جستجوی مقالات مرتبط با کلیدواژه « شبیه سازی » در نشریات گروه « آب و خاک »

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.

The use of hydrological models in watersheds has always been of interest to water resources researchers. Hydrological simulation models are valuable tools for investigating challenging issues related to watershed management, such as the effect of climate change on water resources and the effect of urbanization on floods and droughts. Spatial distribution hydrological model WetSpa is used to simulate river flow at basin scale. The model uses the observed topography, land use, soil map, and daily meteorological time series (rainfall, evaporation and temperature) to predict hydrographs and distributional-spatial hydrological parameters of the basin. In this article, the object-oriented, modular and process-oriented model of WetSpa, which is prepared based on the flexible modeling approach, is applied to simulate the daily hydrograph in Khorramabad basin.

The inputs of the model include digital elevation maps, soil type, land use, and time series of precipitation, temperature, and potential evaporation and transpiration, which are from the statistics of 6 meteorological stations in a ten-year period (water year 84-85 until 93-94) is used. After preparing the inputs of the model, at first the maps of the distribution parameters are automatically generated in the map format by the GIS pre-processing component of the model. After that, the model is calibrated using a 5-year statistical period (water year 84-85 to 89-88) of precipitation, temperature, and potential evaporation and transpiration data. The model uses Thiessen polygons to apply precipitation, temperature, and evaporation data. For this purpose, the daily discharges of Jam Anjir hydrometric station located at the outlet of the studied watershed are used. Model calibration is done manually by determining the values of 11 global (general) parameters of the model, so that the best match between simulation and observational hydrograph is obtained. And finally, the validation of the model is carried out based on a 5-year statistical period (water year 89-88 to 94-93) and the values of the global parameters obtained in the calibration stage.

The maps of distributed parameters are produced, which after preparing the inputs of Mashdand's production model showed that the average potential runoff coefficient of the area is 63% and the concentration time of the area is 17 hours. In the following, according to the 11 global parameters, which symbol and range of changes are specified in table (3), the model global (general) parameters values  are obtained in the calibration stage. Comparing the simulated hydrograph by the model and the observed hydrograph in the calibration stage shows that the best match between the observed and simulated data is established with a correlation coefficient of 0.39. Validation of the model is also based on a 5-year statistical period (water year 89-88 to 94-93) and the values of global parameters. The output files of the model illustrate that 26.15% of the precipitation becomes runoff during the calibration period. During the validation period, the share of total runoff from precipitation is 26.42%. Moreover, the simulation results of the model demonstrate the ratio of evaporation to precipitation in the calibration and validation periods is 57.18 and 69.20%, respectively. Additionally, the results of the evaluation of the model based on the Kling-Gupta index (KGE) present the value of 0.68 for the calibration period and 0.74 for the validation period.

In this article, the effectiveness of WetSpa model is investigated in order to simulate the daily flow of Khorram Abad River at Cham Anjir hydrometric station. According to the results obtained from this research, it can be said that the Wetspa spatial distribution model has the ability to simulate the hydrological behavior of the basin with acceptable accuracy. The graphical comparison of the calculated and observed hydrographs for the calibration and evaluation period also shows a relatively good match between the two hydrographs. Examining the results of calculating of the water balance components by the model demonstrates that the outflow in the calibration and validation period accounted for 26.15 and 26.42% of the total precipitation respectively, seems logical considering the major land use of mountains and pastures in the irrigation basin.

Nowadays, numerical modeling is known as a powerful method and tool in investigating practical phenomena. So that this method is used in many fields of engineering. Although the numerical model of an engineering problem is usually prepared based on several simplifying assumptions, it is possible that this numerical model does not reflect all aspects of the real problem and is not able to show the real behavior in reality. Direct updating methods use the analytical solution of the problem for this purpose, although these methods specify the necessary corrections without the need to repeat calculations and in one step, but in most cases such corrections do not have a physical meaning in reality but iteration-based updating methods require sensitivity analysis of the effective parameters in the problem in order to find the impact of each of them. One of the solutions to deal with the time-consuming problem of multiple re-analyses in numerical models prepared by commercial software during model updating based on sensitivity analysis is to replace the numerical model with an approximate representative model known as meta-models. The response surface method is one of the common methods for building such meta-models. The response surface technique is actually a test design method to select the design parameters in the experiment with the aim of optimizing some system response functions. From this point of view, the research regarding the response of the network of open channels to the arrival of the flood wave and predicting the characteristics of the flow in the branches of the network can be the solution to minimize the damages caused by it.

