شبیه سازی

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.

Changing in precipitation pattern and incidence drought stress shows the necessity of optimal use of water resources and saving in consumption is emerged more than before. The soil moisture content under the root zone can provide part of the plant's water requirement. Currently, the zone under the root of plant is not considered in the calculation of the water balance and the plant water requirement so water entering to this zone is considered as a part of deep percolation losses. In this research, HYDRUS 2D/3D software was used to simulate water flow in the soil in the area under the roots of the sugar beet plant. The investigated scenarios included 4 buffer treatments with different depths under the root zone and three different soil textures including loam, silty clay loam and sandy loam. The results showed that in all three soil textures, increasing the depth of the buffer caused more water to be uptaken by the roots, the best result was obtained for the loam texture, with the buffer depth of 50 cm below the root zone, so that the amount of water uptake compared to the condition without buffer depth, was increased by 7.9%. This value was obtained for the texture of silty clay loam and sandy loam at the buffer depth of 75 cm, with an increase of 8.8% and 4.3%, respectively. Deep percolation in loam, silty clay loam and sandy loam texture decreased by 81, 56 and 90% respectively with increasing buffer depth.

Sugarcane is a plant that has the most water requirement in the summer when the least rainfall occurs, and there is a need to irrigate this plant. In this research, the simulation and modeling of sugarcane cultivation with a focus on the water-environment-food nexus, utilizing the system dynamics approach, have been conducted at the Hakim Farabi Khuzestan Agro-Industry Company. This research was modeled using Vensim software. The model is an integrated and interconnected simulation of water consumption, product production, drainage water volume, salinity, and soil salinity. The information of three years 2015 to 2017 was used for calibration and the information of two years 2018 to 2019 was used to validate the model. MAE, MBE, and MAPE statistical parameters were used to evaluate the model results. The modeling results showed that the model has high accuracy in the calibration period with an MAE index of 6.31 ton/ha for crop yield, 53.56 mm for water drainage volume, 1.21 dS/m for water drainage salinity, and 0.09 dS/m for soil salinity. Also, the results of the same index in the validation period, which were 3.04 ton/ha for crop yield, 48.76 mm for water drainage volume, 1.11 dS/m for water drainage salinity, and 0.04 dS/m for soil salinity, indicate that the model is highly accurate in simulating the existing conditions. The highest water productivity was achieved at a rate of 3.75 kg/m³ in 2019.

Water scarcity and the need for optimal water utilization in arid and semi-arid regions, including Iran, have encouraged water authorities and farmers to adapt modern irrigation systems likedrip irrigation, to make optimal use of water resources. The most important advantage of drip irrigation over other irrigation methods is its ability to control the amount of water applied to each plant. New irrigation methods focus on plant irrigation and not on land irrigation. In arid and semi-arid regions, a drip irrigation system is used to use water optimally and prevent wastage and evaporation. Factors such as soil texture, type of cultivated plant, amount of available water, distance of drippers and laterals, the wetted surface, and the dimensions of the moisture bulb under the soil surface are involved in the design of the drip irrigation system. Due to the variety of soil textures in the earth, the movement of water under the soil surface is different in all kinds of textures, therefore, knowing exactly how water moves in the soil and how the moisture bulb is distributed under the soil surface is of particular importance. The purpose of this study is to investigate the movement of moisture bulbs, check their dimensions under the soil surface in different soil textures and flow rates, and evaluate the capability of the Moment analysis method to simulate this process under various conditions.

To simulate the moisture bulb in different soil textures, detailed information on the physical properties of the soil, including the percentage of particles that make up the soil texture, bulk and real density, porosity, and saturated hydraulic conductivity, is required. In this research, the simulation of the moisture front in different soil texture was conducted using Rosetta software, which defines 12 types of soil textures. In these tests, the source of soil power was considered as surface and point. The total feeding volume of each type of soil texture is 24 L, and this volume was used with different flow rates of two, four, six, and eight L s-1. To numerically simulate the progress of the moisture front, Hydrus software was used. Then the analytical simulation of the moisture front was done using the equations of the Moment analysis method. In this study, an ellipse was drawn to represent the moisture bulb simulated by Hydrus software at different times for the applied flow rates. Coefficient k was used to draw the ellipse, and its appropriate value was determined by minimizing the difference between the model and Hydrus results.

To calculate the moments, the first step is to obtain the values of M00 According to the applied flow rates of two, four, six, and eight L s-1 and the amount of volume intended to feed all types of soil texture, i.e., 24 L, the duration of irrigation is 12, 6, 4, and 3 hr, respectively. The comparison of moisture distribution over all periods and soil textures showed acceptable results, and the distributed subsurface moisture values were similar. In the study of clay texture, with time from the start of irrigation, the difference in the total amount of distributed moisture increased, and the reason for this result is the decrease in the permeability of the clay due to the filling of fine pores. The results indicated that σx2 values changed with the increase in irrigation duration. The highest variance was found in sandy clay with a flow rate of 8 L s-1 (1503.3 cm2), while the lowest variance was observed in clay texture with a flow rate of 4 L s-1 (368.6 cm2). By increasing the amount of applied discharge, σz2 increases and the slope of this increase is different in each soil texture, according to the characteristics of that texture. Also, the effect of irrigation duration on the value of σz2 is evident. In other words, the longer the duration of irrigation, the more the amount of variance changes.

