Soil and Water Science

Introduction

Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resource management, particularly in arid and semi-arid regions. This study employed the SEBAL model in conjunction with Landsat 8 and 9 satellite imagery to estimate ETa in the Gareh Bygone Plain, Fars Province, during 2018–2021. The model integrated corrected satellite data and meteorological inputs to compute key surface energy balance components, including net radiation, soil heat flux, and latent heat flux. Calibration was enhanced by incorporating wind speed data, improving the model’s accuracy. ETa values varied seasonally, ranging from 0.8 to 3.2 mm/day in colder months and 1.8 to 6.5 mm/day during warmer periods. The model’s results were validated against FAO Penman-Monteith reference ET and field measurements, confirming strong agreement. Crop coefficient (Kc) estimates highlighted significant variability based on vegetation type and growth stages. The complementary use of the SEBS model yielded ETa estimations with a low error margin (3–5%), further confirming the reliability of remote sensing-based approaches. These findings support the application of calibrated satellite models and localized parameters in optimizing irrigation strategies and addressing water scarcity in semi-arid environments.

Accurate estimation of actual evapotranspiration (ETa) is crucial for water resource management and evaluating the efficiency of artificial recharge projects in arid regions. Traditional methods relying on point-based measurements often fail to represent large-scale and heterogeneous areas. Remote sensing technologies, utilizing satellite data and surface energy balance models such as SEBAL, enable precise estimation of ETa and crop coefficients (Kc) over extensive spatial and temporal scales. This study aimed to assess water consumption of various vegetation covers and the effectiveness of the flood spreading system in the Gareh Bygone plain, Fars Province, by developing improved models and analyzing the spatiotemporal distribution of ETa.



Materials and Methods

This study was carried out at the Kosar floodwater spreading station in the semi-arid Gareh Bygone Plain of southern Iran’s Fars Province.The Gareh Bygone Plain, with an approximate area of 192 square kilometers, is located downstream of three main watershed areas. The area features an semi-arid to arid climate, an average annual precipitation of 229 mm, and geology comprising limestone, marl, and conglomerates. Satellite data from Landsat 8 and 9, processed with geometric, radiometric, and atmospheric corrections, served as input for the SEBAL model. This model estimates actual evapotranspiration by integrating satellite imagery and meteorological data using the surface energy balance equation. Vegetation indices such as NDVI and SAVI, surface albedo, surface temperature, and other surface energy parameters were used in ETa estimation. Model results were validated with field measurements, including reference evapotranspiration, soil water balance, and estimates of return flow.



Results and Discussion

Satellite imagery from Landsat 8 Level 2, covering 2018–2021, was used to derive ETa maps via the SEBAL model. Due to data acquisition limitations in certain provinces, temporal coverage was incomplete. The SEBAL model, calibrated with meteorological data and incorporating wind speed as a parameter, showed good performance in estimating ETa, particularly in the Gareh Bygone plain. Comparison of SEBAL-derived ETa values with FAO Penman-Monteith reference estimates, using a crop coefficient (Kc=1.05), demonstrated the model’s reasonable accuracy. ETa values ranged from 0.8 to 3.2 mm/day during the cold season and from 1.8 to 6.5 mm/day in the warm season, reflecting a seasonal pattern consistent with the region’s climatic conditions. These results highlight the importance of using satellite data and optimizing model parameters for more accurate ETa estimation. Analysis of SEBAL model results revealed seasonal fluctuations in ETa influenced by temperature, solar radiation, and vegetation cover. ETa values ranged from 0.8 to 6.5 mm/day, peaking during the warm season. The crop coefficient (Kc) was closely linked to vegetation type and growth stage, emphasizing the need to consider these differences in water management. Field measurements of soil (moisture, texture, bulk density) in the Gareh Bygone plain indicated a progressive increase in water infiltration depth and return flow volume during the irrigation season. Comparison of SEBS model-derived ETa with field data confirmed the high accuracy of the SEBS model (error margin of 3–5%), demonstrating its superiority over traditional methods and its effectiveness for agricultural water resource management in semi-arid regions. Computational results showed an increasing trend in return flow volume throughout the 11 irrigation cycles, reaching a significant amount by the end of the season. This finding has important implications for water resource management in the region.



Conclusion

This study employed Landsat 8 imagery to estimate actual evapotranspiration (ETa) using the SEBAL model in the semi-arid Gareh Bygone Plain. Results indicate a strong correlation between seasonal ETa variations and temperature, solar radiation, and land surfaces vegetation activity, with peak ETa during the warm season (April-October) and minimum during the cold season (December-February). This seasonal pattern aligns with the semi-arid climate and highlights the importance of considering spatiotemporal variability in ETa estimations. Furthermore, incorporating wind speed significantly improved SEBAL model accuracy.

Introduction

Soil salinity can cause the destruction of arable land and sustainable production. Increasing the concentration of salts beyond the tolerance threshold causes irreversible physiological damage to the plant. The essence of soil salinity control measures is to regulate the movement of water and the transport of salts downward and out of the area of root development and to prevent their accumulation and transport to the soil surface due to evaporation and transpiration. Subsurface drip irrigation helps improve irrigation efficiency and reduce salinity. But careful design and management are essential to control water salinity. Considering the necessity of optimal irrigation water consumption and the development of subsurface drip irrigation in sugarcane fields in Khuzestan and the important role of soil salinity, studying changes in soil salts and their distribution in different irrigation methods and irrigation management in arid and semi-arid conditions is of great importance.Therefore, this study was conducted to investigate the effect of fertilization stages in subsurface irrigation on salt accumulation, sodium absorption ratio, ammonium and nitrate levels at different soil depths in the root development zone of sugarcane under a subsurface drip irrigation system.

Materials and Methods

The study was conducted in the Farabi Sugarcane Agro-Industry in during the agricultural year 2021-2022, in 35 km from the Ahvaz-Abadan road, east of the Karun River. Before cultivation, the physical and chemical properties of the soil were determined. After preparing and creating the furrow and ridge, sugarcane cuttings of the CP69-1062 variety were planted in two rows with a distance of 40 cm from each other. The Water tube was placed in the middle of two rows of cuttings. In-line dripper pipes with a diameter of 20 mm, dripper spacing of 50 cm, and dripper flow rate of 2.4 L/ha were installed at a depth of 20 cm below the soil surface. The average EC of irrigation water during the research period was 3.18 dS/m. Irrigation planning and water requirements for sugarcane plant were based on the five-year average of evapotranspiration, plant coefficients, and evaporation pan coefficient, taking into account the appropriate irrigation interval. Nitrat fertilization was done at a rate of 300 kg/ha, 25 kg/ha per time. Sampling to evaluate soil salt accumulation was carried out in six stages, a one week after fertilization. EC, pH, concentrations of calcium, sodium and magnesium, ammonium and nitrate dissolved in the saturated extract were measured. In order to investigate the effect of fertilization, a factorial split-plot experimental design with three replications was used. The fertilization stages treatment consisted of six stages (T1, T2, T3, T4, T5, and T6), while the soil depth treatment included five depths (0-20, 20-40, 40-60, 60-80 and 80-100). For analyze the results was used SAS ver 9.4 software, also using the LSD test method, the means of main effects and interaction effects were compared.

Results and Discussion

The results showed that the highest and lowest pH measured occurred in T3 and T4 respectively.The highest electrical conductivity of 4.55 dS/m in stage T4 and the lowest electrical conductivity of 3.01 dS/m was observed in stage T1.Also, the highest EC at a depth of 20 cm is equal to 5.34 dS/m.NH4+ was highest in T1 stage at all depths compared to other fertilization stages.The highest and lowest NO3- were measured in T5 and T2equal to 30.47mg/kg and 20.11mg/kg, respectively. The upward trend in nitrate in each stage compared to previous stages is likely due to increased nitrification that occurred from the application of each fertilizer stage. In depth of 20 cm, the concentration of NO3-soil, equal to 33.54 mg/kg, is higher than at other sampled depths. This trend could be due to the subsurface drip irrigation system, where the depth of penetration of the moisture bulb is less than this range. The interaction between fertilization stages and sampling depth showed that the highest SAR was at the time of fertilization stage T4 and a depth of 20 cm from the soil surface (11.31) and the lowest SAR was at the time of fertilization stages T1 and T6 and a depth of 80 cm (4.66 and 4.57, respectively). In this study, the increase in sodium absorption ratio (SAR) was not affected by the quality of irrigation water, but rather by the type of irrigation method (subsurface drip) and the depth of drip installation.

Conclusion

The results of this study showed that nitrogen fertilizer application had a variable effect on soil pH during the period. This process could be due to microbial activity, mineralization, and nitrification processes. The highest pH measured was in the third stage and the lowest in the fourth stage of fertilization. The highest soil salinity is due to the interaction between fertilization stages and soil depth at the fourth fertilization stage and a depth of 20 cm, which has increased by 54.7 percent compared to the same depth in the first stage. The highest ammonium concentration was in the first stage, which was 28.3 percent higher than the lowest concentration measured in the second stage. Also soil nitrate concentration showed that the highest and lowest concentrations were in the fifth and second stages, respectively, with a difference of 67.15 percent between them. Totally, the concentration of ammonium and nitrate in the soil depth has been decreasing. The behavior of the exchange sodium adsorption ratio is similar to electrical conductivity and increased during the study, which was intensified at the soil surface due to the depth of soil moisture penetration and irrigation method. Therefore, the results of this study indicate that in order to manage salts and prevent their accumulation at the soil surface in sugarcane cultivation areas in Khuzestan using subsurface drip irrigation, it is recommended to consider flow rate and installation depth of the drippers should be considered further.

Introduction

Rice is one of the most important cereals in providing food security and the main food of billions of people in the Asian continent and many other parts of the world. Drought is the most important factor limiting rice production in paddy fields, which affects all stages of rice growth and development. Researches has shown that avoiding the conventional flood irrigation methods in rice cultivation and using alternative methods such as intermittent irrigation have a great effect on increasing water productivity and reducing its consumption. But in determining the most suitable alternative method of irrigation in each region, it should be noted that the effects of water shortage change with changes in the intensity, duration and time of its application. Today many Crop Growth Models (CGM's) have been designed to avoid the huge costs of conducting field research, speed up finding suitable solutions, and help to better understand and solve problems related to water movement in the soil and plant growth. Studies show that CGM's are able to consider the effect of different stresses such as water stress on dry matter production and grain yield during the growth period. One of the CGM’s is the AquaCrop model developed by FAO. The presence of factors such as moderate to severe water stress causes a significant decrease in the simulation accuracy by model, which is mentioned as one of the defects of the model. Most of studies with AquaCrop model have been limited to data collected in short periods. Therefore, the aim of this research is to evaluate the efficiency and accuracy of the AquaCrop model in simulating rice grain yield and biomass under multiple water stresses and during different years.



Materials and Methods

In order to evaluate the accuracy of the model in predicting the grain yield and biomass of rice, data collected from several research projects carried out in different years, the model was first calibrated and validated. The Hashemi variety used in all these projects, which is the most common variety cultivated in Guilan province. All agricultural operations of planting, growing and harvesting were carried out according to regional customs and the amounts of chemical fertilizers, herbicides and pesticides based on the recommendations of experts in agriculture and herbal medicine of the rice research institute. For the present study, a total of 45 irrigation treatments were selected from the previous projects carried out in the lands of the Country Rice Research Institute, Lahijan and Soumesara, of which 31 treatments were used for the calibration section and 14 treatments were used for model validation. The model was implemented for each irrigation treatment separately and the grain yield and biomass values obtained from the simulation were analyzed with the measured values based on the statistical indicators used in this research.



Results and Discussion

Based on the results of calibration of the model, the range of observed grain yield was 2100 to 4870 with an average of 3765 kg.ha-1. This is while the corresponding values simulated in the calibration conditions by the model were equal to 1749 to 4704 with an average of 3748 kg.ha-1. The accuracy of the model is low at the low limit of performance, but very accurate at the high and average performance limits. This phenomenon can be attributed to the estimation error of the model in the presence of environmental stresses such as water and fertilizer stress, which has also been mentioned in the studies of other researchers. Also, the values of RMSE and NRMSE for yield simulation were equal to 309.65 kg.ha-1 and 8.22%, respectively, which indicates very good accuracy in model calibration. Also, RMSE and NRMSE values for biomass simulation in calibration conditions were equal to 596.31 kg.ha-1 and 6.41%, respectively. The values of RMSE and NRMSE for the simulation of performance in the validation conditions were equal to 168.42 kg.ha-1 and 10.30%, which indicates good accuracy in model validation. Also, the values of RMSE and NRMSE for the simulation of biomass were equal to 554.71 kg.ha-1 and 12.90%, which shows the good accuracy of the model in validation. Examining the results of the model in different water stresses showed that with the increase of water stress from permanent waterlogging to high stress with the addition of irrigation cycles, the amount of model error in yield simulation increases.



Conclusion

In general, the AquaCrop model has good accuracy in simulating the grain yield and biomass of Hashemi variety rice, but the more severe the amount of water stress, the accuracy of the model decreases and its error increases. This problem is attributed to the structure of the model and the mathematical equations used in it, as well as the measured data. However, the AquaCrop model has many advantages such as the need for less data, the ability to be used for a wide range of crops and the user-friendliness of the model and it is recommended to use the AquaCrop model in different irrigation managements, especially in conditions without severe water stress, where the model has very good accuracy. But it is recommended due to the advantages of the AquaCrop model, such as the need for less data, the ability to be used for a wide range of crops and the user-friendliness of the model, its use in different irrigation managements, especially in conditions without extreme water stress, where the model has very good accuracy.

Introduction

Agriculture plays a crucial role in fulfilling human needs, but it is also the world's largest consumer of freshwater. This has a significant impact on water resources worldwide, particularly in arid and semi-arid regions. With the growing global population, agricultural production must be increased to meet demand. This has led researchers to explore various methods, including deficit irrigation techniques, soil nutrient enhancement, and water retention agents, to improve crop performance and productivity. Numerous studies have investigated these methods to enhance crop performance and to reduce water consumption. Previous research has also highlighted the potential benefits of biochar application at a 2% level to improve plant performance. This study aimed to evaluate the effects of continuous deficit irrigation (CDI) along with use of compost as a soil nutrient enhancer and biochar as a water retention agent on the performance, growth, and productivity of bell pepper plants. The study examined the effects of these treatments individually and in combination, as well as the combined effects of biochar and sugarcane bagasse compost at 1.5%, 2%, and 2.5% weight levels and their interactions with CDI on water savings and performance improvement of bell pepper plants in a sandy soil.

Materials and Methods

To investigate the research objectives, during 2023, in the research greenhouse of Tarbiat Modares University, a factorial experimental design was implemented in the form of a randomized complete block design with three factors and a total of 48 treatments in four blocks with a total of 192 pots. The first factor includes three levels of irrigation: 100% (D100), 75% (D75), and 65% (D65) of the plant's water needs. The second factor is sugarcane bagasse biochar with four levels: 0% by weight (B0), 1.5% by weight (B1.5), 2% by weight (B2), and 2.5% by weight (B2.5). The third factor is sugarcane bagasse compost with four levels: 0% by weight (C0), 1.5% by weight (C1.5), 2% by weight (C2), and 2.5% by weight (C2.5). In this research, plant growth rate and plant diameter were investigated, and fruit characteristics such as fruit drop, fruit length, fruit flesh thickness, and fruit fresh weight were investigated. In order to check the functional characteristics of the plant, the number of fruits per plant and the total weight of the fruit per plant, as well as the productivity using the ratio of the total weight of the fruit produced per plant to the volume of water used in each pot, were investigated. The watering of bell pepper plants was not like this. During the 3-day watering cycle, the amount of water used in each pot was calculated and added to the pot, and this cycle continued until the end of pepper cultivation.

Results and Discussion

The results showed that the application of biochar and compost at all levels along with full irrigation had a positive and significant effect on the physical characteristics of the fruit (diameter, length, flesh thickness and fresh weight) compared to the control treatment. The lowest value for fruit characteristics was observed in the control group with 65% plant water requirement. The results for the plant characteristics showed that C2B2.5, C2.5B2 and C2.5B2.5 treatments showed a significant difference in plant height compared to the control treatment. C2.5B2 and C2.5B2.5 treatments also showed the greatest increase in stem diameter and had a significant difference compared to other treatments. Also, the results for the number of fruits per plant and yield showed that C2.5B2 treatment had a significant difference compared to other treatments. It caused a 156% increase in plant yield compared to the control treatment. The lowest number of fruits and plant yield was related to the control treatment. In addition, the C2.5B2 treatment achieved an average productivity of 39.3 kg/m3, which was significantly higher than the other treatments, representing a 139% increase over the control. The control treatment had the lowest productivity with an average productivity of 16.43 kg/m3, which was significantly lower than other treatments. The results of mean square variance analysis related to fruit characteristics showed that the triple effect of biochar, compost and deficit irrigation was significant at the level of 1%, but the triple effect of biochar, compost and deficit irrigation was related to plant characteristics and functional characteristics and productivity. The plant was not significant.



Conclusion

The present study demonstrated that the combined application of biochar and compost in bell pepper cultivation offers substantial advantages. This innovative approach not only significantly enhances crop yield but also improves productivity by optimizing water use. Applying 2% biochar and 2.5% compost to bell pepper plants along with continuous irrigation up to 75% of the plant's water requirement can yield promising results for sandy soils. These findings are particularly crucial for arid and semi-arid regions facing water scarcity. In a world facing water shortages, this method can contribute to water conservation and sustainable agricultural development.

Introduction

Water repellency is a property that commonly affects the soil surface layer. It results from hydrophobic coatings on soil particles that originate from organic matter. The most significant effect of soil water repellency is a reduction in infiltration rates. The infiltration rate is one of the primary processes of the hydrological cycle. Hydrogeological and subsurface phenomena as infiltration, percolation mainly affect natural or man-made geotechnical soil. Understanding these phenomena are essential for estimation of runoff process, groundwater seepage, erosion, transport substances, evapotranspiration in surface and into groundwater are mainly influenced by precipitation. It is the property of water by which it moves through the soil particles. Infiltration process plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. Also, Soil infiltration is one of the key processes in design of irrigation systems, water resources management and soil protection and soil erosion control in watershed management and good knowledge of the infiltration rate is useful in calculating the natural and artificial groundwater recharge and surface runoff. Therefore, the prpose of this study was Performance assessment of the artificial intelligence models for prediction of the infiltration rate in the surface soil of geological formations in Alashtar watershed, Lorestan province, Iran.



Materials and Methods

The study area is a part of Kashkan watershed, Lorestan province, Iran. So, it was selected as a suitable watershed to Modeling of infiltration rate in different vegetation types by the various soft computing techniques. The study area located between 48°10′28″ - 48°23′29″ N latitudes and 33°45′ 17″ - 33°51′ 23″ E longitudes, and covers an area of 112.54 Km2 approximately. Elevation of watershed varies from 3613 to 1481 m a.s.l. The studied area has a cold and semiarid climate with a mean annual rainfall Less than 570 mm. Most parts of Alashtar watershed are rangeland, while forest, dry farming, and irrigation lands are in considerable quantities and The surface lithology in the Alashtar watersheds are covered by the Eocene, Quaternary, Cretaceous, Miocene, Oligocene, Paleocene, and Pliocene geologic formations. In this study, The double-ring infiltrometer was used to measure the infiltration in the surface soil of some geological formations in the study area. After determining the infiltration rate, Gaussian Process (GP), Classification And Regression Tree (CART), and Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) were used to Modeling of infiltration rate in different Surface Soil of Geological Formations. Total data set consists of some physical characteristic of soil out of which 70% data used to train the model and 30% data were used to test the models. Finally, the models’ accuracy was assessed using three statistical parameters, Root Mean Square Error (RMSE), Nash-Sutcliffe model efficiency (NSE), and Coefficient of Correlation (CC), were selected to compare the efficiency of all models. Also for rapid and reliable comparisons, we also used Taylor diagrams. The Taylor diagram displays Root Mean Square Error (RMSE), Coefficient of Correlation (CC) and standard deviation (SD) values with closer positions on the diagram indicating better model performance.