In this research, in order to update the numerical model of unsteady flow in the network of open channels by using the combined method of response surface and meta-exploration methods, a branch of the Garmabadr River (Ziquon) has been selected, and the desired data has been prepared and sorted. have been Hec-Ras software has been used as a numerical method. Neural network is also chosen for simulating the numerical method so that the desired parameters can be optimized in the process of using the genetic algorithm method. This area is located in the northern part of Iran and in the northeast of Tehran province. The geographical coordinates include longitude 51 degrees 32 minutes to 51 degrees 38 minutes and latitude 35 degrees 51 minutes to 35 degrees 58 minutes. MATLAB software has been used to build a meta-model or an alternative model. MATLAB is a software that can be called the language of mathematics and modern engineering sciences.

As it is clear from the results of the Hec-Ras program, the discharge of stations number 13 and 22 is equal to 308.03 and 27.89 cubic meters per second, respectively. The estimation error is equal to the difference between the estimated flow rate and the observed flow rate, which is 18.03 and 2.11 cubic meters per second, respectively. The percentage of the estimation error is equal to the ratio of the estimation error to the observed value is equal to 6.2% and 7%, respectively, which is an acceptable value for this research, and this result shows the appropriate performance of the genetic algorithm for optimizing the flow simulated by It shows the neural network, on the other hand, the low percentage of error also determines the accuracy of the intended program. As a result, the error of the genetic program is at an optimal level. As it is known, Manning's coefficients are very important and influential parameters in the behavior of the flow, the change in which causes significant changes in the simulation results. The effect of this uncertainty on the performance of numerical methods was clearly identified in this research. As a result, examining inputs that have inherent uncertainty and determining their appropriate values is a suitable solution for improving numerical methods.

 Genetic algorithm is a powerful method to determine the values of effective parameters in simulated flow performance, and its combination with neural network is a powerful tool for engineering purposes, including the analysis of the behavior of open channels, whose fast convergence is a strong point to choose in these problems. in similar cases.
 Manning's coefficients are very important and influential parameters in the simulated flow behavior, with a small change in it, the simulated model undergoes significant dispersion, which should be considered in the analysis process so that the results are not significantly different from reality.
 Optimizing an effective parameter in simulated flow behavior is an erosive and time-consuming process, and the use of neural network becomes much faster and more reliable due to the high convergence speed of this process.

Managing and monitoring the quantity of groundwater resources, along with assessing the intensity and direction of their flow, constitute fundamental principles for sustainable utilization of such resources. This study aims to utilize GMS software to simulate and analyze groundwater resources in Nobandegan plain, Fars, Iran. The objectives include deriving long-term variations in these resources, evaluating the water balance, and ascertaining the direction and intensity of groundwater flow.

Data related to pumping wells, observation wells, topographic features of the plain, and rainfall, among other pertinent information, were gathered using reports from Fars Regional Water Company. Subsequently, a comprehensive hydraulic model was developed to simulate, calibrate, and validate the groundwater flow within the plain. Following this, the overall trajectory and flow intensity within the plain aquifer were simulated.

The hydrograph pertaining to groundwater within the plain reveals a ~50 meter water table drop over the course of the investigation period.  The predominant groundwater flow within this plain is directed from the northern mountainous regions toward the southwestern expanses. The maximum hydraulic head is recorded at approximately 1283 meters, while the minimum stands at 1175 meters above mean sea level, indicating a 108-m drop in the aquifer water table elevation. The simulation results of the groundwater flow within the plain reveal an increased velocity in the northern regions and, to some extent, in the southern reaches of the plain. The findings of this study may be employed for groundwater quality studies on this plain.

