite formations, Marne and also the abundance of sloping surfaces are in it, and sub-areas F4 and F5 are placed in the next level of flooding, which should be considered in management and executive planning. The priority map of the sub-basins in terms of flood potential shows that in the southeast margin overlooking the heights of the basin, there are factors such as high slope and rocky outcrops without vegetation cover next to the land use, the risk of flooding is very high and therefore the priority of any watershed engineering operation. In this area, it is more priority than other points.

Groundwater is the sole source of water for drinking, irrigation, and industrial uses in many arid and semi-arid regions of the world. Groundwater can be contaminated by natural as well as anthropogenic influences. Residential, municipal, commercial, industrial, and agricultural activities can all affect groundwater quality. Groundwater contamination results in poor drinking water quality, loss of water supply, high cleanup costs, high costs for alternative water supplies, and/or potential health problems. In Iran, dependence on groundwater has increased tremendously in recent years. Groundwaters can be contaminated by natural as well as human influences. Land use changes, residential and agricultural activities can affect the quality of groundwater. Groundwater contamination can lead to poor drinking water quality, loss of water resources, high cleanup costs, high costs for alternative water sources, and problems for watershed health. In Iran, dependence on underground water in most watersheds has increased greatly in recent years. Therefore, the evaluation, protection and proper management of underground water resources is very necessary for optimal and sustainable use of water resources. Water quality assessment includes the assessment of the physical, chemical and biological nature of water in relation to its natural quality, human effects and intended uses, especially uses that may affect human health and the health of the water system itself. The use of geographic information system technology has greatly simplified the assessment of natural resources and environmental concerns, including groundwater. In groundwater studies, GIS is commonly used to analyze site suitability, manage watershed assessment data, estimate groundwater vulnerability to contamination, model groundwater flow, model solute transport and leaching, and integrate groundwater quality assessment models with spatial data to create Spatial decision support systems are used. This study attempts to assess the influence of changing land-use patterns on the groundwater quality in the Dehram, Fars province. The study area is an agricultural developing region with land development progressing at a fast pace. The objective of this article is to demonstrate the influence of land-use transformations, land-use transition, on the quality of groundwater using geographical information systems. The study also aims at evaluating the significance and applicability of a groundwater quality index (GQI) generated using geographical information system (GIS) approach for the assessment of groundwater quality in a medium sized catchment. Moreover, a simple methodology for the preparation of a groundwater quality sustainability map, for use in planning and management decisions by local government authorities, is developed in this work.

The water samples were collected from 6 wells in the study area in 2021, which are added to the urban water system for drinking purposes. For 2014, the available data were used to observe the general changes in the quality of underground water during this period. Sampling in the summer season, due to the lack of water in this season, by taking water from wells in the area and injecting it into the drinking water system, in three repetitions for the year 1400 according to the standard method of the American Public Health Association (APHA) with field sampling and the available data of year 2013 were used to show seasonal changes in different water quality parameters during both years. Therefore, samples were taken from each well of the available data three times in 2013 and three times in summer (July to August) 1400. The geographic coordinates of the sampling wells were recorded manually using a Garmin e-Trex GPS receiver. The hydrochemical data obtained from the laboratory analysis of the water samples were linked to the spatial database of the sampling points. Spatial data shapefiles were prepared in vector format showing the locations of sample wells along with associated hydrochemical data. To evaluate the location of the sampling wells according to the potential sources of groundwater pollution, these shapefiles were placed on the land use map. Then, point shape files were used to prepare variable concentration maps by applying Kriging interpolation method. The GQI proposed was used for quality assessment. To generate the index, seven parameters listed in World Health Organization guidelines for drinking water quality were selected from the main dataset. Six parameters (Cl-, Na+, Ca+2, Mg+2, SO4-2, and TDS) can be categorized as chemically derived contaminants that could alter the water taste, odor, or appearance and affect its ‘‘acceptability’’ by consumers. NO-3 was categorized under chemicals that might inflict ‘‘potential health risk’’ and a guideline value of 50 mg/l was assigned. The GQI integrates the different water quality parameters to give a quantitative index value that can be used for spatio-temporal provides in groundwater quality. In the present study, the land use/cover map for two different periods (2013 and 2014) was prepared to evaluate land use and land cover transition patterns using Landsat satellite images from ETM+ sensors (2013) and OLI (2014).

The land-use pattern has changed drastically with the increased agricultural and built-up area at the expense of other land uses. The analysis reveals a rapid deterioration of groundwater quality related mainly to the increase in built-up land, drought and land-use change in agricultural lands and uncontrolled withdrawal of water by farmers from wells in the region. Mean GQI decreased from 86.42 to 57.36 over a period of 7 years from 2014 to 2021, which indicates a decrease in water quality. The quality of groundwater in the region in 2014 has a desirable quality and is in a very suitable range. But in 2021 the water quality changed from very good and good to poor and bad.

