calibration

In recent years, basins hydrological modeling has been one of the important issues, which its results have a significant impact on water resource planning. One of the tools in basins hydrological modeling is SWAT (Soil and Water Assessment Tools), this tool can be used as an extension in GIS software. The purpose of this research is to evaluate the accuracy of SWAT in Maroon basin hydrological modeling.

The calibration and validation of model, used from the flow data measured at the Idenak and Tange-Takab stations in the period of 1991 to 2017.

The results showed that the accuracy of the model in using daily data is lower than monthly data. The reason of the reduction of the accuracy in using of daily data compared to using of monthly data is that the daily calibrated data in each hydro-station is 3450 numbers and for the monthly data is 108 numbers. The simulation results show that the SWAT has an acceptable ability in the basins hydrological simulation with a mountainous nature and the process of flow changes in the calibration and validation stage is simulated with acceptable accuracy.

Modeling is one of the most effective methods in river water quality management, planning to reduce the entry of pollutants and control environmental damage. In this article, the water quality of Babolrood river using the Qual2kw model and meta-heuristic model of support vector machine and SVM-GA genetic algorithm with the approach of simulating six water quality parameters including temperature, alkalinity, electrical conductivity, total nitrogen, dissolved oxygen and biochemical oxygen demand in four time periods from 2018 was investigated. Genetic algorithm is used for simulation and optimization, and the hybrid of this model with support vector machine has improved the simulation performance. In order to compare the observed and simulated data, the explanation was used coefficient R2 of the average absolute error MAE and the root mean square of the normalized error NRMSE. The results showed that the best SVM-GA simulation for total nitrogen parameter in February is with MAE 0.15 and NRMSE 1.97. Also, the results of Qual2kw numerical model showed that the best simulation for May is with MAE 0.14 and NRMSE 1.2. The water quality of Babolrood River was evaluated using the NSFWQI index. Based on this, the best water quality is in the month of Bahman with an index of 56 in the medium to good range and the worst quality is in the month of May with an index of 49 in the bad range.

The aim of the research is quantitative modeling of the underground water flow of the plain aquifer and the investigation of management solutions to deal with the drop in the underground water level. This research was investigated using hydrogeological information, hydrology, meteorology and basic studies of underground water sources. Quantitative underground water modeling was done using GMS software and Modflow code and the underground water level was simulated for the years 2011-2015 with 60 monthly time steps. Manual calibration of the model was done for 2011-2014 and its validation was done for 2014-2015. Future management solutions, in the form of combined scenarios:One- 10% reduction in withdrawal from wells with a 5% reduction in feeding from return of agricultural water. Two- 20% reduction in withdrawal from wells with a 10% reduction in feeding from return of agricultural water. Three- Their comparison with the continuation of the existing trend was predicted for the years 2015-2019. The result of the aquifer modeling for the above time periods showed the deficit of the reservoir 33.09 MCM. But by applying a 10% reduction in withdrawal and a 5% reduction in feeding, 21.38 MCM/year and by applying a 20% reduction in recharging from the return flows of agricultural consumption and 10% reduction in feeding, 25.94 MCM/year were added to the volume of the water balance. But with the continuation of the current trend, the deficit volume of the aquifer was estimated to be more than 54 MCM.

Soil water content is measured by different methods, such as oven, gamma ray, and time reflectometry methods. Direct methods are time-consuming and lead to disturbance of the soil sample, and some indirect methods have risks of radioactive materials. Among these methods, the TDR method, which is one of the reflectometry methods, is a quick and safe method, and has received attention for this reason and gives acceptable results in soils with normal conditions. However, for saline soils, this method cannot measure water content accurately. The objective of this research was to make TDR sensors that can measure the water content of saline and fine-textured soils. By making these sensors, it would be possible to measure water content of saline soils by a TDR; and if an error is observed, the actual amount of soil water content can be obtained by providing suitable calibration models. For this purpose, a fine-textured soil was obtained from Tikmehdash Research Station and by adding different amounts of salt, soil  samples with electrical conductivity of 20, 35 and 50 dS/m with natural density were prepared. Through trial and error, the type of coating suitable for covering the moisture sensor rod, which is a polymer material, was determined. Then, the length of the coating used in the middle moisture sensor rod was determined for the mentioned soil that had the prescribed salinities and water content. This length was equal to the length of the moisture sensor rod. Then, the coated sensors were tested to measure other soil water contents, and suitable calibration models were presented. Thus, by entering into the models the volumetric moisture content measured by TDR, the moisture content of fine-textured saline soils can be estimated with good accuracy.

