simulation

The concentration of suspended sediments is one of the most important water quality indicators in surface water resources and a significant hydrological phenomenon. However, traditional methods, such as sediment rating curves (SRCs), lack accuracy due to not considering all effective parameters. In this context, hybrid models that include SRCs and artificial neural networks (ANNs) have emerged as a promising approach for enhancing SSC prediction accuracy. These models, with their ability to use complex nonlinear patterns, outperform traditional methods. This study aims to develop and implement an SRC-ANN hybrid model for SSC prediction. The proposed model is predicted to significantly improve prediction accuracy by combining the strengths of both methods, aiding in optimal water resource management and the proper functioning of hydraulic structures.

This research introduces a novel hybrid model that integrates sediment rating curves (SRCs) and artificial neural networks (ANNs) was used for a more accuracy prediction of suspended sediment in the Naroun (Afejeh) hydrometric station. For this purpose, data from 222 sample of flow discharge and suspended sediment over a 50-years period (1971 to 2021) were used. Additionally, 14 different were employed, including 6 sediment rating curve methods, 6 artificial neural network methods and 2 hybrid methods, to simulate suspended sediment. The performance of each method was evaluated using statistical criteria such as coefficient of determination (R2), efficiency coefficient (ME), and mean relative error percentage (RME).

The results showed that among the sediment rating curve methods, the most accurate simulation of the observed sediment discharge conditions compared to other methods was related to the midpoint method, with a coefficient of determination (R2) of 0.840, a modeling efficiency (ME) of 0.820, and a mean relative error (RME) of 0.211%. Also, among the artificial neural network methods, the most accurate simulation was related to the CANFIS method, with a modeling efficiency (ME) of 0.8123 and a mean relative error (RME) of 0.248. Finally, to improve the prediction results, hybrid models 1 and 2 were used. The results showed that he best estimate of suspended sediment was related to hybrid method 1, with a modeling efficiency (ME) of 0.8761 and a mean relative error (RME) of 0.06359%. In the mentioned method, both the estimation of peak flow discharge and the estimation of base flow discharge were very accurate, and it was introduced as the most accurate method for predicting suspended sediments. These results highlight the potential of using hybrid model 1 to significantly improve prediction accuracy and to better fit the observed data.

Among the sediment rating curve methods, the mean category sediment rating curve method was identified as the best approach for predicting suspended sediment due to its consideration of data distribution and flexibility. The performance of artificial neural networks (ANNs) in simulating sediment for base and normal flows was good, but were weaker in predicting sediment during flood events. The most accurate method for suspended sediment prediction is the Hybrid model 1, which use a MSM and ANN methods. Improper selection of a sediment prediction method can lead to inaccurate results. Also, it is essential to examine the impact of other variables beyond flow discharge on sediment. The results of this research showed that it is possible to significantly increase the accuracy of suspended sediment prediction using hybrid models, and that these models can be utilized as an effective tool for managing and predicting suspended sediments, as well as improving water resource management. It is recommended to use advanced sampling facilities and a larger number of samples at hydrometric stations, especially in high-flow and flood conditions, for the development and optimization of hybrid methods. Additionally, it is suggested that engineers and water resource managers utilize the findings of this research to develop optimal strategies for suspended sediment management.

Today, soil erosion is recognized as one of the major contemporary environmental and socio-economic problems. Because it affects agriculture, natural resources, environment and social economy. Although effective measures have been taken in different countries to reverse the adverse effects of soil erosion on a large scale, the rate of soil erosion on a global scale is still very worrying. For example, it has been estimated that approximately 75 billion tons of surface soil are lost annually on a global scale as a result of erosion. The results of studies in Iran also indicate a high rate of soil erosion. The available evidence indicates that the quantity of water erosion in Iran ranges between 1 and 4 billion tons per year. Therefore, efforts to reduce soil erosion should be continued more than before. In this regard, it is very necessary to quantify and identify the spatial distribution of soil and sediment loss areas to provide up-to-date information for policy makers and experts to devise effective strategies for water and soil protection and reduce the adverse consequences of soil erosion.

The study area of Rudzard watershed is located in the southwest of Iran and the east of Khuzestan province, which leads to Ramhormoz city in the south and Izeh city in the north. The average annual rainfall of the area is 500 mm, with an annual average discharge of 3.69 cubic meters per second. The region is primarily utilized for forest, pasture, and agricultural activities, which collectively occupy 35%, 30%, and 35% of the total area, respectively. Rainfall data from stations inside and outside the basin during a period of 15 years (2005 to 2019) on a daily basis from the General Directorate of Meteorology of Khuzestan Province and runoff and sediment data from hydrometric stations from the General Organization of Water and Electricity of Khuzestan which was prepared. The data of the 30-meter elevation digital model and Landsat 7 satellite images were obtained from the website of the United States Geological Survey. The satellite images were used to calculate the vegetation factor. Soil science information was also collected through sampling and previous studies. Finally, the amount of erosion was calculated using the RUSLE model. Sediment volume and sediment delivery rate were also estimated using the SEDD model.