In this research, the accuracy of the Moment analysis method in predicting moisture distribution from drip irrigation was evaluated using results from Hydrus and Moment analysis. The Hydrus results demonstrated that the moisture bulb expanded over time in both the horizontal and vertical directions. The results also indicated higher flow rates increased the horizontal expansion of the moisture bulb, while the duration of irrigation affected both horizontal and depth expansions. Using the moment analysis method, the center of mass of water distribution in the soil and the changes in the moisture front along the x and z axes were determined. By examining and comparing the dimensions of the moisture front resulting from Hydrus and ovals, it was observed that there is a suitable compatibility between the two methods. Therefore, the Moment analysis method can be relied upon to estimate the dimensions of the moisture bulb in drip irrigation. It also provides an efficient and accurate approach to reducing the time and cost of field experiments.

Using the most accurate methods and models to simulate the impact of climate change on meteorological variables in different regions of the world is of utmost importance. In this study, the accuracy of 10 AOGCM models related to the sixth assessment report of the IPCC (CMIP6) was investigated for simulating temperature and precipitation in the Sefidrood Basin. For this purpose, observational data of temperature and precipitation from 16 weather stations located in the basin during the time period from 1980 to 2014 were compared with the output of AOGCMs. The Kling-Gupta Efficiency (KGE) index was utilized for this comparison. The comparison was conducted on both annual and monthly time scales, and the more accurate models were identified for each time period. The results indicated that the accuracy of AOGCM models in estimating temperature in the study area was higher than their accuracy in estimating precipitation. Additionally, different models exhibited varying capabilities in simulating these variables across different months. Based on the results obtained, the MIROC6 and MRI-EMS2-0 models performed better than other models in estimating the temperature of different months. Furthermore, the HadGEM3-GC31-LL model showed a better performance than other models in estimating historical precipitation for most months of the year. Based on the results obtained, it is necessary to select and use the best AOGCM models for each month before conducting climate change simulation studies in the study area.

Crop modeling is one of the widely used methods for simulating the crops yield under different farm management. However, paying attention to the sensitivity of these models to the input parameters can improve the calibration operation and reduce the simulation error. For this reason, in the current research, we analyzed the sensitivity of the AquaCrop model under planting date treatments (D1: March 5 and D2: March 15) and irrigation water salinity (S1: Karon water with an average salinity of 2 dS.m-1, S2: combination of Karon water and drains with an average salinity of 4 dS.m-1 and S3: drain with an average salinity of 6 dS.m-1) and cotton cultivars (C1: Khursheed, C2: Golestan, C3: Sajedi and C4: Khordad). For sensitivity analysis, the normalized water productivity (WP*), maximum crop transpiration coefficient (KCTrx), initial crop coefficient (CCo), crop growth coefficient (CGC), crop decline coefficient (CDC) and harvest index (HI) were used. Sensitivity analysis was done using Beven method. The results showed that the highest sensitivity was in parameters of HI (0.69) and WP* (0.6) and the lowest sensitivity was in parameters CDC (0.02) and CCo (0.3). The increase in planting date from D1 to D2 increases the sensitivity of the AquaCrop model to the HI (19%) and WP* (15%), and the increase in salinity from S1 to S3 increases the sensitivity of AquaCrop to the HI (42%) and WP* (50%). Therefore, in the farm management similar to the current research, paying attention to the two aforementioned parameters can cause the accuracy of the results in the calibration (and validation) stage.

Due to the lack of water resources (quantitatively and qualitatively) and application of different irrigation management in terms of interval and depth of irrigation water (irrigation scheduling) in agriculture and orchards, it is necessary to apply proper irrigation management to prevent the accumulation of salt in the root zone. Pistachio trees are resistant to drought and can produce an average yield even with low amounts of water. The irrigation interval of pistachio trees depends on factors such as soil texture, evapotranspiration, water and soil salinity, irrigation method, tree age and available water amount. Various irrigation methods are used for pistachio trees, the most important of which are surface irrigation, surface drip irrigation, subsurface drip irrigation, and bubbler irrigation. Given that there is no comprehensive information on irrigation management and quality of irrigation water for pistachio orchards in the drip irrigation method of main pistachio growing areas of Semnan (Damghan), identifying different pistachio irrigation managements provides this possibility by using different irrigation scenarios and valid and practical models, suitable irrigation management can be performed in order to increase yield and simulate soil salinity. Three orchards were selected from Damghan city of Semnan province (with a focus on pistachio orchards and surface drip irrigation systems) in order to investigate performance, irrigation management and soil salinity changes. In this regard, information including longitude and latitude, area of orchards, number and arrangement of drippers, irrigation scheduling and soil sampling performance for measuring salinity was done.