GQI and land use were integrated into GIS to yield groundwater quality, in terms of water quality. Zones of sustainable and unsustainable groundwater use were demarcated for better decision making related to land use allotment in this rapidly changing region. The GQI index provides the possibility of mapping the spatial changes of groundwater quality in the study area, which shows that the water quality of the area is generally good, but the deterioration has started with the onset of urbanization. The main sources of pollution identified in this study are agricultural and residential activities. Although agricultural activities and the application of fertilizers related to it have been the main factor in reducing the quality of groundwater, in addition, this study showed that the increase in urbanization has a dominant contribution to pollution in the region. Agricultural activities must comply with methods that ensure minimal impact on groundwater. This study also shows the effectiveness of GIS in groundwater quality assessment. Similarly, GIS-based assessment techniques can be used to characterize groundwater contamination preferably in large watersheds. Of course, the selection of parameters and weights may be different in each location depending on the prevailing land use conditions.

Streamflow prediction is a challenging and critical task for water resource management. Streamflow prediction is one of the essential steps for reliable and robust water resources planning and management. On the other hand, streamflow prediction plays a crucial role in water resources systems planning and mitigating hydrological extremes such as floods and droughts. Since a variety of uncertainties exist in streamflow prediction, it is necessary to enhance our efforts to robustly address uncertainties and their interactions for improving the reliability of streamflow prediction. It is highly vital for hydropower operation, agricultural planning, and flood control. Physical process-based models are developed based on the understanding of the runoff generation processes, transport in channels, and mathematical formulations or parameterization of these physical processes. Data-driven models have the advantages of low demand for model input and ease of use. Due to the influence of various factors, including climate change, human activities, and socio-economic development, the hydrological time series leads to its complicated stochastic characteristics. Statistical models are the typical examples of this category and have been widely used in streamflow predictions over different regions. Different statistical models, machine learning methods are effective in describing the nonlinear characteristics of the observations and offer an alternative approach for streamflow prediction. Over the last two decades, a variety of machine learning techniques, including the artificial neural network (ANN) and the support vector machine (SVM), have been extensively applied in water resource management and hydrological prediction.

In this research, from the data of precipitation (Pt), precipitation with a delay of one day (Pt-1) until precipitation with a delay of three days (Pt-3) and discharge with a delay of one day (Qt-1) until discharge with a delay of 3 days (Qt- 3) are used as input variables and discharge (Qt) is used as output variable to predict the flow of Kurkursar river in Nowshahr. The Kurkursar river basin in the north of Iran is considered a sub-basin of the North Sea, its area is 75.495 km2, the average height of the basin is about 860 m, and the overall average slope is about 80.21%. This basin has an oblique shape and is limited to the Caspian Sea from the north, the Meshlak river and part of the Chalus watershed from the south, and the Chalus watershed from the west. Various geomorphological forms such as floodplains, alluvial cones, and sediment dams have been identified in the river basin area. The time series is daily and 70% and 30% of the data are respectively used for the training and test processes. The models used in this research include three individual models (random forest model - artificial neural network and regression support vector machine) and three hybrid models including bagging tree model - random forest (BA-RF), neural network - creative rifleman (ANN-AIG) and Support Vector Machine Regression-Crow Search (SVR-CSA) was used. In order to evaluate the model, the Mean square error (MAE), root mean squared error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE), and the PSR were used. Pearson's correlation coefficient (PCC) is used for the relationship between input and output variables. Therefore, we calculate the correlation coefficients between input and output variables, then we evaluate the composition of the input model based on different scenarios. The model combination that has the highest correlation is selected as the selected model. Then, the internal coefficients of each model are optimized with the help of meta-heuristic optimization algorithms. Therefore, the prediction results and observational data of the model are compared with each other. Finally, time series graphs, data dispersion, and box plots will be drawn and we will compare different evaluation indicators quantitatively and qualitatively. Finally, we compare the results of all models with each other and choose the model that has the best result as the best model. In the final step, we will compare the accuracy increase of the hybrid model (error reduction) with the standalone model to determine the error reduction percentage.

The Koukursar Nowshahr river flow was predicted using seven model with combinations of predictive variables such as R (t), Q (t-1), Q (t-2), R (t-1), Q (t -3), R (t-2) and R (t-3) as input variables and flow rate (Qt) as output variable. The results show that the precipitation variable (Rt) with a value of 0.563 has the highest correlation with the output variable, followed by Q (t-1) with 0.463, Q (t-2) with 0.297, R (t-1) with 0.281, Q (t-3) with 0.251, R (t-2) with 0.124, and R (t-3) with 0.072. Variable R(t) with 0.563 has the highest correlation, and variable R(t-3) with 0.072 has the lowest correlation with the output variable. Based on this, the relationship between the input and output variables was shown based on the correlation coefficient using the radar chart. In this regard, in this research, three individual models and three hybrid models were used to predict the river flow. Among the different scenarios and combinations of the input model, model 7 was used as the final model for forecasting. The evaluation results show that the RF model in the training phase has R2= 0. 957 and in the test, a phase has R2=0.717. The RF model has the highest correlation coefficient among all research models in the training phase. Also, in the test phase, it has the third rank among all models. ANN-AIG model improved the error of the single model by 32.94 %, the single SVR-CSA model by 23.17%, and the BA-RF model by 17.74%.