Rainfall erosivity and topography are the most important factors influencing soil erosion with an average of 10.1 (MJ mm ha-1 h-1 y-1) and 9.3, respectively. The soil erodibility factor varied between 0.023 and 0.034 with an average of 0.028. The vegetation factor also varied between 0.14 and 0.27 with an average of 0.19. Finally, based on these five factors, the annual average of soil erosion in the region was found to be 7.2 tons per hectare per year based on the RUSLE model. The analysis of the results showed that the highest amount of soil erosion was obtained in the forest areas with steep slopes, and the second and third highest amount was obtained in the pasture and agricultural areas, respectively. The reason for the high erosion rate in the forest areas is the slope of more than 50% in these areas. Based on the average of categories method, the average annual sediment production of the whole area was calculated as 210,000 tons per year, which is equivalent to 2.45 tons per hectare per year, considering the area of the area. The average sediment delivery ratio of the basin was estimated to be 34% based on the average erosion and sedimentation. The results of the SEDD model also showed that the sediment delivery ratio in the whole area varied between 0 and 0.99, and its highest value was obtained in the areas adjacent to waterways. Further investigation showed that the highest sediment delivery ratio was associated with forest and pasture land. This is because these two land uses are mainly located in sloping areas. On the other hand, the lowest ratio of sediment delivery was related to agricultural lands due to the low slope.

The results showed that the use of simulator models with small and easily accessible inputs, such as RUSLE and SEDD models, has an acceptable efficiency in estimating the amount of erosion and sedimentation, especially in large areas limited by lack of data. Elevation and slope factors were found to be the most important erosion and sediment transfer factors in this area, such that the highest erosion and sedimentation rates were obtained on forest lands with slopes greater than 50%, and the lowest erosion was associated with agricultural lands with low slopes. Although the use of experimental models such as RUSLE and SEDD to evaluate soil erosion and sedimentation in watersheds is common, the accuracy of the results obtained from them is often due to the lack of real data of erosion and sedimentation in most watersheds of the country. Therefore, it is suggested that the accuracy and validity of the results of the SEDD model as a model with low and accessible inputs in several watersheds, especially representative watersheds that have an appropriate database or through the method such as cesium 137 should be investigated.

Land use/land cover (LULC) change in a specific area results from environmental and socioeconomic issues, as well as human activities over time and space. Increases and decreases in population growth in the system, climate, financial development, and physical elements, such as landscapes, soil grade, and type all influence LULC changes. The examination of conducted and documented research regarding the impact of land use change on the hydrological response of a watershed indicates that numerous studies have focused on this field both in Iran and globally. However, the effect of the pattern of landscape and the trend of its characteristics has not been observed to date. In this study, the processes of changing the landscape of the land,  management scenarios, and the runoff of the Tajan watershed were investigated taking into account a 20-year period in the basin using the SWAT model under the scenarios of the continuation of land use changes. The relationship between flow and topography parameters was examined to take into consideration the potential possibilities of using topography parameters in watershed management.

The study area is the Tajan River basin (about 4000 km2) surrounded by the Alborz Mountains in the south and the Caspian Sea in the north, located in Mazandaran Province. The environmental hydrological flow index was investigated using the SWAT hydrological model, which is a semi-distributed, continuous hydrological model developed for water resource managers to select the most appropriate strategy or solution by considering the impact of various management practices on river flow and non-point source pollution. Then, the flow intensity of the area was investigated using the land use maps of the region under the periods 1991, 1995, 2000, 2010, 2016, and 2020 in terms of the changes in the landscape. Calibration and validation periods were selected based on the availability of hydro-metric station statistics located on the Tajan River for the years 1997-2012 and 2013-2018, respectively.

The simulation results showed good simulation capability of the SWAT model in estimating the flow of Kordakhail, Parvij, Rigcheshmeh, Varand, Vastan, Garmrood, Karchai, and Aliabad hydrometric stations. EFC indicators, which are categorized into low monthly flows, very low flows, high flow pulses, small floods, and large floods, were also considered in this study because they are crucial for maintaining the ecological integrity of the river. The first EFC group, low monthly flow, represents surface flow, indicating that changes in these parameters could be associated with the availability of surface flow. Essentially, a minor decrease in low monthly flow can be observed in April, May, and October to December, ranging from 0.2% to 1.1% based on the average forecast of the ensemble. Conversely, low monthly flows from July to September are expected to increase significantly by 4.3% to 9.4% at a 95% confidence level. Overall, the flow process was estimated using the SWAT OUTPUT VIEWER. After examining the capability of the model for the effect of its effect on the amount of runoff, the results showed that the amount of runoff will increase in the land LULC, it is necessary to develop strategies for the river runoff management. The reason for the low results at some stations was the lack of a sufficient number of the rain gauge and synoptic stations in the areas to record heavy rainfall on a daily scale with better spatial coverage.

In the present study, the SWAT modeling and land change analysis were investigated as new planning tools to investigate land dynamics in the Tajen watershed from 1990 to 2020. The results of the analysis of changes during the years 1990-2020 showed the reduction of forests by 1211 hectares. The most deforestation was observed in the margins of former agricultural lands due to better access. In addition to the increase in population growth, the use of forest wood as fuel and the construction and repair of villagers' houses are other reasons for deforestation in the study area. The destruction of forest lands can also be attributed to livestock grazing. Similar to other modeling studies, the results presented in this research include various inherent limitations, and the modeling uncertainty was multiplied due to the application of land cover flow simulation in the hydrology model. This study clearly predicted the effects of vegetation change conditions in the Tajen watershed in several intervals. However, the decrease in water availability in late spring and summer can jeopardize the amount of water for irrigation downstream the basin, unless proper water management and storage is implemented along with better use and management of runoff. The results of this study especially confirm the importance of land cover, which can have a small effect, decrease, or increase the amount of water. Moreover, this study provides valuable and important information for decision-makers to design compatible scenarios in accordance with the principles of sustainable development, which aims to manage the flow. Finally, the results of this study especially confirm the importance of land cover, which can have a small, decreasing, or increasing effect on water quantity.

