نشریه مدل سازی و مدیریت آب و خاک
سال پنجم شماره 1 (بهار 1404)

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.

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.

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.

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.

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.

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.

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.

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.

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.

Management of underground water resources in arid and semi-arid regions is of particular importance. Because the drinking water of a huge part of the countries that live in arid and semi-arid regions relies on underground water sources. The ever-increasing need for water following severe socio-economic changes in the country along with successive droughts in recent years has led to the expansion of the use of deep, semi-deep wells and dams. More extraction of underground water by digging deep wells has caused a drop in the underground water level.The underground water level is one of the most important parameters affecting the qanat discharge, preventing land subsidence and permanent rivers recharge. Using new methods to know the changes in the underground water level and simulating the effects of different management methods can be of great help in managing the water resources of an aquifer. Recently, the use of different models and simulation of aquifers has been able to help mankind to achieve its management goals, and groundwater modeling has become an important tool for managing water resources in aquifers. Since Khorasan Razavi province is located in the arid and semi-arid climate zone and exploitation of underground water has a great effect in providing part of the water needed for agriculture, drinking and livestock drinking Management of this valuable resource is essential. But for management, a series of tools are needed to predict and simulate the effects of various factors. One of these tools is model and modeling, which can achieve acceptable results by using some measurable parameters.

Mashhad-Chenaran plain is under the main basin of Kashfroud River, which has an area of 990,914 hectares, is located in the north of Razavi Khorasan province, and is under the basin of Qaraqom watershed. Mashhad-Chenaran plain includes a part of Mashhad city, Targaba-Shandiz, Chenaran and a part of south Qochan. The GMS model was used to investigate the effect of implementing the cultivation pattern and its effect on the changes in the underground water level in the northern part of the Mashhad-Chennaran aquifer. The required layers of GMS model are: a: layer of observation wells b: layer of harvesting values, c: layer of feeding aquifer d: layer of boundary of aquifer and e: layer of bottom rock of aquifer. In the simulation of the studied area, the size of the cells in the entire studied area was considered to be 300 x 300 meters. According to the available data and information for running the model in steady state, September 2006 was entered as the initial hydraulic load. Then, the LPF package was used in MODFLOW model to solve the groundwater flow and level, and the groundwater flow of the studied area was simulated in two stable and unstable states.

After preparing the coverage needed for the model, the model was calibrated in steady state. After calibrating the model in the steady state by introducing stress periods, the GMS model was calibrated in the unsteady state. The results showed that the GMS model has simulated the water level with proper accuracy after calibration. Examining the chart of observed and simulated values shows that in some of the investigated piezometers, the simulated groundwater level is higher than the observed ones, and the model tends to overestimate.To implement the management scenario, six percent of the water used in all the wells used in the agricultural sector was reduced and the model was re-implemented. The results showed that the six percent reduction of water consumption in the agricultural sector on the level of piezometers was about 5 to 15 cm on average and caused the rise of the underground water level in the studied areas.

One of the main sources of water supply for agriculture, animal husbandry and drinking in Mashhad-Chenaran Plain region is underground water sources. But in recent years, due to excessive exploitation of underground water resources and climate changes, the level of underground water has decreased and has had many economic-social consequences. Therefore, for the optimal management and exploitation of this underground water source, it is necessary to know the changes in the underground water level and predict the effects of various factors on it. One of the tools that can be used to determine the changes in the underground water level and the effect of various factors on it are computer models. One of the important tools for managing underground water resources are the models designed for this issue, therefore the MOFLOW model used in this research has a good ability to simulate the level of underground water in the studied areas in permanent and non-permanent conditions. Examining the chert of the observed and simulated balance by the GMS model showed that a six percent reduction in water withdrawal in the agricultural sector increases the level of underground water, but it does not compensate for the amount of damage caused to the aquifer due to excessive withdrawal and only reduces its effect.

Flood events are the most complex natural hazards that endanger human and animal lives, social and economic settings, and environmental resources more than any other natural disaster. This phenomenon is caused by the water flow exceeding the river's channel capacity. The expansion of flood zone along river banks in recent years due to climate change and inappropriate use of natural resources is associated with irreparable socio-economic and environmental damages. Simulation of potential flood zones is crucial for management purposes of flood prone areas. Hydraulic models are proved to be useful in simulating flood zones, identifying hotspot areas, and thus, estimating potential damages. The Arazkuseh River is situated downstream of three watersheds, namely Minodasht, Narmab and Nodeh Khandooz. It is prone to flooding during periods of heavy rainfall in the watersheds. The aims of this research are to assess the performance of the HEC-RAS 2-D hydraulic model in simulating the flood zone for the event of March 17, 2019, and to estimate the direct-tangible damages incurred in a 9-km river reach from the Arazkuseh River due to floods in different return periods.

The HEC-RAS software has the ability to calculate water level in rivers while considering hydraulic structures. To estimate the velocity vectors, the two-dimensional diffusion wave was used, which takes into account more stable numerical solutions and reduces the calculation time. A Digital Elevation Model (DEM) map with a resolution of one-meter was used to create input terrain data for this model. The flood event hydrograph on March 17, 2019 with a peak discharge of 355 cubic meters was recorded at the Arazkuseh hydrometric station located at the joint outlet of the upstream watersheds. Manning's roughness coefficient values estimated based on field observations in the channel and flood plain were also calibrated with the index F during the evaluation of the model's performance. In order to evaluate the performance of the HEC-RAS 2-D model, the outputs of the model for the flood event on March 17, 2019 were compared with the flood zones identified by the Sentinel-2 satellite images at two different days (23 March and 2 April, 2019). The Pilgrim's computational method was used to identify temporal distribution model of the design rainfalls for the Arazkuseh watershed in different return periods. Additionally, the CoKriging geostatistical method was used to estimate the spatial pattern of the design rainfalls. Thus, the hydrographs simulated by the HEC-HMS hydrological model for the design rainfalls were considered as inputs to the HEC-RAS software. Following identifying the elements exposed to flooding, direct and tangible damages caused by the simulated floods to different land uses were estimated through collecting information from different sources accompanied by field observations.

The F index values (79% and 71% for 23 March, 2019, and 2 April, 2019, respectively) indicate that the HEC-RAS 2-D model has an acceptable performance in simulating the flood zone areas in the Arazkuseh River. However, the area of the simulated flood zone shows an overestimation compared to the flood zone observed by the Sentinel-2 images. The overestimation of the flood zone areas by the HEC-RAS 2-D model can be related to the accuracy of the DEM map and the Manning's roughness coefficient estimation. Analysis of the flood zone for the 100-year return period, as a base flood, reveals that crops, trees, dirt roads, residential areas, and asphalt roads are most likely to experience inundation, respectively. Even in 10-year return period, crop lands are likely to place in the flood zone due to gentle slope and the proximity to the river bank. The highest amount of direct-tangible costs for the 100-year flood, is associated to crop lands, residential areas, trees, dirt road, and asphalt road with values of 20889, 8650, 7503, 2250, and 1750 million Iranian Rial, respectively.