Using simulation models is a key strategy in agricultural water management and an effective way to predict the impact of irrigation management and water quality on crop yield. This study aimed to evaluate the performance of the SWAP and AquaCrop models in simulating the growth and biomass production of three maize varieties under the conditions of using saline water for irrigation with a drip irrigation system at the research farm of Tehran University of Agriculture and Natural Resources Campus. In order to calibrate and validate the models, field data obtained from a factorial experiment with two factors of maize variety (SC704, 400, and 260) and irrigation water salinity (0.7, 3, and 5 dS/m) were used. In the validation stage of the AquaCrop model, the R2, RMSE, and NRMSE statistics for the canopy cover (CC) showed a high level of agreement between the measured and simulated data, with values of 0.953, 5.69, and 8% respectively. For the SWAP model, the calculated statistics for the leaf area index (LAI) were 0.477, 1.610, and 54.2%, respectively. Despite the poor performance of the SWAP model in estimating LAI, both the SWAP and AquaCrop models effectively simulated the biomass of maize cultivars under different treatments. In the calibration and validation stage, RMSE and NRMSE of both models were less than 0.5 ton/ha, with 3% (calibration) and 1 ton/ha, 7% (validation), respectively. In general, both models can be used in various studies for different maize cultivars under irrigation water and soil salinity conditions.

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

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

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

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

Today, fresh water sources are very important in human life. During the past years, population growth, development of industries, and agriculture have increased the need for water and subsequently increased the withdrawal from groundwater sources. This increase in withdrawal has occurred without considering the capacity of the aquifers. While logically, the exploitation of an aquifer should be limited to its dynamic storage, in most regions, the static reserves of aquifers have been used to a large extent, and it seems impossible to return these aquifers to their original state. The dynamic storage of the aquifer is actually a part of the storage volume of the aquifer, which has fluctuations, and by examining and determining these fluctuations, it is possible to estimate the permissible amount of exploitation of the aquifer. But the static storage of the aquifer, which is very important, is among the old and stable storage that has been stored for many years. The use of static aquifer storage causes events such as subsidence. The drying up and lack of water in many aqueducts, springs, and extraction wells across the country is one of the consequences of the overexploitation of groundwater resources. In the arid and semi-arid climates of Iran, factors such as climate change and water scarcity, along with overexploitation of water resources, especially groundwater resources, have caused the groundwater levels to decrease. In some areas, we are witnessing irreparable subsidence. As a result, it is necessary to manage and control the extraction of groundwater resources before the crisis occurs and reaches an irreversible level. In this regard, monitoring and controlling the condition of the aquifer using computer models is essential.

In the present study, the modeling and quantitative investigation of the groundwater condition of the South Mehyar-Dasht Asman aquifer in the Gavkhoni catchment area has been done by the MODFLOW 2005 model and groundwater modeling system )GMS 10.4.5( software. GMS software is one of the few with good performance and has been used in groundwater studies in many countries worldwide. This software simulate groundwater both quantitatively and qualitatively. The MODFLOW (Modular Ground Water Flow) model is used to simulates groundwater flow in aquifers with specific boundary conditions, assuming the necessary values for hydraulic conductivity and other aquifer parameters. The program allows the user to select only the modules needed to study the desired system for specific hydrogeological processes and activate or deactivate a particular part. These features have made the MODFLOW model the most efficient and accessible groundwater model today. For this purpose, the statistics and information from 15 years ending in 2013 of the Iran Water Resources Management Company and topographical information extracted from satellite data have been used to model the groundwater of the South Mehyar-Dasht Asman aquifer.