Surface water scarcity and overexploitation of water have brought about a serious crisis in the contemporary age. Therefore, if the factors contributing to such a crisis remain unexplored and proper management is not implemented, water resource systems will face more severe problems. On the other hand, as complex systems possess their own specific elements, their analysis requires a kind of simulation that interconnects all components and phenomena within and outside the system using a specific boundary, helping to obtain more realistic results. Moreover, unlike linear thinking, the variables of a system are governed by causal relationships in dynamic thinking, indicating that changing one componential element of the system affects other components. However, the review of the related literature reveals that the majority of studies already conducted on drought have applied a linear perspective, and only a few studies have followed a dynamic approach. Moreover, it could be argued that no study has investigated the influence of long-term hydrological droughts on water resource systems with hydraulic structures on a daily scale. Therefore, when dams are constructed on water currents, it is necessary to investigate and simulate hydrological drought and the consequent changes made in downstream. This study used a dynamic perspective to simulate hydrological drought from 1991 to 2022 in the Qhorveh Dehgolan watershed in Kurdistan, Iran, on a daily basis, using the Vensim software. 

The study area included the Qhorveh Dehgolan sub-basins in Kurdistan province. The study simulated the daily dam outflow and the hydrological drought using the data regarding the river flow statistics, crop types, cultivated area, cropping patterns, irrigation requirements, precipitation, and hydrograph network from 1991 to 2022. As one of the most commonly used methods for analyzing hydrological droughts, the sequence theory, which was also applied in the current study, determines the drought index threshold, according to which the daily flow rates below the specified threshold indicate the occurrence of drought. However, there are instances where, for a short period, the flow rate exceeds the threshold level, leading to the classification of the drought period into smaller, dependent droughts. The problem mostly occurs in studies carried out on a daily basis, where small droughts with shorter durations are also counted as severe droughts. The problem can be solved by aggregating such small droughts.As the Internal Criterion method exhibits greater accuracy than other similar approaches used for aggregating small droughts, the current study used both sequence theory and the Internal Criterion method.  The primary advantage of the current research model lies in its multidimensionality, obviating the need to separately obtain the feedback and variables for all stations. This characteristic allows for the introduction of equations, feedback, and all variables for a single station without necessitating the expansion of equations and other factors to accommodate multiple stations. Another advantage of the model is that it uses feedback mechanisms to determine the drought tolerance level at each station, indicating that the model initially calculates sequences and volumes at various drought levels using the data collected from stream gauging stations. Then, through feedback loops to flow-dependent variables at each station, the model adjusts the relevant figures within permissible limits to ensure that flow rates approach the minimum flow over the statistical period without reaching zero. Accordingly, based on a long-term time series of flow rates for each station and the related dependent variables, the drought tolerance level for each station was determined, considering the fact that drought conditions at any given station do not necessarily conform to a specific level. 

While the precipitation trend was not decreasing throughout the study period, drought has significantly increased in terms of frequency and intensity during certain years. The precipitation trend in the region indicated an increase in the 1999–2001 period, concurrently accompanied by severe drought conditions, substantiating that an external factor has brought about such drought. The construction of dams and the declining outflow volumes in proportion to the entire period have affected both the slope of the flow duration curve and the drought conditions. Therefore, on a daily scale, the flow rate and its variations determine the intensity and severity of drought events. Accordingly, when flow rates are similar, droughts occur over longer durations in less severe intensity, whereas sudden changes in flow rates result in droughts with shorter durations but greater severity. For instance, at Sang Siah station, the minimum drought intensity (0.0006 MCM) with a long duration (172 days) was recorded in 2013. Similarly, at Gol Bolagh station, a drought lasting 216 days with an intensity of 0.122 MCM was recorded in 2013. However, at Shadi Abad station, which did not exhibit significantly distinct or varying flow rates, the maximum drought duration (215 days) coincided with the maximum drought intensity (0.73 MCM) in 2014. The same was true for Delbaran station, where the maximum number of dry days was recorded in 2014 (305 days), with a corresponding maximum intensity being 0.46 MCM.

This study carried out simulations in a dynamic environment, finding that according to the dam outflow rates when river flows exhibited minimal fluctuations, droughts were characterized by higher frequencies and less severe intensity. On the other hand, when flows were higher with significant variations in peak values, drought showed greater intensity and lasted for shorter periods. It can be concluded that analyzing the trend of changes made in the flow rate as a result of constructs built in the region and investigating the precipitation variations are required for investigating hydrological droughts that may occur over the dams’ inflow rates and simulating the discharge rates. This is consistent with the results found in some other studies (Hamedi and Paimozd, 2023; Alishahi Chegeni et al., 2023). The similarity of the behavior of the stations near the newly constructed dams was also notable. The Sural, Sang Siah, and Gol Bolag dams were constructed and filled in the region in 2011 and 2013, respectively, coinciding with the recorded drought events, which also resulted in rivalry over water harvesting and the development of farmlands. Hassan Khan and Dehgolan stations recorded completely different flow duration curves throughout the period. Therefore, severe and very severe drought levels in these stations will have a lower threshold compared to other stations. In this study, a simulation was designed to calculate the tolerable drought level of the station, which was achieved by employing feedback loops and flow duration curves to assess the severity of drought conditions, particularly when flow rates were low and severe drought events occurred at levels beyond those officially declared. Hassan Khan and Dehgolan stations, which were under the influence of their upstream dams, showed severe droughts in lower values than the declared average drought tolerance threshold of 25%.