The qualitative results of the models show that all the models performed very well in predicting the model. Among the research models, the ANN-AIG model has performed best in forecasting.

Groundwater is one of the most important water resources in many parts of Iran as well as in Sirjan plain. Although in comparison with surface waters, groundwaters are less vulnerable to pollution, their pollution abatement is much more difficult. It is thus necessary to protect them from being polluted. Additionally, in contrast with stream waters which are concentrated in linear areas across the world, groundwaters are present in vast areas. This means they are much more accessible and human beings are much more dependent on them. They are also present under terrains having different uses. That is, their protection against pollution and preparing their vulnerability maps is of utmost importance. There are different methods to determine the pollution potential of groundwater, the most widely used and the most comprehensive method is DRASTIC. DRASTIC is a model that considers the main hydrological and geological factors which potentially may impact aquifers. It considers seven relevant parameters which include depth to water table (D), recharge rate (R), aquifer material (A), soil characteristics (S), topographic slope (T), vadose zone impact (I), and aquifer,s hydraulic conductivity (C). The depth to the water table (D, in meters) is the distance from the land surface to the groundwater level, which means the distance the polluter must pass to reach groundwater. The net recharge (R, in mm per year) is the amount of infiltrated water that reaches the aquifer from the surface. The soil characteristics determine the ease with which a polluter can pass the soil layer toward groundwater. The probability of a surficial polluting agent reaching groundwater is inversely related to surface slope, which is expressed as T in DRASTIC. The material comprising the vadose zone plays an important role in blocking the movement of polluting agents to reach the water table. In DRASTIC, each parameter has devoted a rate of 1 (the least important) to 10 (the most important) which depends on its value. Subsequently, the DRASTIC index is determined using the weights considered for each of the seven parameters. Sirjan plain is one of the most developing plains in Iran which has many different land uses, including agriculture, urban areas, roads, and factories. The majority of these uses may pollute groundwater. It is, therefore, necessary to adapt the different land uses with groundwater vulnerability rates. So, the purpose of this article is to determine the groundwater pollution potential of this plain using the DRASTIC model.

In this study, the relevant seven parameters were first prepared. For preparing the D layer, the depths of the water table measured from monitoring wells across the plain were employed. The (R) layer was prepared using the data of rainfall infiltration, runoff infiltration, and infiltration from residential and agricultural areas. For preparing the (A) layer, 70 geological logs of the area were used. The (S) layer was prepared according to field studies and geological logs. The digital elevation model (DEM) was used to prepare the (T) layer. The (I) layer was prepared using the geological logs of the area. Finally, the (C) layer was prepared by dividing the transmissibility values of the aquifer by its thicknesses. All maps were rated according to and combined to calculate the DRASTIC index (DI). DI values were validated according to nitrate concentrations, and sensitivity analysis was undertaken by omitting the layers successively.

The relevant DRASRIC layers are given in Figures 2 to 8. In the depth rating map (Fig 2 ), the majority of the surface belongs to rate 1. This means that due to high water-level depths, this parameter has lowered pollution potential. In the recharge rating map (Fig 3), the major part of the plain is green (rate 1), meaning very low levels of recharge and a low role in pollution. The aquifer material map (Fig 4) is mainly covered by 5, 6, and 7 rates. The soil rating map (Fig 5 ) indicates that the 7, 8, and 9 rates cover the major parts. This means a rather high pollution potential share of this parameter. In the topographic slope rating map (Fig 6) the 9 and 10 rates cover vast surfaces at the west and south, and the 4-7 rate values of the vadose zone rating map (Fig 7) represent the average contribution from this parameter. The aquifer's hydraulic conductivity map (Fig 8) indicates a rather wide range contribution of this parameter. DI values of the study area range from 60 to 128, representing rather medium vulnerability. The higher vulnerability values are mostly observed in the western part, where recharge from agriculture is high, the topographic slope is gentle, and the water level is rather high. Low and medium values of DI are mostly observed at the north center and east. In a qualified vulnerability map comparatively low, medium, and high vulnerability values are separated. The high values mostly belong to residential and agricultural areas, which are at the risk of nitrate, pesticide, saltwater, detergent, and heavy metal risk factors.

The DRASTIC index values of the Sirjan alluvial aquifer range from 60 to 128, meaning low- to high vulnerability values. The most vulnerable locations mainly include residential and agricultural areas which may deliver considerable amounts of nitrate, detergents, pesticides, heavy metals, and dissolved salts to the aquifer. It is, therefore, necessary to reduce the pollution potential in these areas by such means as reducing pesticide use, using green agricultural practices, and collecting and treating sewage from residential areas. Considering the high water level dropdowns in this plain, which means decreasing groundwater resources, it is necessary to abate the pollution risk of these limited resources via such means as adapting land use with vulnerability at every location.