The knowledge of crops response to salinity stress application methods in growth stages, can lead to better management of stresses. This research was done on the S.C 704 maize, in the mini-lysimeter space and in Qazvin region. The experiment was performed factorial and in a completely randomized design. The salinity treatments of soil saturated extract (the main factor) were applied at four levels of 1.7(S1), 3(S2), 5(S3) and 7(S4) dS.m-1. The crop growth stage treatments (sub-factor) were defined as one-stage of 6 leaves (C1), flowering (C2), seeds milking (C3) and two-stages of C1C2, C1C3 and C2C3. The research target was to simulate the maize yield by AquaCrop model, in conditions of pulsed salinity stress application in crop growth stages. The stress application data in one and two growth stages, were used for calibration and evaluation the AquaCrop model, respectively. From S1 to S4 treatment, the crop dry matter was decreased from 157.2 g. plant-1 to 115.9, 53.2, 77.7, 86.1, 97 and 46.5 g. plant-1 in the C1, C2, C3, C1C2, C1C3 and C2C3 treatments, respectively. The sudden application of salinity stress in a sensitive growth stage (C2 and C3 treatments), was caused the more damage relative to C1C2, C1C3 treatments. Because once application of salinity stress in the 6-leaf stage (C1) has caused the crop adaptation to future stresses (increasing the tolerance threshold). In AquaCrop model evaluation, the statistical parameters of CRM, EF, R2, RMSE, NRMSE and ME were equal to -0.084, 0.833, 0.91, 12.05, 11.34% and 18.32, respectively. Those showed good accuracy of AquaCrop model in simulation the maize yield. As a result, by management the salinity stress in crop growth stages, will be reduced the negative effects on the maize yield. With simulation the crop yield by AquaCrop model, different stress application states can be evaluated.

As a result of droughts and human interventions, the Urmia Lake basin has faced increasing unallowable withdrawal of water resources and environmental requirements share. Over the past two decades, a series of natural and human factors have gradually changed the water balance of Lake Urmia. In addition, decreases in precipitation have been the main reason for the gap between water resources and increasing water consumption in the basin, especially in the agricultural sector. In order to deal with the Urmia Lake catastrophe, a working group was formed for the Urmia Lake restoration. The working group planned and proposed solutions to reach the ecological level of the lake in three stages within 10 years through in-basin water supply and inter-basin water transfer programs. The present research, while investigating the solutions for providing the environmental water requirement of Lake Urmia using the Analytic Hierarchy Process (AHP) method, changes in the water volume of Lake Urmia in the scenarios of water transfer from the surface flow of each entering river in the lake in the years of high intensity severe to mild drought, the implementation of the approvals of the national working group to restore Urmia Lake, including a 40% reduction in agricultural consumption from the sources of dams and other rivers without dams, releasing water from dams in non-agricultural seasons, transferring water from urban wastewater and finally transferring water between the Zab basin has quantitatively been evaluated using the MODSIM simulation model. This research will help to predict the amount of transferable water in drought, normal, and drought conditions and to plan the water accounting of the Urmia catchment. 

The watershed of Lake Urmia, located in the northwest of Iran, with an area of 51,876 square kilometers, is one of the six main watersheds of the country, which is located between the provinces of West Azerbaijan, East Azerbaijan, and Kurdistan. In terms of its territorial location, the catchment area of the Zab Kochak river is in the western part of the international border with Iraq and in the catchment area of the Western Border River basin. A significant part of the Zab watershed (source of water transfer) is located outside the country and in Iraq. The route of water transfer between the basins of the Zab basin is from the place of two dams, Silweh and Kaniseeb, which after entering the Godarchai river will eventually enter the Urmia lake water body. The different solutions from the working group’s approval have been selected for developing six scenarios. Then, the Scenarios have been evaluated by the MODSIM model and AHP analysis method. The scenarios are including reducing agricultural expenses from dams in operation, saving by reducing agricultural expenses from other rivers, transferring water between basins, transferring effluent to Lake Urmia, releasing water from dams in operation to Lake Urmia. A hierarchical structure was developed with the aim of evaluating solutions to save Lake Urmia through internal sources and transfer water sources. In this structure, there are four criteria of climate, consumption, economic-social, and environmental status. Sub-criteria of drought, surface water extraction from rivers, extraction from dam sources, extraction from groundwater resources, development of agricultural lands, and ensuring the sustainability of river flow. and providing solutions for inter-basin water transfer, a 40% reduction of agricultural water consumption from surface water sources of rivers and dams, wastewater transfer, and a 40% reduction of agricultural water consumption from underground water sources in Expert Choice software, were analyzed. 