In many fields of water sciences, the moisture movement in the soil profile is very important. Due to the non-linearity of the governing equation in moisture movement in the soil profile, the comprehensive analytical solution of this equation is impossible, and there is a need to solve it numerically. The abundance of data and simulation steps reduces the flexibility in using existing software to solve the Richards equation and using this data as initial conditions. Therefore, having a numerical solution plan for the Richards equation will be very useful. Among the existing methods, the fully implicit finite difference method can continue the simulation unceasingly due to its unconditional stability in providing a non-iteration solution. However, using the finite difference method to solve the Richards equation leads to significant mass balance error. Some usual methods to reduce the mass balance error could not be applied to the non-reverse solution. By creating the Richards equation numerical solution in MATLAB software, this research introduces a method to reduce mass balance error, which is a simple method to apply to all kinds of numerical solutions.

In this research, a Non-Iteration implicit solution for the Richards equation was developed. After creating the Richards equation numerical solution, the difference between the entering flux to the soil column and the added water to the same soil column, is added to each node to reduce the mass balance error. This amount is added to the nodes that their matrix pressure head difference (PD) compared to the initial state, is more than a threshold value. This amount applies as a coefficient proportional to the humidity of each node. To investigate the effect of various factors on the performance of the created numerical solution, a series of 11 experiments were designed with different characteristics in terms of infiltration rate, infiltration duration, duration of moisture redistribution in the soil profile, and initial volumetric water content. The optimal value for PD in each experiment was determined in such a way as to minimize the mass balance error. For the integrity of the results, the average PD values equal to 10 cm were used to simulate all cases. These series of experiments were simulated by two-dimensional HYDRUS software, and the results were used to verification of the created numerical solution.

The results showed that the most mass balance error is forced in the infiltration phase. Using the proposed method to preserve the mass balance caused a significant reduction in the mass balance error. The absolute value of the mass balance error varied in a narrow range and was always less than 3.5 percent. It is expected that time and space steps have a considerable effect on the mass balance error. The absolute mass balance error decreased linearly from 3.36 to 3.33, with the infiltration duration increasing from 10 to 30 minutes at a constant infiltration rate of 1 mm 〖min〗^(-1). The absolute mass balance error increased linearly from 3.36 to 3.37% by the initial volumetric water content increasing from 13 to 17% and decreased from 3.40 to 3.35 percent by the increase in the duration of the moisture redistribution in the soil profile from 300 to 900 minutes. Also, the Mean Absolute Relative error (MAE) increased from 1.17 to 9.53% by the infiltration time increasing from 10 to 30 minutes; the MAE increased from 1.17 to 8.89% by the infiltration rate increasing from 1 to 3 mm 〖min〗^(-1); and the MAE increased from 1.17 to 6.89% by the volumetric water content increasing from 13 to 17%.

The results showed that this method leads off the simulation to a significant reduction in the mass balance error; so that, in all experiment cases, the mass balance error ever fluctuated in a small range, less than 3.5%. The results comparison of the created simulation and the HYDRUS software showed that this method provided acceptable results, in cases such as summer rains where the infiltration duration is much less than the water redistribution time in the soil profile. Also, the highest value of the MAE in comparing the created simulations with the obtained results from the HYDRUS software is less than 10%. It proves a good agreement between the created simulation and the HYDRUS software.

Surface waters or rivers are one of the most important water sources that play an important role in supplying water for various activities. In recent years, the optimal use of artificial intelligence models has been common to predict and simulate of the quantitative and qualitative parameters of water to prevent the pollution of surface water and rivers. The Kakareza river is located in the city of Selseleh, Lorestan province, the water of this river is mostly used for irrigation, thus it is very important to assess the quality and estimate the quality parameters of the water of this river. In this research hydrogeochemical methods and artificial intelligence have been used. The findings showed that bicarbonate, calcium and magnesium ions have a major role on the water quality of this river. The water quality index of Kakareza River is favorable in the period 1347 to 1398 and can be used for drinking and agriculture purposes. Also, the results of the artificial neural network show the high ability of the neural network in simulation and prediction the parameter index, the model with 9 inputs (HCO3, Cl, SO4, Ca, Mg, Na, TDS, pH, Q) is shown the best performance in estimated value of EC. Finally, in order to implement the management program of water organizations to make optimal use of water resources in the region (such as the Kakareza River) by using hydrogeochemical studies and quantitative and qualitative predictions of water, it is possible to have a more accurate understanding of the water quality conditions, which makes better decisions about the use of these water resources.