After running the model, the parameters of hydraulic conductivity, anisotropy, or HANI, RCH, SY, and parallel lines of the groundwater level were obtained. In the next stage, or the calibration stage, the model was calibrated by the parameters mentioned to minimize the difference between the observed water level and the calculated water level. In the sensitivity analysis stage, the calibrated value of the parameters is systematically changed to determine the model's sensitivity to the parameters. In this stage, it was found that the model received the most influence from the parameters of anisotropy and hydraulic conductivity. To validate the model and measure its accuracy, validation was done. The RMS error was 2.21, which is a good value, but for more certainty, the RMSE was also calculated, and its value was 23 %, which is very appropriate considering the long-term simulation period. Finally, the numbers extracted from the FlowBudget engine based on the calibrated MODFLOW model revealed that during the 12-month simulation period, a particular share of the aquifer's fixed storage was reduced on a daily average basis, resulting in an average drop of 7.5 m in the aquifer's groundwater level during this period. We see that the most significant drop is related to the eastern part of the aquifer.

In this research, the South Mehyar-Dasht Asman aquifer was simulated and modeled by the MODFLOW model and GMS software, and the groundwater level was investigated during the simulation period. The model was calibrated to reduce the difference between the observed and the calculated water level. The sensitivity analysis section also examined the influence of the model parameters. Validation was also done to increase the accuracy of the model's performance. The coefficient of determination or R-squared correlation was 0.9971, which seems to be a good number compared to other studies. The results showed that the water withdrawal is greater than the aquifer's recharge, and a specific volume of the aquifer storage is always reduced during the simulation period, which caused a drop of 7.5 m in the groundwater level. The groundwater level of the South Mehyar-Dasht Asman aquifer continuously decreases from the western part to the eastern region, and the lowest groundwater level is related to the east part of this aquifer. Declining groundwater levels, water shortage conditions, and overexploitation, increase the risk of subsidence, which is an irreversible event. To prevent this from happening, the amount of water taken from the aquifer should be proportional to its capacity, and control and management measures should be carried out on a large scale. Computer models and up-to-date methods help to maintain and manage these water resources as well as possible. Factors such as the dryness of the Zayandehroud River, excessive harvesting, and lack of water have led to the aggravation of the adverse environmental effects of the Gavkhoni International Wetland, and this has increased the importance of simulating the existing aquifers in this basin. Due to the fact that no study has been done on the simulation of the groundwater of South Mehyar aquifer-Dasht Asman and also the duration of the simulation period is 15 years, this research can be a basis for future studies and provide the possibility of comparison with other studies.

Conventional leak detection methods are costly and time-consuming, so attention has recently been drawn to methods that detect leakage by network modeling comprehensively.

In these methods, the difference between the measured pressure and flow rate and those obtained by the model is minimized as a fitness function by calibrating the network and adjusting the nodal demands. It has been established by analysis that the fitness function in itself cannot be a good guide to achieving the minimum point. This drawback was solved by using the ACO method. In addition to the fitness, this method employs a parameter called heuristic guidance to improve its capabilities.

This research compared three ACO-based optimization methods. The first was ACO with fixed heuristic guidance already introduced in the literature. In the second method, the heuristic guidance varies by the values of the observed pressures and pressure decline versus the no-leak state. The third is studied and compared by integrating SSEM and ACO models. These methods were investigated on a network derived from the literature and the Birjand network.

The results revealed that the methods proposed here achieved more precise results in a shorter time and with fewer iterations.

Land use changes are one of the main factors in the amount of surface runoff changes in watersheds. Therefore, it is necessary to investigate it to reduce the damages (human and financial) caused by floods and to modify watershed management. The watershed of Nahre Azam is located in the north of Shiraz city and a lot of loss of life and money to the residents of Shiraz due to floods has occurred in previous years. The present research was conducted to investigate the relationship between land use change and runoff in the Nahre Azam watershed in Shiraz using the SWAT model in the period of 2004-2020. The model was calibrated using data from 2004 to 2014 and validated for 2015 to 2020. These images were classified into 6 main land uses using the supervised classification method after performing necessary pre-processing, and a land use map was prepared for 2040 using the Markov chain method. Then, the effect of the land use change in 2003 and 2040 on the amount of simulated runoff was evaluated with the recalibrated model. The calibration results of Nahre Azam watershed for the values of statistical parameters in the calibration step for the coefficient of determination, P-Facor and R-Facor are 0.77, 0.72, and 2.43, respectively, and for the validation step we obtained 0.69, 0.65, and 2.3 respectively. The analysis of the land use map showed that the main land use change in the region related to the conversion of pastures to agricultural land and urban land, which caused a decrease in pastures. Also, the results of the model simulation using the land use maps of 2003 and 2040 indicated that the amount of runoff decreased. The results revealed that if all the uncertainties are minimized, the calibrated SWAT model can produce acceptable hydrological simulation results for the user, which is useful for water resource and environmental managers and politicians as well as city managers of Shiraz.