Results and Discussion

The results indicated that the surface soil of OML geological formations had a higher cumulative infiltration and average infiltration rate. In this study, Gaussian Process (GP), Classification And Regression Tree (CART), Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) were used for infiltration rate in Alashtar watershed, Lorestan province, Iran. Comparison of these models showed that the M5P model tree (M5P) and Reduced Error Pruning Tree (REP Tree) models, with the combination of time, sand, clay, silt, soil density and soil moisture, could estimate infiltration rate with much less error than the other models. The obtained results suggest that the bagging M5P model tree regression technique in training and testing phase (with CC = 0.99, RMSE = 0.009, NSH = 0.006 and CC = 0.99, RMSE = 0.009, NSH = 0.006 respectivly) is more accurate to estimate the infiltration rate as compare to the GP, CART, RF, MARS and REPTree thegiven study area. Finaly The results showed that M5P model is effective in predicting Infiltration Rate (IR) content in the surface soil of geological formations. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models. The performance of the developed models was also compared using a Taylor diagram, in which an accurate model is indicated by a reference point, with a correlation coefficient of 1 having the same amplitude of variation as the observations. Thus, M5P was shown to be the most accurate model for cumulative infiltration prediction.

Conclusion

Prediction of the infiltration rate is an essential element of hydrologic design, watershed management, irrigation, and agriculture studies. This investigation identifies the optimal model for predicting Infiltration Rate (IR) using several computing approaches, such as Gaussian Process (GP), Classification And Regression Tree (CART), Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) models. In this study, 8 input variables, including time, sand, clay, silt, moisture content, soil bulk density, porosity and infiltration rate, were evaluated using three key performance metrics to assess the efficacy of various predictive models. These metrics comprised the CC, MAE, RMSE. Based on the evaluation results, the soft computing techniques model has a suitable capability to predict the infiltration rate of the soil. Finaly, the results shown that Learning algorithms can be used to quantify the amount of infiltration and also to estimate the amount of runoff in different geological formations. Also, the results shown that these models can be used to quantify the amount of infiltration and estimate the amount of runoff in the Surface Soil of Geological Formations. As well as, the results of this research can be used by the local authority to manage properly, systematically and plan development within their areas.

Introduction

The expansion of cities in the margins of rivers, alluvial cones, low-altitude coasts, deltas and downstream areas of storage dams has led to an increase in the vulnerability of watersheds to the risk of flooding. This study was carried out with the aim of flood risk zoning and prioritization of flood-prone areas using multi-criteria decision making techniques and remote sensing indicators using Fuzzy AHP and VIKOR model in Rakat Khuzestan watershed. One of the solutions used to identify flood risk and prepare maps of its sensitivity is the use of bivariate and multivariate statistical models, data mining and machine learning. But since many of these models require a lot of data and their calibration is complex, therefore, in recent years, many models have been tested to prepare a flood susceptibility map, among which, the combination of statistical models And decision-making with remote sensing techniques and geographic information system has been of great interest to researchers due to increasing the ability of the model in forecasting. The difference between this study and the studies carried out so far is that in this study, for the first time, multi-criteria decision making techniques and remote sensing indicators are used in the zoning of flood risk in the watershed simultaneously in the watershed. Mountainous and flowing rakat will be used in Khuzestan province and its efficiency will be measured.

Materials and Methods

After making the necessary corrections on the Sentinel 2 satellite images of the region, vegetation indices (EVI, NDVI and SAVI), vegetation density and land use of the region were extracted. Then, by using two multi-criteria decision making techniques (FAHP and VIKOR), weighting of indicators and prioritization of sub-basin flooding were carried out. Finally, after extracting the topography, elevation, soil and geological maps and producing 15 morphometric indicators effective in the flooding situation of the basin, using two multi-criteria decision making techniques FAHP and VIKOR, the weighting of the indicators and the prioritization of the flood proneness of the basin were carried out. became In order to validate and evaluate the multi-criteria decision making models, in the future, with field survey, the use of remote sensing indicators such as NDVI and MNDVI, twenty-five points, flood-prone areas of the basin were randomly selected and placed, and the output of the multi-criteria decision-making models FAHP and VIKOR were validated with these points.



Results and Discussion

It was concluded that among all the indicators, the runoff curve number index, vegetation cover and land use and distance from the waterway account for about 50% of the total flood share of the basin and have The greatest impact on the flood phenomenon is in mountain basins, including the Barkat basin in Dehdez County. Also, the direction of the slope range and the rainfall index (due to the uniformity of the index at the basin level) were found to have the least effect (total less than 5%) among the investigated parameters. It can be said that due to the combination of land use and soil maps, vegetation and rainfall of the basin, as well as the simultaneous effect of land use and soil hydrological group on the flood potential of the basin, it can be a more effective indicator in determining the flood benefit of the basin. The results obtained in this study are consistent with the results of Nouri et al., (2019). Another influential parameter in the flooding of the Rakat basin area (19 percent) is vegetation and land use. The vegetation cover of the area includes agricultural lands, medium pastures, oak forest, and high quality pastures, which respectively had the highest and lowest values in the occurrence of floods, belonging to agricultural lands and high quality pastures. The distance from the waterway is the next influential parameter with a weighted value (about 15 percent), the smaller the distance from the waterway, the higher the value in the occurrence of floods, and the greatest flood potential of the region is in this area. The results of EVI, NDVI and SAVI spectral indices in the two methods of Fuzzy AHP and VIKOR showed that the EVI index has an overestimate and vice versa the SAVI index has an underestimation, but the NDVI index has shown more accurate results of locating the flood prone areas of Rakat Basin.

Conclusion

The results of the study showed that out of the 15 indicators used in the flood zoning of Rakat basin, the vegetation cover indicators are 19%, the runoff curve number is 15% and the distance from the waterway is 15%. The effect was among the investigated parameters. The maps extracted from the two fuzzy AHP and VIKOR methods were determined by using the EVI, NDVI and SAVI spectral indices. On the contrary, the SAVI index has shown the percentage of flooding in high-risk areas with a low estimate, but the NDVI index has shown more accurate results of locating the flood-prone areas of the basin. By summarizing the obtained results, it can be stated that the evaluation of the flood risk maps of the Rakat watershed based on the Vikor model and fuzzy AHP shows the highest agreement with an accuracy of about 68% compared to the Vikor model map with an accuracy of about 40%. with the basic information of the region compared to other models and it is suggested as the optimal model in this region. Finally, the final flood risk map of the basin was located using the fuzzy AHP method, the high risk flood prone areas exactly according to the hydrographic network of the basin, it can be considered the reason for the superiority of this method over the VIKOR method.

Introduction

Climate is one of the most important ecological factors, and its changes are currently the most important threat to sustainable development. The phenomenon of climate change causes different processes in the atmosphere and the earth. Phenomena such as rising sea levels, changes in meteorological variables such as temperature and precipitation, impact on surface currents, occurrence of floods and droughts, and changes in air currents and storms are only part of the effects of climate change. Therefore, it is necessary to model the future conditions of the climate to know the future conditions. There are various methods for simulating and predicting climate variables in future periods under the influence of climate change, the most reliable of which is the use of General Circulation Model (GCM) data. GCM models are only able to simulate the data of the atmospheric general circulation model at large levels. Even if global climate models are set up with high technical power to predict the future, the need to downscale the results of these models at station scales is felt. Therefore, in this research, the effects of climate change on the threshold values of precipitation and temperature have been evaluated using SSP scenarios.

Materials and Methods

General circulation models (GCMs) can provide the best information about the response of the atmosphere to increasing greenhouse gas concentrations. In this research, the climatic data of three synoptic stations of Abadeh, Shiraz and Lar, related to Fars province, were used. The data from three models ACCESS-ESM1-5, CNRM-CM6-1, and MRI-ESM2-0 are used from the general rotation models of the sixth report. Daily precipitation and maximum temperature data from 1990 to 2017 were used. Using the statistical model LARS-WG and three scenarios SSP126, SSP245 and SSP585, precipitation and maximum temperature have been downscaling. In this model, the process of generating artificial weather data is done in three parts: model calibration, model validation, and weather data generation. To evaluate the LARS-WG model, coefficient of determination (R2), root mean square error (RMSE) test statistics have been used. To investigate the relationship between precipitation and maximum temperature with different return periods, Gumbel distribution was used. The appropriate distribution for maximum precipitation, temperature, and flood data is Gumble's method; In this study, the distribution of precipitation and maximum temperature for different return periods is presented. In this method, the mean value and standard deviation of the data and the length of the data return period are considered to be the most important effective factors in estimating the maximum values.

Results and Discussion

Validation of the LARS-WG model was done by comparison between observation data and generated data. To evaluate the efficiency of the model, error test criteria have been used. The results show that the LARS-WG model was able to estimate the maximum temperature and precipitation. The accuracy of the modeling in the maximum temperature parameter has been more appropriate than the precipitation ratio. The monthly precipitation changes of the near future period (2021-2040) compared to the base period (1990-2017) of the three ACCESS-ESM1-5, CNRM-CM6-1 and MRI-ESM2-0 models of Abadeh synoptic station showed the amount of precipitation in April, May, June, August, and September has had a decreasing trend compared to the base period. The amount of precipitation in January, February, and December has also increased compared to the base period. At Abadeh station, it shows an increase in temperature under all three models and scenarios in the near future. At the Shiraz synoptic station, precipitation in April, July, and September has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. At the Lar synoptic station, the precipitation in April, September, and October has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Examining the Gumbel distribution of precipitation values also shows an increase in precipitation in the specified return period in the ACCESS-ESM1-5 model.

Conclusion

The changes in the maximum temperature of the near future period (2021-2040) compared to the base period (1990-2017) were incremental in three stations and three models. In Abadeh synoptic station, the maximum temperature changes show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.57, 1.59, and 1.63 °C, and the amount of precipitation in the spring and summer seasons is decreasing and Winter precipitation is estimated to be increasing compared to the base period. In the Shiraz synoptic station, the maximum temperature shows an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, 1.37, 1.50, and 1.48 °C, respectively, and in the ACCESS-ESM1-5 model, in all three scenarios, the amount It is estimated that the winter precipitation is decreasing and the amount of spring precipitation is increasing. The changes in the maximum temperature of Lar synoptic station show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.23, 1.37, and 1.28 °C. In the CNRM-CM6-1 model, the winter precipitation of this station is estimated to be a decreasing trend. Fall precipitation is also estimated in the MRI-ESM2-0 model in two scenarios, SSP126 and SSP585, but the ACCESS-ESM1-5 model has estimated an increase in the amount of precipitation in the Lar synoptic station in all seasons and scenarios. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Therefore, extreme and heavy precipitation and the increase in the frequency of extreme events related to it, such as floods and droughts, are among the results of global warming.

Introduction

Water scarcity and drought are becoming global problems, particularly in arid and semiarid regions. Drought stress is one of the factors that negatively affect the quantitative or qualitative growth of plants. changes outside the desired range of environmental factors. Due to the severe limitations of water resources in most regions of the country, moisture stress has been defined as one of the most important stresses adversely affecting plant growth and yield. Drought stress generally occurs when water levels of soil and atmosphere decrease through evaporation and transpiration. Almost all plants are somewhat drought tolerant, but the degree of tolerance varies from species to species. Magnetic water can be one of the promising methods to overcome the problem of lack of water resources, improve the production of agricultural products, and deal with drought stress on the plant in different stages of growth, at the same time, it is environmentally friendly. Adding biochar to the soil is another method of dealing with drought stress, increasing organic matter and, as a result, increasing water retention in the soil. Therefore, this study was conducted to investigate the combined effect of moisture stress, biochar, and magnetic water on spinach growth and chemical composition under greenhouse conditions.

Materials and Methods

A factorial greenhouse experiment in the form of a completely randomized design with three replications was conducted with drought stress at three levels of field capacity (FC), 75% of field capacity moisture (0.75FC), and 50% of field capacity (0.5FC); and four levels (0, 1, 2, and 3% by weight) of sugarcane bagasse-derived biochar prepared at 400 ºC and two types of water including magnetized water and non-magnetized water. The required soil was taken from a depth of 0 to 30 cm of a calcareous soil, air-dried, passed through a 2-mm sieve, and analyzed for physical and chemical properties. Sugarcane bagasse was collected from Imam Khomeini Sugar Factory located in Khuzestan Province and converted to biochar at 400 °C under limited oxygen conditions for 4 h. Magnetic water of 0.21 Tesla was prepared by repeatedly passing drinking water through a water magnetizing device. According to the results of the soil test, nutrient elements were added to the soil. 15 spinach seeds (Spinacia oleracea L., var. Virofly) were planted in each pot and they were maintained in greenhouse conditions. After one month, the number of plants was reduced to 9 in each pot. All pots were treated by the mentioned moisture levels through daily weighing. Drought treatments were started two weeks after planting and continued throughout the growing season for two months. After harvesting, plant samples were prepared and chemically analyzed. Statistical analysis was performed using Excel and SAS statistical software and means were compared using Duncan's test at a probability level of 5%.

Results and Discussion

The results showed that in plants irrigated with magnetized water, the application of 1, 2, and 3% of biochar caused an increase of 3.9%, 7.8%, and 8.3%, respectively in the shoot dry weight of spinach, although the changes were not statistically significant, Furthermore, moisture levels of 0.75FC and 0.5FC in the magnetized water caused a decrease of 3.7% and 15.7%, respectively, and in normal water, it caused 8.3% and 24% decrease in shoot dry weight, respectively. In plants irrigated with magnetized water, application of 1, 2, and 3% biochar compared to the control caused a decrease of 19.2%, 32.5%, and 30.6% respectively in the shoot Cu concentration. Whereas, the use of 2% and 3% biochar caused an increase of 6.2% and 11.9% in the shoot Mn concentration. Applying 0.75FC and 0.5FC moisture stress levels compared to the normal conditions caused a significant decrease of 19.5% and 29.7% in shoot Cu concentration, 21.2% and 21.1% decrease in shoot Fe concentration, and 18% and 20% decrease in shoot K concentration, respectively. Whereas, the mentioned moisture levels caused an increase of 24.7% and 47% in the shoot Mn concentration.

Conclusion

In general, the application of magnetized water compared to normal water significantly increased the shoot Mn and Zn concentration by 3 times and 40.4%, respectively, compared to that of the control. Using magnetized water increased shoot dry weight by 10.8% compared to normal water. The results showed that the application of magnetized water can be used as a suitable solution to increase the concentration of some nutrients and some growth characteristics of spinach. In general, the results showed that the application of magnetized water and biochar, which have been introduced as two strategies to reduce the adverse effects of drought on plants, can be effective on the chemical composition of plants and nutrient concentration of the plant. Further studies are recommended to evaluate the impacts of other biochar derived from livestock manure and plant residues, as well as different levels of biochar, on spinach and other crops, under drought or other stress conditions.

Introduction

Improving water efficiency in agriculture especially in the face of global warming, requires an accurate of evapotranspiration. The Gharehsu-Gorganrud Watershed with a complex topography is located in Golestan province,north of Iran. Remote sensing methods can provide acceptable estimation of ET in larfe areas with inadequate ground observations. However, these methods have lower accuracy compared to ground-based techniques and require regional validation using water balance or lysimeter approaches. Selecting suitable satellite datasets for water management planning in a specific study area is a fundamental challenge that needs validation through physical methods and ground data. Previous studies show that the GLEAM model wwhich is based on satellite data provides reliable outputs for the Karkheh basin, west of Iran and can be used as an alternative to empirical and conventional methods for estimating crop water requirements. Gharekhani et al. (2020) investigated the uncertainty of actual evapotranspiration in the Gharehsu-Gorganrud basin using two climate databases and a remote sensing-based model. The study demonstrated that the ERA-Interim, GLEAM, and ETPT-JPL databases performed well in reducing uncertainty. Another study by Hafezparast et al. (2022) utilized GRACE satellite data to monitor changes in groundwater levels in the Mianrahān aquifer, revealing critical conditions in some aquifers.Overall, the research aimed to investigate the uncertainty in actual evapotranspiration estimates using GRACE satellite data and climate databases in the Gharehsu-Gorganrud Basin.

Materials and Methods

The study area is Gharehsu-Gorganrud basin which is a sub- basin of the main Caspian Sea Basin..The meteorological data used in this study were collected from synoptic stations of Iran meteorological organization. These data included average, minimum and maximum temperature, relative humidity, precipitation, wind speed, and total sunshine hours. The satellite-derived data provides estimates of actual evapotranspiration, as key variable for this study. However, to compare these estimates with the Penman-Monteith equation (FAO 56 PM) and determine the potential evapotranspiration, we need to consider the vegetation cover factor specific to the study crop of wheat. Hence, remote sensing techniques was employed to retrieve and acquire satellite images exclusively for the wheat fields, allowing us to accurately calculate the potential evapotranspiration by multiplying the actual evapotranspiration by the corresponding vegetation cover factor.The Global Land Evaporation Amsterdam Model (GLEAM) is an algorithm that estimates various components of evaporation and transpiration using satellite observations. The model outputs include potential evaporation, root zone soil moisture, surface soil moisture, and evaporative stress.This model utilizes solar radiation and temperature data to calculate potential evapotranspiration and multiplies it by the evaporative stress to obtain actual evaporation. The data is available on a daily, monthly, and yearly basis,and the grids are divided into 0.25-degree geographical resolution.GRACE satellite data is obtained from the GRACE spacecraft,measuring changes in Earth's gravity field due to water variations. These data, along with ground-based information like precipitation and runoff, enable the calculation of actual evapotranspiration. By assuming a water balance for a specific watershed and utilizing variables such as precipitation, runoff, and ΔS from GRACE, actual evapotranspiration can be determined.

Results and Discussion

Based on the comparisons, the best performance GLEAM model was obtained in Rezvan station, with an elevation of 1447 meters, dominant agricultural land use pattern, with statistical metrics of RMSE=0.32, MAE=0.30, R2 of 0.78, and MAPE =13.67.The lowest agreement was related to the "Kalaleh" station, with an elevation of 127 meters, non-agricultural land use pattern, and statistical indices of RMSE =0.77, MAE =0.60, R2=0.49, and MAPE =18.05. Overall, the results indicate that estimating evapotranspiration using the Penman-Monteith FAO equation performs better in high-elevation areas with agricultural land use patterns, while it yields less reliable results in low-elevation areas with non-agricultural land use patterns. The study by Gharekhani et al. (2020) also showed that the GLEAM model exhibits less uncertainty at elevations between 1400 and 1800 meters above sea level and in areas with agricultural land use patterns. For more precise explanations, further examination of the environment and comparison with field data is required. Based on the conducted comparisons, the best GRACE performance is associated with the "Rezvan" station, which has a drier climate compared to other stations, and statistical indices of 0.41 RMSE,0.38 MAE, 0.66 R2, and 17.46 MAPE. The worst performance is related to the "Kalaleh" station, with an elevation of 127 meters, non-agricultural land use pattern, and statistical indices of 0.91 RMSE, 0.77 MAE,0.45 R2, and 23.17 MAPE.



Conclusions

The use of satellite imagery can provide broader insights into various topics. In this study, the estimation of actual evapotranspiration was conducted using GRACE satellite data and the GLEAM model in the Gharehsu-Gorganrud region of Golestan province. The FAO Penman-Monteith equation was employed for evapotranspiration calculation. The results indicated that the best estimations belongs to Rezvan station, while the worste case performance was observed in Kalaleh station in estimating evapotranspiration based on the FAO Penman-Monteith equation measure and GLEAM data.More precise informatin on land cover maps in the region for ET estimation using vegetation cover dependent coefficents is necessary.

Introduction

About 70% of the total freshwater withdrawal from resources is used in the agricultural sector. Water and soil salinity is one of the most important problems in the agricultural sector in arid and semi-arid regions. In such areas that are facing water shortages, saline water is commonly used in irrigated lands and it will be of great help in preserving freshwater resources. Crop modeling in combination with field measurements is an efficient method to improve water productivity in the field and investigate the crop's biological response to different field conditions. Several crop models have been developed for crop growth simulation. Among these models, AquaCrop software has been widely studied in recent years. AquaCrop software is able to simulate the growth process under different conditions with few input data that can be easily measured in the field. Few studies have been conducted on saffron crop modeling with AquaCrop software, but this model has not yet been calibrated to simulate salinity during the growing season. This research was carried out to calibrate the AquaCrop software for simulating the variations of soil salinity in the root zone of two-year-old saffron. In addition, in the present study, the effect of different levels of salinity and the application of different levels of organic mulch and their mutual effect on the yield of daughter corms, biomass, and water productivity were investigated.