Climate change is one of the biggest challenges that humanity has faced in the 21st century, because it can have severe effects on water resources, agriculture, energy, and even human bioclimate. The purpose of this study is to investigate the changes of climatic parameters in four sites of Plour, Rineh, Kangarchal and Poshtkoh in Mazandaran province using general atmospheric circulation models. For this purpose, the data of IPSL-CM5A-MR and CSIRO-MK36 models under RCP4/5 and RCP8/5 scenarios have been scaled with LARS WG statistical micro-rotator to evaluate the impact of climate change. After carrying out calibration, verification and modeling of data in two stations (Abali and Kiasar), the effectiveness of the models in terms of the amount of compliance of the observed data with the simulated value using the RMSE index, R2, MAE was evaluated. The obtained results showed that IPSL-CM5A-MR and CSIRO-MK36 models obtain acceptable results. Considering that in Abali station, the amount of rainfall in the base period is 542.08 mm under RCP4.5 and RCP8.5 scenarios in both IPSL-CM5A-MR and CSIRO-MK36 models in the future between (2021-2060) will increase by 0.91-0.97 percent. The average maximum temperature will increase by 1.7-2.2 centigrade and minimum temperature by 1.56-1.88 centigrade compared to the base period. Accordingly, the amount of rainfall in the base period in Kiasar station is 528.46 mm, which under the scenarios of RCP4.5 and RCP8.5 in both models in the future between the years (2021-2060) is 96. 0-87% will increase. The average maximum temperature will increase by 1.72-18.2 centigrade and the minimum temperature will increase by 1.62-1.82 centigrade compared to the base period.

Climate change has had a significant impact on the global production of agricultural products. Studying the impact of climate change on the production of agricultural products is very important for making preventive decisions to improve agriculture. The aim of the current research is to simulate the cultivation pattern of Jiroft plain agricultural products under the influence of different climate scenarios. For this purpose, at the beginning, the effect of temperature and precipitation climatic variables on the yield of onion and potato crops in the period 1991-2022 was investigated using regression analysis. The reason for choosing these two crops is that they occupy a high area under cultivation and are cultivated in spring and autumn. Then, using HasGEM3 general circulation model, the climate variables of Jiroft plain under RCP 8/5, 4/5, 6/2 series scenarios available in the latest report of the International Panel on Climate Change (5th report) for the periods (2011-2045) , (2046-2065), (2066-2079) and (2080-2099) were predicted. In the end, by applying the positive mathematical programming approach, the cultivation pattern of the region was simulated and the effects of climate change on the cultivation pattern in the mentioned periods were investigated. The results showed that the climatic parameters of temperature and precipitation have a significant effect on the performance of selected products. Also, by applying the forecast of climate variability in the cultivation pattern model of all selected crops in the periods (2011-2045), (2046-2065), (2066-2079) and (2080-2099) based on The noses of the HasGEM3 model are affected by different climate scenarios. Considering the effects of climate change and improving the productivity of agricultural products, it is one of the bad effects of this phenomenon. The results of this research can be useful in agricultural planning and economic development of Jiroft city.

Many human civilizations have collapsed due to the climate. Historical and geographical evidence demonstrates that the majority of significant and stable civilizations were established in specific geographical-climatic areas with suitable biological conditions. Human civilizations have faced serious damages and collapses due to unfavorable climatic conditions. The existence of most human civilizations can be attributed to climatic conditions, with historical and geographic evidence, especially the special environmental condition of the world in the current situation. Countries that deal with biological, economic, social, and even political issues may not give special importance to this issue. The climate conditions around the world are constantly changing, and there will be severe problems in different dimensions in the not-too-distant future. Their existence could even be threatened by economic problems they will face. The world's population increase and energy demand in today's industrial world also result in damage to the planet's climate and atmosphere, as well as the effects of natural disasters. The human burden has not been spared and has been affected by many fundamental changes, of which the phenomenon of climate change is one of the clear examples. The most important indicators of climate change are temperature and precipitation. The change of each of these factors causes climate variability, which also has consequences on people's lives. The increase in greenhouse gases and suspended particles in the atmosphere, along with variations in the earth's surface, are among the most apparent consequences, and the damage caused by the world's industrial activities and human societies in the past two centuries. Different economic sectors, such as agriculture, industry, tourism, water, and health, are affected by this phenomenon. Based on what has been stated, it can be concluded that climate change will definitely occur in Iran. The agricultural sector will face significant challenges due to the occurrence of such things, given the climatic situation, hydrological characteristics, and limitations of the country's agricultural sector. It has created a unique climatic situation in Iran due to its location. The high-pressure rule in the subtropical zone causes the total amount of precipitation in Iran to be low.