Due to inherent limitations of global climate models, their outputs are significantly biased in comparison to observed values which could provide unreliable climate projections. This study evaluates the performance of 10 global climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating precipitation in the Rafsanjan study area over calibration (1986-2005) and validation (2006-2014) period. For correcting simulated precipitation, various quantile mapping-based bias correction methods applied in these two periods. Evaluating the performance of various climate models and quantile mapping-based bias correction methods and approaches is carried out through multiple statistical metrics including NSE, PBIAS, MAE, and KGE as well as Taylor's diagram. Finally, simulated precipitation of selected model extracted for projection period under SSP1-2.6, SSP2-4.5 and SSP3-7.0 scenarios and corrected by suitable bias correction method. Results showed that the MPI-ESM1-2-LR model has better performance in simulating precipitation over calibration and validation periods compared to other climate models. The results of evaluating the performance of quantile mapping-based bias correction methods in both periods also showed that bernlnorm method performs better than others for the correction of simulated precipitation by climate models. In addition, the evaluation results of quantile mapping approaches including NTP, PT, and DDT in these periods demonstrated that NTP and PT have an acceptable performance compared to the DDT approach. Present study can help to improve the credibility of future climate projections using CMIP6 climate models.

This study aimed to calibrate the HYDRUS-2D model for simulating soil moisture distribution under tape irrigation in transplanting sugar beet, with and without mulching. The research was carried out during the cropping seasons 2017-2018 in 14 plots (7 plots with mulch and 7 plots without mulch) with 2 rows of 40x50 tape. To monitor soil moisture distribution, soil samples were collected from 12 plots, and access tubes of the PR2 device were installed at depths of 10, 20, 30, 40, 60, and 100 cm in two plots. The PR2 device was calibrated for a 7- day irrigation frequency and 6 irrigations. The calibration results showed that the default parameters a0=1.6 and a1=8.4 proposed by the PR2 manufacturing company were not appropriate for the loam-textured soil of the study area. For this research, a0 and a1 were modified to 1.8 and 8.3 for soil water contents higher than the filed capacity and to 1.2 and 8.6 for soil water contents lower than the field capacity, respectively. The PR2 device was then used to measure soil moisture contents for five irrigations (6 hours each irrigation), during and after irrigation until the next irrigation. The results showed that the HYDRUS-2D model performs appropriately in simulating soil water content in five irrigation intervals with and without mulch application. The mean absolute value of error (MAE) was less than 0.035, root mean square error (RMSE) was less than 0.042; correlation coefficient (R) was above 70%, and the sum of squared error (SSQ) was less than 0.41 percent. If the hydraulic parameters of the soil were recalibrated for the use of mulch, the HYDRUS-2D model provided the same efficiency. Therefore, the HYDRUS-2D model is recommended for designing and managing tape irrigation due to its good simulation during and after irrigation.

Exploitation, management and protection of water resources, especially groundwater resources, are very important. The management of water resources requires accurate knowledge of the characteristics of the aquifer, the tension to it and finally the groundwater balance. Simulation models help planning managers to determine the amount of aquifer utilization for long-term and sustainable use. For this purpose, changes in the water level of the Kashan plain aquifer in the statistical period of 1387-1397 were simulated by the GMS model for 125 stress periods. The model was calibrated in two permanent and non-permanent modes for a period of 94 months (1387-1395) and validated for a period of 31 months (1395-1397).The result was a drop of 4.4 meters of groundwater during the 10-year study period. To predict the behavior of the aquifer in the near future, the reservoir level was modeled until 1407. Due to the location of this plain in the climatic dry belt and the occurrence of climate change and the lack of effective rainfall to properly charge the aquifer, in this study the focus was on the management of extraction from the aquifer and according to these three scenarios, extraction with the current trend,15% increase and 15% reduction definition and simulation was done. The simulation results of these three scenarios showed a drop of 9.866, 11.895 and 6.993 meters at the end of 1407 respectively.