Deficit irrigation is a solution for less water use with the aim of maximum use of the unit volume of water use and water storage and saving for the development of agriculture or the development of other consumption sections. Although the direct result of the lack of irrigation is the reduction of yield at the unit level, the reduction of production costs and the optimization of net profit will compensate for the reduction of yield. In order to adapt and deal with the limitation of water, it is necessary to use mechanisms to increase the efficiency of water use and water resources. Deficit irrigation is used as a technical and economic method in irrigation to improve the relationship between drinking water and the functioning of agricultural plants, and also as one of the productivity solutions in conditions of water shortage. This method requires consistent, accurate, and efficient management, which is different from traditional irrigation management. Another to solve this problem is the use of crop models that simulate crop yield under different soil and climate conditions. The great advantage of these models is the low cost and the short time spent to obtain results, besides helping better agricultural planning and management towards higher profits. The DSSAT model is a computational system that includes a database management system, crop models, and application programs. Plant simulation models can be useful for predicting crop yield and investigating the effect of drought stress on plant growth and development. The DSSAT model is able to evaluate the impact of environmental factors such as weather, soil properties, and field management decisions. The data and information needed to run the model include spatial location (longitude and latitude, altitude above sea level, average annual temperature) and meteorological information (daily minimum and maximum temperature, solar radiation, and radiation), soil science (such as soil texture, soil structure and depth of each layer, apparent specific weight, nutrients, wilting point, and electrical conductivity), and agricultural operations (type of number and Its type is planting date, planting depth, line spacing, planting density, irrigation dates, amount of irrigation water). In this research, the DSSAT model was used to simulate seed yield, pod, biomass, soil water balance components, and water consumption efficiency in cowpea cultivars.

This experiment was conducted for two consecutive cropping seasons in 2018 and 2019 in Guilan province and in Astaneh-Ashrafiyeh city with an average height of -5 meters at sea level. The amount of rainfall during the growing season of cowpea in the first and second years was 93 and 94 mm, respectively. This research was done in the form of split plots and in the form of a randomized complete block design with three replications, and the main factor in it includes irrigation at three levels: 100% of water requirement (I1), 75% of water requirement (I2) and 50% of water requirement (I3), and the secondary treatment included three cultivars of cowpea, Kamran cultivar (C1), Khuzestan local variety (C2) and Dehsari local cultivar (C3). The amount of water supplied to each experimental unit was measured by a counter. The amount of water used during the plant growth period included the total amount of irrigation water and the amount of rainfall.

The results showed that the average relative error between the observed and simulated values in 2018 and 2019 for biomass yields were -0.88 and -0.89%, respectively. With full irrigation and providing 100% of water needs, the amount of biomass, seed, and pod yield in cowpea cultivars increased and the model was able to simulate the process of yield changes in different water needs. The relative error (MRE) between the observed and simulated values in biomass yields in 2017 were between -1.14 and -0.55 percent and in 2018 between -1.19 and It was -0.55 percent. The root mean square error in estimating the productivity of water use based on biomass yield based on water use, for Kamran, Khuzestan, and Dehsari cultivars in 2017 respectively 0.0106, 0.01078, and 0.01087 kg.m-3 and per the year 2018 were estimated as 0.01044, 0.01079, and 0.01091 kg.m-3 respectively. Examining the values of simulation and observation on biomass, seed, and pod yield showed that root means square error and average relative error rate and other statistical tests were within the acceptable range and the DSSAT model was able to reproduce the yield of cowpea cultivars in simulate well the conditions of deficit irrigation. The average relative error between observed and simulated values in water productivity based on water use in biomass, seed, and pod yields in 2018 were -0.95, 0.29, and -0.47 % respectively, and in 2019 it was -0.67, 0.001, and -0.40 % respectively. The mentioned values in water productivity based on transpiration and also based on evaporation and transpiration in the yield of biomass, seeds, and pods were -0.88, 0.08, and -0.45% respectively in 2018 and in 2019, were -0.89, 0.07, and -0.45%, which indicates the appropriate evaluation of the model in simulation and observation conditions.

In the field experiment, with the increase in the amount of irrigation water, the seed and biomass yields in cowpea cultivars increased and the DSSAT model was able to change these traits at different levels of irrigation, in accordance with the results observed in the simulation field. However, with the increase of drought stress, the percentage of simulated relative error was higher in stressed conditions than in irrigated conditions. The values of RMSE and RMSEn statistics for each function and water productivity parameters showed that the DSSAT model has an acceptable error. The simulation of the yields of seed, pod, and biomass under the influence of deficit irrigation was acceptable with the mentioned model and it is able to be an efficient tool to support decisions and improve research in management. Water use in cowpea should be recommended for the study area.

Water is a basic element for human stability and social economic activities. However, water scarcity is one of the biggest problems facing many societies around the world. Today, many regions of the world are affected by water shortages. Population growth in the future has caused a greater demand for food, which has a direct impact on water consumption in the agricultural sector. Hormozgan province is in the south of Iran and has an arid and extra-arid climate, and the problem of lack of water resources is an important and undeniable fact. Therefore, it is necessary to investigate the changes in the agricultural water footprint and the lack of water resources for providing an optimized cultivation model to reduce the water footprint and preserve water resources. Based on this, the main goals of this research are: (1) Estimation of water footprint components of the agricultural section, (2) Calculation of water scarcity indicators including water stress, agricultural water stress, and Blue Water Scarcity (BWS), (3) Estimation of water poverty and (4) Calculation of self-sufficiency and water dependence indicators during from 2008 to 2019.