The use of DEM data with appropriate spatial resolution is very important in creating terrain data and simulating flood zones in two-dimensional models. Damages to agricultural products and costs attributed to the removal of sediments and cleaning up in crop lands will be significant due to the spreading out of the flood water in this land use. The high damage incurred to the residential areas is because of costly repairs required after flooding and also the price of houses' contents. The total area of land uses which are exposed to 100-year flooding is about 23 hectares and the total damage imposed is approximately, 41042 million Iranian Rial. Therefore, due to the expansion of the residential areas along the river, it is necessary to reduce the hazard of flooding, to enhance adaptive capacity and coping capacity, and to decrease the level of exposure. In crop lands, managers and practitioners should reduce the amount of damages to this land-use by appropriate actions such as flood insurance promotion and introducing resistant varieties to inundation in the study area.

As a result of the development and expansion of cities, growth and development of industries and technology, various pollutants have entered the environment. The pollutions resulting from the discharge of industrial effluents, consumption of fuel, discharge of municipal sewage and the use of sludge from sewage treatment as a soil fertilizer have caused harmful effects affecting humans and living creatures and changing ecosystems. Meanwhile, as a heavy metal, cadmium is very important due to its high mobility and toxicity in low concentrations.Various methods have been developed to remove heavy metals from contaminated water and soil, and surface adsorption is one of the most efficient methods. Various surface adsorbents such as clays, zeolites, dry plants, agricultural waste materials, biopolymers, metal oxides, microorganisms, and volcanic ash have been used to remove heavy metals. Iron nanoparticles with zero charge (nZVI) is a new technology that has successfully been used to remove various metal ions. Nanoparticles are special adsorbents that are used for remedial purposes due to the presence of significant specific surface area that leads to high density of exchange sites and metal removal capacity. Basically, iron nanoparticles have been introduced as effective reductants and catalysts for a wide range of common pollutants such as organochlorine compounds and metal ions.Cleaning water environments of heavy metals can happen as a result of surface absorption by absorbent materials such as nanoparticles. The aim of the present study is to investigate the effect of iron nanoparticles in removing cadmium from water environments.

Nanoscale iron particles with zero charge can be prepared in aqueous environments by reduction of ferric iron or ferrous iron by sodium borohydride or by decomposition of pentacarbonyl iron in organic solvents or argon. In this study, the synthesis of iron nanoparticles by sodium borohydride has been used. For the synthesis of nanoparticles, 7.823 grams of iron sulfate II (FeSo4.7H2O) was dissolved in 200 ml of distilled water in the presence of ethanol. After setting the pH of the suspension at 6.8, 0.8 grams of starch and 1.8 grams of sodium borohydride were added to the solution. The nanoparticles were separated from the solution using a centrifuge and washed with ethanol. All steps were performed in the vicinity of nitrogen gas and finally the synthesized nanoparticles were dried under nitrogen gas. Finally, by using microscopic methods, images with very high magnification of the material are obtained. In this study, the shape and morphology of produced nanoparticles were investigated using SEM.To investigate the effect of nanoparticle and pollutant concentration on removing efficiency, factorial statistical design with cadmium concentration treatment at 9 levels (10, 25, 50, 75, 100, 200, 300, 400 and 500 mg/liter), and treatment with zero amount of nano iron in three levels (0.025, 0.05 and 0.1 g) was applied. The effect of contact time on removal efficiency was investigated at 10, 30 minutes and 1, 4, 8, and 24 hours, and the effect of alkalinity was investigated at pHs of 4, 6, 8, and 10. In all stages, the concentration of cadmium in the purified solution was measured using an atomic absorption device.

With the passage of time, the amount of absorption or removal efficiency increases, but after 4 hours, its changes are not statistically significant. The increase in removal efficiency with the passage of time is due to the fact that with the passage of time the formation of holes and corrosion on the surface of iron increases, as a result of which the cross-sectional area of absorption and removal efficiency also increases. In addition, the active sites for cadmium absorption change and the number of products resulting from the reaction of iron in the water environment increases, which also causes an increase in the removal efficiency with increasing retention time.The results showed that the absorption efficiency decreases with the increase of the initial concentration of cadmium, which means that iron nanoparticles have a limited capacity to absorb cadmium. Examining the effect of the initial concentration of the ions of the adsorbed material showed that firstly, the more concentrated the solution is in terms of the number of ions, the better the absorption is, and secondly, the number of active sites for absorption gradually increases with the increase in the process time and the increase in the number of ions absorbed on the adsorbent decreases, so that the rate of absorption decreases significantly, leading to the formation of balance in absorption.The greater the amount of iron nanoparticles, the greater the number of active surfaces participating in metal absorption, and as a result, it holds the absorbed cadmium with greater force. The results of placing cadmium-contaminated nanoparticles indistilled water (Figure 5) showed that in high doses of iron nanoparticles, a smaller amount of cadmium was absorbed and re-entered the environment. With the increase in the initial concentration of cadmium, its release has also increased, because, as mentioned above, the number of occupied sites has increased and the amount of holding energy per ion has decreased, causing release.

As the concentration of the pollutant increases, due to the limited capacity of the adsorbent, the efficiency of absorption decreases, which means that with the saturation of the absorption sites, it is not possible to absorb more of the pollutant. On the other hand, with the increase in the amount of adsorbent, the absorption efficiency increases due to the increase in the number of absorption sites. Also, by increasing the amount of absorbent and pollutant, the possibility of collision between cadmium and iron nanoparticles and the occurrence of absorption reactions increases. The high ratio of absorbent to pollutant causes a stronger bond and as a result less pollutant is released. When the adsorption surfaces of nanoparticles are occupied by the pollutant, it is no longer possible to release the pollutant and reuse these absorption surfaces, and in general, nanoparticles of iron have zero and cannot be used more than once to clean cadmium from the water environment.

A significant portion of the flow of perennial rivers originates from groundwater. The changes that occur in a watershed due to natural and human factors are indicative of physical changes and artificial mismanagement of water resources. These situations change the contribution of groundwater to streamflow. Therefore, understanding baseflow allows for the identification of the potential and dynamics of the groundwater system. In principle, the separation of base flow and quick flow is difficult to distinguish from the measured discharge data in a river, because the measured discharge in a river is a combination of the two flow components. Separation of riverbed flow is essential for water resources management and can significantly contribute to the calculation of water availability in the dry season (relatively short discharge period). In addition, comparing different watersheds in terms of flow recession characteristics can provide valuable information about storage and recharge properties in the watershed. The main objective of this research is to estimate base flow using several hydrograph analysis techniques, as there has been neither organized research on groundwater resources at the watershed level nor studies on different methods for estimating the base flow contribution in these streams.