Optimal use and management of water resources is very important. Also, choosing the appropriate irrigation method plays a vital role in saving water consumption in the agricultural sector. Therefore, in the current research, the effect of the irrigation method on the simulation of sugar beet yield was investigated using the AquaCrop model. In this study, the effect of two methods of drip irrigation (tape) and surface irrigation (furrow) and three levels of irrigation water (50%, 75%, and 100% of the plant's water requirement) on the yield of sugar beet plants was investigated in Sarayan-Ayask region, Iran. A factorial experiment was conducted based on a completely randomized design with 4 replications in 2021-22. The results showed that irrigation method had a significant effect on leaf diameter, leaf length, leaf weight, tuber diameter, and tuber length and weight of sugar beet tuber. Also, according to the results, drip irrigation had greater effect than furrow irrigation on the studied traits. Then, the grain and biomass yield was simulated using the AquaCrop model and the simulated values were calibrated and verified using observational data. Calibration was done using two replications of 100% and 50% stress levels, and validation was done using the replication of 75% stress level. The NRMSE, RMSE, RD and R2 coefficients of the model calibration values showed that the simulated and validated values were close to each other and these values were more precise in drip irrigation than in furrow irrigation. Validation values of two irrigation methods also showed the ability of model in simulating grain yield and biomass.

Today, human activities are one of the most important challenges in issues related to the quality of water resources, which is currently considered one of the management threats in the exploitation sector. This issue will require the identification and evaluation of water resources from different perspectives, of which the vulnerability of water resources is one. This study evaluates the vulnerability of water resources by combining two DRASTIC vulnerability indices in the aquifer sector and WRASTIC vulnerability index in the upland sector of Astana-Kuchsefahan region. In this study, after assessing the vulnerability with the relevant indicators, two methods of entropy and hierarchical analysis were used for scaling based on maximizing the correlation with nitrate concentration. The results showed that the most importance in both indices based on recalibrated weights include industrial use in the WRASTIC index and underground water depth in the DRASTIC index. The analysis of the calibration results showed that the correlation of nitrate with the DRASTIC vulnerability index using the AHP method was 0.61 and with the WRASTIC index was 0.58 using the Shannon entropy method. Also, the measurement results showed that the WRASTIC index ranges from 17 to 46 and the DRASTIC index ranges from 119 to 180. Based on the consolidated results in the western and eastern parts of the highlands, the level of vulnerability using the WRASTIC index is lower and there is a higher vulnerability in the western part of the aquifer compared to the eastern part.

A significant course of action to planning agricultural operations and further maintaining and developing performance on a regional scale involves the accurate and timely estimation of crop yield prior to harvesting using crop growth models. Modeling dynamic changes during crop growth can assist researchers in planning crop management strategies aimed at increasing crop yield. Such models include several parameters that can be calibrated according to the characteristics of the study area. However, insufficent information on location/spatial-wise components or the lack of  thereof in these models along with uncertainties in parameter values may lead to errors in the estimated outputs. In this light, remote sensing data assimilation can be useful for resolving such complications and evaluating the spatial variability of lands, particularly at the regional scale. Remote sensing can estimate values of input parameters for crop growth models such as Leaf Area Index (LAI), fCover, biomass, and soil characteristics. This review paper seeks to introduce and compare different methods of remote sensing data assimilation in crop growth models and examine their advantages and disadvantages. In addition, a literature review conducted in this field can guide the readers in slecting the appropriate crop growth model, relevant remote sensing data assimilation method, and pertinent state/control variables. The literature review indicates that with new sensors and methods in the estimation of remote sensing state/control variables such as LAI and the development and improvement of crop growth models, it is possible to improve the accuracy of crop yield estimation.