In order to evaluate the effectiveness of the defined scenarios, the simulation model was implemented for future conditions during the next 10 or 15 water years for different scenarios. The results show that the implementation of scenarios 4 and 6, will bring the largest increase in the volume of lake water, of course, with the occurrence of precipitation and proper input. Regarding the results of scenario 1 and the sameness of its results in two periods of 10 and 15 years, it can be stated because in the definition of the scenario, it is a dry year and the amount of evaporation of the lake water is more than the amount of input into the lake. There has been no increase in the lake's water volume for several consecutive years. Also, the results show that in wet and dry years, if the maximum transfer flow scenario is implemented, we will not have a flow from the Zab river to the downstream side, and it will not be accepted from the hydrodiplomacy point of view for the neighboring country in terms of influencing the use of the Dukan dam. Therefore, water diplomacy solutions are needed to reduce environmental threats. Therefore, the issue of transferring water from the Zab basin to Lake Urmia cannot be considered guaranteed in the long term due to the impossibility of reducing the outflow of water from the country to zero. 

In conclusion, in order to evaluate all scenarios to satisfy the environmental needs of Lake Urmia shows that the protection of the lake requires correcting the mistakes of the path traveled in the current and past years, and preferably with a 40% reduction in agricultural consumption from dams and other rivers, groundwater in the basin is the main supplier of Urmia Lake. Inter-basin water transfer is the next priority because the source catchment is able to transfer water in the face of droughts due to the reduction of runoff and related challenges as well as its negative economic and social effects. It will not be as much as predicted and included in the program (600 million cubic meters per year). In addition to that, water diplomacy is necessary in terms of impacting downstream transboundary uses and reducing environmental threats. The results of this research and the high costs of water supply requirements, in parallel with the investigation of controversial options and costs of inter-basin water transfer projects, macro-management of water resources towards demand and consumption management and water saving programs and the modification of water consumption patterns and the legality of the behavior of water users from within the catchment area of Lake Urmia should be low-cost, sustainable and reliable solutions.

Water management in water basins requires information of some facts and full acquaintance of basin conditions, which is problematic due to the deficiency of data collection stations in the country. One of the most suitable methods for flow simulation is the use of hydrological models. In this study, in order to simulate runoff caused by precipitation and to investigate the apparatus of runoff formation and outflow at the outlet of Shahid Rajaei Dam basin located in Sari, the HBV-Light model was used. The purpose of evaluating the stochastic method is to estimate the model parameters and reduce the uncertainty. The length of the daily statistical period was the variables of temperature, precipitation, runoff and evaporation from 1981 to 2015. The Monte Carlo random method was used to estimate the appropriate parameters for the study area. For a consistent assortment, the values of the parameters with a relatively good coefficient of determination were used in the validation section. Finally, the results show the good aptitude of the model in simulating runoff in the study basin. The values of coefficient of determination 0.87, Nash coefficient (NS) equal to 0.66 in the range of good evaluation and RMSE equal to 0.26 in the validation period indicate this issue. In the analysis of uncertainty caused by model parameters, K1, K2 and UZL parameters were identified as the most sensitive in the model reaction subroutine. Therefore, the results showed the importance of accurate determination of water and soil parameters affecting subsurface runoff.

Awareness of water resources status is essential for the proper management of resources and planning for the future due to the occurrence of climate change in most parts of the world and its impact on different parts of the water cycle. Hence, many studies have been carried out in different regions to analyze the effects of climate change on the hydrological process in the coming periods. The present study examined the effects of climate change on surface runoff using the Atmosphere-Ocean General Circulation Model (AOGCM) in Khomeini Shahr City. The maximum and minimum temperatures and precipitation of the upcoming period (2020-2049) were simulated using a weighted average of three models for each of the minimum and maximum temperatures and precipitation parameters based on the scenario A2 and B1 (pessimistic and optimistic states, respectively) of the AOGCM-AR4 models. The LARS-WG model was also used to measure the downscaling. The HEC-HMS was used to predict runoff. The effects of climate change in the coming period (2020-2049) compared with the observation period (1971-2000), in the A2 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.6 Degrees Celsius, respectively, and the precipitation would decrease 17.8 percent. In the B1 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.4 degrees Celsius, respectively, and the precipitation would decrease by 13 percent. The results of runoff were different in the six scenarios in the way the most runoff reduction is related to the scenario of fixed land use and scenario A2 (22.2% reduction), and the most increase is related to the scenario of 45% urban growth and scenario B1 (5.8% increase). So, according to increase urban texture in the future and consequently enhance the volume of runoff, this volume of runoff can be used to feed groundwater, irrigate gardens, and green space in the city.