In this study, agricultural water footprint components, including green, blue, and gray water footprints were estimated based on the method described by Hoekstra et al (2007) in Hormozgan Province. Also, regional agricultural water shortage in this research is measured using the water stress index, agricultural water stress, water shortage, water poverty, self-sufficiency index, and water dependency, which is the known method for assessing water scarcity. The ratio of the use of water resources (water consumption) to the number of available water resources.Hormozgan province has an area of about 68000 km2, which is the eighth province of the country. In terms of climate condition, this province is located in the hot and dry region of Iran and its climate is influenced by desert and semi-desert climate. The average annual temperature of this area is about 27˚c. The average rainfall in Hormozgan province is 188 mm. Information related to cultivated area, production per unit area, yield, planting and harvesting dates, growth cycle length, fertilizer consumption for the studied agricultural products was prepared from the Agricultural Jihad Organization and the information related to water resources such as available water resources, total water consumption, water consumption in the agricultural sector in two sectors, surface and underground water of the province for the statistical period of 2017 to 2018 was prepared from the Regional Water Company of Hormozgan province. Also, information related to the population and per capita consumption of each product has been collected from the Program and Budget Organization.

The total volume of the water footprint of Hormozgan province is 1698.02 MCM, of which 1484.64 MCM (79.19 %) is blue water, 75.51 MCM (6.65 %) is green water, and 137.86 MCM (14.16 %) is related to gray water. The average water resources available during the studied period is 2583.70 MCM, of which 1584.55 MCM is related to blue water resources and 999.15 MCM to green water resources. The comparison of water stress, agricultural water stress, and blue water scarcity indices in crop productions showed that the average WSI is 0.91 and its value is always higher than 0.85 during the study period, which shows that Hormozgan province facing severe and extreme water stress. The average AWSI and BWS during the studied period are 1.38 and 1.19. These indicators highlight the fact that Hormozgan Province is facing a critical level in terms of water shortage the agricultural production systems. The average water poverty in agricultural section is 4919.59 MCM. In the following, regarding the indicators of water dependency (39.21 %) and water self-sufficiency (60.79 %), despite the severe water shortage in Hormozgan Province, it has a high level of water self-sufficiency in the production of agricultural products. It is necessary to develop crops that have less water footprint by modifying the cultivation pattern.

Bluewater resources are the main water resources available in the agricultural sector in Hormozgan Province. This issue can be a reason for the high-water self-sufficiency index compared to the water dependency index. On the other hand, AWSI is higher than WSI, which indicates high water stress in the agricultural sector. Since the share of the blue footprint is more than other components of the water footprint, BWS is more than WSI. As a result, the water resources of this province are not rich and this province has high water poverty. However, the AWSI can reveal the situation of agricultural water shortages in arid agricultural areas more clearly. Strategies for agricultural development and water use formulation in the Iranian South-producing areas should be made based on the areas' AWSI performance. Moreover, it should be noted that the intensification of water resources in certain areas is caused by producing agricultural products for other regions due to the mismatch of agricultural production and population. This phenomenon has not been quantified or analyzed in this paper but needs to be studied in the future.

Precipitation is one of the most important climatic phenomena affecting the globe. In each round of rainfall, only a part of the rainfall is used by the plant, and the rest is removed from the reach of the plant through different ways such as runoff and passing through the root zone. For this purpose, the concept of effective rainfall is used to express the part of the precipitation that directly responds to the plant's water needs. Estimating effective rainfall is one of the essential components in water resources management, irrigation planning decisions, and a guiding factor for crop production estimation. To make the best possible use of rainfall for the agricultural sector in rainfed lands, estimating the effective rainfall is vital. Since the only source of water supply for rainfed crops is rainfall and the yield of rainfed crops depends on the amount of water absorbed by the plant, almost all of the effective rainfall is spent on evapotranspiration. Therefore, the purpose of this research is to estimate the amount of effective precipitation through the estimation of the evapotranspiration rate of rainfed crops and also to develop a model of effective precipitation estimation based on an artificial neural network.