Material and Methods

To calibrate the AquaCrop software for two-year saffron during the growing season in the research farm of the Ferdowsi University of Mashhad (FUM), growth parameters of saffron crop such as the soil moisture and salinity, the dry weight of daughter corms and the crop canopy cover were continuously measured during the growing season. Soil moisture and salinity were measured at least once a week, and the dry weight of daughter corms was measured biweekly. After model calibration, the accuracy of the model for simulating soil salinity during the growing season was evaluated by comparing the measured and simulated values. Statistical indicators of Pearson correlation coefficient, root mean square error, and Nash Sutcliffe model efficiency coefficient, were used to evaluate the accuracy of crop model simulation. The model was subsequently run for different initial conditions of soil salinity and irrigation water salinity. The dry weight of daughter corm, biomass, and ET water productivity were monitored for different conditions. Then, the model was run in the same conditions of water and soil salinity under the application of organic mulch, and the effect of mulch on the yield of daughter corms and evapotranspiration water productivity under salinity stress was investigated.

Results and Discussion

The statistical indicators between the measured and simulated values of soil moisture, canopy cover, and biomass approved the capability of AquaCrop for simulating saffron growth. Then, according to the measured salinity values of the root zone using the TDR sensor, AquaCrop was recalibrated to simulate soil salinity. Afterward, the changes in the measured salinity values of the root zone during the growing season were compared with the values simulated by AquaCrop. The Pearson correlation coefficient for measured and simulated soil salinity by software was 0.9 and the root mean square error was 0.086 dS m-1. Nash–Sutcliffe efficiency was 0.66, showing the high accuracy of AquaCrop for simulating soil salinity. The results of the crop growth simulation in saline conditions show the sensitivity of saffron to salinity. The results showed that under no initial salinity (0.5 dS m-1) in the soil, increasing the salinity of irrigation water from 1 dS m-1 to 4 dS m-1 caused a decrease of 3.7 % in the daughter corms weight. In addition, considering the initial salinity of 2 dS m-1, increasing the salinity of the irrigation water has a significant effect on reducing the daughter corm weight. In the presence of high-quality irrigation water (0.5 dS m-1), increasing the initial salinity of the soil from no salinity (0.5 dS m-1) to 4 dS m-1 caused a 38% decrease in the weight of daughter corms. The effect of organic mulch was also evaluated under saline water irrigation conditions. The results showed that the use of organic mulch with 100% coverage in water and soil salinity conditions equal to 4 dS m-1 can mitigate the effect of salinity stress by increasing 51% of daughter corm weight.

Conclusion

Water and soil salinity and its related problems are limiting factors in agricultural production in arid and semi-arid regions. The expansion of irrigation methods with saline water without proper management can lead to the risk of soil quality loss and in turn the loss of agricultural lands in the long term. In this research, the AquaCrop model was calibrated to simulate soil salinity during the saffron growing season, and the effect of organic mulch on soil and water salinity conditions was evaluated on the yield of daughter corms. The findings of this research will be of great help to farmers and water experts in improving the performance of saffron in saline soil and irrigation water.

Landslides are one of the most destructive types of domain movements and instabilities, which always cause soil erosion, produce sediment, destroy agricultural lands, gardens, and roads, and cause significant human and financial losses in different parts of the world. Especially in Iran due to the special conditions of the geological structure.

The watershed of Chalus River is located in the northern slope of Central Alborz and in the south of the city in the geographical longitude of 51°, 00'east to 51°, 35' east and latitude 36°, 08' north to 36°, 43' north. The studied basin of Chalus River watershed leads from the west to the Sardabroud River watershed, from the east to the Korkorsar River watershed, from the south to the Karaj watershed, and from the north to the Mazandaran Sea.

In this research, the most important factors affecting landslides have been investigated, which include topography, climate, geology, soil science, land use, distance from the river, topographic humidity index, and vegetation index.

After determining the most important factors affecting landslides in the studied watershed, a layer map was prepared. Then, by using the maximum entropy algorithm with the help of MaxEnt software, one of the capabilities of this model is to identify the most important influencing variables and determine the relative importance of each of the factors affecting the identification of landslide areas and analyze the sensitivity of the model using the Jackknife method.

In this method, after creating a complete model with the involvement of all variables, the modeling is repeated for the number of variables and each time one of the variables is removed from the modeling process. In this way, the effect of each variable in predicting the desired areas was evaluated. Then, in order to evaluate the model, the ROC curve was used, and the area under the obtained AUC chart was taken into consideration as a criterion of the model's discriminating power in detecting presence and non-presence points. In the next step, in order to prepare the stability index, SINMAP plugin and Arc view software were used. Then, by accepting the default values to recalibrate the parameters and apply the corresponding settings and values, the stability index was extracted. In the last stage of the current research, based on the effective factors in the Arc GIS10.8 software environment, a map of the risk of landslides in the Chalus watershed was prepared.

According to the results of the model, the most effective factors in the occurrence of landslides in the study area were rainfall factors, soil science, geological units, slope percentage, land use and distance from the river.

In the present study, AUC chart was used to validate the model. The number of output diagrams of the model is equal to the number of iterations of the model. Finally, the average of model iterations was considered as ROC diagram to evaluate the validity of the model.

The value of AUC for landslide validation is 0.73, which indicates the acceptable prediction and modeling of landslides by the model in the study area.

According to the results obtained from combining the results of the previous sections, the final map was prepared, which according to the findings of the research, the area and percentage of each of the landslide risk classes in the study area were obtained. In the landslide risk zoning map, the sensitivity of the region to the occurrence of this natural phenomenon was determined between zero and one.

Due to the special geographical situation of Iran, each year the phenomenon of landslides imposes a lot of human and financial losses on our country, and one of the ways to reduce these losses is to identify areas prone to landslides with forecasting and zoning methods and providing implementation solutions. In general, it can be said that in all areas where the risk of landslides is more likely, there are formations with low resistance, suitable slopes to provide a landslide bed, and landslide prone landforms. Due to the fact that the role of each factor depends on other effective factors, so its role in the occurrence or non-occurrence of landslides is not the same, therefore, the combination of factors has created a suitable platform for the occurrence of this natural phenomenon. In this regard, in this study, with the aim of zoning landslide risk using the maximum entropy method in the Chalus watershed, it was planned that according to the results obtained from the current study, the risk classes were low, relatively low, medium, relatively high and high, respectively 13.29 , 18.57, 23.73, 35.90 and 8.49 percent of the studied area, which indicates the high potential of the area to cause landslides, so the results of this research can be useful to managers and planners. It helps a lot so that they can make better decisions based on location data.

Introduction

Evapotranspiration that includes evaporation from the soil surface and transpiration from vegetation, is one of the most important factors of water loss. Also, it is one of the most effective components of the water balance in a catchment in arid and semi-arid regions of the world. Therefore, it is an important physical parameter for water resource management and determining the plant water requirement in the agricultural sector. so far, many experimental methods have been proposed to calculate evapotranspiration, but, they are only suitable at the local scale and cannot be generalized to large areas due to regional dynamics and changes. whereas the accurate estimation of it is also very difficult and expensive, Therefore, in the present study, calculated the amount of evapotranspiration in the irrigated agricultural sector by using of landsat 8 satellite images and Surface Energy Balance Algorithm (SEBAL) in Nahavand Plain. in SEBAL algorithm by estimating all energy components on the earth's surface, including net radiation flux, soil heat flux, and sensible heat flux and using the energy balance equation, evapotranspiration is calculated. Remote sensing also has the ability to show evapotranspiration spatial distribution in addition to estimating the amount of its, because, it is the only technology that extracts factors such as surface temperature, albedo coefficient and plant index in a way compatible with the environment and is also economically affordable.

Materials and Methods

in this research, in order to estimate daily actual evapotranspiration of the irrigated agricultural and gardens of Nahavand Plain, extracted irrigated agricultural land use map by using of Sentinel 2 satellite images, Then, by using of Landsat 8 satellite images (13 images, from 13 April to 22 October during the growth period of the irrigated crops) and Surface Energy Balance Algorithm (SEBAL), evapotranspiration maps were obtained during the irrigated crops growth period in 2021. These Landsat 8satellite images are obtained by the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard the satellites and are widely used for water resource applications. The OLI sensor has 9 bands and the TIRS has two bands (10th and 11th are the thermal bands). Landsat images are at intervals of 16-days with a spatial resolution of 30 m. In all images, the imaging time was 7:21. Then, due to FAO- Penman monteith method is one of the most important and reliable reference methods in evapotranspiration calculations, in this research, this method was used as a basis for evaluation and comparison. Finally, in order to evaluate the efficiency of SEBAL method in estimating the actual evapotranspiration of irrigated crops and gardens in Nahavand Plain used RMSE function (Root Mean Squares of Errors).

Results and Discussion

According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration was related to images 2021.09.04 and 2021.08.19 which fall in the middle of the growing period of irrigated crops. In addition, the surface albedo is noted to be relatively low for these days with the high NDVI values indicating high absorption of radiation by the vegetation during this period. Net solar radiation is directly contingent upon the incoming longwave and shortwave radiations, both of which directly influence the surface temperature. Therefore, areas with higher surface temperatures have higher net solar radiation. The net radiation flux has a direct relationship with NDVI, Greenness, and wetness parameters and is inversely related to albedo, Brightness, and Ts. The vegetative moisture and sensible heat flux are higher on days with high NDVI. Higher NDVI values are an indication of an increase in vegetation greenness, therefore essentially an increase in evapotranspiration is expected to be observed. The lowest mean of actual evapotranspiration is related to the northeast of the case study; due to lack of sufficient surface and ground water resources and consequently the reduction of agricultural lands in this region. Finally, in order to investigate the accuracy of SEBAL method in calculating evapotranspiration, compared the results of SEBAL method with the results of FAO- Penman monteith method. The results of this comparison showed that the SEBAL method with RMSE 0.82 has appropriate efficiency for estimating evapotranspiration.

Conclusion

Due to an increase in population and shortage of water resources, especially in the agricultural sector, researchers are looking for ways to better manage the available water resources. Evapotranspiration rate is one of the most important components of the global hydrologic cycle and has a significant influence on energy balance and climate. the using of indirect methods such as remote sensing can be an important step for estimating the water need of agricultural products, planning and management the country, s water resources. Therefore, according to the position of Nahavand city as the agricultural hub of Hamedan province, in this study, the actual evapotranspiration of the irrigated agricultural land use using of landsat 8 satellite images and SEBAL Algorithm was investigated in this area. According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration in all of the investigated images is related to the southeast and center of the studied area. that the reason of this matter is location of this area in the main branch of the Gamasiab River and focused the irrigated agricultural and gardens in this area. The final results of this research indicated high precision of SEBAL algorithm in estimating evapotranspiration. Thus, the high accuracy and low error indicate that the SEBAL method could be aptly used to estimate evapotranspiration on a regional scale, in the respective time range. Also, the results obtained from the SEBAL method assisted in understanding the spatial and temporal changes in different stages of plant growth.

Evapotranspiration, which includes evaporation from the soil surface and transpiration from vegetation, is one of the most important factors of water loss. Also, it is one of the most effective components of the water balance in a watershed in arid and semi-arid regions of the world. Therefore, it is an important physical parameter for water resource management and determining the water requirement of plants in the agricultural sector. So far, many experimental methods have been proposed to calculate evapotranspiration. However, they are only suitable at the local scale and cannot be generalized to large areas due to regional dynamics and variations. Moreover, the accurate estimation of evapotranspiration is also very difficult and expensive. Therefore, the present study calculated the amount of evapotranspiration in the irrigated agricultural sector using Landsat 8 satellite images and the Surface Energy Balance Algorithm (SEBAL) in Nahavand County. In the SEBAL algorithm by estimating all energy components on the Earth's surface, including net radiation flux, soil heat flux, and sensible heat flux and using the energy balance equation, evapotranspiration is calculated. Remote sensing can also show the spatial distribution of evapotranspiration in addition to estimating the amount of its, because, it is the only technology that extracts factors such as surface temperature, albedo, and vegetation indices in a way compatible with the environment and is also economically affordable. In this research, to estimate daily actual evapotranspiration of the irrigated agricultural lands and gardens of Nahavand County, an irrigated agricultural land-use map was extracted using Sentinel 2 satellite images. Then, using Landsat 8 satellite images (13 images, from April 13 to October 22 during the growth period of the irrigated crops) and Surface Energy Balance Algorithm (SEBAL), evapotranspiration maps were obtained during the irrigated crops growth period in 2021. These Landsat 8 satellite images were captured by the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard the satellites and are widely used for water resource applications. The OLI sensor has 9 bands, and the TIRS has two bands (10th and 11th thermal bands). Landsat images are taken at 16-day intervals with a spatial resolution of 30 m. In all images, the imaging time was 7:21 AM. Then, since the FAO-Penman-Monteith method is one of the most important and reliable reference methods in evapotranspiration calculations, this method was used as a basis for evaluation and comparison. Finally, to evaluate the efficiency of the SEBAL method in estimating the actual evapotranspiration of irrigated crops and gardens in Nahavand County, the Root Mean Square Errors (RMSE) function was used. According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration occurred in the images from September 4, 2021 and August 19, 2021, which fall in the middle of the growing period of irrigated crops. In addition, the surface albedo was noted to be relatively low on these days, with high NDVI values indicating high radiation absorption by the vegetation during this period. Net solar radiation is directly influenced by the incoming longwave and shortwave radiation, both of which directly impact the surface temperature. Therefore, areas with higher surface temperatures receive higher net solar radiation. The net radiation flux has a direct relationship with NDVI, greenness, and wetness parameters and is inversely related to albedo, brightness, and surface temperature (Ts). Vegetative moisture and sensible heat flux are higher on days with high NDVI. Higher NDVI values indicate an increase in vegetation greenness; therefore, an increase in evapotranspiration is expected. The lowest mean actual evapotranspiration was observed in the northeastern part of the study area due to a lack of sufficient surface and groundwater resources and, consequently, a reduction in agricultural lands in this region. Finally, to investigate the accuracy of the SEBAL method in calculating evapotranspiration, the results of the SEBAL method were compared with those of the FAO-Penman-Monteith method. The results showed that the SEBAL method, with RMSE 0.82, has appropriate accuracy for estimating evapotranspiration. Due to population growth and water shortage, especially in the agricultural sector, researchers are looking for ways to optimize the management of available water resources. The evapotranspiration rate is one of the most important components of the global hydrological cycle and has a significant influence on the energy balance and climate. The use of indirect methods such as remote sensing can be an important step in estimating the water demand of agricultural crops, as well as planning and managing the country's water resources. Therefore, considering Nahavand County's position as the agricultural hub of Hamedan Province, this study investigated the actual evapotranspiration of the irrigated agricultural land using Landsat 8 satellite images and the SEBAL Algorithm. According to the SEBAL algorithm results, the highest mean actual evapotranspiration was observed in the southeastern and central parts of the study area. This can be attributed to the presence of the main branch of the Gamasiab River, which supports irrigated agricultural lands and gardens in this area. The final results of this research indicated the high precision of the SEBAL algorithm in estimating evapotranspiration. Thus, the high accuracy and low error rate indicate that the SEBAL method could be effectively used to estimate evapotranspiration on a regional scale over the given time period. Additionally, the results obtained from the SEBAL method assisted in understanding the spatial and temporal changes at different stages of plant growth.

Introduction

Wheat, with the scientific name Triticum aestivum L., is the world's first agricultural product, which is consumed by 35% of the world's population as the main food source. The cultivation area of this crop in the world is 219153830 ha, of which about 48% of it is under irrigation. The area of wheat cultivation in Iran is 6908545 ha, and about 34.3% of it is under irrigation. The area of irrigated wheat crop in Kermanshah is about 102236 ha. Based on the statistics of the crop year 2021-2022, Kermanshah province ranks sixth, fourth and third in the country in terms of the amount of cultivated area, production and yield of water wheat. Wheat plant is one of the main and major agricultural products of Kermanshah province and it is cultivated under irrigation in a large area of their lands. Therefore, determining wheat physical water productivity is an important indicator in wheat production planning. Considering the increase in population, climate changes, lack of water resources, and the increasing need for wheat production and food supply, it is necessary to improve the wheat water productivity. To improve water productivity, the first step is to know and determine its amount. Unfortunately, there is no accurate information about its amount in Kermanshah province, and only information related to the results of research projects in certain conditions is available, which cannot be generalized due to the difference between those conditions and the conditions of farmers' fields. The purpose of this research is to determine the water productivity of wheat crops in cold, moderate and hot climates of Kermanshah province.

Materials and Methods

Kermanshah province is located in the geographical position of 45° 25ʹ to 48° 6ʹ East longitude and 33° 41ʹ to 35° 17ʹ North latitude. Kermanshah province with an area of 24434.25 km2 covers about 1.5% of the country's area and its average height is 1200 m above sea level. This province generally has three climates: cold, moderate and hot. Based on the statistics of the agricultural year of 2020-2021of the Organization of Agricultural Jahad of Kermanshah province, and in the mentioned climates, Sonqor, Kermanshah and Sarpol Zahab cities respectively have the largest area under wheat cultivation and were selected as the research areas. To carry out the current research, 34 farms were selected under the conditions of farmers and during the growing season, the total volume of irrigation water of each farm was measured. The effective precipitation was determined using the data of the closest synoptic meteorological station to the selected farms and the USDA relationship. The volume of water consumed by each selected farm during the growing season was also calculated from the sum of the total volume of irrigation water and effective precipitation. After harvesting the crop and determining the yield of wheat by dividing it by the amount of water consumed, the amount of physical water productivity in each of the farms was determined. Then, the data obtained in the studied cities were statistically analyzed using SPSS software.

Results and Discussion

The results showed that the average total volume of irrigation water measured in Sonqor, Kermanshah and Sarpol Zahab cities was 5204, 5795, and 4236 m3 ha-1, respectively, and the average volume of wheat water consumption was 6297, 7737, and 5844 m3 ha-1, respectively. Therefore, the total volume of wheat irrigation water in Sarpol Zahab city was 19% and 27% less than in Sonqor and Kermanshah cities, respectively, due to the short duration of the wheat growth period and growth in the cool months of the year. This causes the amount of wheat water consumption volume in this city to be 7 and 24% less than the two cities of Sonqor and Kermanshah, respectively. The average yield of wheat in the mentioned cities was 5799, 7082 and 4937 kg ha-1, respectively. The average physical water productivity of wheat in the mentioned cities was 0.97, 0.95 and 0.86 kg m-3, respectively. Therefore, the results showed that the amount of physical water productivity of wheat in Sarpol Zahab city was lower than the other two cities, and the most important reason was the low yield of wheat in this city.

Conclusion

In this research, the values of the total volume of irrigation water, the volume of wheat water consumption and the physical water productivity of wheat in cold, moderate and hot climates of Kermanshah province were determined. The results generally showed that the total volume of irrigation water and the volume of wheat water consumption in the hot climate of the province were less than in the cold and moderate climates of the province due to the short growth period of wheat and the growth of this plant in the cool months of the year. Therefore, due to the smaller amount of the volume of wheat water consumption in hot climate, it was expected that the physical water productivity of wheat in this climate would be higher than the other two climates of the province. However, due to the lower yield of wheat in the hot climates, this did not happen and the physical water productivity of wheat in the hot climate of Kermanshah province was lower than the two cold and moderate climates of the province. Therefore, it is possible to increase the yield and finally increase the physical water productivity of wheat by managing agronomic and breeding in this city.