The current research includes estimating base flow from daily streamflow data using the Flow Duration Curve (FDC) technique, the Web-based Hydrograph Analysis Tool (WHAT), and the Baseflow Index (BFI+) program. Using the FDC technique, the long-term annual average fraction of flow from base flow is estimated after obtaining the values of Q90 and Q50. The Web-based Hydrograph Analysis Tool includes three algorithms: the local minimum algorithm, the one-parameter algorithm, and the two-parameter algorithm. The web-based WHAT system provides an efficient tool for hydrologic model calibration and validation. Baseflow information from the WHAT system can also play an important role in sustainable groundwater and surface water management, including irrigation and industrial uses, and estimating pollutant loads from both baseflow and direct runoff. The Baseflow Index program also uses the following algorithms: Fix Interval, Sliding Interval, Local Minima, Lynne-Hollick, Chapman, One Parameter Algorithm, Two Parameter Algorithm, Exp. Weighted Moving Average, Eckhardt, BFLOW, IHACRES, and Fure & gupta. For this purpose, daily streamflow and precipitation data were obtained from the Regional Water Organization of Lorestan Province. The time series of data for streamflow and precipitation was selected from 2009 to 2019.

The results of the data analysis indicate that most of the automatic filter techniques used with assumed parameters have overestimated the baseflow above the average compared to the FDC. In addition, the FDC analysis showed that the contribution of groundwater storage to streamflow was below average. The WHAT automatic digital filter tool has been widely used for long-term baseflow separation using a two-parameter digital filter (α and BFImax). In this study, the BFImax value was set to 0.80 for alluvial streams and the filter parameter (α) was set to 0.995 for the Rahim Abad stream. The BFI values for the Rahim Abad River are consistent and are estimated to be around 45% for the entire watershed. The results obtained from BFI+ showed that the calculated baseflow values for the one-parameter and two-parameter algorithms, except for RDF-IHACRES, BF-BFLOW, BF-Chapman, and BF-Furey, were higher than the mean flow. In addition, the long-term baseflow to flow ratio or BFI is equal to the ratio of Q90/Q50. This ratio indicates the discharge of groundwater or other delayed sources to the streamflow. Hydraulic structures built upstream of hydrological gauging stations can affect flow conditions. These BFI values are related to the geology and hydrogeology of the watershed. The Q90/Q50 ratio has an annually varying decreasing and increasing trend for flow, indicating that the contribution of groundwater to streamflow varies from year to year with decreasing and increasing changes. Finally, by employing all methods, the range of groundwater contribution to Rahim Abad streamflow was obtained between 2 and 84 percent, and its average value was also determined to be 63 percent.

Considering the average of all BFI values obtained from all methods with values less than the mean, an overall average of 45% was obtained, which provided a better estimate for the entire watershed. In this regard, the modified RDF methods (one-parameter and two-parameter algorithms), IHACRES, BF-BFLOW, BF-Chapman, and BF-Furey were selected as the top algorithms for the entire watershed. Further studies are necessary for future groundwater resource problems in the watershed. The interaction of groundwater and surface water and pollution problems, water quality management of rivers, estimation of groundwater potential using other techniques, and estimation of the contribution of groundwater under climate change are among those that can be mentioned. Introduction of suitable methods of separation of daily flow in hydrological modeling, regional analysis of minimum flows and determination of base flow share can be used. It is hoped that the output of this study will help the planning, development and management of water resources in the Silakhor watershed.

Faults are among important geological structures, affecting groundwater flow and its quality. The faults’ function can cause the complexity of the groundwater flow in alluvial aquifers. Faults have different hydrogeological behaviors in geological environments. Fault zones can act as a conduit for groundwater flow, a barrier to groundwater flow, or a complex conduit/barrier system. The displacement of rock and sedimentary layers around the faults may lead to clear changes in the bedrock level, aquifer thickness, and groundwater depth. Faults can also channelize the groundwater flow and solute transport. Determining the hydrological behavior of fault structures can play an important role in the sustainable management of water resources. The Sabzevaran fault system is one of the most important fault systems in southern Iran. This system is a right-lateral transverse fault having a length of 150 km. Sabzevaran fault striking N-S (from the west of Jiroft city to the southwest of Kahnuj city in Kerman province, Iran) shows the evidence of Quaternary deformation. The fault scarps, cutoff ridges, and tilted rivers represent the activity of this fault. This study aims to investigate the hydrogeological behavior of the Sabzevaran fault and to evaluate its impact on the water fluctuations and groundwater quality.

Various data were used to investigate the hydrogeological behavior of the Sabzevaran fault. Geophysical surveys are one of the best indirect methods to study the subsurface geological situation. Geophysical studies were conducted by geoelectri method of Abkav- Louis Berger Company. The results were used to investigate the effect of the fault on changing the aquifer geometry and the situation of the subsurface layers. Investigating the groundwater level’ fluctuations is very important in water resources studies. The groundwater level’ fluctuations were investigated using the depth water data of the monitoring wells. The groundwater depth and isopotential maps were also plotted. Fault systems can affect the groundwater quality. To evaluate the impact of the Sabzevaran fault on the groundwater quality, 57 water samples were collected from abstraction wells and their parameters such as electrical conductivity and concentration of major cations (calcium, sodium, and magnesium) and anions (bicarbonate, sulfate, and chloride) were measured in the laboratory of Kerman Regional Water Authority. The isotopic ratio of the chemical elements of water molecules is a useful and new method in water resources studies. The isotopic contents of oxygen-18 (18O), tritium (3H) and carbon-14 (14C) were also used in this research.

The geoelectrical results confirmed the activity of the Sabzevaran fault and its role in changing the aquifer geometry. The Sabzevaran fault changed the bedrock topography, aquifer thickness and depth to water level. A significant difference in the depth of water can be observed around the Sabzevaran fault. There was a significant difference in the groundwater level due to fault action. The difference in water level on both sides of the fault ranged from 20m to more than 30m in different areas. The pattern of temporal fluctuations of groundwater was completely different on both sides of the fault. The groundwater level had a sinusoidal pattern on the west side of the fault, while the groundwater showed a completely downward trend in the east of the fault. The direction of groundwater flow was also different around the Sabzevaran fault. Groundwater flow was parallel to the fault line in the eastern part of the fault, while groundwater was channelized in the western part of the fault. The highest amount of electrical conductivity and concentrations of sulphate and chloride ions were recorded in the wells adjacent to the Sabzevaran fault. The highest concentrations of oxygen-18, tritum, and carbon-14 were observed in the middle part of the fault. Also, groundwater of the fault zone has a distinct isotopic content compared to the eastern part of the aquifer. The difference in groundwater residence time and its entrance into the aquifer through the fault were the reasons of the isotopic difference.