The LARS-WG model is a model that is utilized for exponential micro-scale GCM models. This model is a great choice for generating random weather data, which can be used to generate rainfall, radiation, and maximum and minimum daily temperatures at a station for both current and future climate. In order to model meteorological variables, the LARS-WG model uses a complex statistical distribution. Modeling is based on the length of dry and wet periods, daily precipitation and radiation series, and semi-empirical distribution. Climatic parameters such as minimum temperature, maximum temperature, precipitation, and solar radiation are inputs for the LARS-WG model, which are all in the daily time frame. In this model, temperature is not taken into account when modeling radiation, and the sundial can be utilized instead. From the semi-empirical distribution of the rainfall for the month in question, and without taking into account the wet series or the amount of precipitation from the previous day, the amount of precipitation for one day is calculated. In this model, the temperature is estimated using Fourier series. The daily minimum and maximum temperatures are modeled as random processes using the average and standard deviation of the daily standards, which depend on whether the day is wet or dry. The mean and standard deviation of the seasonal temperature are simulated using the third-order Fourier series. Residue values are determined by subtracting the average values from the observed values, and the minimum and maximum data are utilized in time autocorrelation analysis. Minimum temperature, maximum temperature, precipitation, and radiation are the outputs of this model. LARS-WG model data generation involves three stages: calibrating, evaluating, and creating meteorological data. In the next step, using LARS-WG models under RCP 2.6, 4.5, and 8.5 scenarios and the micro-scale exponential LARS-WG generating model of Jiroft City climate changes during the planting period of each crop in the periods of 2011-2045, 2065-2046, 2066-2079 and 2080-2099. In the final stage, a positive mathematical programming model was utilized to investigate the impact of climate change scenarios on Jiroft City's planting patterns using predicted climate change results. The cultivation pattern of Jiroft city today and the predicted effects of climate change parameters during the periods 2011-2045, 2046-2065, 2066-2079, and 2080-2099 are examined in this section. The current cultivation pattern will be discussed in the previous step. A positive mathematical programming model and GAMS25 software have been employed in this regard. Separate information about the area under cultivation, production, and performance of selected agricultural products, as well as the consumption of inputs, for the agricultural year 2021-2022 is presented in the table.

The results indicate that the performance of selected products is significantly affected by the climatic parameters of temperature and precipitation. Also, by applying the forecast of climate variability in the cultivation pattern model of all selected crops in the periods 2011-2045, 2046-2065, 2066-2079 and 2080-2099 based on the noses of the HadCM3 model are affected by different climate scenarios. The improvement of agricultural productivity and climate change are both negative effects of this phenomenon. The findings of this investigation can be advantageous for agricultural planning and economic development in Hamadan province.

SWAT model is a suitable tool for simulating hydrological processes. This model requires many inputs that often cannot be measured directly and is considered one of the main sources of uncertainty in these models. The recalibration process can reduce the uncertainty in the model results by adjusting and adapting these inputs. The researches showed that calibrating a hydrological model by using the common automatic CV calibrating algorithms will not provide proper accuracy in the prediction of hydrological variables during the validation period, so PSO algorithm was used to calibrate the SWAT model. Since there is no mathematical and logical rule to determine the best combination of PSO algorithm parameters and these combinations are selected based on trial and error and among many different combinations, therefore trial and error based methods are very time-consuming and sometimes impossible. In this research, Taguchi method was used to determine the best combination of PSO algorithm parameters.

In this research, the ability to use the SWAT model to simulate monthly runoff in the Javanmardi Watershed, one of the main sub-basins of the Lordegan Watershed with an area of 380 square kilometers, was investigated. In this study, the PSO algorithm parameters, including the number of simulations (A), the number of repetitions (B), the speed calculation weight (C) and the movement parameter (D), were defined in four levels. Then, these parameters were designed and implemented according to the experiments in the L16 orthogonal array (using the Taguchi experiments design method). The performance scale used to evaluate the algorithms was RPD (Relative Percentage Deviation). Considering the variable nature of the response in this study, the S/N index "the lower the better" was used to analyze the Taguchi test results. The selection of arrays and calculations were done in Minitab 16 software.

In the sensitivity analysis stage, which was performed before the model recalibration, among the 28 parameters studied in this research, the model showed sensitivity to the changes of 22 parameters, and they were identified as variables influencing the simulation of runoff in Javanmardi Watershed. The results showed that the parameter of the runoff curve number (CN) is the most important factor and the parameters of soil apparent density in the wet state (SOL_BD) and average water usable by the plant (SOL_AWC) are among the most important factors controlling the flow rate in the study basin, respectively. Based on the results simulated by the PSO algorithm, it was found that the SWAT model has an acceptable accuracy for estimating the monthly runoff in the study area. So, in the recalibration phase, the r-factor and p-factor indices were 1.23 and 0.88, respectively, and the explanatory and Nash-Sutcliffe coefficients were 0.77 and 0.75, respectively. In the validation stage, the r-factor and p-factor indexes were 1.31 and 0.84, respectively and the explanatory and Nash-Sutcliffe coefficients were 0.72 and 0.73, respectively. In this study, the best combination resulting from the application of Taguchi method for the parameters of the number of simulations, the number of repetitions, the speed calculation weight and the appropriate parameters in the PSO algorithm were determined as 40, 100, 0.2 and 0.15 respectively (A4B4C4D3).

The results show that the SWAT model has an acceptable accuracy for estimating the monthly runoff in the Jawanmardi Watershed, and the PSO method is an effective algorithm in calibrating and determining the uncertainty of the model in this basin, and the use of the Taguchi test design method is a suitable way to determine the best combination of PSO algorithm parameters is for researchers who use this method to optimize the SWAT model.

In recent years, the use of SWAT model has been used as a common tool for simulating flow rate, sediment production and evaluating different scenarios to reduce sediment production and runoff. The current research also aims to evaluate the changes in runoff and sediment under the influence of irrigation water reduction, using the SWAT model in the irrigation and drainage network of Dez River.

Recalibration and validation of the model for simulating runoff (Shoshtar, Arab Asad, Shavor Bridge, Harmeleh and Bamdej stations) and sedimentation (Shoshtar, Shavor Bridge, Harmaleh and Bamdezh stations) using statistical data from 1995 to 2012 and 2013 It was done until 2017. The simulation results were also evaluated using R2 and NSE coefficients. Then the scenarios of 10, 20 and 30% reduction of irrigation water were introduced to the model to evaluate their effect on runoff and sedimentation of the study area.