Groundwater is the largest source of water in semi-arid regions. It is, therefore, essential to develop and exploit underground water resources to meet these needs. Precipitation is the main source of recharge for many aquifers. Changes in precipitation and groundwater level depth are closely related. It is more difficult to quantify groundwater availability and the long-term effects of climate change on groundwater than surface water. Although groundwater resources are more resilient than surface water, they are increasingly vulnerable to overexploitation, drought, pollution, and the lack of permanentconsistent precipitation, reducing quality and availability. The decrease in aquifer quality and storage capacity as a result of extraction exceeding the available surplus is due to the development of urban areas, the use of water-based industries, and an increased cultivation area, which jeopardizes underground water sources. Although human factors have a strong influence on groundwater, the natural hydrological cycle plays a key role in regulating the condition of the aquifer. In arid and semi-arid countries and regions of the world where surface water resources are relatively scarce, groundwater is often the most important or even the only source of water for regional food security, drinking water, economic development, and environmental conservation. Quantitative information about groundwater resources related to wells, springs, and qanats was provided by relevant organizations, including the Iranian Water Resources Research Organization (Tamab) and the Yazd Regional Water Organization, as well as previous research. For a more detailed investigation, the level and depth maps of underground water were drawn for five years based on the available information. The zoning map of fiveyear underground water changes was prepared using ArcGIS software to analyze the amount of water level drop in the observation wells. This research considered the distribution of wells, springs, and qanats and the trend of changes in their number, discharge, and annual consumption in different parts. There are 25, 122, 0, and 11 semideep wells, deep wells, springs, and qanats. It is worth mentioning that the statistics of the selected wells in each plain were used in the calculations and for the underground water maps, which have the most complete statistics during the selected period, so the number of wells mentioned in each plain is not necessarily the same as the number of wells in the piezometric network, and the length of the statistical period used did not necessarily cover the entire statistical period. The maximum depth of underground water in the Bahadoran and Shams aquifers in 2018 was 68 m and 47.7 m, respectively, with the deepest points in the northern areas of the Bahadoran aquifer and southern Shams aquifers, toward the southern areas of the Bahadoran aquifer and the eastern parts. The water depth in the west of the Shams aquifer has decreased; therefore, the minimum depth of underground water in both aquifers is approximately 11.8 m. The highest groundwater level in the aquifers of the Bahadoran area in 2018 was approximately 1538.91 m in the southwestern and western areas of the aquifers. In the eastern part of the Bahadoran aquifer, it reached 1447.3 m, and in the southern part of the Shams aquifer, it reached 1190 m in 2018. The groundwater level in 2013 and 2008 was higher than in 2018, but it did not change significantly. The decline in groundwater level from 2008 to 2013 and from 2013 to 2018 was approximately 13.14 m and 6.68 m, respectively. Examining the changes in the groundwater level during the statistical period shows that the overall trend has been downward. This study investigated the level and depth of underground water sources in the Bahadoran watershed in Yazd province. The results indicate an alarming drop in the groundwater table. The spatial distribution of groundwater extraction differed throughout the watershed, so some areas experienced a severe decrease in the water level. In addition, due to the use of these resources, the seasonal variation in water level reduction differs. In the absence of surface water sources due to a decrease in precipitation and prolonged droughts, the majority of groundwater extractıon is done for various purposes, such as increasing the area of agricultural cultivation. This excessive extraction occurs through additional water wells. Therefore, it is necessary to carry out comprehensive studies to investigate the relationship between the extent of vegetation in the area and the amount of groundwater extractıon from aquifers using satellite images. In addition, it is suggested to evaluate the balance between groundwater extractıon and recharge in all watersheds of the country. The results of this study provide policy makers and managers with information on changes in groundwater resources in aquifers, which can be used for optimal management .

Groundwater is the largest source of water in semi-arid regions. It is therefore very important to develop and exploit underground water resources in order to meet these needs. Rainfall is the main source of nutrients for many aquifers. Changes in rainfall and groundwater level depth are closely related.It is more difficult to quantify groundwater availability and the long-term effects of climate change on groundwater than surface water. Although groundwater resources are more resilient than surface water, they are increasingly vulnerable to overexploitation, drought, pollution and lack of permanent rainfall, leading to reduced quality and availability. The decrease in the quality and storage capacity of aquifers as a result of extractıon more than the available surplus is due to the development of urban areas, the use of water-based industries and an increase in the area under cultivation of agricultural products, which jeopardises underground water sources. Although human factors have a strong influence on groundwater, the natural hydrological cycle plays a key role in regulating the aquifer situation. In arid and semi-arid countries and regions of the world where surface water resources are relatively scarce, groundwater is often the most important or even the only source of water for regional food security, drinking water, economic development and environmental conservation.

Quantitative information about groundwater resources related to wells, springs, and ghanat over 17 years was provided by relevant organizations, including the Iranian Water Resources Research Organization (Tamab) and the Yazd Regional Water Organization, as well as previous research. For a more detailed investigation, the level and depth maps of underground water were drawn for a five-year period based on the available information. The zoning map of five-year underground water changes was prepared in the Arc GIS software environment to check the amount of water level drop in the observation wells. This research considered the distribution of wells, springs, and ghanat in the region and the trend of changes in their number, discharge, and annual consumption in different parts. There are 25, 122, 0, and 11 underground water sources in the region, including semi-deep wells, deep wells, springs, and ghanat. It is worth mentioning that the statistics of the selected wells in each plain were used in the calculations and drawing of the underground water maps, which have the most complete statistics during the selected period, so the number of wells mentioned in each plain is not necessarily the same as the number of wells in the piezometric network, and the length of the statistical period used was also not necessarily the entire statistical period.

According to the results, the maximum depth of underground water in the Bahadran and Shams aquifers in 2018 was 68 and 47.7 m, respectively, which reached the maximum value in the northern areas of the Bahadran and southern Shams aquifers and toward the southern areas of the Bahadran aquifer and the eastern parts. The water depth in the west of the Shams aquifer has decreased; therefore, the minimum depth of underground water in both aquifers is approximately 11.8 m. The highest level of underground water in the aquifers of the Bahadran area in 2018 was approximately 1538.91 m in the southwestern and western areas of the aquifers. Thus, in the eastern part of the Bahadran aquifer, it reached 1447.3 m and in the southern part of the Shams aquifer, it reached 1190 m this year. The level of underground water in 2013 and 2008 was higher than that in 2018, but it did not change significantly. The drop in underground water level from 2008 to 2013 and from 2013 to 2018 was approximately 13.14 and 6.68 m, respectively. Examining the changes in the underground water level during the statistical period shows that the underground water level generally has a downward trend.

In this study, was investigated the level and depth of underground water sources in the Bahadran watershed in Yazd province. The results indicate an alarming drop in the underground water table. The spatial distribution of underground water resource extraction differed throughout the watershed, so some areas experienced severe decrease in the water level. In addition, due to the use of these resources, the time distribution of water level reduction may also be different in the seasons. In the absence of surface water sources due to a decrease in adequate rainfall and resulting droughts, the majority of underground water sources are extractıon for various purposes, such as increasing the area of agricultural cultivation. This amount of underground water extraction is carried out through other water wells. Therefore, it is necessary to carry out comprehensive studies to investigate the relationship between the extent of vegetation in the area and the amount of water extractıon from underground aquifers using satellite images. In addition, it is suggested to evaluate the amount of underground water resources extractıon and charge in all watersheds of the country. The results of this study provide politicians and managers with information on changes in underground water resources in aquifers, which can be used for optimal management.

Introduction
Evaluating the effectiveness of various water and soil protection measures on soil properties and erosion and sedimentation is of great importance in soil planning and management. And it is considered to be one of the measures of moving towards sustainable development. Based on this, this research has evaluated the effects of water and soil protection measures in terms of erosion and sedimentation and soil characteristics in the Rimele watershed of Lorestan province.

Materials and methods
EPM model was used to predict erosion before and after the implementation of water and soil protection measures. In order to investigate the characteristics of the soil, the apparent specific mass factors, soil texture, acidity, electrical conductivity and soil permeability were evaluated at two depths of 0-30 and 30-60 cm.

Results
The results of comparing the physical and chemical properties of soil in two areas under protection operation and the control area, showed that with water and soil protection measures, the apparent specific gravity, soil texture, acidity and electrical conductivity of surface soil did not increase significantly. The comparison of permeability data in control areas and protected areas showed that the implementation of water and soil protection operations had a positive effect on soil permeability. Also, the amount of total annual erosion and sedimentation in the current conditions compared to before the implementation of water and soil protection measures have decreased by an average of 6793 cubic meters and 5712 tons, respectively. The results of the paired t-test showed that the water and soil protection measures implemented in the Rimele basin have been able to create a significant difference in reducing erosion and sedimentation.

Conclusion
According to the findings of the research, it can be stated that water and soil protection measures have a significant positive effect in reducing erosion and sedimentation, but its effect on improving soil quality is not absolute and is completely dependent on the different climatic and geological conditions of each region.

Healthy soils contribute to climate change mitigation by sequestering carbon and reducing greenhouse gas emissions. Moreover, water resources are crucial in addressing climate change impacts, especially as they are increasingly affected by shifting weather patterns. Conservation efforts can enhance resilience to the effects of climate change. Water and soil conservation play a significant role in economic stability. Sustainable management of these resources can enhance agricultural productivity, leading to food security and increased income for farmers. Additionally, protecting watersheds can help reduce costs related to erosion control, water purification, and disaster recovery from floods and droughts. Today, the role of soil and water conservation management measures in increasing vegetation cover is of particular importance due to their effects on soil stabilization. Vegetation cover can act as a barrier between the ground surface and raindrops, and by preventing raindrops from directly impacting the soil surface, it prevents soil particles from disintegrating. Evaluating the effectiveness of different water and soil conservation measures on soil properties, erosion, and sedimentation is crucial in soil planning and management. It is considered one of the steps towards achieving sustainable development. Therefore, this study evaluated the effects of water and soil conservation measures on erosion, sedimentation, and soil characteristics in the Rimeleh Watershed of Lorestan Province. The Rimeleh Watershed area is about 7319 ha. The minimum and maximum elevations are 1500 and 2783 masl, respectively, and the average slope of the watershed is about 28 percent, with an average rainfall of 499 mm per year. Its average annual temperature is 11.2 C. In this study, the normalized difference vegetation index (NDVI) was used to prepare the land use layer using Landsat satellite images and ILWIS software. In addition, the land use of the 1990s was prepared using existing aerial photographs and compared with the current land use map. First, the data were checked for heterogeneity, including the presence of outliers. To test the null hypothesis that the means of the studied variables were equal to zero, a one-way analysis of variance (ANOVA) was used, and to compare the means between treatments, Duncan's test was used at a 5 % significant level. The Erosion Potential Model (EPM) was used to predict erosion before and after the implementation of water and soil conservation measures. To investigate the soil characteristics, bulk density, soil texture, acidity, electrical conductivity, and soil permeability were evaluated at depths of 0-30 and 30-60 cm. Comparing the soil physical and chemical properties in the two areas under conservation measures and the control area showed that the bulk density, soil texture, acidity, and electrical conductivity of surface soil did not significantly increase with water and soil conservation measures. A comparison of permeability data showed that conservation measures had a positive effect. In addition, the amount of total annual erosion and sedimentation under the current conditions have decreased by an average of 6793 m 3 and 5712 tons, respectively, compared to before the implementation of conservation measures. The results of the paired t-test showed that the water and soil conservation measures were able to reduce erosion and sedimentation significantly. In general, no significant effect of measures on soil acidity was found. The results indicate that in both the first and second soil depths, a significant difference in electrical conductivity was evident between the conserved areas and control areas. The study of soil bulk density for both surface and deep depths showed that there was no significant difference. The soil bulk density value in the protected area was lower than in the unprotected areas, which could be due to the higher organic matter content in the protected areas. The implementation of soil and water conservation measures such as engineering and bioengineering measures (drywall, check dam, Turkinst, gabion and earth dam), terracing, pond construction, and gardening development in the Rimeleh Watershed could significantly increase the physical and chemical properties of the soil. The results also showed that the amount of total sediment output after project implementation has significantly decreased. Finally, it can be concluded that soil and water conservation measures have had a significant positive effect on reducing erosion and sedimentation, but the effect of soil and water conservation measures on improving soil quality is not absolute, and their effectiveness is highly dependent on the different climatic and geological conditions.

ntroduction
The reuse of wastewater in agriculture, especially for crop irrigation, can be one of the most important options to alleviate the water shortage problem and reduce environmental pollution through land based wastewater treatment. The presence of more organic matter and food and the presence of fecal matter in sewage effluent, increase the activity, survival, growth and development of bacteria. Those are as factors increasing soil cloiform in the condition of irrigation with sewage. Subsurface irrigation is one of the proved method that can minimize soil contamination in comparsion with other method of irrigation. It has been showen that, subsurface irrigation systems with geotextiles can reduce environmental pollution and the risk of soil and plant contamination when using wastewater. So far, many studies have investigated the effects of wastewater application on soil characteristics; but there is no information on the effect of wastewater application using SSTI (Subsurface Textile Irrigation) systems on soil properties. In this study, the effects of wastewater application in subsurface irrigation with geocomposite sheets on the chemical and biochemical characteristics of soil were investigated. In the present study, the changes in chemical and biochemical characteristics of soil due to the application of Shahrekord University wastewater using subsurface irrigation with geocomposite sheets were investigated.
Materials and Methods
This research was conducted as a factorial experiment based on a completely randomized design with three replications. The studied factors included the measurement location at two levels above (0-40 cm) and below the injection layer (40-80 cm) and the distance of the drain from the injection layer at two levels of 35 (D35) and 70 (D70) cm. To conduct the experiment, four meter long, 40 cm wide and 75 (D35) and 110 (D70) cm deep trenches were dug in the soil. The bed and walls of the trenches were covered with plastic and greased to prevent preferential flow from the plastic walls. Then the geocomposite layers were used as a drainage layer with a length of four meters and a width of 20 cm. After that, according to the desired treatment (35 and 70 cm thick), soil was poured on the drain to a depth of 40 cm from the ground surface. To conduct the research, geocomposite-sheets were used for the water influx layer and drainage layer. The treatments included two distances of 35 and 75 centimetres between the water influx and the drainage layer. In both treatments, the water influx layer was 40 centimetres below the ground surface. Wastewater was injected 12 times with a weekly frequency. At the beginning and end of the study period, soil samples were taken from two depths of 0-40 and 40-80 cm and pH, EC, total calcium and magnesium, carbonate and bicarbonate, nitrate and fecal and total coliform were measured.
Resullts and discussion
The results showed an increase in electrical conductivity, nitrate, carbonate and bicarbonate, total coliform and faecal coliform in the soil at the end of the study period compared to beginning of the study period. The pH of the soil has decreased in both the upper and lower areas of the water table. Probably, the decrease in soil pH under the conditions of using wastewater is due to the nitrification of ammonium and the leaching of cations from the soil. However, the results of the ANOVA of the effect of depth of measurement, depth of drain application, and their interaction on soil pH changes showed that the effect was not significant. The electrical conductivity of the soil has increased in both areas above and below the water table for both treatments. The results of ANOVA of the effect of measurement depth, depth of drain installation, and their interaction on percentage changes in soil electrical conductivity showed that the effect was not significant. The total soil calcium and magnesium in D70 and D35 treatments decreased on average by 6.66 and 8.48% compared to the beginning of the period, but this difference was not significant. According to the presented results, the amount of soil nitrate has increased as a result of irrigation with wastewater at both depths compared to the beginning of the research period. The amount of total coliform and faecal coliform in the soil at the end of the period has increased compared to its value at the beginning of the period.

Conclusion
As a conclusion, the use of geocomposite sheets for land treatment during the period of study did not have a negative effect on the chemical and biochemical properties of the soil, and this method can be used without worrying about soil contamination.

Introduction
Potato is one of the most important food sources in the world and is considered one of the basic sources in different countries. Iran is the 13th producer of this product with the production of about five million tons of potatoes. The yield of potatoes is high compared to other crops, and for this reason, it has a higher water efficiency than other crops. However, potato yield is strongly dependent on the amount of irrigation water. This issue has caused special attention to be paid to the amount of irrigation water and many studies have been conducted on determining the optimal irrigation water. However, providing the right amount of irrigation water in each climate and irrigation system requires many experiments. These tests require spending a lot of time and money, which cannot be done in research centers in the current situation. To solve this problem, various plant models have been presented. The SALTMED model is such tool, accommodate different plants, soils, irrigation systems, irrigation management solutions, water qualities, and environmental stresses.

Materials and Methods
To carry out this research, the data collected from two research projects were conducted (the first project in 2012 and 2013, the second project in 2017 and 2018) in Shahrekord's research station. In the research projects, to manage irrigation in potato cultivation, two irrigation factors were investigated: the first factor includes different irrigation methods (S: drip, Su: subsurface drip, and F: furrow), and quantitative management of irrigation water as the second factor (FI: providing 100% of water needs, RDI80: providing 80% of water needs and RDI65: providing 65% of water needs) considering three repetitions. Data collected from the first year were used to calibrate the SALTMED model. The area of the plots was 40 m-2. Within each plot, four rows were planted with a row spacing of 75 cm and a seed spacing of 20 cm. The subplots were separated by one meter. In the surface drip irrigation method,  16mm drip pipes with an average flow rate of 1.75 L h-1 were used for each crop row. In subsurface drip irrigation, 16mm drip pipes with an average flow rate of 1.85 L h-1 were installed at a depth of 20 cm in the soil for each crop row. To evaluate the SALTMED model at this stage, the statistics of root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), model efficiency (EF), agreement index (d), and determination of coefficient (R2) were used. Validation of this model was done using second-year data.

Results and Discussion
The highest and lowest differences between the observed and simulated values were 4.2 and 1.4 tons per hectare, respectively. The average difference between the observed and simulated values was 2.7 tons per hectare. The differences between the observed and simulated yields for drip, subsurface drip, and furrow irrigation methods were respectively 3, 1.9, and 3.1 tons per hectare. In addition, the accuracy of this model for simulating yield in the furrow irrigation method was lower than the other two methods. The highest and lowest difference between observed and simulated water productivity were 0.3 and 1.3 kg.m-3, respectively. The average of this difference was determined to be 0.7 kg.m-3. The difference between observed and simulated water productivity for drip irrigation, subsurface drip, and furrow irrigation methods was 0.8, 0.5, and 0.8, respectively. Based on the values of MBE statistics, the SALTMED model had an underestimation error in simulating yield and water productivity. The results of the NRMSE statistic showed that the accuracy of the SALTMED model for yield simulation was in the excellent category. The efficiency of the SALTMED model for yield simulation was acceptable based on two statistics EF and d. The R2 statistic for the simulation of potato yield by this plant model ranged from 0.88 to 0.99.

Conclusion
This research evaluated the SALTMED model for simulating potatoes' yield and water productivity under three irrigation methods: furrow, drip, and subsurface drip. Using the recalibrated SALTMED model, a simulation of yield and water productivity was done to meet the water needs of 90, 55, and 45 % of potatoes for all three irrigation methods. A downward trend was observed between the reduction of water supply and potato yield in all three irrigation methods. The yield differences between the 100 and 90 %, and 90 and 80 % water supply in surface irrigation were 4.8 and 5.7 %, respectively. These values were respectively 18.6 and 8.7 % for drip irrigation and 13.3% and 1.7% for subsurface drip irrigation. Therefore, the slope of yield reduction until providing 80% of the water requirement was low in all three irrigation methods. The results showed that the SALTMED model had an underestimation error for simulating both parameters (MBE<0). However, the obtained error is negligible and the accuracy of this plant model was in the excellent category (NRMSE<0.1). Based on the results of EF (<0.88) and d (<0.99) statistics, the effectiveness of the SALTMED model was favorable for simulating both yield and water productivity. To determine the optimal amount of irrigation water, the recalibrated SALTMED model was used. The results showed that providing 80% of the potato's water requirement led to the achievement of an optimal yield and high water productivity.

Introduction
Rice (Oryza sativa L.) is one of the most popular cereals in the world and is known as the second most consumed grain in Iran after wheat. For this reason, it is essential to pay attention to the quantity and quality of this agricultural crop. The cultivation of ratoon rice, which has become common in some areas in the north of Iran, will enable the re-production of rice in the next cropping season. The lack of water resources in recent years in Iran and the prediction of the severity of the shortage of available water in the coming years have caused the use of methods to reduce water consumption in the agricultural sector, which is the largest water consumer in the country, to be considered Rice plant has a large share in water consumption per unit area among other agricultural products. For this reason, some methods of reducing water consumption in the cultivation of this product have been suggested. According to the existing restrictions on the use of irrigation water and nitrogen fertilizer, it is necessary to determine the optimal amounts of these factors in the field. However, this work requires many field experiments, which require a lot of time and money. To solve this problem, the use of simulation and optimization models has been suggested. Therefore, this research aims to optimize the two factors of irrigation water and nitrogen fertilizer to achieve the most appropriate amount of production and improve the quality of rice using the response surface methodology (RSM).

Materials and Methods
Considering the effects of two aforementioned factors, in this research the optimization of the amount of irrigation water and nitrogen fertilizer on the quantitative and qualitative characteristics of ratoon rice was applied using the response surface method. The study site was the research farm of the Rice Research Institute located at latitude 37º 16’ N and longitude 49º 63’ E in Rasht, Iran. The studied factors included the amount of irrigation water (with upper and lower levels of 0 and 5 mm of cracks appearing in the soil, respectively) and nitrogen fertilizer (with upper and lower levels of 90 and zero kg ha-1 of pure nitrogen, respectively). the RSM refers to a multivariate function. To compare the obtained model results with observed values, from the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Bias Error (MBE), Efficiency Factor (EF), agreement index (d), and the Coefficient of Determination (R2) was used.