The results of this study indicated that the Sabzevaran fault caused the hydraulic disconnection of the western part of the aquifer of the Jiroft plain. This disconnectivity had affected the groundwater flow and its quality. Due to the action of the fault, clear changes in surface topography, bedrock level, aquifer thickness, groundwater level, hydraulic gradient, groundwater flow direction, and water quality had occurred. Examining the groundwater fluctuations, water quality parameters, and isotopic content of water samples also confirmed these findings. This study showed that the Sabzevaran fault behaves as a hydraulic barrier to groundwater flow in Jiroft plain and led to the channelization of the groundwater flow, especially in the western margin of the aquifer. In order to more accurately investigate the effect of the Sabzvararen fault on the groundwater system, it is necessary to carry out geoelectrical studies at the fault zone. Measuring groundwater depth, water quality and isotopic content of wells adjacent to the fault zone is very important. It is suggested to consider the hydrogeological behaviour of the Sabzvararan fault in the preparation of mathematical models of the groundwater system and the development of aquifer management strategies.

One of the most significant environmental challenges today is soil pollution, which is particularly evident in developing countries. This issue has consistently been a focal point for urban and environmental officials. Soil, as a fundamental component of living ecosystems, is essential for sustaining life on Earth. In addition to its crucial role in life continuity, soils have a significant impact on evolution and even the formation of life. They serve as a primary source for generating societal wealth and play a vital role in development projects and community health initiatives. Today, the accumulation of heavy elements and soil pollution in agricultural lands is recognized as one of the vital issues in the field of environmental biology globally. This not only compromises the quality of agricultural products but also jeopardizes the sustainability of agricultural production. Entry of heavy elements into soil ecosystems occurs through human activities, and researchers believe that these pollutant sources may pose long-term risks to human health. This is because prolonged exposure to surface soils containing heavy metals can lead to serious health hazards through inhalation, ingestion, and skin absorption

The study areas in this research include two counties: Birjand in South Khorasan Province and Zabol in Sistan and Baluchestan Province. In this study, a systematic grid sampling method was used for soil sampling. A total of 90 samples were collected, with a distance of 200 meters between each sample on both sides of the road. The sampling depth was 0-25 centimeters. After sampling, the samples were transferred to the laboratory. Finally, 12 elements (Al, Mn, Fe, Cr, Zn, Ni, Ti, Co, Cd, Cu, As, Pb) were analyzed in the soil samples using the ICP-OES instrument model Varian 710-ES. Then, three indices, including Muller's Accumulation Index, Contamination factor, Cumulative pollution index and Enrichment Factor, were used to assess the contamination intensity of the soil samples. Pearson correlation matrix in R software was also employed to examine the relationships between heavy metals present in the soil samples from the cities of Birjand and Zabol.

The levels of metals present in the soil (concentration in the earth's crust) of Birjand city indicated high concentrations of lead, Nickel, Cadmium, Zinc, Copper, and Chromium related to human activities. According to the results obtained from the analysis of background soil samples, among the measured metals in Zabol city, the highest average concentration was related to aluminum and iron, while the lowest average concentration was related to nickel. The results of the Pearson correlation analysis for heavy metals in Birjand city showed that Copper, Zinc, and Arsenic have the highest negative correlation with other heavy metals, while the rest of the heavy metals had a positive correlation with each other. The results of the Pearson correlation matrix for the concentration of heavy metals in the soil of Zabol city also showed that there is mostly a negative correlation between most elements, while in other cases, they, the highest average concentration was related to aluminum and iron, while the lowest average concentration was related to nickel. The results of the Pearson correlation analysis for heavy metals in Birjand city showed that Copper, Zinc, and Arsenic have the highest negative correlation with other heavy metals, while the rest of the heavy metals had a positive correlation with each other. The results of the Pearson correlation matrix for the concentration of heavy metals in the soil of Zabol cit all have a positive correlation. Based on the accumulation index, it can be stated that the origin of pollution in Birjand city with chromium and lead is mostly anthropogenic. Additionally, the origin of other elements that were within the non-polluted range can be attributed to the earth's crust. On the other hand, in Zabol city, the accumulation index indicates pollution of the soil in the area with Nickel.

Based on the results of the study in the two cities of Zabol and Birjand, it can be concluded that the pollution status with heavy metals in these two cities differs significantly. Considering the correlation between heavy metals in soil samples from the cities of Birjand and Zabol, it was found that in Birjand, copper, zinc, and arsenic had negative correlations with other heavy metals, while the rest of the heavy metals had a positive correlation with each other. However, in Zabol city, most elements had negative correlations. Nevertheless, the strongest positive correlations were observed between titanium and iron, titanium and manganese, arsenic and lead, and manganese and cobalt. These results indicate differences in the pattern of heavy metal pollution between the cities, which may be attributed to local factors such as industrial activities, traffic, and fuel consumption.This study overall underscores the need for further actions to control and reduce heavy metal pollution in both cities.

Reference evapotranspiration is one of the important processes in the water cycle that has a great impact on water resources and agriculture. Climate change can affect this process and requires accurate spatial-temporal analysis. The standard method for calculating reference evapotranspiration is the FAO-56 Penman-Monteith method that requires meteorological data. But in some areas, there is not enough data and therefore other methods such as machine learning and remote sensing are used. These methods can estimate reference evapotranspiration with high accuracy using different variables such as vegetation indices, temperature, humidity, and wind speed. Some of these methods are random forest, support vector regression, generalized regression neural network, and gene expression programming. These methods can also help in assessing the importance of predictor variables and their uncertainties.

The aim of this article is to model daily reference evapotranspiration (ET0) using data collected from meteorological and satellite sources and implementing random forest (RF) algorithm. The standard FAO-Penman-Monteith method, which is based on the Penman-Monteith equation that integrates radiometric and aerodynamic parameters, was adopted as the base method for calculating ET0 of a reference crop. However, this method demands a large amount of meteorological data such as solar radiation, relative humidity, wind speed, and maximum/minimum temperature, which can be challenging to obtain. To overcome this limitation, satellite images from Google Earth Engine system for the years 2001 to 2021 were processed using Landsat and MODIS sensors to extract parameters such as land surface temperature (LST), enhanced vegetation index (EVI), leaf area index (LAI), and normalized difference vegetation index (NDVI). These parameters can be used to estimate effective evapotranspiration continuously in the short term by applying models and interpolations. One of the problems of planning and management based on satellite image data is the lack of daily images of the study area. One of the ways of time microscaling of this valuable information is interpolation. In other words, interpolation is a mathematical process that estimates unknown data at other points using data available at specific points. This process is used to fill in gaps, increase resolution, or create continuous maps from satellite data. The importance of satellite data interpolation is that it can help improve the quality and accuracy of data and use them to study and predict various meteorological, agricultural, geological, etc. phenomena. In this research, LST (8 days) and vegetation cover data (16 days) were converted into daily data using spline and cubic spline interpolation functions. This work has been done using spline and Bezier interpolation functions and for days without data with equal intervals by coding in Mathematica programming environment.