Investigating the effects of climate change on improving agricultural productivity is of great importance. It is predicted that the rising temperature trend will continue in the 21st century, leading to changes in the climatic conditions of  regions. Changes in precipitation distribution ، temperature and water resources are considered as harmful consequences of climate change، which could have detrimental effects on agricultural production. . In this study، the impact of climate change and cultivation on different dates was examined in terms of the yield of the 'Parsi' spring wheat variety in the Qazvin Plain. This study covered the period from 2021to 2100, comparing two information sources, LARS-WG and DKRZ, for producing annual climate change data and using the Aquacrop model to simulate the plant’s response to the mentioned changes. During the periods 2021-2040، 2041-2060، 2061-2080، and 2081-2100، the best planting date (February 4, February 20, March 5, March 20, and April 4) for increasing wheat yield was evaluated.  According to the model results in both scenarios 4.5 and 8.5، in all four periods (2021-2040، 2041-2060، 2061-2080 and 2081-2100), the spring wheat yield will increase compared to the baseline.. The highest yield in all of these periods and models is predicted for the period 2081-2100 under the LARS-WG climate model of in scenario 8.5,  assuming planting on February 4, with an estimated yield of 12.43 tons per hectare and a standard deviation of 0.31 tons per hectare. the lowest yield is expected for the period 2021-2040 under LARS-WG climate conditions in  scenario4.5, with planting on April 4, with an estimated yield of 7.87 tons per hectare and a standard deviation of 0.36 tons per hectare. In all four periods (2021-2040، 2041-2060، 2061-2080 and 2081-2100), February 4 is recommended as the most suitable planting date to increase wheat yield in the Qazvin plain.

Soil contamination due to heavy metals is a global environmental issue. One vital aspect for understanding the impact of a contaminant in porous media is to describe their transport behavior using appropriate models. The governing equations for solute transport in soil consist of the convection–dispersion equation (CDE) and the mobile–immobile model (MIM). Mathematical models are usually used to evaluate solute transport in porous media. The first model used to express the transport of solutes and pollutants in porous media is CDE it provides acceptable and satisfactory results in homogeneous soils in laboratory tests. Hydrus-1D is a modeling environment for simulating water, heat, and solute movement in one-dimensional variably saturated media. Sensitivity analyses and model identification are standard approaches in modeling applications to investigate the relative importance of model components that control the system’s behavior. The sensitivity analysis is applied to identify the parameters that influence the model performance most. The sensitivity analysis is defined as the rate of variation in the model outputs due to changes in the input parameters. This study is a fundamental practice for analyzing the behavior of a model under different conditions of an application. The sensitivity analysis could be a practical and powerful tool for investigating the role and importance of model components, such as parameters and forcing data on the model responses.

The loamy soil samples were collected in both disturbed and undisturbed forms from a farm in the Qaramalek area with appropriate humidity located in western Tabriz, Iran, at 38º 5' 59.89'' north and 45º 12' 38.57'' east. To determine and present breakthrough curves, concentration values are required throughout the laboratory columns at different times. To simulate the CDE model, Hydrus software was used. Solute transport parameters such as diffusion coefficient (D), distribution coefficient (Kd), and dispersion coefficient (β) were estimated using soil hydraulic parameters and data related to the metal concentration of cadmium, nickel, and zinc by an inverse modeling method. A sensitivity analysis was carried out for the identification of the most influential factors on the model output. This method examines the impact of input data on a given model and its actual conditions. In line with this purpose, in each run, one input data was changed to a value equal to Positive and negative five to 15%, and the other input data was kept constant. To identify the effect of the input parameters of a given model on its output, the sensitivity analysis for the Hydrus model was utilized. The parameters of hydrodynamic dispersion coefficient (D), distribution coefficient (Kd), and spreading parameter (β) were changed between five to 15 %. Sensitivity analysis was carried out on cadmium, nickel, and zinc metals with densities equal to 50, 100, and 150 mg.l-1 in two disturbed and undisturbed soils.

Examining the breakthrough curves of cadmium in disturbed and undisturbed soils shows that the fitted curves using the Hydrus model and the measured curve almost coincide with each other, which is more obvious in disturbed soils. It should be noted that the model fits better in the disturbed soil than in the undisturbed soil. This may be due to the disruption of the structure the increase in the contact surface of the particles in the disturbed soil and the presence of heterogeneity in the undisturbed soil column. The simulation results show the transport of heavy metals (Zn, Ni, Cd) and Hydrus output have the highest and the lowest sensitivity to dispersion coefficient β and diffusion coefficient (D), respectively. In general, the impact of input parameters can be reported as follows: spreading parameter (β) > distribution coefficient (Kd) > dispersion coefficient (D). Therefore, it can be observed that D has a negligible effect on the model results; and consequently, measurement errors can be ignored.