Results and Discussion
The results showed that the regression model for predicting the plant height, biomass, gel consistency, and yield had an underestimation error (MBE < 0), and for other parameters had an an overestimation error (MBE<0). The value of the NRMSE was less than 0.1, so the quadratic regression model had excellent accuracy for all parameters. The two statistics EF and d, which show the efficiency of the regression model, had acceptable values (d>0.90 and EF>0). After optimization, the yield of seed and biomass was 10.5% and 0.5%, respectively, higher than the average values of these parameters in field conditions. In optimal conditions, the number of tillers, panicle length, and harvest index were 1.9%, 1.4%, and 3.6% higher than the observed values, respectively, and plant height and weight of 1000 seeds were almost equal in both optimal and observational conditions. The changes in the quality indicators of gelatinization temperature, gel consistency, amylase content percentage, and grain elongation in optimal conditions compared to the observed values were 1.8 (less), 0.4 (more), zero, and 6.2 (more) percent, respectively. The amount of irrigation water and nitrogen fertilizer in optimal conditions was 3.5 mm of cracking in the soil and 68 kg ha-1, respectively.

Conclusion
In this research, the effect of two factors, irrigation water and nitrogen fertilizer, was optimized using the response surface method. For this purpose, the minimum and maximum values of these factors were considered with codes of -1 and +1. The results showed that, except for yield and biomass, other traits did not have uniform and regular changes with the increase of two factors, irrigation water and nitrogen fertilizer. For this reason, the overlap map of these factors was determined and it was observed that the effect of nitrogen fertilizer on most parameters was greater than that of irrigation water. The optimal range of all factors was in the values of -0.3 to +0.5 nitrogen fertilizer -1 to +1 irrigation water +0.5 to +1 nitrogen fertilizer and -1 to +0.7 irrigation water. Of course, a part of the overlapping area of these two ranges also lacked the optimal value for the parameters, which was considered by the surface-response method in the optimization. After optimization, it was observed that the two parameters of gelatinization degree and gel consistency were lower than the average observed values. The parameters of amylase percentage, 1000 seed weight, and plant height did not change compared to the field conditions and the value of other parameters increased. To achieve these results, it is necessary to consider the amount of irrigation water in cracks of 3.5 mm and the amount of nitrogen fertilizer as 68 kg ha-1.

Introduction
Climate change has become a concern among the scientific community, governments, and the public. Climate change will inevitably affect the temporal and spatial distribution of the hydrological cycle and water resources. These changes, in turn, may lead to changes in soil erosion in watersheds, among other environmental problems. Evaluating changes in runoff and sediment performance in the context of climate change and showing the relationships between climate change and hydrological resources and ecological change can provide a scientific basis for the design, use, and management of water resource systems. Temperature changes and precipitation patterns can be predicted by regional climate models (RCM) and global climate models (GCM), which can have a distinct effect on water resources and soil erosion. In some cases, climate change can lead to changes in the amount, intensity, and distribution of rainfall. These changes, such as altering precipitation characteristics, increasing temperature, and reducing water resources have been extensively studied worldwide. Evaluating these impacts on hydrological and ecological systems can provide a scientific basis for the design, use, and management of water resource systems. Therefore, the current research was carried out to investigate the impact of climate change on the runoff and sediment of the Ferizi Watershed located in Razavi Khorasan Province.

Materials and Methods
This research has been done in two main steps. In the first step, the climate of the region was studied. First, the meteorological data of the region, including the minimum and maximum temperature and daily rainfall, were obtained from the Meteorological Organization. To investigate the climate change of the region using atmospheric general circulation models and the sixth IPCC report, the minimum and maximum temperature and daily precipitation for the period 2021-2100 were predicted using the SSP2-4.5 scenario (moderate) and the SSP5-8.5 scenario (very pessimistic). The second step is the application of the SWAT model for simulating past and future runoff and sediment patterns. For this purpose, first, the SWAT model was run using the data of the study area, such as the digital elevation model map, soil characteristics map, land use map, and meteorological data, and then using the observed runoff and sediment from the area and SUFI-2 algorithm in the SWAT-CUP software was calibrated. Finally, the SWAT calibrated model was run with forecasted precipitation and temperature data until the year 2100 and the impact of climate change on runoff and sediment was investigated.

Results and Discussion
The results of SWAT model calibration showed that the Nash-Sutcliffe criterion for discharge and monthly sediment in the calibration period was 0.66 and 0.65, respectively, while for the validation period, it was 0.57 and 0.56, respectively. The results of runoff and sediment from the simulation of the SWAT model under future climate conditions with two scenarios SSP2-4.5 and SSP5-8.5 showed that the average runoff in these two scenarios were estimated to be 0.25 and 0.28 m3 s-1, respectively, which shows a 48.9 % reduction in runoff. In the SSP2-4.5 scenario and a 42.8 % reduction of runoff in the SSP5-8.5 scenario compared to the previous period. Also, the total sediment for the SSP2-4.5 and SSP5-8.5 scenarios is predicted to be 3.57 and 4.94 × 106 ton, respectively, which shows a 7.2% increase in sediment in the SSP2-4.5 scenario and a 48.3 % increase in the sediment in the SSP5-8.5 scenario compared to the previous period. Also the average runoff and total sediment in the pessimistic scenario (SSP5-8.5) were predicted higher than the average scenario (SSP2-4.5). The total average precipitation in the SSP2-4.5 and SSP5-8.5 scenarios is 266.1 and 281.4 mm y-1, respectively, and the average temperature in the SSP2-4.5 and SSP5-8.5 scenarios is 18.4 and 19.3 °C, respectively. Therefore, the reason for the increase in runoff and sedimentation in the pessimistic scenario compared to the average scenario is the higher amount of precipitation in the pessimistic scenario.

Conclusion
The investigations carried out in this research show that in general, runoff and sediment will decrease between 2021 and 2100 compared to the previous period, and the amount of reduction in the very pessimistic scenario (SSP5-8.5) will be higher than the average scenario (SSP2-4.5). This result, attributed to a relative reduction in rainfall and an increase in temperature, can be linked to extreme rainfall events projected for future years. Considering the increase in flood conditions in the basin and the increase in the amount of sediment load on the horizon of 2100, to deal with and adapt to climate change, it is better to take appropriate solutions in the Ferizi Watershed.

Introduction
Gully erosion is a particularly destructive form of water erosion that can lead to alarming rates of soil loss, especially in the vulnerable landscapes of dry and semi-arid regions. This type of erosion is recognized not only for its immediate impact on land but also as a critical environmental challenge that requires our urgent attention. As a result, there has been a growing emphasis on developing effective predictive models that can elucidate the temporal and spatial dynamics of gully erosion-specifically, how it forms, expands, and evolves over time. This endeavor has captured the interest of soil conservation experts and researchers alike, who understand the profound implications of this issueIn recent years, remote sensing and data mining techniques have emerged as valuable tools for identifying and mapping areas susceptible to gully erosion. These innovative methods provide essential insights for land managers and policymakers, enabling them to make informed decisions. Furthermore, the effectiveness of predictive models hinges on their advanced capabilities, which enhance their learning potential and improve the identification of relationships among various factors. Creating a sensitivity map is an essential strategy for land use planning, as it actively contributes to reducing land degradation and safeguarding our natural resources. Understanding the connection between gully occurrences and influential factors is not only beneficial; it is crucial for sustainable land management and environmental preservation.

Materials and Methods
This research investigates the sensitivity of the upper basin of the Boustan Dam to gully erosion using object-based techniques and data mining algorithms. To achieve this, field visits were conducted to select 81 gullies for analysis. The study examines several factors, including slope, aspect, slope length index (LS), elevation, plan curvature, distance from the river, drainage density, topographic wetness index (TWI), height above the nearest drainage (HAND), average annual rainfall, distance from roads, distance from faults, land use, geomorphology, soil texture, and satellite bands B7, B5, and B3. Additionally, the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference water index (NDWI) are considered, along with geological aspects. QuickBird satellite images from 2021 and Orfeo software were utilized to monitor and identify gullies in the area through image segmentation. Initially, a collinearity analysis of 23 effective erosion occurrence indices was performed, resulting in the removal of distance from the fault, digital elevation model (DEM), NDWI, NDBI, and satellite bands B3, B5, and B7 due to their collinearity exceeding five. Following this linear operation, all remaining indices were integrated with the segmentation map obtained from the Orfeo environment. Finally, three models -Random Forest, Maximum Entropy, and Support Vector Machine- were employed to model the selected indices using Python (Colab).

Results and Discussion
The results from the object-oriented method in the Orfeo software further demonstrated its effectiveness in accurately identifying gullies. With an impressive accuracy rate of 91.3%, this method has proven to be highly reliable in generating machine learning maps with high precision. Findings indicate that the key factors contributing to gully erosion include the rainfall index, distance from the river, Height Above Nearest Drainage (HAND) index, distance from the road, and valley index. Torrential rain emerged as a significant driver of gully erosion, while the distance from the river was crucial due to the concentration of surface and subsurface flows toward waterways. The HAND index played a prominent role in modeling the sensitivity of the study area compared to other sub-indices derived from DEM, as it exhibited promising applications in assessing natural hazards. Locations close to roads were found to be more vulnerable to water erosion, and valleys were identified as especially susceptible to gully erosion due to their conducive conditions for rapid water flow and erosion. Extensive field studies support this observation. Furthermore, zoning results generated using these indices indicated that, within the random forest model, 544.23 hectares of the area are at high or very high risk of erosion. This model outperformed the Maximum Entropy and Support Vector Machine models in predicting erosion-prone areas. Finally, the ROC curve was utilized to validate the model, yielding AUC values of 0.95 and 0.94 in the random forest model during the training and validation stages, respectively. These results indicate the model's high accuracy in predicting areas highly susceptible to gully erosion.

Conclusion
This study effectively used object-based image analysis algorithms and data mining techniques to create a sensitivity map of the region. The object-based method efficiently identified the local gullies using the mean shift algorithm, while the random forest algorithm excelled in predicting areas prone to gully erosion. Key factors contributing to gully erosion were identified, including rainfall, distance from the river, soil HAND index, and distance from roads and valleys. The findings from this study provide valuable insights for managing and preserving basin resources. Implementing the recommendations from this research could help mitigate the impacts of gully erosion in the future and ensure the sustainability of the Boustan Dam and its surrounding ecosystem.

Introduction
In recent years, our country has experienced an increase in the frequency and severity of natural hazards such as floods, droughts, and pests. These changes can be attributed to shifts in climate variables. Due to its influential role in other natural hazards such as drought, climate can significantly impact the economy and people's lives. This is particularly evident in the agricultural, animal husbandry, and industrial sectors, where it can cause damage and destruction in various regions. Drought is one of the biggest climatic challenges that our country has been facing in recent years. When a drought occurs, it initially manifests as a meteorological drought. If the drought persists, it can lead to other types of droughts, including hydrogeological, agricultural, and economic droughts. Each of these droughts, as well as their cumulative impact, affects various aspects of the ecosystem within a watershed. Climatic changes cause many problems due to their impact on the temporal and spatial distribution of precipitation in various regions. Due to its gradual process and slow speed, this phenomenon has been operating for a relatively longer period. Its effects may be revealed after a few years and with a longer delay than other risks. This phenomenon has more tangible effects in rural areas because rural communities are more vulnerable. Therefore, understanding this phenomenon and analyzing and evaluating its impact on ecosystems within a watershed is crucial for effective planning and decision-making. This knowledge is necessary for the implementation of appropriate adaptation strategies.

Material and Methods
To carry out this research, the required climatic data were first collected from the Meteorology and Regional Water Department of Mazandaran Province. The SPI (Standardized Precipitation Index) index was also used to evaluate meteorological drought. Additionally, to examine the trend of changes in flow rate, the data from existing hydrometric stations in the region, which have more comprehensive data, were utilized. Lars-WG software was used to project future climate (2023-2050) conditions under two climate scenarios: an optimistic scenario (RCP2.6) and a pessimistic scenario (RCP8.5). After preparing the current and future data, the DrinC software was used to determine drought thresholds based on the standard table for the SPI. In this research, the IHACRES hydrological model was also utilized to forecast future discharge. To achieve this objective, the future precipitation and temperature data obtained from the previous stage were utilized, along with the observed discharge data (1990-2020), to project the trend of discharge in the future (2022-2040). Subsequently, the hydrological drought of the Tajan Watershed was calculated at the outlet station (Kordkhil) using the SDI (Streamflow Drought Index) in the DrinC software. After analyzing the stream flow data and using the standard table associated with the SDI, the thresholds for hydrological drought were determined.

Results and Discussion
The results of this study showed that, based on the SPI standard table, the degree of drought in the area under investigation has fluctuated between -3.3 (indicating very severe drought) and 2.4 (indicating extremely humid conditions) in recent years. Also, the future climate was projected for a period of approximately 27 years (2023-2050) under the influence of two optimistic scenarios (RCP2.6) and one pessimistic scenario (RCP8.5) at three selected stations (Kordkhil, Soleiman Tange, Rig Cheshme). The results of the climate drought conditions and the index values for the projected period (2023-2050) at Soleiman Tange station, in 12-month steps showed that there is a possibility of a severe climate drought occurring in this region during the water year 2049-2050. Also, the results show that there is no significant difference in the future drought index between the two scenarios. The results of the climate drought situation at Rig Cheshme station indicate a high likelihood of a severe climate drought in the region during the water year 2029-2030. Additionally, the results of the climate drought condition at Kordakhil station show that the condition at Kordakhil station differs somewhat from the other two stations, which are located at higher altitudes. This difference is likely due to the proximity of this station to the Caspian Sea, as the climate in that region is influenced by the coastal humidity. It can also be expected that there will be a severe drought in the water year 2032-2033 in this region. The results of the IHACRES model showed that the comparison chart of the simulated discharge and the observed discharge is in good agreement. Additionally, the error coefficient values obtained during the two stages of evaluation and recalibration were 0.48 and 0.53, respectively. These values indicate the model's acceptable ability to simulate future discharge. The results indicate that the discharge rate of the Watershed is unlikely to undergo significant changes in the future as a result of climate change. The probable cause for this is the occurrence of intense rainfall events, which lead to flooding and subsequent increases in river discharge. Considering that these results only show the effect of climate change on discharge, there is a possibility that other factors, such as land use changes, may also contribute to changes in the amount of discharge.

Conclusion
The results of this analysis indicate that in both climate scenarios, there is a possibility of experiencing severe droughts in some years in the future. However, it is also possible for the climate to be very wet in certain years. These changes in the state of drought over approximately 30 years indicate climate fluctuations, which are one of the signs of climate change in a region. Therefore, based on the obtained results, it cannot be concluded that we will experience either a completely dry or completely wet state in the next 30 years. Thus, it is necessary to employ adaptation strategies to mitigate the impacts of drought during certain years. The hydrological drought situation of the Tajan River in the next 20 years (2020-2040) was evaluated using both optimistic and pessimistic scenarios. The drought situation in this river has also exhibited fluctuations, with a decreasing trend of flow in some years and an increasing trend in others. These fluctuations also indicate another effect of climate change in a region, which causes heavy rains and floods, leading to increased river flow during certain times of the year. However, relying solely on the study of the flow rate of a region cannot accurately indicate the drought situation. This is because certain measures implemented in watersheds can significantly impact the flow rate of a river. Therefore, considering all the conditions and climatic factors that govern our country, it is not surprising to see such changes occurring in different periods.

Introduction
The average weather condition in a specific region is defined as climate. The diversity of climatic variables is effective in determining the climate of a region and causes the formation of diverse and different climates. One of the effects of climate change is that causes an increase or decrease in a climate zone and, as a result, a shift in climate zones. Climate classification is an attempt to identify and recognize the differences and similarities of climate in different regions and to discover the relationships between different components of the climate system. Climate classification indicators are used to visualize current climate and quantify future changes in climate types as predicted by climate models. The studies conducted on these methods show that climatic variables affecting experimental methods such as temperature and precipitation should be considered effective variables in determining climatic boundaries in a new way. The De Martonne aridity index is an empirical index for climate classification based on two components, precipitation and temperature. Due to its high accuracy, and the use of variables that are more accessible and can be measured at most meteorological stations, De Martonne’s index has received more attention from researchers and has been used in many studies of climate change. Therefore, the purpose of this research is to evaluate the effects of climate change on the climatic classification of Iran.

Materials and Methods
To investigate the effects of climate change on the climatic classification of Iran, the De Martonne aridity index has been used. To show the effects of climate change in the past and the future on Iran's climate, data from 120 meteorological stations of Iran, which are distributed in different locations with different climates, were collected and analyzed in the statistical period of 1933-2022. The climatic condition of Iran in the base period was determined according to the De Martonne aridity index. In addition, to investigate the effects of climate change in the coming periods on the climatic classification of Iran, the data related to the output of the CanESM2 model, which is one of the CMIP5 models that is hybridized by the Canadian Center for Climate Modeling and Analysis (CCCMA) by combining CanCM4 and CTEM models, were used. To examine the changes in climatic classes of Iran under different scenarios and conditions, the output of two release scenarios, RCP2.6 and RCP8.5, were utilized. Due to the large-scale output of General Circulation Models (GCM), the output of this model was downscaled using the LARS-WG model. The LARS-WG model, which is considered one of the most famous and widely used models for downscaling weather data, was used to generate precipitation values, minimum and maximum temperatures, as well as daily radiation, under base and future climate conditions.

Results and Discussion
According to the results, the majority of Iran (90.49%) has an arid and semi-arid climate. The percentage of arid climate is 68.82%, while that of semi-arid climate is 21.97%. Therefore, Iran should be called an arid and semi-arid country in terms of climate. By analysis of the effects of climate change indicates that in future periods, the precipitation and average temperature will increase. This increase will be greater under the RCP8.5 scenario than the RCP2.6 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The sum of arid and semi-arid climates will reach its lowest level in the period of 2020-2041. This is following the RCP2.6 scenario, after which these climates are expected to expand once more. According to the RCP8.5 scenario, during the periods of 2021-2040, 2041-2060, and 2061-2080, the total area of arid and semi-arid climates will decrease. However, from 2081 to 2100, this trend will be reversed, increasing in these climates. According to the results of this research and according to the forecast, although according to different release scenarios, the difference in the area of different classes can be seen, in the future, arid and semi-arid climatic zones will still form the majority of Iran.

Conclusion
In this research, by using the latest available data, Iran's climate is classified by the De Martonne aridity index, and then the changes in Iran's climate classes under the effects of climate change in the future periods, according to the output of the CanESM2 model from the CMIP5 modes, which is downscaled using the LARS-WG model. It has been investigated according to two emission scenarios, RCP2.6 and RCP8.5. The results indicated that the arid climate with 68.82% and the semi-arid climate with 21.97% constitute the largest area of Iran. The remaining climatic classes collectively comprise less than 10% of Iran's area. Therefore, Iran should be called an arid and semi-arid country in terms of climate. Investigating the effects of climate change on precipitation and temperature showed that both precipitation and average temperature will increase in future periods. However, the increase in both variables will be greater under the RCP8.5 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The findings of this study indicate the necessity of addressing the issue of climate change and the importance of involving experts and macro planners in the analysis of the effects of climate change. It is suggested to use the output of other GCM models in future research due to the uncertainty of climate scenarios. Also, the use of diverse climate classification methods that incorporate other variables is suggested for more precise identification of climate characteristics

Introduction
Water scarcity is an increasingly critical global issue, causing a rise in arid lands and highlighting the need to address wasteful water usage in agriculture. Population growth, climate change, industrialization, and human conflicts have exacerbated water shortages, particularly in arid and semi-arid regions. According to the Falcon Mark index and the United Nations, Iran is experiencing water stress and a severe water crisis, threatening food security, economic development, public health, and national security. With over 92% of water consumption attributed to the agricultural sector, efficient water usage and reducing irrigation system losses are paramount. This study focuses on improving furrow irrigation efficiency by investigating surface irrigation efficiency and providing appropriate solutions.

Materials and Methods
The SIRMOD model, capable of simulating hydraulic surface irrigation, was employed to obtain the cut-off to Advance Time Ratio (CR) indicator. Diagrams based on soil texture, inflow rate, farm length, and the CR indicator were generated to enhance the design and efficiency of furrow irrigation systems. To ensure the accuracy of the simulation and results, the SIRMOD model was validated, and optimal CR indicators were determined for a furrow irrigation system with four different lengths and sandy-loam textured soils. A suitable field under the row irrigation system was selected, and the soil texture was determined using double-cylinder tests. Field operations included establishing forward and backward stations and smoothing the water path to prevent water from exiting the furrow. A pipe at the end of the furrow measured the output runoff using the volumetric method, and standard siphons were chosen to maintain the input flow rate. After turning on the siphons, advance and retreat times were recorded, and instantaneous runoff was estimated during irrigation. This process was repeated for three inflow rates: 0.5, 0.8, and 1.15 l s-1, with three repetitions for each rate l s-1.