Rainfall-runoff modeling is considered one of the important methods in the study of hydrology and environmental management, especially in urban areas, in which sudden floods lead to significant financial and human losses. Thereupon, various models have been developed for urban flood simulation, among which selecting the best, is of significant consideration in the literature.

Runoff peak flow and hydroghs measured in three nodes of an urban drainage network were applied for both spatial and temporal evaluation of the SWMM hydrological model in EPA SWMM, ASSA and Bently SewerGEMS V8i softwares. Other rainfall-runoff models also evaluated were SCS TR-55, SCS TR-20, Rational, Dekalb Rational, Santabarbara UH models (in ASSA software) and SCS UH model (in SewerGEMS V8i software). The models were calibrated considering the measurements during three precipitation events, and then validated by the measured data of three other events. The Nash-Sutcliffe coefficient along with the BIAS coefficient, the Coefficient of Determination and the Root Mean Square Error were used as the efficiency indicators.

The measured runoff peak flow and hydrograph were most compatible with those simulated by SWMM models of EPA SWMM, SewerGEMS V8i and ASSA softwares, respectively. Regarding the results of the partial parameter and the Spearman correlation coefficient methods, model outputs were most sensitive to the percentage of impervious areas, equivalent width, roughness coefficient of impervious areas, the depth of depression of impervious and pervious areas, the percentage of impervious areas without surface storage and the curve number, respectively. The model was not sensitive to the roughness of the permeable areas. The results suggest EPA SWMM as the software with more reliable simulation results for runoff management projects in the study area and urban basins alike.

Soil organic carbon is a key element in determining soil quality, health, and fertility. Due to the complexity of the structure and relationships of soil organic carbon pools, the use of models is beneficial in identifying the reaction of these pools to the change in ecosystem conditions. So, by using the RothC model, the effect of global warming and climate change on the amount of soil organic carbon pool of the agricultural ecosystem of southeastern of Mashhad was investigated. Therefore, the model was calibrated and validated using data measured in 2020 and available long-term data. Comparing the measured values of soil organic carbon and the simulated values by the model, the coefficient of determination (R2) was 0.89. Root means square error (RMSE): 3.45, mean difference (MD): 1.84, mean absolute error (MAE): 2.79, and model efficiency (EF) was 0.73, demonstrating the validity and suitability of the model. The modeling of the future climate changes of Mashhad showed a decrease in rainfall and an increase in temperature and evaporation, leading the amount of total soil organic carbon (TOC) would decrease by 1.13% compared to the current conditions. Considering the decomposition rate constant of the model's four active carbon pools, humus exhibited the slowest decomposition rate of 0.96%. At the same time, decomposable plant materials (DPM), resistant plant materials (RPM), and microbial biomass (BIO) were decreased by 1.18%, 2.21%, and 2.10%, respectively, compared to the current climate condition. Moreover, over time, the decomposition rate decreased due to the decay of active organic matter pools that are easily decomposed.

Due to the importance of determining the behavior of plants in water shortage conditions, it is necessary to determine the coefficients of plant sensitivity to water at different stages of plant growth. Water shortage in the Sistan plain is a serious and important issue, so that the lack of water supply to the Helmand border river, which is the only source of water supply in the region, leads to the destruction of agriculture. The low possibility of increasing new water resources and the need to increase agricultural production from limited water resources in this region requires the use of appropriate scientific and technical methods to increase the productivity of agricultural water consumption. Also, changing the pattern of cultivation and replacement of high-income crops in recent years in arid areas has received much attention from farmers. The purpose of this study is to investigate the effects of water stress on the behavior of garlic in dehydrated conditions to calibrate the production function of this plant to obtain yield response coefficients in conditions of water shortage.