Sensitivity analysis is used to analyze model behavior under different conditions. This analysis is used to investigate the relative importance of model components that control the system’s behavior. In this research, the transfer of hydraulic parameters of heavy metals Cd, Ni, and Zn in disturbed and undisturbed loam soil columns with initial concentrations of 50, 100, and 150 mg.l-1 was performed under the simulation of the Hydrus-1D model. The comparison of the simulated BTCs of the Hydrus-1D model and the measured data indicates a high agreement between the simulation curves and the measured data. Solute transport parameters such as hydrodynamic dispersion coefficient (D), distribution coefficient (Kd), and spreading parameter (β) were estimated using soil hydraulic parameters and data related to Cd, Ni, and Zn metal concentration by inverse modeling method. Based on the results of sensitivity analysis, the spreading parameter (β) and hydrodynamic dispersion coefficient (D) had the highest and lowest sensitivity, respectively. In other words, due to the significant effect of β changes on the output values of the model, this parameter should be measured more accurately and on the other hand, the measurement errors of parameter D can be ignored. The degree of sensitivity of the parameters was independent of the initial concentration of the elements.

The amount of sediment production, the manner and time of sedimentation, the size and composition of sediment grains, and transport among the waterways network are important features of the sedimentation regime of Watersheds; Because changes in each of these factors cause changes in Watershed performance. Therefore, sediment production is a reflection of the importance and amount of erosion processes and sediment sources in the upstream parts of the Watershed and how sediment is transported and deposited from the moment of movement of erosion materials from the point of separation to the exit of it. Sediment connectivity indicators indicate the spatial changes of connectivity patterns in different parts of the Watershed and provide a suitable estimate of the contribution of sediment sources and sediment transport routes. For this purpose, investigating the spatial pattern of sediment flux at the Watershed scale is of particular importance in developing comprehensive management plans and measures to control erosion and sedimentation. Although various indicators and models have been developed in this field, their performance has not been evaluated based on observational data and statistical methods. This research aims to analyze the sediment flux pattern of the Neyriz Watershed located in the east of Fars Province, based on sediment connectivity and sediment transfer capacity indicators, and compare their performance based on field sediment evidence.

At first, a digital model of the ground elevation of the Neyriz Watershed with a spatial resolution of 12.5 m was prepared and its drainage network was extracted. Then, the sediment connectivity index was calculated by considering the upstream and downstream features of the Watershed and by considering the roughness factor as the sediment movement resistance factor, and the sediment connectivity map of the Watershed was made. Then, the sediment transport capacity index map was also prepared based on the concept of topography and using the digital model layer of land height. For this purpose, the sediment transport capacity index was calculated for each pixel of Neyriz Watershed in SAGA-GIS software, and a sediment transport map was prepared. Based on field visits to different parts of the Watershed, 30 positions that had evidence of sediment transfer were recorded as observation points of the sediment transfer event. Also, 30 other positions that did not have signs and evidence of sediment transfer were added to the validation database as observation points of no sediment transfer event. The corresponding values of each index were also extracted in the geographic information system and based on the available information, using evaluation methods based on the error matrix including true skill statistic (TSS), efficiency (E), and F score (F-score), the validation of the mentioned indicators was done quantitatively.

The value of the sediment connectivity index of the Neyriz Watershed in Fars Province varied from -7.24 to 2.43 and its median value was -4.36. The spatial pattern of the sediment connectivity index in this Watershed is such that the middle and western parts have a low amount and the northern, southern, and eastern parts have more amounts. In this research, the drainage network of the Watershed was introduced as the target of receiving the sediment; Parts of the slopes of the Watershed level, which had the conditions of sediment production and transfer were connected to the drainage network in terms of the transfer path, have shown a higher value of connectivity index. This index provides valuable information for land management by considering the upstream characteristics of each point as well as the characteristics of the transfer path to the sediment-receiving target. The value of the sediment transfer capacity index varied from 7.2 to 23.16 and the average value was 10.19. The value of this index is high in the marginal parts of the Watershed where there are sloping lands, and the middle parts of the Watershed have a small value. Based on the findings, the index of sediment connectivity with the true skill statistic (TSS) of 0.833, the efficiency value (E) of 0.916, and the F score of 0.915 is a better performance than the sediment transport capacity index (TSS= 0.633, E=0.816, F-score=0.825). In addition, based on the values of the false positive component in the error matrix, the sediment transport capacity index predicts high sediment flux potential in many situations; While in the field observations, it was not true.

Based on statistical evaluation criteria, the sediment connectivity index has been able to better describe the state of sediment flux and is more consistent with field realities. So that the sediment connectivity index, in addition to considering the characteristics of the upstream area of each point of the Watershed, is possible to consider the path of the sediment particle to the target location of the receiver (such as the nearest branch of the network drainage) has made it possible. Based on the findings obtained in this research, although the sediment transfer capacity index in some parts of the Neyriz Watershed of Fars Province was in line with the sediment connectivity index; However, due to the energy-based nature of the flow, this index only considers the local conditions of the points and ignores the features of the upstream area as well as the process of transporting sediment particles to the downstream. Therefore, it is suggested to use the sediment connectivity index in the erosion and sedimentation studies of Watersheds. Because when information about the amount of sediment production is not available; the Sediment connectivity index can provide useful information about the sediment transfer process in the Watershed.