Results and Discussion
The advance times in the first, second, and third furrows were 44.22, 45, and 42.88 min, respectively, while the water recession times were 293.1, 290.73, and 292.7 min, respectively. The relatively high water speed in light-textured soils and the short water regression times indicated the light texture of the farm soil and validated the test results. For an inflow rate of 0.5 l s-1, measurements revealed that half of the water volume entered the furrow along its length, while the other half exited as runoff. Simulation results for an inflow rate of 0.5 l s-1 yielded CR values of 8.37 for a 100 m length, 6.99 for a 120 m length, 5.41 for a 150 m length, and 3.31 for a 200 m length. For an inflow rate of 0.8 l s-1, optimal CR indicators were 8.0, 7.25, 6.19, and 4.63 for lengths of 100, 120, 150, and 200 m, respectively. At an inflow rate of 1.15 l s-1, the optimal CR indicators for lengths of 100, 120, 150, and 200 m were estimated to be 7.42, 6.53, 6.11, and 5.06, respectively.
 
Conclusion
The study's findings highlight a significant breakthrough in optimizing water usage in agriculture, a sector heavily reliant on water resources. By meticulously experimenting with different inflow rates and furrow lengths, the highest water application efficiency was attained with a specific set of parameters. An inflow rate of 0.5 l s-1, coupled with a furrow length of 200 m, resulted in an impressive water application efficiency of 83%. This efficiency correlates with a cut-off to advance time ratio (CR) value of 3.31, indicating a well-balanced water distribution system. The implications of this discovery are far-reaching, especially in regions facing water scarcity and agricultural challenges. By implementing these optimized settings, farmers can maximize their water usage while minimizing waste. This not only ensures the efficient utilization of a precious resource but also contributes to sustainable agricultural practices. Furthermore, the consistency in results, achieved through the SIRMOD model's validation, underscores the reliability of these findings, providing a solid foundation for future irrigation system designs and improvements. The expanded text emphasizes the significance of the study's findings, highlighting the efficient water usage and its potential impact on sustainable agriculture, especially in water-scarce regions. It also underscores the reliability of the results through the model's validation, providing confidence in the optimized settings for furrow irrigation systems. The best water application efficiency in the farm, 83%, was associated with the inflow rate of 0.5 l s-1 and the length of 200 m, and the CR equaled 3.31.

Introduction
Biochar is a potential soil amendment produced by pyrolyzing waste organic materials. Biochar with improving soil quality indicators could increase soil sustainability. Amending soil with biochar enhances soil quality and stimulates plant growth. So far, most studies have investigated the potential impacts of biochar on soil fertility, soil biota, soil chemical properties, soil greenhouse gas emissions, and remediation of contaminated soils. Comparatively, a minimal number of research has been carried out on the implications of biochar application on soil's physical and mechanical properties on the field scale and in the presence of plants. This study aimed to investigate the effect of Conocarpus erectus biochar as a modifier on some mechanical properties of soil (shear strength (SS), coefficient of linear extensibility (COLE), liquid limit (LL), plastic limit (PL) moistures, and plasticity index (PI)) as well as some physical properties includes soil porosity, soil moisture retention (field capacity (FC), permanent wilting point (PWP), and plant water available content (AWC)), soil air capacity (SAC), and bulk density (BD).

Materials and Methods
The research experiment was conducted in a completely randomized block design with three replications. The treatments including biochar at three levels (zero, three, and six ton ha-1) were added into a calcareous soil. The biochar was produced from Conocarpus erectus wood through the slow pyrolysis process at 550 °C. Before being applied to soil plots, the biochar was crushed to pieces smaller than 0.5 cm. The biochar was mixed to around 20 cm soil depth and soil moisture was kept at 70% of field capacity for three months. The corn plant was then planted and harvested after three months. Then soil samples were collected and used for physical and mechanical experiments. Some physical and mechanical properties of soil include SS, COLE, LL), PL moistures, PI, BD, porosity, SAC, FC, and PWP moisture were measured. The surface functional groups analysis of the biochars was detected using Fourier transform infrared spectroscopy (FTIR). Furthermore, the surface morphology of bulk biochar was portrayed by a scanning electron microscope (SEM).

Results and Discussion
The results of the analysis of variance (ANOVA) indicated that the addition of Conocarpus erectus biochar had a significant effect on the soil's physical properties (P< 0.01). The results revealed that the biochar significantly enhanced soil porosity, air capacity, and moisture content at FC and PWP, while diminished soil bulk density (P <0.01). The amount of soil porosity, air capacity, FC, PWP moisture, and AWC in the treatments of three and six ton ha-1 of biochar had a significant difference with the control treatment (P< 0.01). The increase of FC, PWP, and plant available water by raising the amount of biochar was attributed to the porosity of the biochar particles. The results of SEM images revealed that synthesized biochar is a porous material that significantly can enhance the total soil porosity and water retention capacity. Furthermore, the FTIR spectra of the synthesized biochar functional groups such as carboxylic acid, phenolic, ketone, ester, and, amine were detected. The findings of the ANOVA also show a significant effect (P< 0.01) of Conocarpus erectus biochar on the soil mechanical properties (SS, COLE, LL, PL moistures and PI). Moreover, the results of the mean comparison test revealed that three and six ton ha-1 of biochar treatments had a significant difference with the control treatment (P< 0.01); The difference between three and six ton ha-1 of biochar was also significant (P< 0.01). Application of the biochar increased LL, PL, and PI; while diminished shear strength and COLE index. In the treatments of three and six ton ha-1 of the biochar, the amount of LL increased by 40.32 and 77.74%, respectively, and PL increased by 40.8 and 70%, respectively, compared to the control treatment. Furthermore, the value of PI was enhanced by 38.33 and 71.66% in the biochar treatments of three and six ton ha-1 compared to the control treatment. While, the amount of shear resistance in the treatments of three and six ton ha-1 of the biochar decreased by 23.94 and 34.75%, respectively, compared to the control. The amount of decrease in COLE index at the three and six ton ha-1 of the biochar compared to the control was 20.28 and 36.95%, respectively. The results also revealed that the application level of six ton ha-1 biochar treatment increased the amount of porosity, SAC, FC, PWP, and AWC by 70, 13.7, 6.2, 5, and 8 %, compared to the control treatment. The application of biochar reduced the COLE index significantly; therefore, biochar has the potential to improve the mechanical characteristics of expandable soils.

Conclusion
This study showed that the biochar of Conocarpus as a suitable modifier improves the quality of the physical and mechanical properties of calcareous soils. According to the findings, it can be concluded that biochar by reducing soil bulk density, and shear strength, increasing porosity, water retention, plant available water, and air capacity, and improving soil consistency (Atterberg Limits) can provide suitable conditions for plant growth. The application of biochar not only has positive effects on the transport of nutrient elements, gases, heat, and water movement in soil but also by increasing soil porosity and water retention capacity provides beneficial conditions for plant growth. Therefore, in arid and semi-arid regions such as Khuzestan Province, which is facing the problem of lack of water resources and organic matter, biochar can be a valuable soil amendment. Overall, the use of biochar of Conocarpus could improve soil physical and mechanical properties at the field scale but long-term studies in different soils under plant cultivation are needed for a better understanding of its performance as a soil amendment.

Introduction
Accurate quantification of environmental trends must consider variation at different temporal scales when ignoring variation at one scale could lead to incorrect conclusions about variation at another scale. Many environmental monitoring programs collect temporally resolved but irregular time series data to quantify trends for regulatory, management, or research purposes. Conducting a study to understand the trends and predict future conditions in hydrological aspects such as river water quality is essential. During the last decades, river water quality monitoring has increased by measuring several water quality parameters. Therefore, the analysis of water quality trends is important in providing information about changes or variations in water quality through time series data .Furthermore, determining the quality status of water resources is necessary to adopt proper policies to prevent and enhance the reduction of water quality. Additionally, based on this information, it is possible to identify the quality of river water and implement protective measures to improve and manage rivers and drainage basins in a more integrated way. In recent years, the water quality of the Karun River has been affected by various pollutants, including agricultural runoff and industrial wastewater; Therefore, it seems necessary to monitor the quality of the river and the process of its changes over time and place to know the current situation and provide the necessary measures in the future. Therefore, this research analyzed the Karun River's water quality trend over 20 years at four water quality monitoring stations.

Materials and methods
To check the quality of river water in hydrometric stations, the obtained data were assessed from physical and chemical parameters, including Total Dissolved Solids (TDS), Electrical Conductivity (EC), Sodium adsorption ratio (SAR), Na, and Cl in 20 years from 1998 to 2017 in four hydrometric stations including Gotvand, Shushtar, Mollasani, and Ahvaz of Karun river in the wet season (first six months of the water year) and dry season (the second half of the water year). The process of river water quality and inspecting the changes were conducted using the Mann-Kendall test and a geographic information system, respectively. Wilcox's classification was used to check the water quality from an agricultural point of view, as there are relevant standards. By putting the sodium absorption ratio against salinity, Wilcox presents a chart for the water quality assessment for agricultural purposes and can classify water into different classes based on EC and SAR values.

Results and Discussion
According to the results, the river water salinity in the wet season in three hydrometric stations significantly increased. The increment was at the level of 10% at the Shushtar and Mollasani stations. However, at the Ahvaz station, it rose to the level of five percent. Due to the different annual rainfall amounts during the study period, the river water’s electrical conductivity had relatively large fluctuations in all the investigated stations. The range of electrical conductivity (EC) alterations in the wet season was between 490 and 2800 µS/cm and in the dry season between 397 and 2806 µS/cm. TDS increased in the wet and dry seasons. Moreover, the p-value showed that the value of this statistic was significant at the level of 10% in the Shushtar and Mollasani stations and at the level of five percent in the Ahvaz station. The range of changes in the wet period was between 250 and 1750 mg/liter and in the dry period between 220 and 1700 mg/liter. The alterations in total dissolved solids were more in the wet than in the dry season and did not have a uniform trend. In fact, the decrease or increase in the amount of precipitation affected the intensity and weakness of the TDS amount during the year.

Conclusion
The results of the Mann-Kendall test showed that the parameters of TDS, SAR, Na, Cl, and EC increased during the last twenty years, indicating the expansion of the entry of sewage and industrial and agricultural effluents. According to the Wilcox index, water quality for agricultural purposes was in the average category in all the studied stations. Meanwhile, Na, Cl, and TDS parameters were in the average and inappropriate range in some years, being an alarm regarding the low water quality. Additionally, there is a risk of water quality decline in the investigated stations. In general, the watershed of the Karun River is noteworthy due to the presence of a large population, cities, and centers. Specifically, the city of Ahvaz and the heavy steel industries located in this watershed are important fundamental in terms of water consumption and producing pollutants affecting the quality of water resources, which faces many quantitative and qualitative challenges in water. The study of the changes in the water quality parameters of the stations located in the Karun River during the study period demonstrated that the amount of dissolved salts in these rivers increased and caused the reduction of water quality due to incorrect utilization and failure to comply with the principles of river exploitation.

Introduction
River flow forecasting has been one of the important challenges of water resources management in recent decades, so many researchers have proposed different methods to improve the performance of forecasting models. In the last decade, artificial intelligence methods have been most widely used in the simulation of various processes, including hydrological processes, due to their flexibility and high accuracy in modeling. However, the results of these methods have always been associated with uncertainty due to several factors such as structure, algorithm, input data, and the type of method chosen for data calibration. One of the methods that can somewhat solve this problem is the uncertainty analysis of the predictions made by these models.

Materials and Methods
In this study, the uncertainty of the results of artificial neural network (ANN) and support vector machine (SVM) models in predicting the monthly flow of the river has been evaluated. In this research, the time series of the monthly flow of the Ghezelozan River using the data of the Bianlu-Yasaul Stream gauging station in 39 years from 1976 to 2014 was used, where 75% and 25% of the data was used for training and testing the models, respectively. In these models, to estimate the monthly flow of the Ghezelozan River, six different input combinations including the flow of one, two, and three months before and the number of months of the flow were used. Then, the accuracy and performance of the models were compared using the coefficient of determination (R) and root mean square of errors (RMSE). Finally, the uncertainty of the results of ANN and SVM models in predicting the monthly flow of the river was evaluated by the Monte-Carlo method using d-factor and 95PPU values.

Results and Discussion
The evaluation of the performance of the ANN model shows that the best performance is related to the combination where the flow of the previous two months and the number of the month of the flow are the inputs of the model so that R and RMSE indexes were obtained as 0.757 and 9.45, respectively. In the SVM model for the monthly river flow series, the best performance is related to the combination where the flow of one, two, and three months ago and the number of months of the flow were the inputs of the model, and the R and RMSE indexes for this input pattern were 0.729 and 10.946, respectively. After studying the uncertainty of the models, the results showed that the ANN model has more uncertainty in the output values compared to the SVM model, and this is while the d-factor of the ANN model, both in the training and test phase, it was more than the SVM model. The comparison of the uncertainty analysis of the results of the ANN and SVM models showed that the SVM model with d-factor and 95PPU values equal to 0.155 and 17.241, respectively, compared to the ANN model with d-factor and 95PPU values equal to 0.993 and 85.470, respectively, has less uncertainty in the output values. So the number of observation data placed in the 95% confidence range (95PPU) of the ANN model compared to the SVM model has increased significantly in both the training and testing phases. Also, the results showed that both models have more uncertainty in the months with a large volume of water, which can be due to the complexity of the process and the involvement of uncertain factors in these months, as well as the effect of factors that are not considered in the structure of predictive models.
Conclusion
The results of ANN and SVM models in predicting the monthly flow of the Ghezelozan River showed that although the ANN model with R-value equal to 0.757 and RMSE value equal to 9.45 has a good performance compared to the SVM model with R-value equal to 0.729 and RMSE value equal to 10.946 in predicting the river flow, the results of this model are associated with high uncertainty. The comparison of the uncertainty analysis of the results of ANN and SVM models by Monte-Carlo method showed that the SVM model with d-factor and 95PPU values equal to 0.155 and 17.241, respectively, compared to the ANN model with d-factor and 95PPU values equal to 0.993 and 85.470, respectively, has less uncertainty in predicting the monthly flow of the Ghezelozan River and it is better than ANN model. According to the results of this research, taking into account the fact that advanced artificial intelligence models also have uncertainty, it is necessary to apply these methods in the fields of risk management and future planning to obtain the best performance.

Introduction
Effective approaches and policies including identifying priorities and optimal water allocation techniques, especially in basins with different users are considered essential for sustainable development in each region. With 1100 m3 of renewable water per person per year, Iran is considered to be the most critical region in the world in terms of water resources. Unfortunately, most plans in the water sector of such countries are based on local economic growth, and no attention is paid to the amount of available water resources. Considering the issue of a water crisis and the droughts of the last few years, the issue of water resources management has gained high importance. To overcome the mentioned problems, it is inevitably essential to use newly developed water management techniques based on advanced approaches. Although optimization techniques are well-known tools in these issues, the simulation method is utilized as a helpful approach. To simulate water management in the basin, there are various available models. RIBASIM, MIKE BASIN, WEAP, and MODSIM models are famous and user-friendly ones in this collection. WEAP software is a comprehensive and advanced water resource system simulation tool widely used in watershed management and can consider physical and hydrological processes. The scenarios that can be investigated with this software include population growth, economic development, changing the policy of operating reservoirs, extracting more from underground water resources, saving water, allocating ecosystem needs, integrated use of surface and underground water, reuse of water, etc.

Materials and Methods
This study was conducted in the Nahand catchment area which is located in East Azerbaijan province. Nahand river is the main draining course of this catchment, on which a dam has been built to supply a part of Tabriz's drinking water. To control the performance indicators of the reservoir, several management and exploitation scenarios were developed and evaluated in the WEAP model. The WEAP model was presented in 1990 by the Stockholm Environment Institute (SEI). It is a comprehensive and advanced model for simulating water resource systems, which is extensively used in the management of water resources in watersheds. This model has provided a practical tool for water resource planning and policy analysis to put all the issues related to water resources and uses in a single environment. The WEAP model is capable of simulating issues related to consumption such as water consumption patterns, water reuse strategies, costs, and water allocation patterns, as well as issues related to resources such as river flow, groundwater resources, reservoirs, and water transmission lines. The inputs of the WEAP model include data on the population of Tabriz City, per capita consumption of drinking water per person, the amount of water wastage in the distribution network, the inlet discharge of the Nahand reservoir, the information of the Nahand dam, the amount of cultivated area, etc., and to evaluate the model R^2, RMSE, and MAE statistical indicators were used in two periods of calibration and validation. Then, various operating conditions were investigated by compiling the Reference (continuation of the status quo), SC1 (increase of input flow by 10%), and SC2 (decrease of input flow by 10%) scenarios. Besides, Reservoir performance indicators are used to measure its performance under different operating circumstances.

Results and Discussion
The simulation results of the studied area indicated that the WEAP model with evaluation criteria including R2, RMSE, and MAE in the calibration stage was 0.89, 1.16, and 1.01 MCM, respectively, and in the validation stage were 0.88, 6.22, and 6.01 MCM, respectively. The results also showed that the amount of water demand for the near future period (2021-2040) will increase due to the increase in population, and therefore, the resources in the basin will not be able to meet all assumed needs. The findings showed that the studied system for the near future period (2021-2040) under the reference (continuation of the status quo), SC1 (increase in flow by 10 %) and SC2 (decrease in flow by 10 %) scenarios from the drinking water supply point of view, will result in a shortage of 28.1, 7.3 and 44.3%, respectively, and from the supply of agricultural needs point of view will result in 31.4, 18.3 and 44.4%, respectively. Also, by evaluating the reservoir's performance indicators, it was found that under all assumed scenarios, the system will fail under the condition of supplying 100% and 80% of the needs, whereas the reservoir will be more sustainable by applying the SC1 scenario in comparison with the other two scenarios.

Conclusion
To choose the best management and exploitation scenarios, due to existing circumstances and limitations such as time limitation, cost, possible risks to the environment, etc., it is not possible to apply all scenarios in the basins and, thence, it is logical to choose the most suitable scenario. Therefore, software tools can help experts to make decisions by considering all limitations. By examining the results of the reservoir performance indicators, it can be seen that the reservoir will encounter failure in supplying 100 and 80% of the needs in the future period under all scenarios and the sustainability index of the reservoir (remedial stability) in supplying 100%. The needs under the Reference, SC1, and SC2 scenarios will reach 31, 49, and 22%, respectively, and in meeting 80% of the needs, the sustainability index will be slightly higher.

Introduction
A landslide is one of the mass movements on the top surface of the earth. Landslides have resulted in notable injury and damage to human life and destroyed infrastructure and property. Landslides represented approximately Nine percent of the natural disasters worldwide during the 1990s. According to studies, this trend is expected to continue due to increased human development. Many studies have been done to determine the factors affecting mass movement. In large part of Iran including the mountain areas, tectonic activity and seismic high with diverse geological and weather conditions led to many countries prone to landslide. Landslides cause wide damage to natural resources, human settlements, infrastructure, mud floods, and filling reservoirs. Landslides cause extensive property damage and occasionally result in loss of life. Besides, should not be ignored the social and environmental impacts resulting from the occurrence of this phenomenon, such as immigration and unemployment. One of the strategies for reducing losses due to a range of movements is the identification and management of unstable slope areas. To identify unstable regions pay to landslide hazard mapping. The main purpose of this research is to assess the effective parameter on landslide occurrence and to compare different machine learning models including SVM, GP regression, and RF for landslide susceptibility zoning.

Materials and Methods
The study area is a part of the Haraz Watershed, Mazandaran Province, Iran, occurrence many landslides are damaged after each heavy rain. So, it was selected as a suitable Watershed to evaluate the landslide susceptibility mapping (LSM). The vegetation covers and land mainly consists of rangeland. The geology of the study area consists mainly of Quaternary and Shemshak formations. The first step for the assessment of landslide susceptibility is gathering the necessary data and preparing information. These data were determined based on several factors. Considering the literature review, the local conditions, and previous studies. In this study, nine parameters such as slope angle, slope aspect, elevation, geology, land use, the distance of fault, the distance of the road, the distance of the river, and precipitation were identified as key factors for the prediction of landslide susceptibility. To assess the effectiveness of GP-PUK, GP-RBF, SVM-PUK, SVP-RBF, AND RF to estimate the landslide susceptibility map (LSM), data used in the present study were taken from field data. In this study, the dataset contains 148 observations of landslide occurrence and landslide non-occurrence points. The landslide data have been randomly separated into training (70% of landslides; 103) and testing (30% of the landslides; 45). To judge the performance of the soft computing techniques, statistical evaluation parameters were used. In this research, three statistical evaluation parameters were used. These parameters are the correlation coefficient (C.C.), root mean square error (RMSE), and Nash–Sutcliffe model efficiency (NSE).