Considering the importance of estimating the effective rainfall and since rainfall is the only source of water supply for rainfed crops and the yield of rainfed crops is dependent on the amount of water absorbed by the plant, almost all of the effective rainfall is spent on evapotranspiration. Therefore, to more accurately estimate the effective rainfall, by having the yield of rainfed crops for a region and using the relationship between evapotranspiration and crop yield, it is possible to obtain the actual evapotranspiration ration and, as a result, the effective rainfall amount. The study area of ​​this research is Kermanshah Province, one of the western Provinces of Iran, where extensive rainfed crops are cultivated every year. The meteorological parameters of 10 meteorological stations in Kermanshah Province were received and calculated a result of the weather condition of Kermanshah Province. Then, the potential evapotranspiration was calculated using meteorological parameters and CROPWAT software. In addition, the amount of cultivated area (ha) and the amount of production (t) of rainfed wheat in Kermanshah Province were extracted from the agricultural statistical yearbooks, and the yield of rainfed wheat for 14 crop years (crop years 2005 to 2019) was calculated. Then, with the crop factor and potential evapotranspiration rate in hand, using the Doorenbos and Kassam equation, the actual rate of evaporation and transpiration during the crop growth period for the aforementioned 14 agricultural years was estimated. Then the correlation between effective precipitation and meteorological parameters (such as maximum temperature, minimum temperature, humidity, wind speed, sunshine hours, growing degree days, and precipitation) was investigated and the most effective parameters were used to develop a model using feedforward neural network (FFNN). 5 networks were developed under different scenarios (with different inputs and outputs of effective precipitation) and the error of the networks was evaluated with the evaluation criteria of root mean square error (RMSE), mean bias error (MBE) and index of agreement (D). Finally, the best network with the least error was introduced to predict effective rainfall.

The total rainfall during the growing period of the dry wheat crop in crop years varied between 204.59 and 748.79 mm and the yield of the crop varied between 0.3 and 1.63 t ha-1. The results showed that the highest amount of yield in crop years is not related to the highest amount of rainfall and this result increases the importance of estimating the effective rainfall. The results show that the amount of effective precipitation estimated using Doorenbos and Kassam's equation during the studied period (14 years) and during the growth of the dry wheat crop varied between 119.85 and 279.90 mm. Then, in order to accurately estimate, an effective rainfall estimation model was created in Kermanshah Province with the help of a neural network. In this model, the effect of each of the meteorological parameters on the effective precipitation estimated by the inverse solution method was investigated with the Pearson correlation method, and the most effective parameters were used for modeling in several scenarios. Among all the meteorological parameters such as temperature, humidity, sunshine hours, wind speed, growing degree days (GDD), and precipitation, the precipitation parameter with a correlation of 0.99 was recognized as the most effective parameter in estimating effective precipitation. Meteorological parameters were prioritized based on correlations and used for modeling by a neural network. Then, networks were trained under different scenarios (various inputs individually and together), among which the network with rainfall input had the best performance in estimating effective rainfall. R^2 (Coefficient of Determination) of effective rainfall prediction with the help of this model was estimated to be 0.99 and its RMSE (root mean square error) and MBE (mean bias error) value was 4.61 and -1.4 mm and index of agreement (D) was estimated 0.997.

In order to use water optimally in agriculture and drainage projects, it is necessary to know and estimate effective rainfall. Several experimental methods have been presented to estimate effective precipitation. However, considering that these experimental methods were developed for areas with special characteristics and their generalization to all areas is not error-free, in this research effective precipitation was estimated by the inverse solution method, and a suitable model was used. Meteorological information and information from crop yearbooks and CROPWAT software were used to calculate effective precipitation by the inverse solution method. In order to present a model based on artificial intelligence, the correlation of meteorological variables with effective precipitation was investigated. Finally, a model based on a neural network was proposed in Kermanshah Province to estimate effective rainfall. The results of this research showed that neural networks, which are based on mathematical and natural theories, are more successful in predicting effective rainfall based on the amount of rainfall directly.

Water is a renewable resource that naturally follows a hydrologic cycle. Huge groundwater tables are essential resources that are utilized as underground water. Irregular population increase in the last three decades, limited surface waters, and too much utilization of groundwater tables damaged the Iranian aquifers irreparably, both quantitatively and qualitatively. Today, with the introduction of extensive industrial activities and chemical fertilizers in agriculture, the most important source of human life is at risk. Groundwater pollution is often caused by toxic wastewater from industries, agriculture, or sewage storage sources. The effects of different pollutants in the environment are different. One of these pollutants is nitrate. The contamination of underground water with nitrate ions is an important global problem, the most important source of which is arbitrary agricultural activities and the excessive use of nitrogenous chemical fertilizers. Numerical simulation of groundwater flow and contamination transport is a vital aquifer resource management tool that estimates the hydraulic and hydrologic parameters of the aquifer. In previous studies conducted by researchers, only flow modeling or only pollution transfer has been discussed, which we have studied both cases in the present study. Therefore, in this research, the flow and transfer of pollution has been investigated separately and completely.

In this research, the flow and transfer of pollution were investigated separately and completely. In this paper, the general aquifer software simulation algorithm is first discussed. The Damaneh aquifer is then introduced. Damaneh is located in Isfahan Province in the catchment basin of the Falat Markazi. The region’s specifications are given, and its hydrologic and hydrogeological features are stated. The process of creating the conceptual and numeral model is discussed, and the aquifer modeling process is examined in detail. The last section discusses the modeling of contaminant transport. According to the climatic conditions of the region, the Damaneh plain faces alternating wet and dry periods. For this reason, for the model to better match the nature of the aquifer, a wet and dry period has been applied to the model. According to the monthly rainfall statistics, from 2005 to 2008 was a dry season and from 2009 to 2013 there was a large relative drop in rainfall, which was applied to the model as a drought period. This study presents the results of mathematical modeling of the groundwaters’ flow and contaminant transport and their simulation in the Damaneh aquifer. Groundwater modeling system (GMS) software was used. The necessary information, such as geological, hydrological, hydrogeological information, and topographic maps are gathered. The aquifer flow model in stable and unstable states was run next. The model is optimally calibrated for hydraulic conductivity and specific yield up to a particular standard error. It was verified using an unsteady period to ensure the simulation results’ accuracy. To predict the water level fluctuations and the response of the two optimized coefficients, the Damaneh aquifer was simulated for a specific time interval using two distinct scenarios.