To Estimate Parameters and Evaluation of Cropsyst Model to Predict the Growth and Yield of Two Wheat Cultivars in Northeast Khuzestan, an field experiment was conducted in the form of randomized complete block design in a research farm in Ramhormoz with three replications. Factors were included irrigation regime at two levels (full irrigation and stop irrigation after flowering), cultivation date (six dates including: October 30, November 15, November 30, December 15, December 30, and January 15) and two varieties of wheat. (Chamran 2 and Sirvan). Based on the results, the critical day length, the ceiling day length, the number of basic vernalization days, the minimum growth rate in the absence of vernalization, and the saturation point of vernalization for Chamran 2 variety are equal to 14, 8, zero, and 0.9, respectively. and 24, and for the number of Sirvan, it was 13.8, 7.8, zero, 0.93 and 25, respectively. The results obtained from the simulation of flowering and physiological ripening stages based on RMSE, d, CRM and EF statistics showed that the Cropsyst model simulated the mentioned stages with good accuracy for different figures. The simulation results of dry matter of aerial organs for different figures showed that this model was able to simulate 95 to 98 percent of this parameter in different treatments of planting date and irrigation. The error value of the model based on the RMSE statistic for the dry matter of aerial organs in Chamran 2 and Sirvan cultivars was 627 and 351 kg per hectare, respectively.

Evaporation, the process by which water molecules escape a surface after absorbing sufficient energy to overcome vapor pressure, is a major contributor to water scarcity, especially in arid and semi-arid regions where heat readily facilitates this escape. Accurately estimating evaporation losses is crucial for effective water resource management, crop water demand prediction, and irrigation scheduling. Machine learning (ML) has emerged as a powerful tool for tackling the complex and stochastic nature of environmental problems. ML models excel at identifying relationships between predictor variables and outcomes (predictands), often surpassing traditional methods. However, their performance can vary depending on input factors and climatic conditions. Recently, hybrid techniques that combine multiple models have gained traction in climate and hydrology studies. These techniques leverage the strengths of different approaches within a single algorithm, potentially capturing more complex patterns in data series. This research will explore the potential of various individual ML models and propose a novel hybrid approach for estimating pan evaporation in Sistan and Baluchistan Province.

This study investigates pan evaporation simulation and prediction in Sistan and Baluchistan Province, Iran. Synoptic station data (1980-2019) served as model inputs, while pan evaporation measurements from these stations provided the observed values. In this research, in the approach of individual performance of data mining models, eight data mining models were used to simulate and predict evaporation from the pan. In addition to the individual performance approach, the combined VEDL approach was used to provide a hybrid model (a combination of the mentioned eight individual models of deep learning). In this hybrid approach to regression issues, the estimators of all models are averaged to obtain an estimate for a set called vote regressors (VRs). There are two approaches to awarding votes: average voting (AV) and weighted voting (WV). In the case of AV, the weights are equivalent and equal1. A disadvantage of AV is that all of the models in the ensemble are accepted as equally effective; however, this situation is very unlikely, especially if different machine learning algorithms are used. WV specifies a weight coefficient for each ensemble member. The weight can be a floating-point number between Zero and one, in which case the sum is equal to one, or an integer starting at one denoting the number of votes given to the corresponding ensemble member. the weight of each model was selected based on the accuracy of the model's performance using the evaluation criteria obtained from the training implementation section of individual models. the model’s performance was assessed using statistical measures, including R2, RMSE, MAE, and Taylor diagram.

The results showed that all the models had very good results in both the training and testing stages. All models exhibited excellent performance during training and testing. The Artificial Neural Network (ANN) achieved the highest accuracy in both phases at the Zahedan station (R² = 0.89, RMSE = 45.95 in training; R² = 0.96, RMSE = 44.18 in validation). It emerged as the best model for monthly pan evaporation prediction at this station. Other models also performed well, with the Support Vector Machine (SVM) and Random Forest (RF) models achieving R² values of 0.89 and 0.88 in training, respectively. Notably, the BART model ranked second in validation (R² = 0.96). The Tree Model (TM) had the lowest accuracy (R² = 0.84 and 0.93 in training and validation, respectively). Across all stations, ANN, SVM, and RF consistently delivered the best results in both training and testing. In the test phase, the SVM model outperformed others in Khash, Iranshahr, and Chabahar stations (R² = 0.94, 0.96, and 0.94, respectively). At the Saravan station, the RF model achieved the highest R² (0.94) during testing. To develop a hybrid data mining model, the Voting Ensemble for Deep Learning (VEDL) technique was employed with weighted voting in the training stage. The combined model significantly improved upon the best individual model. RMSE decreased from 45.95 to 33.1, R² increased from 0.89 to 0.94, and MAE improved from 32.92 to 23.9. Evaluation using the Taylor diagram further confirmed the superior performance of the VEDL model compared to the individual ANN model.