This research used satellite and meteorological data and the random forest machine learning method to estimate the ET0 at Tabriz and Ardabil stations. The results showed that the saturation vapor pressure and the land surface temperature at night and day had the highest correlation and coefficient of determination with the ET0. The highest accuracy of ET0 estimation at Tabriz was in scenario 10 with error of 0.364 and at Ardabil in scenario 12 with error of 0.430. The best model was the combination of meteorological and satellite parameters. The spline interpolation method provided a better modeling than the bezier method. Additionally, increasing the parameters involved in machine learning and the LAI parameter reduced the accuracy.
Douna et al. (2021) investigated the ability of the RF method to predict daily ET0 in three regions in Australia from 2010 to 2014, using satellite data of LAI and LST and regional meteorological parameters. They stated that the LAI is the most important variable, and they also obtained satisfactory performance in three regions, with RMSE errors of about 1 mm per day. At the same time, for Tabriz and Ardabil stations, the LST values were more important than the LAI and more correlated with ET0. Therefore, by combining meteorological and satellite data, the amount of RMSE errors was reduced to 0.364 and 0.430 mm in Spline and Bezier intyerpolation functions.

The results of the research showed that 1) at Tabriz station, among all meteorological parameters, saturation vapor pressure and among satellite parameters, land surface temperature at night had the highest correlation with daily ET0. 2) At Ardabil station, in the same time period, saturation vapor pressure also had the highest correlation of 0.887, but among satellite parameters, land surface temperature at day had the highest correlation of 0.737 with daily ET0. 3) The highest accuracy of daily ET0 estimation at Tabriz was in spline and random forest methods in scenario 10 with error of 0.364 and in bezier methods in scenarios 14 and 16 with error of 0.380. 4) At Ardabil station, both spline and bezier methods had the highest accuracy in scenario 12 with errors of 0.430 and 0.453, respectively. 5) In both spline and bezier interpolation methods, the most accurate model was the combination of meteorological and satellite parameters. 6) In general, spline interpolation method provided a better modeling than bezier. Increasing the parameters involved in machine learning, which were calculated using the available data, had no positive effect on the accuracy of the model. 7) Adding the LAI parameter, which was calculated using the EVI data, to the machine learning model, reduced the accuracy in spline method.

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.

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.

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.

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.

Runoff is an important hydrological component in the assessment of water resources. Most water resource applications rely on runoff as an essential hydrologic variable. The hydrology of basins is influenced by many factors, including climate change. Basin discharge estimation is an important step in planning and managing surface water resources, especially in basins that lack reliable flow data. In this study, due to the inappropriate spatial distribution of meteorological stations in the study area of Takab, satellite images and products were used to evaluate the possible effects of climatic factors including rainfall and temperature on runoff. For this purpose, in order to investigate the changes in rainfall and temperature from 1998 to 2020, TRMM and FLDAS satellite products, respectively, were evaluated using different statistical criteria with Takab synoptic station data. The evaluation results indicate the appropriate accuracy of these satellite products compared to the observed values. Choosing a suitable rainfall-runoff model for the catchment area is important for the efficiency of planning and management of water resources. Also, choosing a model requires recognizing the capabilities and limitations of hydrological models of the catchment area, which requires access to meteorological parameters such as rainfall and temperature. According to the studies, the IHACRES hydrological model has been used to estimate the amount of changes in discharge and runoff in many basins. Its purpose is to help water resources engineers describe the relationship between basin runoff and precipitation.

Finally, the trend of changes in rainfall and temperature was investigated with the non-parametric Mann-Kendall and Sense's slope tests. Examining the rainfall data of the study area of Takab indicates that the highest amount of rainfall occurs in the 3 months of April, March and November respectively. which is approximately equivalent to 45% of the total annual rainfall of the study area and its values are estimated as 53.1, 40.4 and 39.6 mm per month respectively. Also, the highest and lowest average temperatures of the range are in the months of July and January, respectively, which are estimated at 24.2 and -3.4 degrees Celsius, respectively. In the following, the IHACRES model was used to simulate the river discharge using temperature and rainfall data from satellite products. Also, in this study, the IHACRES model was used to predict production runoff under the influence of climate change and evaluate different climate scenarios. For this purpose this study focused on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) for the coming years until the year 2100 were used.

The flow simulation results from the RCP2.6 scenario indicate that the greatest increase in discharge in the coming period for the Takab study area was estimated for the months of December, November and January. Also, according to the RCP8.5 scenario, the largest flow increase in the future period was calculated in the months of August, July and January, respectively. In addition, the maximum decrease in discharge in both scenarios was simulated in the months of April and May, respectively. According to the obtained results, according to the RCP2.6 and RCP8.5 climate scenarios, the average annual discharge was predicted to be equal to 8.3 and 1.5 cubic meters per second, respectively. In order to evaluate the IHACRES model, the determination coefficient (R2), Nash-Sutcliffe efficiency coefficient (ENS), root mean square error (RMSE) and bias error (Bias) were used in the calibration and validation period. According to the obtained results, these values for the 14-year period of model calibration are 0.82 and 0.80 (-), 1.4 (cubic meters per second) and 0.42, respectively. Also, these values for the validation period were calculated as 0.71 and 0.68 (-), 4.7 (cubic meter per second) and 0.1, respectively.

In this study, to investigate the trend of rainfall and temperature changes from 1998 to 2020, TRMM and FLDAS satellite products, respectively, were evaluated using different statistical criteria with Takab synoptic station data. According to the obtained results, the annual changes of rainfall in the entire study area of Takab are incrementally insignificant. Also, in general, the percentage of annual rainfall changes in the highlands is higher than the average annual rainfall in the plains. After examining the trend of temperature and rainfall data, the IHACRES rainfall-runoff model was used to simulate the discharge. After simulating discharge for the statistical period, different climate scenarios were used to predict production runoff. According to the obtained results, according to the RCP2.6 and RCP8.5 climate scenarios, the average annual discharge was predicted to be equal to 8.3 and 1.5 cubic meters per second, respectively. According to the RCP2.6 scenarios, the predicted discharge has an insignificant upward slope. Also, the percentage of annual changes in river flow according to this scenario was calculated as 19.4%. Also, in the examination of the output of the IHACRES model resulting from the RCP8.5 scenarios, it was observed that the future trend of the river is a significant downward trend. In other words, the percentage of annual changes in the simulated discharge according to this scenario was estimated as -68.1%.

As the population continues to grow, the significance of boosting food production becomes increasingly evident. Consequently, it is imperative to find solutions to address water limitations and enhance food production in regions facing water scarcity. In this context, it is proposed to implement strategies that involve expanding cultivated areas and optimizing the utilization of available water resources, particularly in scenarios where irrigation is restricted. Successful water engineering projects necessitate precise estimation of plants' water requirements across various regions. The goal is to maximize the efficiency of water usage per unit volume to ensure optimal agricultural output. This study aims to explore fluctuations in the water requirements and hydromodule of crops within different cultivation patterns in selected regions of northwest Iran, in response to climatic variables.