The results of model evaluation using R2 and NSE coefficients indicate the appropriate performance of the model in simulating the mentioned parameters. The results of the evaluation of scenarios of 10, 20 and 30 percent reduction of irrigation water show that applying the 10 percent scenario had the least impact on the amount of sedimentation. On the other hand, applying the scenario of 30% reduction of irrigation water will increase the runoff (30-60%) due to the reduction of the collection of runoffs for irrigation purposes and consequently the increase of the amount of sediment (20-50%) in the irrigation and drainage network of Dez River. The obtained results indicate that the increase in the amount of runoff in the basin, especially in the rainy season, has caused an increase in erosion and consequently an increase in sediment in the basin. Therefore, it can be stated that the time of applying the scenario and the characteristics of the soil in the area are the most effective components on the amount of runoff and sediment in the basin.

According to the results obtained, according to the effect of runoff on erosion and the amount of sediment in a watershed, it is necessary to use methods to control and reduce runoff, such as watershed operations, structures to contain and store runoff, and use modern irrigation methods to prevent soil erosion. Because traditional irrigation methods, in addition to water losses, cause a decrease in soil fertility and an increase in sediment in the drains of agricultural lands. In lands that are irrigated by traditional methods, sometimes the amount of water used is more than the infiltration capacity of the soil. This causes erosion of the surface layer of the soil by the runoff from water accumulation. Therefore, agricultural management approaches in basins prone to soil erosion should be focused on modern irrigation methods with minimum runoff and drainage output.

Excessive exploitation of groundwater in Damghan plain has caused many problems such as land subsidence in the study area, along with the reduction of the groundwater level and the loss of a part of the aquifer and the reduction of the specific yield of aquifer. Therefore, in order to control the withdrawal from the aquifer and as a result to reduce the drop in the groundwater level in that plain, it was investigated. For this purpose, in this research, the mathematical modeling of Damghan Plain was done for the optimal management of exploitation and providing the optimal cultivation pattern in order to save water consumption with the focus on controlling the groundwater level and limiting land subsidence.

In this research, Damghan plain aquifer was first simulated using MODFLOW mathematical model. Then, taking into account different scenarios of aquifer exploitation, using the NSGAII genetic algorithm, optimization of multi-objective exploitation of water resources and optimal management of water supply and demand in the agricultural sector was done. The results of the model were evaluated based on statistical error parameters to predict the ground water level.

The results of this research showed that the MODFLOW numerical model was able to simulate the groundwater level of the studied plain well in steady and unsteady conditions in the calibration and validation stages. Also, the results of the simulation-optimization algorithm showed that by changing the cultivation pattern and also by changing the type of irrigation system from traditional to modern irrigation and as a result of increasing the irrigation efficiency (90 percent), the average drop of the groundwater level of the plain can be reduced. It reduced the groundwater level of the study area from 0.49 m in existing conditions to 0.07 m in optimal conditions. This will compensate the deficit of the aquifer from 31.90 to 5.1 million cubic meters per year.

Using management strategies such as changing the cultivation pattern, increasing irrigation efficiency, and also by controlling surface water and injecting it into groundwater, the process of reducing the groundwater level in the studied plain can be completely controlled and provided 100% of the water needs of the plain.

Climate change affects extreme events such as floods and droughts. This research was conducted in the watershed of Kashmar City in Khorasan Razavi province and the output of general circulation models from the CMIP5 model series was used to check the performance and validity of these models in predicting the climatic parameters of precipitation, average temperature, maximum temperature, and minimum temperature during the period 1989-2005. The best model was selected using the evaluation indices of the determination coefficient, the root means squared error, the absolute mean error, the correlation coefficient, and the mean squared error. Finally, the BCSD method was employed to downscale the data for three time periods near future (2020-2038), middle future (2039-2069), and far future (2070-2100) under the IPSL-CM5A-MR model and future emission scenarios (RCP2.6, RCP4. 5, RCP6, RCP8.5). The Mann-Kendall statistic test was used in order to investigate the trend of changes in monthly, seasonal, and annual rainfall and temperature variables. The results of the model evaluation indices for four climate models indicated the highest performance of the IPSL-CM5A-MR model in simulating precipitation and temperature with a high correlation coefficient and relatively low error indices compared to other models. The results of the investigation of the rainfall trend showed that in the far future, the rainfall component will be completely decreasing and it will have the largest decrease under the RCP8.5 scenario with a value of -41.12 mm. The average temperature is also increasing in all months and the maximum increase in the average temperature in the far future under the RCP8.5 scenario is 7.58 °C. The amount of minimum temperature and maximum temperature increases in all months, and the maximum increase of minimum temperature in the far future interval under the RCP8.5 scenario is 8.35 °C. Furthermore, the maximum increase in the maximum temperature in the far future interval under the RCP8.5 scenario is 7.09 °C.

Protecting water, soil, and organisms, rangeland vegetation is naturally spread over the earth like an umbrella. On the other hand, vegetation changes either cause an improvement or irreparable damage to water and soil resources. Therefore, knowing the effect of vegetation changes on hydrological components of the watershed, including base and peak flow, is a prerequisite for any management planning at the watershed scale. However, the issue takes on particular significance in arid and semi-arid areas, where there is no sufficient quantitative data and the land cover and land use classes are widely dispersed. Therefore, this study sought to calibrate and validate a model for simulating runoff and sediment in the Sarbaz watershed using the soil and water assessment tools (SWAT). What follows presents a scenario concerning the simulation of the influence of improving rangeland vegetation on hydrological components of the Sarbaz basin using a recalibrated and validated model.