The results showed that among all the models, ANN, SVM, and RF models had the best performance in the two stages of training and verification. In the validation stage, the SVM model with R2 values equal to 0.94, 0.96, and 0.94 performed best in the Khash, Iranshahr, and Chabahar stations. At the Saravan station, in the Sensji validity stage, the RF model with an R2 value of 0.94 had the best performance among the models. The excellent performance of the models in the two stages of training and validation is another finding of the research, These results are consistent with the results of researchers who have expressed the appropriate efficiency of machine learning models in estimating evaporation/evaporation and transpiration in different climatic regions of Iran. The results of the combined model showed that the combined model improved the results compared to the best individual model so that the RMSE values increased from 45.95 to 33.1, the R2 values increased from 0.89 to 0.94, and the MAE value improved from 32.92 to 23.9. The use of the VEDL approach to estimate evaporation from the pan was a new approach that has not been used in past studies. Therefore, according to the results of this research, the proposed deep sensing model is proposed to estimate the evaporation of arid and semi-arid areas for water resources management and agricultural planning.

Climate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods.

The LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model.

The results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.

Due to population growth and Iran's location in arid and semi-arid regions of the world, the need for water and food has increased and as a result, the pressure on water and soil resources will be more than before. On the other hand, the risk of drying up Lake Urmia, which causes environmental problems in the region, requires macro-water planning for the region and the use of optimal cultivation pattern to deal with water scarcity. Therefore, optimal use of water preserves water resources and increases the quality of products. Today more than ever, increasing the production of strategic crops such as wheat requires the proper use of water resources. The main source of food for the Iranian people is wheat and related products, and any action that increases the yield of wheat due to limited soil resources, especially water resources, is important and necessary at the same time. In recent years, significant advances have been made in modeling product growth and development using mechanical models. Plant growth models are increasingly used in the analysis of agricultural systems and simulate the plant's response to growth factors using mathematical equations. The AquaCrop model is one of the dynamic and user-friendly models developed by the FAO. The AquaCrop model receives information about farm, plant, soil, irrigation and climate, and ultimately predicts important parameters such as crop. Wheat yield simulation allows efficient management and better planning under various environmental inputs such as soil and water. To achieve higher accuracy and less model error, field parameters must be properly calibrated by the model to achieve proper performance. Also, calibration of the model, if not done correctly, causes a high error prediction by the model, which leads to incorrect management, water loss, plant drought and other cases. Therefore, using a model that has accurate and close prediction to the AquaCrop model and requires fewer input parameters is essential, which saves time, reduces costs and eliminates calibration errors. However, this model requires relatively large input parameters and is a time-consuming model in the presence of multiple scenarios.3

In recent years, smart models have been able to show high accuracy and become reliable models. Therefore, in the present study, to solve this problem and develop a model with less input data, using the ANN, SVR and SVR-FFA intelligent models and creating 440 scenarios in 2 farms, the performance of the AquaCrop model was compared.99WestW2 farm is located in Miandoab city and has a yield of 6.588 (ton ha-1) and WestW10 farm is located in Mahabad city and has a yield of 5.05 (ton ha-1).

The results of the model are performed using 5 evaluation criteria of Correlation coefficient, Root mean square error, Nash-Sutcliffe coefficient, Wilmot’s index of agreement and, Mean absolute percentage error. The results of this study showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model could have the lowest error rate, so that for the yield index, the RMSE value for the mentioned farms was 0.033 and 0.069 (ton ha-1), respectively. The use of three models SVR, SVR-FFA, and ANN and their comparison with the AquaCrop model to predict wheat yield has been done for the first time in this study. The SVR model was able to show the highest accuracy after the SVR-FFA model. For 99WestW2 farm, it can reduce the error rate to 0.043 (ton ha-1) and for WestW10 farm to 0.077 (ton ha-1) and show good performance. The ANN model, after the SVR model, was able to show acceptable accuracy. The ANN model for 99WestW2 farm was able to reduce the error rate to 0.123 (ton ha-1) and for WestW10 farm to 0.094 (ton ha-1). Finally, the ANN model had a relatively higher error than the SVR-FFA and SVR models, respectively, and showed a relatively lower performance than the two models.

Finally, the intelligent SVR-FFA, SVR and ANN models, despite having the least number of inputs, were able to predict yield values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. For further studies, it is suggested that the ANN model be combined using the firefly algorithm (MLP-FFA) to increase the accuracy of the ANN model and make more accurate predictions of wheat yield.