The objective of this study is to evaluate the variations in plant water needs in cultivation patterns across different regions in northwest Iran, taking into account climatic parameters. Initially, meteorological data from stations in Tabriz, Kalibar, Parsabad, Germi, and Bukan were collected from the national meteorological organization.Cultivation pattern of agricultural plants in the investigated stations was extracted from the Jihad Agricultural Organization of the respective province. Considering the vastness of the studied areas and the impossibility of detailed soil investigation, the soil of the areas was considered medium. According to Ministry of Energy researches, the irrigation efficiency for all the studied areas averagely was considered to 60%.Subsequently, utilizing CROPWAT 8.0 software based on the FAO56 equation, factors such as evapotranspiration of grass (used as a reference plant), solar radiation, and effective rainfall were determined for the selected stations. The hydromodule, representing water requirements, was then calculated monthly for the desired cultivation patterns in the respective areas. Finally, employing the Weibull transformation coefficient, the irrigation hydromodule with various return periods was derived for the study region.

The results indicated that the average potential evapotranspiration of grass, serving as the reference plant, was calculated as 4.12, 3.03, 2.86, 3.32, and 3.86 mm per day for the Tabriz, Kalibar, Parsabad, Germi, and Bukan stations, respectively. Furthermore, the average irrigation hydromodule for these stations was determined as 0.73, 0.35, 0.6, 0.7, and 0.62 liters per second per hectare, respectively. Utilizing the linear variation function, the average irrigation hydromodule for return periods of 2, 5, 10, 25, 50, 100, and 200 years for the aforementioned stations were obtained as 0.813, 0.552, 0.707, and 0.721 liters per second per hectare, respectively. Similarly, using the exponential function, the corresponding values were extracted as 0.818, 0.632, 0.719, 1, and 0.73 liters per second per hectare, respectively. Specifically, for the Tabriz, Kalibar, Parsabad, Germi, and Bukan stations, the irrigation hydromodule values with a return period of 2 years, employing the linear function, were calculated as 0.742, 0.439, 0.622, 0.821, and 0.67 liters per second per hectare, and with the exponential function, they were determined as 0.74, 0.444, 0.618, 0.829, and 0.672 liters per second per hectare, respectively. Additionally, for these stations, with a return period of 200 years, using the linear function, the calculated irrigation hydromodule values were 0.851, 0.613, 0.754, 1.015, and 0.748 liters per second per hectare, while employing the exponential function, they were determined as 0.862, 0.749, 0.777, 1.1, and 0.762 liters per second per hectare, respectively.

The irrigation hydromodule values in the Tabriz station increased by 0.851 liters per second per hectare, which is equivalent to a 10.88 percent increase compared to the average. Similarly, in the Kalibar, Parsabad, Germi, and Bukan stations, the increases were 0.613, 0.754, 1.01, and 0.748 liters per second per hectare, respectively, representing increments of 8.44, 13.17, 38, 19.7, and 7.78 percent compared to the average. Utilizing the exponential changes function, when the return period was adjusted from 2 years to 200 years and the probability of occurrence was reduced, the irrigation hydromodule increased by 0.862 liters per second per hectare in the Tabriz station, which corresponds to a 12.23 percent increase relative to the average. Similarly, in the Kalibar, Parsabad, Germi, and Bukan stations, the increases were 0.749, 0.777, 1.1, and 0.762 liters per second per hectare, respectively, representing rises of 30.5, 15.82, 09, 27.04, and 9.04 percent compared to the average. Given the water scarcity in various regions of the country, it is recommended to use the minimum values of functions (linear or exponential) to estimate the irrigation hydromodule for different return periods. With respect to linear function changes, and considering that the irrigation hydromodule does not decrease significantly (around 20% on average) with an increase in the return period (up to 200 years), it is advisable to design and implement storage facilities, transfer systems, and water distribution networks in the studied plains with a low probability of occurrence (high return period). This approach minimizes the increase in costs and reduces risks during water transfer and distribution operations.

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.

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).

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.

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.

Floods are among the most devastating natural disasters, causing extensive damage and significant loss of life globally. Developing countries are particularly vulnerable due to inadequate infrastructure, financial resources, and advanced technology for mitigating flood impacts. Therefore, there is a critical need to develop high-performance flood forecasting models to delineate flood-sensitive areas. The frequency, lethality, and economic impact of floods have spurred the scientific community to create sophisticated algorithms and models to manage the inherent complexity of ntural events. Data mining algorithms have revolutionized scientific research by extracting patterns from vast, unstructured datasets and predicting future trends and complex natural phenomena. Machine learning techniques, a vital subset of data mining methods, excel in making accurate predictions by addressing data limitations and preventing overfitting with proper configuration. Previous studies have demonstrated that machine learning algorithms significantly improve the speed and accuracy of mapping potential flood risks. Consequently, this study aims to develop a sensitivity map for a region in Khuzestan province using advanced machine learning algorithms. This region has experienced frequent floods, leading to substantial human and financial losses. Notably, during the floods of 2018, villages near the Dez and Karkheh dams encountered severe challenges.

The preparation of the flood risk map is based on two key hypotheses: (1) the past is indicative of the future, implying that future hazards will occur under conditions similar to those of past events, and (2) flood risk conditional factors are spatially related and can be utilized in forecasting models. To test these hypotheses, the locations of past floods were obtained from relevant authorities and verified through field visits. These locations were randomly divided into two groups: a training group (70%) and a validation group (30%).Data on flood risk conditional factors, including topography, hydroclimatic conditions, and geological information, were collected and used to create raster maps of these predictive factors. The locations of flood points were treated as dependent variables. Machine learning algorithms, specifically Support Vector Machine (SVM), Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), and Random Forest (RF), were applied to generate the flood risk map. The performance of the models was assessed using the area under the receiver operating characteristic curve (ROC) with the validation group data (30% of the flood points), and the best-performing model was selected. The final flood risk map was then produced based on this optimal model.

According to the collinearity analysis of the 13 factors influencing floods, all factors had tolerance thresholds greater than 0.1 and variance inflation factors less than 5. Therefore, collinearity was not an issue, and no factors needed to be removed. Flood susceptibility modeling was conducted using four models: SVM, GLM, FDA, and RF. The resulting flood hazard maps from these models were classified into five risk categories: very low, low, medium, high, and very high. The results indicated that all four models identified flat lands and surface runoff margins as areas with higher flood susceptibility. In all models, more than half of the study area was classified as having low and very low flood risk. Specifically, the SVM, GLM, FDA, and RF models identified 73.9%, 69%, 72.6%, and 63.9% of the area, respectively, as low and very low risk, with the remainder falling into medium to very high risk categories. Additionally, the RF and GLM models indicated a larger portion of the region was at high to very high risk, with 4.7% and 3.9% of the area classified as high risk, respectively. Comparing model accuracy, the RF model demonstrated the highest performance, with an area under the curve (AUC) value of 98.8%.