Results and Discussion
According to the results of the comparison of methods, RF was the best model and the accuracy of the RF model was more suitable for the estimation of the landslide occurrence. So, in this study, RF was used for the landslide susceptibility map. Single-factor ANOVA test suggests that there is an insignificant difference between observed and predicted values of landslide occurrence and landslide non-occurrence using GP_PUK, GP_RBF, SVM_PUK, SVM_RBF and Random Forest approaches. According to the results of the comparison of methods, RF was the best model and the accuracy of the RF model was more suitable for the estimation of the landslide occurrence. The map of landslide susceptibility map was divided into five classes from none susceptible to very high susceptibility. According to the final Landslide susceptibility map, the area belonging to the “non-susceptible” class covers 35.86 km2, “low susceptibility” class 36.19 km2, “moderate susceptibility” class 15.06 km2, “high susceptibility” class 10.95 km2 and “very high susceptibility” class 14.46 km2 of Haraz Watershed. Sensitivity analysis was performed to find the most significant input parameter in the prediction of landslide occurrence and landslide non-occurrence. The result shows that aspect has a major role in predicting landslide occurrence and landslide non-occurrence in comparison to other input parameters, respectively.

Conclusion
Due to all results, some zones are potentially dangerous for any future habitation and development. Thus, there is an immediate need to implement mitigation measures in the very high-hazard and high-hazard zones, or such zones need to be avoided for habitation or any future developmental activities. The results of this research can be used by the local authority to manage properly, and systematically and plan development within their areas.

Introduction
The agriculture sector, as the biggest consumer of water to produce more food, has faced the challenge of water shortage. One of the problems ahead in the agricultural industry is the sustainable use of available resources such as land, water, and labor to increase agricultural production and development, which requires proper planning and management policies. Plant models can be used to investigate the long-term effects of quantitative and qualitative changes in irrigation water on crops, soil salinity, evaporation and transpiration, deep infiltration, and surface runoff. One of the widely used plant models is the AquaCrop model, which was presented and developed by the World Food and Agriculture Organization. The Aquacrop model is one of the crop yield estimation models that can be used for a wide range of crops including fodder crops, vegetables, grains, fruits, oil crops, and tubers. In this model, the state of various stresses including water and soil salinity, simulation of lack of irrigation, and crop yield are considered. Various studies have been conducted regarding the calibration and validation of crop forecasting models in our country, and much research has been conducted on wheat at the global level. In this research, the AquaCrop model was used to predict the biomass and grain yield of wheat in Qazvin. This model can be a good substitute for field measurements and can be used in areas where there is a lack of ground information.

Materials and Methods
In the present research, the data of water wheat cultivation in a lysimeter in Ismailabad, Qazvin were used. The input information of the AquaCrop model includes information on climate, soil, management, and plant characteristics. To calibrate and verify the model, some farm information was needed to be compared with the output of the AquaCrop model. The biomass of the wheat plant was determined by taking random samples of the 0.5×0.5 m2 with two replications per sampling hectare. To measure grain yield in the fields, four samples were taken at the end of the growing season at the end stage. The validated AquaCrop model was used to estimate the effect of three planting dates and three low irrigation conditions on wheat grain yield. In this step, the average regional information around the farms was used so that the implementation of the model is not unique to the conditions of a particular farm.

Results and Discussion
In terms of the investigated meteorological factors, the model has moderate sensitivity to maximum and minimum temperature and low sensitivity to rainfall. The change in the maximum temperature in this region increases the error of the simulation on average. Regarding the soil parameters, the sensitivity of the model to the crop capacity moisture, wilting point, saturated moisture, and saturated hydraulic conductivity, especially in saturated conditions, is low to medium. The most sensitive of the AquaCrop model was the change in the reference harvest index. The model simulated biomass values with higher accuracy than yield. In the calibration stage, the values CRM, NRMSE, and d for biomass were -0.15, 0.17, and 92% respectively. These values were obtained in the validation stage for biomass -0.1, 0.24, and 92 % respectively, and for yield -0.03, 0.06, and 80 % respectively. By running the model in different climatic scenarios, it was determined that the maximum delay in the planting date is on November 15. A 25% reduction in irrigation water reduced grain yield in wet, normal, and dry years by 15%, 20%, and 28 %, respectively, and a 50 % reduction in irrigation water reduced its amount by 20, 25, and 45 %, respectively.

Conclusion
Evaluation of the AquaCrop model for common plants in a region plays an important role in comparing crop performance in different conditions. In this research, the ability of AquaCrop 6 model to estimate the yield and biomass of wheat in Ismailabad Qazvin was investigated. The results showed that the model is capable of simulating these factors with high accuracy. The accuracy of the model in biomass simulation was higher than the grain yield. By implementing the calibrated model in different climatic scenarios, planting dates, and irrigation deficits in two regions, it was determined that to achieve optimal performance, the wheat planting date should not exceed 15 November. It was the use of calibration coefficients by spending a long time in the AquaCrop model so that a calibrated model can be used in many areas with proper accuracy. More accuracy in the simulated results can be achieved by using more calibration factors, but it is clear that the use of more calibration factors requires spending more time and money. Finding a general recalibrated model that can be used in large areas is a good solution in crop management at the farm-to-regional scale. Comparing the statistical parameters obtained in this study with previous studies on wheat yield modeling by the AquaCrop model shows that the results of this study are within an acceptable range.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Soil erosion and sediment yield from watersheds are among the key limitations to achieve sustainable use of land and to maintain the water quality in water bodies. Studying the soil erosion process and and evaluation of dominant factors on erosion is therefore considered fundamental in watershed management. The accurate measurement of sediment yield using sedimentgraph data is accordingly of major importance. The present research was conducted to analyze sedimentgraphs in Chehelgazi Watershed upstream Gheshlagh Dam basin in the Kurdistan province in Iran with an area of 27233 hectare. The hydrographs and sediment graphs of 11 storms from winter 2006 to spring 2007 were investigated. The results revealed that the sediment graphs with average sediment yield of 79.56±47.93 tones followed the general trends in corresponding hydrographs. All hydrographs preceed the sedimentgraphs with an average lag of 2.94±1.04 hours.

In open-pit mining, particularly in large-scale operations, the stability of slopes and rock walls is a crucial factor determining the safety and operational status of the mine. A slight change in the slope of the mine walls can lead to a significant increase or decrease in the amount of waste material extracted, thereby profoundly affecting both the economics and safety of the mining operation. Therefore, geotechnical investigations and studies for the design and stability of open-pit walls hold particular importance. The Mouteh Gold Mine is considered one of the major mines operated by the Iranian Mineral Production and Supply Company. Estimating the rock mass strength parameters is one of the most significant challenges in rock engineering. Determining geomechanical and structural properties is essential for modeling and designing the extraction pit. One approach to controlling the behavior of mine walls involves monitoring the final walls and analyzing their displacement magnitude and rate, which helps identify areas with potential instability and issue warnings to prevent accidents. This monitoring can be conducted in both surface and subsurface conditions, each requiring specific tools and methodologies. In this paper, laboratory results and field surveys are utilized to determine these properties.

Precipitation is one of the most important and variable climatic elements, exhibiting more complex behavior compared to other climatic factors. This element shows significant temporal and spatial variability. The aim of this research is to examine the trend of precipitation changes in the Caspian Sea basin and its relationship with teleconnection patterns. In this context, monthly precipitation data from the ERA-Interim dataset, with a resolution of 2° x 2°, covering the statistical period from 1970 to 2023, were utilized. Given the non-normality of the data, the non-parametric Mann-Kendall test was employed to calculate the trend. The precipitation slope was also determined using the Sen's slope estimator. The results indicated an increasing trend in precipitation during the months of january, february, march, november, december, and to some extent in may and october, while a decreasing trend was observed in other months. The increasing trend in precipitation was most pronounced in february, while the decreasing trend was most significant in june and august. The correlation between precipitation and teleconnection patterns showed that, temporally, correlations were observed in january, february, march, june, august, september, october, and december. The EA.WR pattern exhibited the highest correlation with the basin's precipitation compared to other patterns, followed by the TNA, AMO, and AMOS patterns. Overall, february showed the highest correlation between teleconnection patterns and precipitation, and in terms of pattern type, the EA.WR pattern had the strongest correlation with the basin's precipitation.

Introduction
Most of the regions of Iran are located in arid and semi-arid climates, the characteristic of this climate is dry and long seasons without rain. In these areas, the lack of water is one of the most important factors limiting the production of agricultural products, in such a situation, the competition for water is increasing with the increase in population, urbanization and industrialization, and on the other hand, the lack of water is aggravated by improper irrigation management in agriculture. Accurate estimation of irrigation water quantity, water productivity of cultivated sugar beet in Iran are important and key indicators in agricultural sector planning. The purpose of this study was to investigate the results of field and farm measurements of irrigation water and sugar beet yield under farmers' management and compare it with estimating the NIAZAB system in 16 Township during a crop season (2016-2017) as well as determining sugar beet water productivity. The results showed that the average amount of sugar beet irrigation water in the method measured in farms and systems was 13088 and 13856 cubic meters per hectare, respectively, and the average grain yield of sugar beet in the method measured in farms and systems was 71846 and 64206 kg, respectively. The average water productivity of sugar beet in the field measurement method and NIAZAB system was 5/8 and 4/9 kg / m3. The results showed that the NIAZAB system with a normal root mean error of 0.21% and agreement coefficient of 0.89 estimates the amount of sugar beet irrigation water in the township scale and sugar beet grain yield with a normal a root mean error of 0.27% and agreement coefficient of 0.81 and estimates the productivity of sugar beet water with a root mean a normal error of 0.34%,. The values of the efficiency coefficient of the model show that the model provides acceptable results in determining the amount of irrigation water and sugar beet water productivity in the country farms. Therefore, the NIAZAB system can be used to estimate the volume required for the irrigation of plants in the Iran and also in farm water management.

Materials and Methods
This study was conducte based on field and field measurement and system-oriented approach (NIAZAB system) on a national scale to estimate the amount of sugar beet irrigation water. The main variables and implementation steps of this study are as follows: Theoretical foundations:. First step: estimation of the reference evaporationtranspiration, second step: determining the phenology stages of sugar beet plants, third step: estimating the amount of effective precipitation using, fourth step: estimation of the net irrigation water requirement of the sugar beet, fifth step: Estimating the gross irrigation water requirement of the sugar beet plant, ,sixth step: Accessible performance in the water demand system, seventh step: estimating the actual amount of irrigation water.
Practical foundations: first step: statistical population and sampling method: for an accurate estimation of the amount of sugar beet irrigation water in the country, targeted sampling was needed, based on this, a statistical population was used that included sugar beet farmers of the country, based on the statistical method, first of all 31 provinces of the country, the provinces that had a greater share in sugar beet production were selected. The selected provinces include 8 provinces (Ardebil, West Azerbaijan, Razavi Khorasan, Fars, Khuzestan, Semnan, Kermanshah, Hamedan) with an area equivalent to 105,875 hectares and a share equal to 75 The percentage of the cultivated area of the country was taken into account in selected provinces of 16 cities with an area equal to 71671 hectares.. Figure 1 presents the coordinates of the measurement points. second step: the method of measuring the selected field, third step: measurement of sugar beet yield, fourth step  Converting the measuring points to the city average and then compared with the estimated results of the amount of irrigation water of the NIAZAB system, it was compared, compared and validated.

Results and Discussion
In order to compare the results of the water requirement estimation system with the field and farm measurement values of the amount of irrigation water from farmers' sugar beet fields, the following was done. According to table 1, it can be seen that the amount of irrigation water for sugar beet based on the field measurement method in 16 cities of the country is equal to 13088 cubic meters per hectare and based on The estimation of the NIAZAB system at the level of 16 cities of the country was equal to 13856 cubic meters per hectare. Considering that there is a difference between normal averaging and weighted averaging, therefore according to table (3), the weighted average amount of sugar beet irrigation water according to the cultivated area in 16 cities in the field measurement method is equal to 12805 cubic meters and based on the system estimate The NIAZAB system was equal to 13817 cubic meters per hectare. Comparison of the average results of irrigation water measured in the field and estimated by the NIAZAB system shows that on average, the difference in the amount of irrigation water for sugar beet plants in the two methods was about 768.7 cubic meters per hectare and shows an equivalent error of 5.5%. According to table 1, it can be seen that the amount of sugar beet irrigation water varies between 7336 and 17950 mm in the entire growth period in 16 regions. The reason for the difference comes from the basics of estimating the amount of irrigation water. Considering the irrigation methods and calculating the efficiency of irrigation water use in the areas, but in the direct method, it was based on the actual measurement in the field, and however, the error value of 7.7% is reasonable and acceptable. Comparing the results of irrigation water quantity measurement and estimation of the NIAZAB system has a normal error of 21% and an agreement index of 0.89, and this shows the acceptable efficiency of the system. Therefore, NIAZAB system has the ability to estimate the amount of irrigation water for sugar beet plants at the level of the fields in the country scale.

Conclusion
The results showed that the average irrigation water of sugar beet plant from the measurement method in the fields and the estimated NIAZAB system is equal to 13088 and it was 13856 cubic meters per hectare. The results of the weighted average of sugar beet irrigation water which was estimated by the  NIAZAB system equal to 13817.9 cubic meters per hectare and directly measured from the fields equaled 12805 cubic meters per hectare, and it can be concluded for the surface of 140 thousand hectares of sugar beet irrigated lands the volume of irrigation water estimated by the NIAZAB system was 1.94 billion cubic meters and the volume of irrigation water measured in the field was equal to 1.79 billion cubic meters. The comparison of the results of the two methods showed that there is a difference of about 7.7% in the country.

Introduction
Watermelon is a popular fruit that is cultivated in greenhouses and on the ground, and water and fertilizer, as two essential factors for the growth of the product, significantly affect the yield of crops. Watermelon is the twelfth plant in terms of cultivated area in Iran. Due to its importance in export and virtual water, it is necessary to pay special attention to the characteristics of water needs and water consumption. However, water resources are scarce and irrational irrigation and use of fertilizers are common. This caused environmental pollution and waste of resources and also affects the growth and absorption of nutrients. Plant absorption, therefore, affects the yield and quality of the product. China is the first producer of this product in the world with the production of 67% of the total watermelon crop. The next ranks with less than 4% of world production are held by Turkey, Iran, Brazil and Egypt. According to FAO statistics, Iran ranks third in the world in watermelon production. In Iran, 91,000 hectares are cultivated with watermelon, of which 85,400 hectares are irrigated.Considering the special attention given to the watermelon plant as one of the high consumption options in the cultivation pattern, it was necessary to conduct a research in this field to investigate the amount of water required and reduce its yield under deficit irrigation.

Materials and Methods
In this study, Charleston variety watermelon was grown with a density of 8000 plants per hectare on May 29, 2022 in the soil and water research farm in Alborz province. Also, Crimson Sweet cultivar with a density of 11,000 thousand plants per hectare was cultivated on Desember 25, 2021 in the southern research center of Kerman province.In this regard, a research was conducted in the form of randomized block design in 4 full irrigation treatments, 75% of water requirement, 50% of water requirement and 30% of water requirement in the research farm of the Soil and Water Research Institute and Kerman Research Center. After applying low irrigations in different treatments, the performance of each treatment was measured. Also, the water requirement values of watermelon were investigated using the water requirement system of the Soil and Water Research Institute of the country under standard conditions. Also, by using two production functions, the sensitivity coefficients of the Charleston variety were determined. Also, the water requirement values of watermelon were investigated using the water requirement system of the Soil and Water Research Institute of the country under standard conditions. Also, using two production functions, the sensitivity coefficients of the Charleston variety were determined.

Results and Discussion
The results showed that the standard water requirement of watermelon in Karaj is about 488 mm and in Jiroft area of Kerman is 423 mm and it is in good agreement with the water requirement estimated by the water requirement system. The results show that watermelon in both cultivars reacted to the lowest amount of stress, so it is sensitive to lack of irrigation, and therefore it is necessary to investigate the values of the production function in their case according to the applied treatments. On the other hand, the highest sensitivity coefficient occurred in the period of 60 to 80 days after cultivation in the middle period of growth and the recalibrated production function estimated the yield of the plant with appropriate accuracy in the applied stresses, which the statistical indicators of Charleston variety RMSE, NRMSE, MBE , d and EF were 497, 0.02, -119, 0.99 and 0.98, respectively, and the Crimson number was 568, 0.095, -536, 0.98 and 0.96 respectively, so the sensitivity coefficients and production function It is proposed to simulate the performance of both watermelon cultivars under water stress conditions. Also, the highest productivity was obtained in the low irrigation treatment of 70% and Crimson variety has higher water consumption efficiency. Therefore, this variety is recommended for watermelon cultivation with 70% less irrigation.

Conclusion
Summarizing the results showed that the standard water requirement of watermelon in Karaj is about 488 mm and in Jiroft area of Kerman is 423 mm and it is in good agreement with the water requirement estimated by the NIAZAB system. The sensitivity coefficients and the production function presented for simulation The performance of both watermelon cultivars is suggested under water stress conditions. On the other hand, the highest sensitivity coefficient occurred in the period of 60 to 80 days after cultivation in the middle period of growth and the recalibrated production function estimated the yield of the plant with appropriate accuracy in the applied stresses, which the statistical indicators of Charleston variety RMSE, NRMSE, MBE , d and EF were 497, 0.02, -119, 0.99 and 0.98, respectively, and Crimson number was 568, 0.095, -536, 0.98 and 0.96, respectively. Therefore, the sensitivity coefficients and production function It is proposed to simulate the performance of both watermelon cultivars under water stress conditions. Of course, due to the high performance level of the Crimson variety, the amount of water productivity in this variety is twice that of the Charleston variety, so in terms of water consumption efficiency, the cultivation of the Crimson variety is a priority compared to the Charleston variety. Also, the highest productivity was obtained in the low irrigation treatment of 70% and Crimson variety has higher water consumption efficiency. Therefore, this variety is recommended for watermelon cultivation with 70% less irrigation.

Introduction
Flood is one of the most important threats to human society, which has increased in recent decades with the increase in population and climate change. Therefore, studying the features of the watershed, which are related to the level of flooding, can help in the correct management of this risk. Watersheds are different in morphological characteristics, so they have different hydrological reactions in the same climatic and environmental conditions. The stability of the morphometric characteristics of the sub-watersheds has made them used in flood studies. So the investigation of the morphometric factors of the watersheds can be of great help to the management and prioritization of flood sub-watersheds. In this research, an attempt has been made to introduce and prioritize flood-prone sub-watersheds using morphometric data in the environment of the geographic information system in the north of Birjand Plain, using the maximum entropy method and VIKOR decision-making model.

Materials and Methods
This plain is located at a longitude of 58˚ 45́ to 59˚ 30̋ and a latitude of 32˚ 30̋ to 33˚. The area of the Birjand Plain watershed is 3155 km2, of which 1045 km2 are plains (33 %) and the rest are highlands. In this research, morphometric factors (14 factors), entropy method, and VIKOR decision-making method were used to prioritize 22 sub-watersheds in the north of Birjand Plain. Since the morphological factors of the aquifer basin have different effects on the processes of runoff formation, there is a need to determine the effects of the parameters, which Shannon's entropy method is used in this research. In the following, using the VIKOR method, sub-watersheds are prioritized.

Results and Discussion
Morphometric factors are important in identifying and determining flood risk areas. The effect of morphometric factors on flooding is not the same, so it is necessary to determine the importance of each parameter. Shannon's entropy method was used to determine the impact of different morphometric factors (13 parameters) on flooding in the northern Birjand Plain watershed. The results showed that the Vf index with a relative importance of 0.51 has the greatest effect on flooding and prioritization of sub-watersheds in the study area. In prioritizing the flooding of sub-watersheds, all factors are not the same in terms of influence, so some factors have a positive effect and some criteria have a negative effect on flooding, for example, the more the amount of drainage density increases, the more it has a positive effect and causes an increase in flooding. It is possible that if the parameter of the channel maintenance index is lower, the flooding will be more. This attitude and point of view have not been seen in many studies, but in this research, the nature of the parameters was determined based on the opinions of experts and previous studies. In this study, the increase of watershed asymmetry indices, tissue ratio index, channel maintenance index, and valley width to height ratio index have a negative effect, that is, with their increase, there is less flooding, and other factors have a positive effect, that is, with their increase, flooding, and runoff production also increase.
 