Most of the regions of the globe have faced changes in the precipitation regime and CO2 concentration with the increase in temperature (especially the minimum temperature). Considering that two-thirds of Iran is considered to be arid and semi-arid regions, which will cause climate changes and successive droughts, and this process will affect agricultural ecosystems and their performance. The aim of this research is to simulate the effects of climate change on growth, biomass yield and corn grain under climate change conditions in Shushtar and Safi Abad cities in the northern part of Khuzestan province in 2020-2050. For this purpose, the production data of precipitation, minimum temperature, maximum temperature and sunshine hours of the LARS-WG statistical model using HADCM3 atmospheric general circulation model and under emission scenarios A2 and B1 were used in the periods of 2020-2050. AquaCrop model was used to simulate plant performance and growth. Before use, the AquaCrop model was calibrated and verified by field data collected in the region. Then, the values of seed yield and biomass in future periods were simulated for the treatment of 50% of the water requirement of the plant. According to the results, under A2 emission scenarios, the evaluation results of the LARS-WG model showed that it had a suitable forecast for the climate parameters and the simulation of the future growing season. And the increase in minimum and maximum temperature will be the average increase in precipitation. The length of the growth period for each station will decrease with the increase of GDD according to the climatic parameters of the region under the influence of climate change. Biomass and grain yield will also increase by one to two tons in different horizons and scenarios assuming the current planting date and full irrigation remain constant.

An artificial neural network (ANN) is a powerful data-driven tool capable of learning the linear and nonlinear relationships governing different systems. However, determining the best-performing algorithm in terms of convergence speed and accuracy for a particular problem is still a fundamental challenge for users of artificial neural networks.

We investigated the most effective algorithm among widely used processes to simulate and estimate nonlinear water quality parameters. For this purpose, we constructed 42 models combining artificial neural network topology (single or multilayer) and training processes. The quality parameters’ data acquired at 107 wells throughout the aquifer of Qorveh-Dehgolan plain were used for training (data from 1996 to 2013) and to test (data from 2014 to 2016) each model.

The results showed that artificial neural networks with a hidden layer that benefits from the optimal number of neurons could simulate the aquifer behavior with high accuracy and in less time. Also, increasing the number of hidden layers while increasing the response accuracy increases the number of optimal network neurons and the duration of the problem analysis. Finally, artificial neural networks based on the Broyden-Fletcher-Goldfarb (BFG) method had the highest efficiency in simulating aquifer behavior, although the performance of the Levenberg Marquart (LM) method is very close to BFG. BFG is more efficient than LM due to its lower Mean Square Error and Standard Deviation (3.46 and 3.09, respectively).

The aim of the study is to evaluate the changes of mean temperature and precipitation parameters using IPCC Sixth Report models (CMIP6) under SSP scenarios (SSP1-2.6, SSP2-4.5 and SPP5-8.5) during the period of 2022-2100 in Kashan Plain. The mean temperature and precipitation data was obtained from 7 stations (Kashan, Kavir-e-Hosseinabad, Kamu, Ardestan, Alavi, Noushabad and Sensen) in Kashan plain considering the base period of 30 years (1984-2014). Also, 7 models were selected from the models of the sixth report (CMIP6). The post-processing of the output of the models was carried out using the linear ratio method. Nash-Sutcliffe indices (NSE), root mean square error (RMSE) and correlation coefficient (r) were used to determine the accuracy of the models. The annual trend of changes was investigated using Mann-Kendall test. Finally, the mean of IPSL-CM6A-LR and BCC-CSM2-MR models outputs was used to simulate mean temperature and precipitation changes in the future period. According to the results, in all of the studied stations, precipitation in the coming period will have a decreasing trend compared to the base period. The mean temperature will also increase in the future period compared to the base period. In Kavir-e-Hossein Abad, Ardestan, Noush Abad and Sen Sen stations, the intensity of temperature increase will be higher than Kashan, Kamu and Alavi. According to the predicted conditions, it is necessary to pay attention to comprehensive policies in the field of adapting to climate change in Kashan Plain.

Due to the upcoming climate threats, challenge of water shortage and its impact on the food security of the growing population in Iran, the optimal use of soil and water resources is very important. With the development of remote sensing technologies, free access to a variety of field data has become widely available, which can be used to reduce the uncertainty of simulation models. The aim of this study was to simulate actual evapotranspiration (ETa) and rice crop coefficients (Kc) during its growth stages using the SWAP model updated with satellite data and evaluate the accuracy of the results with/without updating. This research was conducted at the National Rice Research Institute of Iran in Rasht in the year of 2017. Based on the obtained results, total ETa measured by lysimeter and simulated by SWAP model with and without updating were 395.4, 373.2 and 363.6 mm, respectively. The average crop coefficients during the growth stages of vegetative, reproductive and ripening were estimated as 1.13, 1.49, 1.21, respectively. The crop coefficients for the proposed stages estimated by SWAP model without using satellite data were 1.02, 1.39, 1.04, respectively. After updating with satellite data, the crop coefficients were modified as 1.05, 1.43 and 1.07, respectively. Finally, the statistical analysis indicated that the SWAP model has a reasonable performance in estimation of ETa (RMSE=0.89; EF=0.98; R2=0.74) and rice crop coefficients (RMSE=0.53; EF=0.96; R2=0.63). The results indicate that the SWAP model combined with satellite data improved the accuracy of ETa estimation (RMSE=0.75; EF=0.99; R2=0.86) and rice crop coefficient (RMSE=0.40; EF=0.99; R2=0.74) at field scale.