Sensitivity analysis of crop models helps researchers to have the necessary information about the reaction of crop models to changes in input parameters before calibration and application. This makes it possible to introduce the input data to the model with more awareness and increase the accuracy of the model in the calibration stage. Due to using the AquaCrop model in recent years, in this study, the sensitivity of normalized water productivity (wp*), crop transpiration, initial canopy cover (CCo), canopy growth coefficient (CGC), canopy decline coefficient (CDC) and harvest index (HI) was analyzed using a new method. For this purpose, corn yield data collected from Research Institute of Seed and Plant Breeding during 2008-2010 were used. Data consisted of four irrigation levels (W1, W2, W3 and W4 indicate supply of 120, 100, 80 and 60% of water requirement, respectively) and four nitrogen fertilizer amount (N1, N2, N3 and N4 indicate supply of 100, 80, 60 and 0% of fertilizer requirements, respectively). The results showed that the AquaCrop was most sensitive to changes in harvest index (0.65≤Spi≤1.3) and normalized water productivity (0.55≤Spi≤1.2). The lowest sensitivity (0.02≤Spi≤0.07) was observed to changes in crop canopy decrease coefficient (CDC). Sensitivity coefficients were positive for all parameters except CDC. Therefore, by increasing the CDC value, the AquaCrop suffers from underestimated error. The sensitivity coefficients for treatments N1 to N4 were equal to 0.32, 0.41, 0.46 and 0.51, respectively. These results for irrigation treatments W1 to W4 were equal to 0.36, 0.39, 0.44 and 0.5, respectively. So, with increasing water stress and fertilizer, the sensitivity of the AquaCrop model to all parameters increased. The highest sensitivity was observed in W4N4 treatment.

The main goal of this research is management and comprehensive planning to optimally use the available water resources of Karde dam, using the WEAP model, and supply the demand in the agriculture and drinking sectors, considering the growth of their needs in the future. For this purpose, Karde Dam was first simulated in the environment of WEAP model and the model was implemented for basic conditions and seven different scenarios of development plans. According to the results obtained for the reference scenario, this dam alone does not respond to all the needs defined completely in the horizon of the project, except by applying management measures in the form of scenarios, which will reduce water consumption in different sectors of demand. Among these scenarios are demand management, increasing irrigation efficiency, using both at the same time in one scenario, changing or reducing the cultivation pattern, etc. As a result, by applying the low irrigation scenario and increasing the efficiency at the same time, it is possible to reduce the lack of water demand by 37% compared to the reference scenario, and the reservoir storage volume in this scenario increases by 25% compared to the reservoir storage volume in the reference scenario.

IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress is extended to morphological, physiological and biochemical responses. Considering the rapid advancement in computer model development, plant growth models have emerged as a valuable tool to predict changes in production yield. These growth simulation models effectively incorporate the intricate influences of various factors, such as climate, soil characteristics, and management practices on crop yield. By doing so, they offer a cost-effective and time-efficient alternative to traditional field research methods. Material and MethodsThis research was conducted in the research farm of Varamin province, which has a silty loam soil texture. The latitude and longitude of the region are 35º 32ʹ N and 51º 64ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Varamin has a temperate humid climate. The long-term mean temperature of Varamin is 11.18 ° C and the total long-term rainfall is 780 mm. In this study, in order to simulate irrigated wheat cv. Mehregan growth under drought stress, an experimental based on completely randomized blocks (CRBD) including: non-stress as control (NS), water stress at booting stage (WSB), water stress at flowering stage (WSF), water stress at milking stage (WSM) and water stress at doughing stage (WSD) with three replications during growth season 2019-2020 was carried out in Varamin, Iran. Crop growth simulation was done using SSM-wheat model. This model simulates growth and yield on a daily basis as a function of weather conditions, soil characteristics and crop management (cultivar, planting date, plant density, irrigation regime). Results and DiscussionBased on the results, the simulation of the phenological stages of irrigated wheat cv. Mehregan under water stress condition using SSM-wheat model showed that there was no difference between observed and simulated values. Summary, the values of day to termination of seed growth (TSG) were observed under non- stress, stress in the booting stage, flowering, milking and doughing of the grains, 222, 219, 219, 221, 221 days, respectively andsimulation values with 224, 221, 220, 221, respectively. However, with their simulation values, there were slight differences with 224, 221, 220, 221, respectively. Acceptable values of RMSE (11.7 g.m-2) and CV (3.5) indexes showed the high ability of the SSM model in simulating the grain yield of irrigated wheat cv. Mehregan under water stress conditions. Grain yield values were observed in non-stress conditions of 5783, water stress in booting, flowering, milking and doughing of the grain stages in 5423, 5160, 5006 and 5100 kg. h-1, respectively. While the simulated values were 5630, 5220, 4920, 4680 and 4880 kg. h-1, respectively. Based on the findings, observed and simulated values of leaf area index (LAI) were observed under water stress condition in the booting, flowering, milking and doughing of the grain stages (4.3 and 4.47), (4.33) and 4.46), (4.4 and 4.57) and (4.4 and 4.58) cm-2, respectively. Evaluation of the 1000-grain weight of irrigated wheat cv. Mehregan under the water stress showed that the SSM model was highly accurate. RMSE (4.6 g.m-2) and CV (1.8) values indicate the ability of the SSM model to simulate the 1000-grain weight of irrigated wheat cv. Mehregan. Also, the simulated values of the harvest index were 34.7 % in non-stress conditions, which decreased by 6 % compared to the observed value. Harvest index values were observed under water stress conditions in the in the booting, flowering, milking and doughing of the grain stages in 30.2, 29.3, 29.9 and 29.5 %, respectively. Compared to its observed values, it was reduced by 3, 3.5, 5, and 5.5 %, respectively. ConclusionBased on the findings, the slight difference between the observed and simulated values demonstrates the SSM model's capability to accurately capture water stress impacts on the phenological stages, grain yield, and yield components of irrigated wheat cv. Mehregan during critical growth stages, including booting, flowering, milking, and doughing. The results indicate that the SSM model is effective in simulating wheat growth under water stress conditions, showcasing its potential as a valuable tool for modeling irrigated wheat growth. The model's ability to account for water stress and its effects on various growth parameters makes it a reliable and efficient tool for predicting crop performance in water-limited environments.

Groundwater is an essential natural resource that is widely used to meet domestic, industrial, and agricultural needs. In recent years, the amount of withdrawal from groundwater has been more than the amount of its recharging leading to going out of balance. Since groundwater is in the ground and it is not possible to observe directly, identifying its properties is time-consuming and expensive. On the other hand, problems such as inconsistent and incomplete input information, heterogeneous aquifers, etc., have made groundwater study difficult. In many watersheds, groundwater resources are the strategic and primary source of water supply for different users. However, groundwater extraction has exceeded the amount rate of recharge in many regions around the world, resulting in harmful ecological and environmental problems, such as water level decline, water quality degradation, drying up of wells, increased pumping costs, and land subsidence. Assessing groundwater resources for their available water volume and obtaining an accurate prediction of groundwater levels (GWL) is central to sustainable management (i.e., balancing between demand and supply) of groundwater and surface water resources in a watershed. Therefore, tools such as groundwater modeling are used to solve this problem. Simulation of groundwater flow with mathematical models is an indirect approach to solving problems with lower costs than direct methods. In fact, the use of mathematical modeling is to simulate the natural conditions of the water surface with mathematical relationships. Groundwater modeling is done using differential equations, and one of the most widely used methods in solving groundwater problems is the use of finite differences and finite elements. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer. Next, the trend of changes in the groundwater level of the range was analyzed by non-parametric tests. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer.