Statistical analysis of flow model errors in a stable state gived maximum observed and calculated water levels of 0.87 m and RMSE and correlation coefficients of 0.99 m and 99.9 % and 1.113 m and 99.7 % for calibration and verification periods, respectively. The correlation coefficient between the observed and calculated data for transport modeling was 89.1 %. The error values indicate that the designed flow and contamination transport mathematical models match the aquifer’s natural conditions well. They give a good simulation of the hydrological system. In the aquifer, the presented results of these discoveries have high uncertainty, because, in some wells of the adjacent logs, drastic differences between the type and profile of these logs can be seen. To calculate the path length and movement time of pollution particles on the surface of the aquifer, points were determined and the MODPATH model was run in Forward mode. The results showed that the transit time was between 2 and 7 years, and the shortest and longest paths traveled by the particles in this period of time were 312 m and 6200 m, respectively. As the modeling results showed, the concentration of nitrate in most areas of the Damaneh aquifer exceeded 80 mg/l during the past years 2007 to 2008 and according to drinking water consumption standards, it is necessary to reduce the nitrate concentration or clean the Damaneh aquifer. A sensitivity analysis shows that the flow model is more sensitive to hydraulic conductivity and specific yield. The transport model is most susceptible to the surface recharge, porosity, and longitudinal coefficient of variation parameters, respectively. It is not sensitive to recharge from the aquifer’s borders. The results of the transfer model showed that only one halo of pollution is forming in the Damaneh aquifer and it is spreading in the entire aquifer at an approximate speed of 15 m d-1.

According to the error values, it can be concluded that the mathematical model designed for the flow and transfer of pollution has a good match with the natural conditions of the aquifer and has well simulated the behavior of the hydrological system. It is suggested to prepare accurate and new statistics of the exploitation wells in the region, although it is very expensive at the level of plains such as the Doman plain, but the special conditions of this plain require more attention. Providing smart meters is the most effective harvesting control programs in the region.

One of the most critical issues that have always been the concern of researchers in water engineering and hydrology is the simulation of runoff or river discharge to plan, prevent damages, and solve the water shortage problem, and soil erosion. In addition, due to the ever-increasing limitations of extractable freshwater resources, it is very important to predict the river discharge and its changes as accurately as possible. The prediction of runoff, this important hydrological variable, significantly impacts the sustainable management of water resources, engineering designs, environmental protection, water supply planning, water quality management, irrigation systems, and electricity generation worldwide. Besides, for data accuracy and to modify and complete the data, the results of these models can be used. The rainfall-runoff process is one of the most complex, dynamic, and non-linear hydrological phenomena, which is influenced by various factors such as temporal and spatial changes, geomorphology, and climatic characteristics of the catchment area. Knowing the connection between precipitation and runoff is one of the important issues of hydrology because precipitation data are used in flood prediction, and a good prediction is made when a suitable relationship is defined. By increasing the accuracy of river runoff prediction, more efficient management and planning are done. Therefore, improving runoff prediction modeling seems essential.

So far, complex and diverse relationships have been presented to predict the extent of river floodings, such as conceptual rainfall-runoff models, time series linear models, and hybrid models. However, due to the lack of accurate knowledge and the complexity of factors affecting river flooding, the values ​​calculated from various relationships have significantly differed in many cases. In the meantime, hydrological models, with their potential, are considered efficient tools, especially in climate change conditions. One of the models that researchers use to model rainfall and runoff is the RRL (Rainfall Runoff Library) hydrological model. This software package includes integrated and conceptual models (such as AWBM, Sacramento, SMAR, SimHyd, and Tank). In this research, the comparative evaluation of the Sacramento, SimHyd, and SMAR models of this tool has been done in predicting the long-term runoff of the Galikesh watershed and also investigating the effect of parameters on the performance of each model.

In this research, after preparing the input data, the models were calibrated and validated for 1989-2010 and 2010-2019. The simulated and observation runoff results were analyzed to check the potential of the models. Furthermore, after evaluating the model using the optimized parameters, the sensitivity of each of the parameters of the three Sacramento, SimHyd, and SMAR models was investigated in the simulation of runoff from the Galikesh watershed so that the sensitivity of the models to the change of parameters and the effect of each parameter in the simulation makes it clear. To be the results of the evaluations indicate the optimal performance of all models in runoff simulation. However, the Sacramento model with a Nash Sutcliffe coefficient of 0.82 for the calibration period and 0.7 for the validation period, and then the SimHyd model with a Nash Sutcliffe coefficient of 0.71 and 0.76 for the calibration and validation period has the best performance in the runoff simulation of the Galikesh watershed. The SMAR model has shown weaker results than other models in runoff simulation. Finally, the sensitivity of all parameters was checked. The results showed some parameters such as LZTWM (Lower zone tension water capacity) and Zperc (Maximum percolation rate coefficient) in the Sacramento model, impervious threshold parameters and infiltration coefficient in the SimHyd model, and the evaporation conversion (T) parameter in the SMAR model was the most sensitive to reducing their values. Also, increasing the value of the Rexp (Percolation equation exponent) parameter in the Sacramento model and the proportion direct runoff (H) parameter in the SMAR model has the greatest impact on the simulation runoff compared to other parameters.