Predicting high-risk areas is crucial for guiding decisions and implementing corrective measures. This study evaluated the performance of four machine learning models—SVM, GLM, FDA, and RF—in preparing a flood hazard map for a part of Khuzestan province, using the area under the ROC curve as the evaluation metric. The results revealed that the RF model achieved the highest accuracy, with an area under the curve of 98.8%, and was identified as the most suitable model for predicting flood risk areas. According to this model, the areas classified as very low, low, medium, high, and very high risk accounted for 34.2%, 29.7%, 18.9%, 12.4%, and 4.7% of the region, respectively. Additionally, the GLM and FDA models demonstrated acceptable accuracy, with AUC values of 76.3% and 75.2%, respectively. These results underscore the efficacy of machine learning models in predicting flood risk areas. Given the increasing population, urban development, and infrastructure expansion in mountainous areas and floodplains, it is essential to develop various hazard susceptibility maps and multi-hazard maps for sustainable development. Future research should focus on evaluating different machine learning models and creating hazard maps for other potential hazards in the region, ultimately leading to the development of comprehensive multi-hazard maps. The findings of this research will assist decision-makers and policymakers in making informed management decisions for both current and future development.

 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.

Wildfires reveal evidence of forest soil, water, and vegetation disturbances resulting from various interacting natural and human factors that create patterns that vary spatially and temporally. Fire risk assessment allows for identifying these factors and estimating their area of influence, thereby determining locations at high fire risk. Fire risk assessment typically involves the ignition probability (IP) and burn probability (BP) modeling of natural and human-made resources, as well as identifying resource responses to fires of varying severity. In recent decades, wildfires have caused significant damage to the Hyrcanian forests in northern Iran, even in the protected areas. Therefore, this study focuses on the spatial distributions of fire size, fire frequency, IP, and BP as essential components of the fire risk framework. First, a historical fire database (including ignition points, burned area, etc.) was prepared using available resources and field surveying. Second, a modeling approach using a limited number of auxiliary variables representing the fire environment (fuel, topography, and weather) and the historical fires (1992-2022) was implemented to calculate IP and BP. The spatial distribution of these parameters helps improve decision-making in fire prevention and control strategies.

Guilan Province is located in northern Iran and has an area of 14,044 km2 with an average elevation of 741 m above sea level. This province has 20 natural protected areas, which cover a total of 256,488 ha. ArcGIS 10.8 was used to create a historical fire database in the study area by digitizing fires between 1992 and 2022 from maps or by importing information directly from the previous GIS datasets. Point process models (PPMs) were used to analyze the spatial distribution of fire frequency. PPMs are a regression approach to model point data (i.e., geographic coordinates) for the number of times a 100-m pixel burns between 1992 and 2022. An existing raster map of the study area was converted to points by calculating the center of each pixel, and each point was assigned a frequency. Furthermore, IP was calculated as the average ignition probability occurring over a year in a raster pixel. To help use the fire ignition density to plan preventive activities, the output values were classified into five classes reflecting ignition occurrences (from very low to very high). Finally, the fire risk using BP was assessed by considering topography, fuel loads, and weather using FlamMap. To calculate BP, 1000 random ignition points were created based on the distribution of historical ignition points in the study area. The maximum fire simulation time was set to 6 hr (the average fire duration in the area).

There are 186 recorded fires (total burned area of 2232 ha) with an average annual number of 6 fires (average burned area of 12 ha) in the protected areas. Fires <10 ha accounted for 62.4% and 30.8% of the fire number and the burned area, respectively. Fires (10-50 ha) accounted for 32.8% and 46.2% of the fire number and the burned area, respectively. Fires (50-100 ha) accounted for 2.7% and 14.5% of the fire number and the burned area, respectively. Finally, fires >100 ha accounted for <0.5% of the fire number but alone accounted for 8.4% of the burned area. The distribution of fire frequency ranged from 0 to 6. The largest protected areas (40%) experienced no fires. 13% of these areas had 1-2 fire frequencies. Furthermore, 48% of this area had more than two fire frequencies. About 35% of the study area had very low and low IP values, 36% had medium IP, and 18 and 11% had high and very high IP, respectively. 88% of the study area had low and moderate BP values, and 12% had high and very high predicted values. Two fire regimes can be distinguished in the area, one with relatively high fire frequency and BP (mainly at higher elevations) and the other with low fire frequency and BP (at lower elevations). High fire frequency and BP is very limited in extent and occurs in the patches in the southern area. In contrast, low fire frequency and BP regime is the most widespread regime in the area (except for the southern part).

According to the simulated patterns of fire frequency, IP, and BP in the study area, a clear distinction between the actual historical fire perimeters and the predicted burn pattern is that there are areas of moderate to high IP where fires have not occurred in the past 30 years. This is particularly evident in the southern and central parts of the area, where fires have either not occurred or have been very limited in extent. Therefore, a justifiable assessment could be that the likelihood of fire spread and vegetation communities undergoing extensive and long-term changes following the fire is high shortly. Although this study focuses on the protected forest areas, this approach can be applied to fire risk modeling at larger scales. This allows for broader application in natural resources management and planning at regional and national levels. It also provides a comprehensive tool for assessing and managing forest vegetation, soil, and water vulnerability.

Predicting irrigation demand provides valuable information for agricultural planning and decision-making. By accurately predicting irrigation needs, farmers can optimize water distribution and avoid wasting water. Farmers can use this information to set planting schedules, crop rotation, and optimize land use based on water availability. this study introduces a new model for predicting irrigation demand.This study intrudes a new model for predicting irrigation demand. The self-attention-mechanism (SA) is coupled with the long short term memory (LSTM) neural network to predicting irrigation demand. SALSTM incorporates self-attention mechanisms, which enable the model to focus on the most relevant parts of the input sequence while making predictions. The attention mechanism allows SALSTM to assign different weights to different time steps or features, emphasizing the most informative ones for predicting irrigation demands. SALSTM can capture complex non-linear relationships between different input features, such as meteorological data, soil conditions, and crop characteristics.Predicting irrigation demand provides valuable information for agricultural planning and decision-making.