Conclusion
Watershed projects are one of the most important strategies for flood control. The implementation of watershed projects for flood control requires the determination of flood-producing areas and their prioritization. Considering the lack of sufficient hydrometric stations at the level of the sub-watersheds and the lack of recorded statistics and information about floods in the country's watersheds, it is practically impossible to infer the severity of sub-watershed floods from the available data alone. Therefore, it is appropriate to use methods such as morphometric factors that require less hydrometric and quantitative data. There are various methods for determining the amount of runoff and prioritizing sub-watersheds in terms of flooding, and most of these methods are based on graphical methods and the use of empirical formulas, statistical analysis of flood data, and watershed separation. In this research, 14 morphometric parameters were used to prioritize floods in the north of Birjand Plain, because the morphometric characteristics of each sub-watershed are like its fingerprint, and it is possible to prioritize sub-watersheds for flood control based on that. In this research, sub-watersheds 9, 22, and 14 were prioritized from one to three, respectively, based on morphometric criteria.

Introduction
Crop water productivity (CWP) is defined as the crop yield produced per unit of water consumed, which can be improved by increasing the crop yield with a given water usage or reducing water usage with a given yield. Increasing CWP can thus help to alleviate the water crisis while ensuring food security. Physical productivity alone is not enough to determine the crop pattern and economic productivity should also be considered. Economic water productivity (EWP) expressed as the gross income in USS per unit of water consumed, is relevant for farmers to pursue higher net benefits. Both CWP and EWP terms are important indices for water resource managers to consider when formulating planning policies. The simultaneous consideration of CWP and EWP allows for a more comprehensive and robust exploration when planning the process for developing regional agricultural water-saving measures, such as modifying the regional cropping distribution. This allows farmers to reduce irrigation water use and shift the area of water-intensive crops to ones with efficient water use or higher economic value. Determining crop pattern-based water productivity is especially important in countries with dry climates, whose agriculture depends solely on irrigation and also has low water consumption efficiency. Therefore, instead of consuming scarce water resources, in the production of products that consume a lot of water, it is possible to produce products with lower water consumption and avoid excessive pressure on water resources. Knowledge of crop-water requirements is crucial for water resources management and planning to improve water-use efficiency. Crop water requirements in the growing period depends on the crop growth stage, cropping technique, and irrigation method. About 99 % of the water uptake by plants from soil is lost as evapotranspiration (ET), so, it can be stated that the measurement of actual crop evapotranspiration (ETc) on a daily scale for the whole vegetative cycle is equal to the water requirement of the given crop. Evapotranspiration is defined as the water lost as vapor by an unsaturated vegetative surface and it is the sum of evaporation from soil and transpiration by plants. Knowledge of the exact water loss through actual evapotranspiration is necessary for accurate and effective water management.

Materials and Methods
For this purpose, in the first stage agricultural condition of the aquifer was investigated through a questionnaire by farmers and experts. To calculate the reference crop evapotranspiration we used the Penman-Monteith equation in this study crop coefficient curves have been prepared according to the agricultural calendar of the Basht aquifer. Net water requirement is calculated from the difference between effective rainfall and evapotranspiration. Water productivity per crop ( ) (kg.m-3) is an important index for determining the agricultural production system efficiency. Water productivity is defined as the proportion of crop yield (kg.ha-1) to irrigation water consumed by crops in the field (m-3.ha-1). Likewise, water economic productivity is measured about or with the economic benefits in a monetary value of outputs over the number of necessary inputs such as water depleted. To calculate the value of each cubic meter of water, the production costs (minus the water) need to be deducted from the income and the remainder needs to be divided by the volume of water. The calculation results are calculated separately for each product. To determine the suitable pattern crop in Basht aquifer, different cropping patterns were evaluated (eight different scenarios).

Results and Discussion
Based on the results of the Penman-Monteith method, it can be concluded that the gross water requirement (the amount of net irrigation requirement divided by the irrigation efficiency) in dominant crops of aquifer including rice, alfalfa, citrus, watermelon, corn, wheat, rapeseed, legumes, barley respectively were 20234, 14083, 9291, 9170, 7863, 5630, 5411, 5225, and 4821 m-3 ha, respectively. The amount of effective precipitation that provided a part of the crop’s water requirement through soil moisture (green water) for water crops such as Rice and Corn is close to zero. Autumn crops such as canola, citrus fruits, and cereals use green water. To determine the amount of irrigation per hectare of the current crop pattern of the aquifer, the hydro module of each crop was determined. As it is clear from hydromodule, the average required water flow (l s-1 ha-1) for rice, alfalfa, citrus, watermelon, corn, rapeseed, wheat, beans, and barley, equaled 0.63, 0.44, 0.29, 0.29, 0.24, 0.18, 0.17, 0.16 and 0.15 (l s-1 ha-1) respectively. In total, the amount of water consumed by the agricultural products in the aquifer's Basht is 45 millm3, that approximately equivalent to one m3 m-2 of the aquifer cultivation area and this amount is much more than the aquifer agriculture programmable water. The economic productivity of the aquifer’s current cultivation is 45,000 IRR m-3, on average. Also, most aquifer products' physical productivity was less than one. the comparison of different patterns showed that scenarios eight and twohad the highest and lowest amount of water consumption, 45 and 22 millm3, respectively.

Conclusion
The crop pattern will be influenced by parameters such as climatic compatibility of products, water, and soil potentials, needs, interests of agriculture producers, and production income. In the Basht aquifer, the availability of water and the amount of water consumed is one of the most important factors in choosing the cultivation pattern. In the current situation, due to the high temperature and increasing evaporation rate, and the use of seasonal rainfall, crops that spend their growth period in autumn and winter should be included in the cultivation pattern. The simultaneity of water requirements for crops planted together is one of the important parameters in choosing the cultivation pattern. In the Basht aquifer, the water requirement of corn, alfalfa, cucumber, and tomatoes coincide with the citrus water requirement during the time of high water consumption, and the cultivation of one of them may create water limitations for the other crop. In contrast, the cultivation of wheat, barley, and canola have a very small overlap with the citrus irrigation times. Choosing a combination of citrus, wheat, barley, and canola will optimize the cultivation pattern.

Introduction
Accurate estimation of reference evapotranspiration (ET0) is essential in water management in the agricultural sector, especially for arid and semi-arid climates. ET0 plays a vital role in the water and energy cycle and is an essential link between ecological and hydrological processes. Therefore, accurately estimating ET0 is a major issue for understanding the water cycle in continuous soil-plant-atmosphere systems. The traditional ET0 estimation methods are mainly based on physical principles, such as Priestley-Taylor, Hargreaves, and Samani, which have many limitations in accurate ET0 estimation in cases of minimum meteorological parameters (such as radiation solar, wind speed, and air temperature). Numerous studies have focused on ET0 estimation using terrestrial data. However, in the case of a lack of meteorological stations, the conventional methods of estimating ET0 using ground data will be inefficient, so remote sensing (RS) provides the possibility to fill such a gap, in such conditions, satellite images are the most effectivefor evaluating ET0 in large areas. Because satellite images have a suitable spatial and temporal resolution, the time series of satellite images can be used to estimate ET0. The successful estimation of ET0 from satellite images paved the way for its prediction using artificial intelligence models. The primary satellite imagery sources can be obtained from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Land Surface Satellite (GLASS). Remote sensing data provides the possibility of recording more information through satellite images. Remote sensing methods can be used to extract vegetation information and different types of radiation, which help estimate ET0.

Materials and Methods
In the current research, two different agro-climatic locations including Ahvaz and Tabriz stations were selected. According to De Martonne classification method, Ahvaz was classified as dry climate and Tabriz as semi-arid climate. In this research, random forest (RF) and multi-layer perceptron (MLP) algorithms have been used to estimate monthly ET0 in Ahvaz and Tabriz stations. The input parameters were selected from Landsat 8 and MODIS satellite images in the time period of 2014 to 2021. The utilized parameters were the monthly average, Landsat Land Surface Temperature (LSTLand), MODIS Land Surface Temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand) and MODIS Normalized Difference Vegetation Index (NDVIMOD). To evaluate the accuracy of the input parameters and models, the estimated monthly ET0 was evaluated with the monthly ET0 of the FAO-Penman-Monteth equation.

Results and Discussion
The input parameters for implemented models were Landsat land surface temperature (LSTLand), MODIS land surface temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand), and MODIS Normalized Difference Vegetation Index (NDVIMOD). Six possible scenarios were defined to estimate monthly ET0. The first two scenarios were considered as a single parameter (scenarios 1 and 2) and other scenarios were evaluated with two input parameters. Scenarios 3 and 4 were evaluated based on the parameters of the Landsat satellite and MODIS sensor, respectively. In scenarios 5 and 6, monthly ET0 was estimated with Landsat and MODIS NDVI and Landsat and MODIS LST, respectively, to determine the effect of NDVI and LST values on ET0 estimation. According to the obtained results, for the MLP and RF models in Ahvaz station, the value of R2 ranges from 0.440 to 0.972 and 0.271 to 0.983, respectively. In Ahvaz station, the lowest and highest RMSE is 0.279 mm.month-1 (RF-5 model) and 1.396 mm.month-1 (RF-4 model), respectively. Additionally, in this station, the highest and lowest values of NS are 0.962 (RF-5 model) and 0.042 (RF-4 model), respectively. According to the obtained results, in estimating the monthly ET0, the best performance is related to MLP-6 (R2=0.972, RMSE=0.348, and NS=0.940) and RF-4 (R2=0.983, RMSE=0.279, and NS=0.962). The highest and lowest values of R2 in Tabriz station were 0.988 and 0.186, respectively. Moreover, MLP-4 and RF-5 models in this station have the lowest and highest RMSE, respectively. The results showed that in Tabriz station, the best performances were related to MLP-4 (R2=0.988, RMSE=0.299, and NS=0.935) and RF-4 (R2=0.979, RMSE=0.302, and NS=0.933). In addition, in this station, the RF-5 model has the weakest performance among all models with R2=0.186, RMSE=1.169, and NS=0.012.

Conclusion
The results showed that 1) the accuracy of monthly ET0 estimation in Ahvaz (arid climate) and Tabriz stations (semi-arid climate) with scenario 4 including LSTMOD and NDVIMOD was better than other investigated scenarios; 2) in estimating monthly ET0 using a single input parameter including LSTLand (scenario 1) and LSTMOD (scenario 2), in both Ahvaz and Tabriz stations, scenario 2 had better performance with both MLP and RF models; 3) estimation of monthly ET0 in Ahvaz and Tabriz stations has performed best with RF-4 and MLP-4 models, respectively, with LSTMOD and NDVIMOD input parameters (scenario 4); 4) in the comparison of scenario 5 (NDVILand, NDVIMOD) and scenario 6 (LSTLand, LSTMOD) in both RF and MLP models, scenario 6 has the best performance in estimating monthly ET0; and 5) in the comparison of monthly ET0 estimation in both arid and semi-arid climates, the best performance with a high correlation coefficient was obtained with the MLP model in semi-arid climates.

Introduction
Soil organic matter is one of the main indicators of soil quality and soil production capacity. It provides some nutrients for plant growth and improves the physical conditions of the soil. In addition to the importance of soil organic matter from an agricultural point of view, there is a deep and close relationship between the amount of soil organic matter and the amount of carbon dioxide in the air, global warming, and desertification. In addition, due to the lack of organic matter in the soils of arid and semi-arid regions, the use of organic compounds such as animal manures is considered an important and effective management factor on soil quality and improving the physical, chemical, and fertility properties of the soil. The supply sources of organic fertilizers in Iran are very diverse, which include animal manure, green manure, and all kinds of composts including urban waste composts and sewage sludge. Different sources of organic matter and their effect on plant growth have been investigated by various researchers. One of the effective ways to produce and increase crop yield is the combined use of organic and chemical fertilizers. The use of chemical fertilizers is the fastest and most reliable way to ensure soil fertility. Still, the high costs of these fertilizers, pollution, and destruction of the environment and soil are the defects of these fertilizers. Using organic fertilizers reduces the consumption of chemical fertilizers and increases the production of crops and garden crops. According to the recommendations of the Ministry of Agriculture to use fewer chemical fertilizers and to encourage farmers to use more organic fertilizers, and considering that a major part of the nitrogen in animal manure is wasted before consumption, investigating the effect of organic fertilizers on plant growth is a special priority. Therefore, this research aimed to determine the optimal levels of organic fertilizer and nitrogen for wheat cultivation in the Khorramabad region.

Materials and Methods
A split plot experiment in a randomized complete block design with three replications was implemented in the agricultural research station of Sarab Chengai Khorramabad during the crop year 2018-2019. This station has a moderate climate, an altitude of 1171 m asl, and an average annual rainfall of 516 mm. This station has a xeric humidity regime and a thermic temperature regime. Experimental treatments were: A- Organic fertilizer as the main treatment: M1 (control), M2 (10 tons of manure ha-1), M3 (20 tons of manure ha-1), M4 (10 tons of compost ha -1), and M5 (20 tons of compost ha -1). B- Consumption of urea fertilizer based on soil test as a sub-treatment: N1 (control), N2 (100% of fertilizer recommended based on soil test), N3 (75% of fertilizer recommended based on soil test), and N4 (50% of fertilizer recommended based on soil test). The type of crops and their rotations were wheat and corn according to common rotation in the region. Chamran variety wheat was planted on 2 November and wheat was harvested on 25 June with the amount of seed used 150 kg ha-1, single grass 704 corn variety was planted on 1 July and corn was harvested on 26 October with the amount of seed used 30 kg ha-1. It was done manually. All operations, such as the method and amount of irrigation water, fighting against pests and weeds, were carried out according to the advice of agricultural experts. This experiment was carried out in the form of alternating wheat and corn.

Results and Discussion
The results showed that in wheat, the highest straw yield in N3M2 treatment increased by 57.2% compared to control, the highest grain yield in N3M1 treatment increased by 33.6% compared to control, the highest total yield in N3M2 treatment increased by 39.8% compared to control. The highest harvest index related to the N4M4 treatment is an increase of 11.3% compared to the control, the highest number of seeds in the bunch related to the N3M2 treatment is a 50% increase compared to the control, and the highest thousand seed weight is related to the N3M4 treatment, which is a 16.6% increase compared to the control and has no significant difference with N3M3 treatment. In corn, the highest fresh weight of aerial organs related to N3M1 treatment is a 34.3% increase compared to the control, the highest nitrogen concentration of N2M2 is a 16.7% increase compared to the control, the highest seed protein is related to the N2M2 treatment is 15.8% increase compared to the control. Therefore, the use of 75% nitrogen of the soil test for wheat (187.5 kg.ha-1 urea) and 100% of the soil test (200 kg ha-1 urea) for corn, as well as the use of 10 t ha-1 manure have a beneficial effect on the growth characteristics of wheat and corn. In general, for wheat, among the treatments of combined use of organic fertilizer and chemical nitrogen fertilizer, the use of 20 t.ha-1 compost fertilizer along with 50% of recommended urea fertilizer based on the soil test (M5N4 treatment) showed the greatest effect on grain yield. In the case of corn, treatment M4N4 (use of 10 t.ha-1 compost fertilizer along with 50% of recommended urea fertilizer based on soil test) had the greatest effect on fresh weight.

Conclusion
The results of this research showed that in wheat cultivation, the consumption of 75% nitrogen recommended in the soil test can increase yield and yield components compared to control. Combined use of organic fertilizer and nitrogen can significantly increase grain yield, straw, biological yield, the weight of 1000 seeds, harvest index, and the number of seeds in the cluster. The results regarding corn cultivation also showed that the use of 100% nitrogen according to the soil test has a higher efficiency than other treatments. Among the organic fertilizer treatments, 10 t ha-1 animal manure is recommended. Therefore, to reduce the use of chemical fertilizers and increase the quality of soil properties, to prevent the reduction of soil organic matter and the destruction of the environment, the combined use of organic and chemical fertilizers is recommended.

Introduction
Although the exploitation of mineral resources is very useful for the country, it has a negative impact on human and plant life with its destructive effects. These destructive effects appear as pollution in the soil, which can lead to the imbalance of ecosystems and ultimately endanger human health. In addition, economic development and the expansion of industrial areas, especially coal mining mines, cause heavy metal contamination of the soil to become more severe and cause the destruction of soil resources and the deterioration of ecosystems in different regions of the world. The present study was conducted to investigate the contamination of the two elements Pb and Zn in the surface soil of the Komarzd mining area in Mazandaran province (Iran) due to coal mining.

Materials and Methods
Komarzd mines in Mazandaran province are one of the largest and oldest coal-producing areas in the central Alborz coal field. Komarzd mines are located 48 km from the southern district of Qaemshahr and 25 km from Alasht. Four indices, including CF, mCF, RI, and Igeo were calculated for 110 samples from surface soil prepared at a depth of 5-15 cm. After separating the rubble, the collected soils were kept in closed plastic bags with a unique code, and a GPS device recorded their location. The samples prepared in a dry environment were transferred to the laboratory. The ICP-MS method at 75 microns was used to analyze and measure the concentration of heavy elements in the collected samples. According to the presence of different elements measured in the region's soil and the compounds in it, two metals Zn and Pb were selected for evaluation and analysis. Sampling was randomly selected based on saved points and drilling tunnels. PCA statistical test is a type of multivariate analysis widely used in sediment, water, and soil pollution studies. Varimax Rotation is one of the most common types of PCA tests to interpret the results and contamination components of the method. This statistical method can be calculated using R and SPSS softwares.

Results and Discussion
The results showed that the ordinary kriging model is the most suitable model to show the region's concentration distribution of the two metals, PB and Zn. Also, the statistical status of the elements showed that the lowest amount of Zn for the Igeo index is equal to -0.53 and the highest value for the CF index is equal to 0.72. The amount of Pb concentration measured based on Igeo, CF, mCF, and RI indices equals 0.08, 1.03, and 213 mg/kg, respectively (RI<CF<Igeo). The results of the CF and mCF showed that the region has a low or moderate pollution status in terms of the concentration of the two determining elements; in other words, the value of this factor for Zn is less than 1 (CF<1) and the amount for Pb is (3 > CF> 1). The Igeo index showed that Zn with a concentration lower than zero is not polluted, but Pb with a value (0< Igo <1) has a non-polluted to slightly polluted state. The RI also showed that the concentration of the two elements is less than 150 mg/kg, so this index is also in the low-risk category of contamination (RI ≤ 150). By evaluating the graphs obtained from the PCA analysis, it was found that the two elements Zn and Pb in the correlation analysis graph have a good correlation and distribution, and the changes in the two axes show 47.9% and 24%, respectively, for the Igeo. This indicates the presence of environmental factors in the distribution of this element in the environment. The CF also showed that their distribution in region three negatively correlated with environmental factors. Its value for the two axes is 32.5 and 21.9 %, respectively. The correlation of the Zn element is less than -0.5, but the Pb element is more than -0.5. In general, the value of this variable is 8 and 20, respectively, based on the diagram of two minimum and maximum values. The mCF and RI indices showed that environmental factors influence the distribution of the two elements PB and Zn, and among the two elements, the distribution of the Pb element in the mCF and RI, results of 32.8 and 34.4 has the most influence from the distribution of the drilling tunnel and had environmental factors and has a lower correlation than Zn. The effect percentage of these two elements is 32.8 and 23.2%, respectively, and the two elements Zn and Pb have a good correlation and distribution with environmental changes and human factors.

Conclusion
According to the measurements and the results of four indices, the area is uncontaminated or has low contamination in terms of the concentration of two elements, PB and Zn. Therefore, mineral tailings and coal exploitation do not significantly affect the distribution of the mentioned elements. To improve the research results, other indices can be used to determine the role of environmental factors. Also, considering the agricultural lands and water resources of Talar River, it is better to use other elements that directly affect human health and analyze the region's conditions in terms of environmental pollution to achieve better results and accuracy. One of the main disadvantages of measuring the sources of pollution is the many problems in laboratory work and the preparation of laboratory materials. Still, with this research, it is possible to determine the existing doubts in the field of soil pollution for residents and show the impact of mining on the spread of pollutants. A suggestion to improve this research is to consider elements other than Pb and Zn in different environments such as water sources; In general, for a more detailed investigation, other elements that play a role in human health should be investigated.