 This study used the SWAT hydrological model to investigate the effect of vegetation changes on discharge and sedimentation in the Sarbaz River watershed located in Sistan and Baluchistan province, enacting the scenario of improving rangeland conditions from poor to moderate. To this end, HRU was obtained from the combination of land use, soil, and slope class maps. Then, surface runoff, sediment, and chemical elements were calculated for each HRU, each sub-basin, and the watershed, respectively. On the other hand, ninety-seven hydrological response units were obtained based on digital height lines of 21 sub-basins after combining three maps. Then, following the preparation of the intended parameters and input data, the model was calibrated for 17 years from 1999 to 2016, and validated over a five-year period from 2017 to 2021. Finally, the model was implemented for the present time based on preliminary data, and then the scenario of improving the pasture conditions from poor to moderate was enacted to determine the influence of vegetation on runoff and sediment.

 The sensitivity analysis revealed that the alpha parameters in the return flow (v_ALPHA_BF) and the initial curve number of American soil conservation (CN-SCS) for medium humidity conditions exerted the greatest influence on the calibration and validation of the simulation. The results obtained from runoff calibration were found to be 0.76 Nash-Satkif coefficient and 0.86 explanation coefficient, and 0.53 and 0.58 for sediment, respectively, indicating acceptable rates for runoff (on a good floor) and sedimentation. Moreover, the results of the scenario concerning the improvement of pasture conditions from poor to moderate suggested a 46% and 15% reduction in the flood volume and total sediment, respectively, indicating the great influence of vegetation increase on Sarbaz River’s stability.

Preserving rangeland plants requires the protection of water and soil, and ultimately maintaining the balance of an ecosystem. However, quantifying the influence of vegetation on runoff and sediment requires modeling and simulation. Accordingly, the current study used water and soil assessment models to construct its intended scenarios. The area of poor pastures in the study area covers nearly 81% of the region. It should be noted that the status of the pastures could be improved through appropriate grazing methods such as modification and flooding, making the flow continue in dry periods and thus reducing the flood damage in the study area.
Generally, this study found that the average amount of surface runoff was decreased by 46.88%, indicating the effectiveness of the enacted scenario in reducing flood discharge. Moreover, the peaks of sediment in the diagram were decreased. Also, the average sediment was found to have decreased by 15.53% at the outlet of the basin, suggesting a significant reduction of sediment in the basin. Therefore, it could be argued that the scenario enacted in the current study worked well and that relevant organizations, especially the General Directorate of Natural Resources and Watershed Management of Sistan and Baluchistan province may play it out in the study area. Such a change could be made with suitable methods to improve the conditions of the pasture, including modification and flooding, leading to the continuation of the discharge flow in dry periods and the reduction in flood damage and loss of water and soil in the study area.

Flood is one of the main natural disasters that has affected different regions of the country in recent years and caused a lot of damage. Scientists have always invented different methods to know the magnitude and time of flood and to estimate the height of runoff. Rainfall-runoff models are among the useful and powerful tools in achieving flood characteristics. The purpose of this research is to calibrate and validate the HEC-HMS model as well as to simulate floods as a single event in Tajan basin.

In this study, firstly the variables of precipitation and temperature were entered into the model, then the model was calibrated based on 3 single events and its parameters were calculated. Also, the parameters of curve number (CN) and impermeable surfaces were analyzed in Geographic Information System (GIS) environment and finally based on another single event the model was validated and its accuracy was estimated. The hydrographs generated from the scenarios of the adjusted model parameters were compared with the observed hydrographs of Kordkheil station.

Calibration results showed that the simulated data has a high correlation with the observed data (R>0.9). Also, the validation results using the correlation coefficient test and Student's t test showed that the correlation coefficient of the observation and simulation discharge is equal to 0.9, which shows an acceptable significance. Sensitivity analysis of the model was performed to determine the exact value of a sensitive parameter in exchange for changes of increase and decrease of parameters by 20%. The results of the sensitivity analysis of the model showed that the parameter of the curve number and the amount of initial losses are highly sensitive, so that in the range of -20 to +20% change of the parameter of the curve number and the initial losses, respectively, the sensitivity of the model varied from -2.33 to 7.15 and from 0.79 to -0.73. On the other hand, the model simulation results showed that the best objective function for estimating flood peak flow is Percent Error Peak, which the model predicted with a difference of 0.8% peak flow at Kordkheil station. Also, the objective function of the Percent Error Volume as the best estimator for estimated the flood volume with a difference of 0.

In general, the results of the study showed that the use of the hydrological model system using the geographic information system, rainfall–runoff modelling, flood simulation and predict the peak flow and flood volume, was very efficient and the obtained results are citable. Therefore, the tested models can be used in watershed management.