By combining the power of LSTM and attention mechanisms, SALSTM can learn intricate patterns and interactions between these factors, enabling it to make more accurate predictions of irrigation demands. This ability is particularly beneficial in capturing the nuanced relationships that exist in agricultural systems. Relative humidity, temperature, wind speed, rainfall, and potential crop evapotranspiration were used as the inputs to the models. The SALSTM model was benchmarked against the LSTM, recurrent neural network (RNN), Radial Basis Function Neural Network (RBFN), and multiple linear regression (MLR) models. The study also evaluates and compares the performance of SALSTM models for irrigation demand prediction in multiple programming languages, including Python, MATLAB, R, and JavaScript.Accurate prediction of irrigation demand is crucial for efficient water management in agriculture and can contribute to sustainable engineering practices. Overall, the study contributes to advanced engineering informatics by providing a comparative analysis of SALSTM models, incorporating self-attention mechanisms, and exploring their application in irrigation demand prediction. The study combines concepts from various disciplines, including data science, machine learning, and irrigation engineering. By applying advanced informatics techniques to irrigation demand prediction, the study bridges the gap between these areas and encourages interdisciplinary collaboration

In this research, the self-attention mechanism was integrated with the LSTM model to forecast irrigation demands. The SALSTM model leverages self-attention mechanisms, allowing it to concentrate on the most pertinent segments of the input sequence during predictions. This attention mechanism enables SALSTM to allocate varying weights to different time steps or features, highlighting the most significant ones for predicting irrigation needs. The findings showed that the SALSTM model surpassed the performance of other models.By comparing the performance of SALSTM models implemented in Python, MATLAB, R, and JavaScript, the study provides insights into the advantages and drawbacks of different programming languages for implementing machine learning models in climate studies. This knowledge can aid researchers and practitioners in selecting appropriate programming languages for their specific needs, promoting the efficient and effective utilization of computational resources in climate studies.The results indicated that the SALSTM model outperformed other models. The SALSTM model had the lowest mean absolute error (MAE) of 1.212, followed by LSTM (1.345), RNN (1.555), RBFN (1.678), and MLR (1.879).

The SALSTM model the lowest MAE of 1.212, followed by LSTM (1.345), RNN (1.555), RBFN (1.678), and MLR (1.879). The median value of the observed data, SALSTM, LSTM, RNN, RBF, and MLR was 18.5, 18.5, 18.5, 23, 22, and 27.5, respectively. SALSTM could capture complex non-linear relationships between different input features, such as meteorological data, soil conditions, and crop characteristics. By combining the power of LSTM and attention mechanisms, SALSTM could learn intricate patterns and interactions between these factors, enabling it to make more accurate predictions of irrigation demands. By accurately predicting irrigation demands, SALSTM enables farmers to avoid excessive water usage. By proactively adjusting irrigation plans based on SALSTM predictions, managers can minimize the risk of crop losses due to under- or over-irrigation. The next studies can develop current study based on the following comments:• Comparative Analysis: Conduct a comparative analysis of different models for predicting irrigation demand, including other deep learning architectures, traditional machine learning models, or hybrid models.Feature Engineering: Discover additional features that can improve the prediction accuracy of the SALSTM model. Examine how different feature sets affect the performance of the model and identify the most meaningful features for predicting irrigation needs.Model Interpretability: Enhance the interpretability of the SALSTM model by investigating techniques such as attention visualization or feature importance analysis. Transfer Learning and Generalization: Explore the transferability of the pre-trained SALSTM model across different geographical regions, crops, or irrigation systems. Investigate the effectiveness of transfer learning by fine-tuning the pre-trained model on new datasets. Uncertainty Estimation: Incorporate uncertainty estimation techniques into the SALSTM model to quantify prediction uncertainty. This can help decision-makers assess the reliability of the predictions and make informed decisions based on the level of uncertainty associated with irrigation demand predictions..

Surface runoff is considered as one of the main components of the hydrological cycle and one of the important sources of water supply. In today's era, with the increasing trend of urbanization and as a result of changing the use of permeable surfaces to impervious surfaces, there have been adverse changes in the quality and quantity of surface runoff. However, by applying flood management methods, this resource can be used in a controlled manner and in the best possible way to meet water needs. Based on this, in recent years, a concept called the best management solutions with the abbreviation BMPs has been proposed to control the quantity and quality of runoff. In these activities, by increasing the retention time of the flood in the reservoirs, increasing the roughness coefficient and increasing the permeability of the surfaces, an attempt is made to reduce the peak discharge and the volume of runoff, as well as control the concentration of pollutants in the runoff.

Based on this and considering the importance of runoff management in a metropolis like Tehran, in this research, a part of the catchment area of the 22nd district of Tehran municipality was selected and evaluated the effects of BMPs on the amount of runoff using mathematical models of precipitation and runoff. In order to investigate the subject of the research, it has been tried by considering three objective functions of runoff quality (including BOD5 and TSS quality parameters), runoff quantity (including the volume of runoff produced in each sub-basin) and cost (including flood damage and maintenance costs of BMPs) to The comparison of two optimization models NSGAII and MOPSO should be paid.

The results of these two multi-objective evolutionary optimization algorithms conclude that the NSGAII optimization algorithm is more suitable due to the use of features such as crowding distance and the speed of performing different steps in the optimization algorithm. In addition, the use of MOPSO optimization algorithm will be easier due to the inclusion of fewer parameters than NSGAII. It is also necessary to mention that reaching the steady state in NSGAII will take place in fewer generations than MOPSO. . Also, the results of the evaluation of BMPs in the form of different scenarios showed that the application of these solutions can reduce the peak discharge from 16.3% to 1.50% and also reduce the volume of runoff from 9.2% to 37.4% depending on the type and The number of BMPs used at the basin level. Considering that, in general, the phenomenon of rainfall-runoff is a process that is strongly influenced by uncertain factors, and the inappropriate selection of design parameters leads to the incorrect estimation of the flood discharge and as a result, the selection of unfavorable dimensions for structures and technical performance becomes inappropriate or uneconomical. Designs and ultimately financial and human losses will be many. Therefore, the correct selection of design parameters is very important. In this regard, better results can be achieved by applying methods such as uncertainty analysis of inputs and effective parameters on the results of modeling or analyzing the sensitivity of the model to changing parameters. Among the input factors of rainfall-runoff models that have a noticeable effect on the results, we can mention the temporal and spatial distribution of rainfall, the continuity of rainfall and the previous soil moisture conditions., it is shown that the MOPSO model produces higher quality solutions in most cases compared to the NSGA-II model. Additionally, in cases where the NSGA-II model provides higher quality solutions, the execution time or distribution of solutions in the MOPSO model is better. This is while in a real-world problem and depending on the type of objective functions and their application, the results obtained from the application of these algorithms may be contrary to the experimental function results.

In this research, after analyzing the uncertainty of the temporal and spatial distribution of rainfall as well as the initial moisture of the soil using the Monte Carlo simulation method and analyzing the sensitivity of the flood hydrograph to the continuation of the rainfall, flood management strategies in the region were investigated. The results of the investigations showed that the highest peak flow is obtained from rainfall with a duration of 0.5 hours, in this case the range of peak flow changes is equal to 34.8 cubic meters per second, which indicates the presence of high uncertainty in the input parameters of the rainfall-runoff model.Overall, in terms of comparing the capabilities of the NSGA-II and MOPSO optimization models in this simulation-optimization problem, it should be noted that the optimal values related to objective functions on the optimal exchange curve by the NSGA-IIalgorithm exhibit more dispersion compared to the MOPSO algorithm, indicating a wider range of scenarios generated by the NSGA-II algorithm. In fact, using this algorithm can provide decision-makers with more scenarios with significant diversity in objective function values. This is not observed in the results obtained from the MOPSO algorithm.

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.

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.

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.

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.