The study area of Mahabad is located in West Azarbaijan province. GMS software and MODFLOW code were used for groundwater simulation. Using the information of 22 observation wells, exploitation wells information, river information, recharge, and withdrawal from groundwater, the desired model was built. . The model was run in September 2015 for the steady-state and October 2010- September 2011 for the transient state with a monthly time step. The values for hydraulic conductivity and storage coefficient were calibrated for the steady and transient states, respectively. Aquifer thickness varied from 60 to 200 m, and the cell size was considered 200 × 200 m. Rainfall infiltration, return flow, and input flows feed the aquifer. Seventeen percent of the monthly rainfall was considered rainfall infiltration that fed the aquifer. Moreover, based on the wells' primary use, return water from the wells was considered about70, 75, and 20% for drinking water, industrial and agricultural wells, respectively. The GWL is higher in the South part of the aquifer compared to other parts and, as we move from the South part of the aquifer towards its central and southern regions, the GWL declines. In conclusion, the groundwater flows from the upper South part of the aquifer towards its lower part. More exploitation wells are in the aquifer's central section, and most of their extracted water is used for urban and agricultural purposes. It was then implemented in two stable and unstable modes and its performance was evaluated with root mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R2) criteria. Various statistical methods have been provided to analyze the trend of time series. Among them, non-parametric methods are more useful in the time series of hydrological variables. These methods are suitable for time series that have elongation or skewness and are independent of the statistical distribution of the time series.  In the following, the Mann-Kendall method and Sen’s slope were used to determine the trend of the groundwater level at significant levels of 90, 95, 99, and 99.9%.

The simulation results showed that there is a very good agreement between the simulation and observational data. The model evaluation criteria including RMSE, MAE, and R2 for two stable and unstable modes were calculated as 0.84, 0.63, and 0.99, as well as 0.88, 0.72, and 0.98 m, respectively. These values showed the appropriate efficiency of the model. Based on the results, the highest level of groundwater was in the south of the Mahabad aquifer and the lowest level was in the north of the aquifer.  The optimized values ​​of hydraulic conductivity, special yield, aquifer thicknesses, values ​​of exploitation wells, and aquifer transmissivity were determined from the groundwater simulation results. The results of the Mann-Kendall test showed that Haji Khosh, Gapis, and Gorg Tapeh stations had the highest downward trend. So, in these stations, the downward trend was more significant at the level of 0.99%. The Mann-Kendall Z-parameter values were positive for the Qom Qala station, which indicated the rising trend of the underground water level in this area. The results of Sen’s slope test also confirmed the results of the Mann-Kendall test. It was so that the Sen’s slope test showed that the downward slope of the three stations Haji Khosh, Gapis, and Gorg Tapeh occurs more strongly.

The results of this research showed that GMS and MODFLOW codes are suitable tools for simulating groundwater and the condition of the aquifer with proper accuracy. Also, the results of Mann-Kendall and Sen’s slope tests showed that out of 19 wells, almost 18 had a downward trend, which shows that the Mahabad aquifer is not in a favorable condition and with the increase in harvesting and decrease in rainfall, especially in recent years, its situation will worsen. The Mann-Kendall test showed that the Mahabad aquifer is in poor condition so out of the 19 investigated wells, approximately 18 wells had a downward trend in the groundwater level. The age slope estimator method also confirmed the Mann-Kendall results. Examining the obtained results exhibits that the use of new approaches for simulation provides the opportunity to manage and balance the allocation of groundwater resources effectively. Further, the use of new tools can be considered for implementing balancing scenarios related to groundwater resources.

Prediction of evaporation as a key component of the hydrological cycle is one of the most important issues in water resources management and meteorology studies. In this study, the performance of ARIMA, SARIMA, gene expression programming, multiple linear regression, Monte Carlo and Thomas Fairing models in prediction of monthly evaporation values of Ekbatan Dam station, west of Iran in a 47 years period (1971-2017) were evaluated. For calibration of these models, 40 years data (1971-2010), and for validation, data from 2011-2017 (7-year) were used. The statistical metrics of the correlation coefficient, root mean square error, standard error, the Akaike information criterion, and NSE were selected for evaluation and comparison of models. The results showed that the SARIMA model has more accurate performance in predicting monthly evaporation. The GEP model, ARIMA, and MLR are ranked second to fourth. However, since the GEP model is easier to use than the SARIMA model and requires fewer variables than the SARIMA model, it shows promise to generate faster results, therefore, the GEP models can be the preferred option compared to others.