Quantifying land use change dynamics is critical in tackling environmental and socio-economic challenges such as climate change in recent years. This study takes Siahkal County in Guilan Province as the research subject and analyzes the land use changes in two different years: 2000 and 2021, and predicts the change in 2031. We carried out land use change analysis using LANDSAT-7 ETM+ and LANDSAT-8 OLI multitemporal data pertaining to the years 2000 and 2021. For land use data extraction, a pixel-based digital classification using an ISODATA algorithm with a high Kappa index of 0.97 was applied to Landsat images. In addition, the MOLUSCE plugin in QGIS software was used to model land use change for 2000-2021, produce a transition probability matrix, and detect the future of land use for 2042 by using Cellular Automata Simulation. During 2000-2021, the result showed that the areas under agricultural areas, bare, built-up, plantation, shrubland, and woodland classes are found to increase with growth rates of about 31%, 39%, 67%, 88%, 20%, and 5%, respectively. The area under forest, grassland, and water bodies is found to decrease with decrease rates of about 22%, 16%, and 18%, respectively. Mountainous meadows did not change significantly. In terms of projected land use, the result also indicates fluctuations in land use change, especially for built-up land, showing a steady increase over time. We saw evidence of the local expansion of forest plantations, but the continuous decrease of natural forests may negatively impact the natural environment and landscape patterns regionally. In conclusion, the results of monitoring and modeling land use changes can be seen as a warning to managers, policy-makers, and planners. It is also advisable to use more data to analyze the impacts of land use changes on landscape patterns in future studies.

Landslide, as one of the most important natural hazards, causes financial losses and destruction of natural resources, every year. Rabar area, located upstream of Halilrood watershed, is prone to landslides due to the presence of marl formations and hence, a high amount of sediment has entered the Safa reservoir in Rabar city. Therefore, the purpose of this study is to zone this environmental hazard using convolutional neural network (CNN), support vector machine (SVM) and evidential belief function (EBF) models in Rabor region. To achieve this purpose, the parameters of altitude, slope, and distance from the fault, geology, land use, soil type, Normalized Difference Vegetation Index, and distance from the river were used. Then, using the data of the Geological Survey of Iran and field observations using GPS, a landslide distribution map was prepared as a dependent variable. There were 70 landslides, 49 (70%) of which were used for simulating and 21 (30%) for model validation. The results obtained from the validation of the models using the ROC showed that the AUC values for CNN, SVM and EBF models were 0.987, 0.958 and 0.899, respectively. Overall, the results showed a satisfactory correlation between the landslide data available in the area and the landslide susceptibility maps and the deep learning model of  the convolutional neural network had a higher performance compared with the other two models. Finally, the landslide susceptibility map was classified into four classes: low, medium, high, and very high susceptibility. According to the results of all models; the central, southeastern, and southwestern parts of the study area have a high and very high landslide risk. Carrying out appropriate designs such as retaining walls, preventing water infiltration, appropriate drainage, planting vegetation suitable for the environment, and landslide-prone slopes and etc can be appropriate in preventing and controlling this hazard.

The various services that we obtain directly and indirectly from the natural environment are referred to as ecosystem services. The purpose of these services is to communicate with human welfare in four categories of services: provision, support, regulation and culture. The purpose of this study was to investigate the supply of soil conservation services at different land uses and also to compare their amount. InVEST model was used for this research. To calculate soil retention, this model estimates soil erosion potential using the Modified Soil Erosion Equation (RUSLE) and sediment retention rate based on sediment delivery ratio. Therefore, the required input information of the model including biophysical table and maps of digital elevation model, rain erosion, soil erodibility, vegetation and land use were prepared with the help of ArcGIS 10.4 software and entered into the model in the form of raster maps. The results of this study showed that among different land uses, forests and rangeland with good and average condition had the highest soil conservation and the lowest amount was related to barren lands, residential areas and poor pastures. The results also showed that the highest rate of soil conservation occurs in the east and southeast and upstream of the basin and factors such as reduced vegetation and soil type have contributed to the intensification of erosion in this area. The results of this research can be useful in land management programs and appropriate decision making for rangeland management.

Estimation and forecasting of precipitation pattern in different parts of the world, especially in arid and semi-arid regions such as Iran is very important. In addition, various numerical methods such as artificial intelligence methods due to high accuracy and speed have the ability to simulate the phenomenon of precipitation and similar subjects. The use of these techniques plays an important role in saving time and costs in field and laboratory studies. Therefore, the application and popularity of various artificial intelligence techniques to estimate and simulate different issues such as rainfall is increasing day by day. The purpose of this study is to estimate the long-term rainfall in Rasht by a hybrid ANFIS and wavelet transform model.

In this study, long-term rainfall of Rasht city was simulated using an optimum artificial intelligence model for a 62 years period from 1956 to 2017. In other words, the wavelet transform was utilized to enhance the performance of the ANFIS model and hybrid WANFIS (Wavelet-ANFIS) model was defined. Firstly, the effective lags of time series data were detected through the autocorrelation function (ACF). Then, using the lags, eight models were developed for each ANFIS and WANFIS model. It should be noted that 42 years data was applied for training and 20 years data to test the artificial intelligence models. Next, the number of optimal membership functions of ANFIS model was selected equal to two.

results of ANFIS 1 to ANFIS 8 were evaluated. Additionally, different mother wavelets were examined to optimize the ANFIS model. This means that the demy was introduced as the best mother wavelet for increasing the performance of the ANFIS model. The comparison between ANFIS and WANFIS models signified that the wavelet transform enhanced the performance of the ANFIS model. Also, results of the hybrid WANFIS models were analyzed, indicating that WANFIS 8 was the superior model. The model estimated the rainfall with an acceptable accuracy. For instance, the R, MARE and RMSE for the superior model were computed 0.961, 0.855 and 24.510, respectively. Additionally, the values of VAF and NSC for this model were respectively estimated as 92.273 and 0.913.

Results showed that (t-1), (t-2), (t-3) and (t-12) were identified as the most influenced lags for estimation of long-term rainfall of Rasht city using the hybrid WANFIS model.