In this study, garlic plant was implemented in the form of a randomized complete block design with three replications in 1398 and 1399 in Sistan and Baluchistan province. Treatments were applied based on stress levels compared to control 30, 35, 40, 45, 50, 60 and 70% of water requirement. This station is located 20 km southeast of Zabol city at latitude 61 degrees and 41 minutes and latitude 30 degrees and 54 minutes in Sistan region. The altitude of the station is 483 meters above sea level, the average annual rainfall is 55 mm and the annual evaporation rate is 4500 to 5000 mm. This region has a very dry climate with very hot summers and mild winters. The area of ​​the experimental plot was equal to 750 square meters was selected for planting garlic of Chinese cultivar before planting the bed was reinforced with animal manure and after growing based on soil fertilizer test, 300 kg of potassium phosphate, 200 kg of triple phosphate and 100 kg. One kilogram of urea was added to the soil. Then plow and disc and finally leveled. On the 24th of October, grooves were planted with a shovel at a distance of 20 cm and a depth of 5 cm to plant garlic of Chinese cultivar, and garlic tubers were planted at a distance of 8 cm on the rows. Then, based on the obtained information, the two functions of Tafteh and Raes production were investigated and yield response factors were determined during the growing period.

Comparing the two methods used the highest yield of garlic in the study was 8240 kg / ha, for the production of which 833 mm of water was used. The data of the first year were used to calibrate the yield response factors of garlic and after calibrating the two production functions of Raes and Tafteh, the values ​​of the yield response factors of garlic were presented. This coefficient was different in the range of 0.5 to 1.2 in different periods of garlic plant growth, the change curve was presented. Evaluation of Rice method in the second year with root mean square error of 1302 kg / ha and normalized value was about 23%.

In the method of Tafteh the mean root mean square error was 485 kg / ha and the normalized value was about 11%. Therefore, the method of Tafteh is recommended in determining the yield of garlic under water stress conditions.

Runoff measurement has always been difficult due to financial and time constraints and severe physical conditions. To cope with these problems, the use of hydrological models is very common, which has been developed today. Better simulation of hydrological processes requires that the input data of hydrological models be able to accurately describe the actual conditions of the catchment area. The purpose of this study was to compare the efficiency of SWAT model in simulation of runoff in Mahabad watershed in West Azarbaijan province. Calibration and validation of the model were performed using the SUFI_2 model. The runoff statistics of three hydrometric stations have been used to calibrate and validate the model. The model was performed using temperature and precipitation data for 17 years (1996-2012). Years (1999-2009) for calibration and years (2010-2012) were considered for model validation. The indicators of p-factor, d-factor, R2 and NS were used to assess the ability of the SWAT model to simulate the runoff of the three stations. The results of these simulations to calibrate the outlet of the basin were 0.70, 0.71, 1.3, 0.84, and0.5,0.48,0.85,0.9, and also the results for the basin entrance) in the calibration phase for were 0.79 , 0.70 , 0.97, 89.9 respectively, and for validation 0.65, 0.62 , 0.78, 0.98 and for the Kuter station at the calibration stage, it was equal to 0.84, 0.68, 1.1, 0.86 and 0.6, 0.65, 0.85, 1 for validation. The overall simulation results showed that the SWAT model could be a suitable tool for runoff simulation.

The FAO Penman-Monteith (FAO-56 PM) model is proposed as a standard model to estimate reference evapotranspiration (ETO) in various climates. Different models are used to estimate the total solar radiation (Rs) as one of the essential inputs of this model. This study aimed to calibrate and validate four Rs estimation models (Ångström-Prescott, Hargreaves-Samani, Mubiru et al., and Chen and Li) in Ahvaz (with arid climate) and Hamedan (with semi-arid climate) stations during the 1992-2020 climate period and the effect of these models on the ETO estimation. The coefficients of these models were calibrated by the intelligent shuffled frog leaping algorithm (SFLA). To evaluate the efficiency of these models, the estimated Rs values were compared with measured values. Based on the root mean square error (RMSE), the coefficient of determination (R2) statistics and Nash-Sutcliffe, the A-P model with RMSE=1.929, R2=0.918, NS=0.896 and Mobiro et al. model with RMSE=2.925, R2=0.875, NS=0.860 showed better performance than the other models in Ahvaz and Hamedan stations, respectively. A decrease of about 20% was observed in the percentage of ETO difference between, the calculated and the estimated Rs compared to the measured Rs in 2 stations at all of the models.