Different models have been proposed to explain these complexities, considering the importance of runoff forecasting and the non-linearity of converting precipitation into the runoff. Different structures and approaches of studied models have led to their different predictions, which has led to the importance of the comparative evaluation of the models for various purposes. For this purpose, in this research, three hydrological models, Sacramento, SimHyd, and SMAR have been used to simulate the runoff of the Galikash catchment area. The investigations showed that all three models could simulate the outflow of the watershed, and all the models have successfully simulated high amounts of runoff. However, the Sacramento model has performed better than the others. Models parameters sensitivity analysis has been investigated considering the importance and effect on runoff simulation. Finally, The sensitivity analysis showed that some parameters are more sensitive than others in the runoff simulation. The optimal amount of these parameters should be considered during the simulation due to their high sensitivity.

Climate changes and human activities are the two main factors altering the hydrological cycle of watersheds causing changes in the spatial and temporal distribution of water availability. The river flow, as the most critical component of the hydrological cycle, is the most vulnerable component that is affected by these changes resulting in worrying consequences on water demand by different sectors. Since there is growing controversy on the contribution of two main factors (climate change and human interventions) affecting river flow alteration, assessment and quantification of their contribution is of utmost importance to the water resources managers. Quantification of climate change and direct human intervention contributions to streamflow alteration is a prerequisite for developing adaptation strategies and policies for regional water resources planning and management. To this end, various approaches and methods have been developed and proposed during the last decades in order to separate the contribution of natural and human-induced factors on river flow regime conditions. The current research investigates the contribution of climate change and direct human interventions on the discharge decline of the Hablehrood watershed.

In this research, the upstream area of the Hablehrood watershed draining to the Bonekouh hydrometry station, located within the Tehran Province jurisdiction, has been studied. The hydrological condition of the Hablehrood watershed, as a main drainage channel of the watershed, has drastically altered during recent years and its discharge has significantly decreased. In this study, first, the long-term statistics of Simindasht and Dalichay hydrometric stations were collected and subjected to pre-processing. Then, using the Pettit and Lanzante statistical tests, the significant change point in their annual discharge time series was identified. Then, the empirical methods of the slope change ratio of accumulative quantity (SCRAQ) and double mass curve (DMC) were applied to separate the effect of climate change and human interventions on the discharge decline of the Hablehrood watershed and its main tributaries. Since rainfall, temperature, and potential evapotranspiration are considered the main climatic elements in both the used methods, the relationship between discharge, and precipitation, between discharge and precipitation as well as temperature, between discharge and precipitation as well as potential evapotranspiration were analyzed in order to compute the contribution of climatic drivers first. According to the empirical methods, after calculating the contribution of climatic variables to the changes in water flow, the remaining changes in water flow are attributed to human interventions. In this study, the observed data from the hydrometry stations of Simindasht and Dalichay during 1980 – 2017 were used. For SCRAQ, the slope change of the ratio of cumulative discharge and cumulative values of rainfall and temperature as well as the slope change of the ratio of cumulative discharge and cumulative values of rainfall and evapotranspiration were computed to separate the effects of climate change and human interventions.

The results of both tests showed that the hydrological regime of the basin changed in the mid-1990s in both hydrometric stations and the average annual discharge drastically decreased in the period after the change point. Results of different calculation methods of SCRAQ (the slope change of the ratio of cumulative discharge and cumulative values of rainfall and temperature (first method) and the slope change of the ratio of cumulative discharge and cumulative values of rainfall and evapotranspiration (second method)) showed that human intervention is the main cause of discharge decline within the Hablehrood watershed. According to the first calculation method, the contribution of climate change on discharge decline at the Simindasht and Dalichay stations were, respectively, 15.53 % and -37.08 %, and for the second calculation method, 0.55 % and -39.72 %, respectively. The positive values for the contribution of climate change to the Dalichay station indicate that climate change has resulted in an increase in its discharge. Results of different calculation methods of DMC between cumulative values of discharge and climate variables showed that climate change has increasing effects on the discharge of both Simindasht and Dalichay sub-watersheds.

Human intervention is the main cause of discharge decline in the Hablehrood watershed and the contribution of climate change is very small and incremental in some cases, particularly for the Dalichay sub-watershed. Therefore, management policies and priorities should be focused on managing human interventions, promoting public awareness, optimal using of water resources, and preventing over-exploitation of water resources across the watershed. The results of this study can provide a scientific guidefor the development, utilization, and management of regional water resources and ecological environment protection.