جستجوی مقالات مرتبط با کلیدواژه « calibration » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Climate change directly impacts hydrological components, water resources, and watershed outflow. Calculating the amount of possible changes in rainfall and runoff will play an important role in the policy-making and planning of water resources under climate change conditions. In this research, runoff simulation in the eastern watersheds of Mazandaran province (Talar, Tajan, Nekarood, and Babolrood) was investigated under the impact of climate change in the future period (2021-2040).

In this research, one of the atmospheric-ocean general circulation models, named HadGEM2, was used under climate scenarios (RCP 2.6 and RCP 8.5) to investigate climate change. The Lars-WG model was used for the downscaling of climate data during three stages of data calibration, data evaluation, and meteorological data generation for the future period. This purpose was achieved using the daily data of rainfall, maximum and minimum temperatures, and sunshine hours of the selected stations of four watersheds in the current period (1997-2016), and the model was implemented for the base period. After ensuring the ability of the Lars-WG model to simulate meteorological data by comparing the data produced by the HadGEM2 model and the observational data of the base period, this model was implemented to generate future data using climate scenarios (RCP 2.6 and RCP 8.5). The daily values of the climate data of rainfall, maximum and minimum temperatures and sunshine hours were produced and simulated for the next 40 years (2021- 2060). Runoff simulation in the future period and the effect of climate change on runoff were investigated with the IHACRES hydrological model, which is a continuous conceptual-dynamic model for simulating rainfall-runoff at the watershed scale, using climate scenarios. For this purpose, the daily data of rainfall, mean temperature, and discharge of the current period (1997-2016) in four watersheds were introduced into the IHACRES software. Then, the 1997-2008 and 2009-2016 periods were selected for calibration and validation, respectively, to determine the ability of the IHACRES model to simulate future runoff. After calibration and validation, the climate data produced by the HadGEM2 model were introduced into IHACRES, and the daily series of discharge was predicted for the future period (2021-2040). Then, the annual average of the observed discharges in the current period and the simulated discharges in the future period under the RCP 2.6 and RCP 8.5 scenarios, as well as the 20-year average runoff in the stations in the future period, were compared and evaluated with the observation period.

The results of the climate change investigation showed that mean monthly minimum and maximum temperatures in all months of the future period would increase in all four watersheds in both scenarios RCP 2.6 and RCP 8.5 compared to the current period (1997-2016). Compared to the current period, the mean monthly rainfall in both scenarios does not have a constant trend and is different in various months. The results of the runoff simulation show that the changes in the discharge of the watersheds under the effect of climate change are not constant compared to the discharge of the current period. This could be attributed to the inconsistency of the trend of changes in the precipitation of the future period compared to the current period. Despite the increase in the average rainfall, the discharge will decrease in some years in the future period, which can probably be related to the increase in temperature in the future period due to climate change. Despite the decrease in the average rainfall of that year, the increase in discharge could be caused by, for example, the possibility of heavy rains or showers in that year, thereby increasing the amount of discharge. Compared to the current period, the 20-year mean of the predicted discharge has decreased in the selected stations (Talar, Tajan, and Nekarood watersheds) in the future period for both scenarios. In the station considered for the Babolrood watershed, however, this value has increased in the RCP 2.6 scenario compared to the current period. In the Polsefid station of the Talar watershed, the simulated discharge under the RCP 2.6 and RCP 8.5 scenarios was equal to 6.18 and 6.12 m3/s, respectively, which shows a decrease compared to the observed discharge of 7.41 m3/s. In the Soleimantange station of the Tajan watershed, the predicted discharge under the RCP 2.6 and RCP 8.5 scenarios was equal to 7.33 and 7.29 m3/s, respectively, which has decreased compared to the observed discharge of 9.82 m3/s. In the Sefidchah station of the Nekarood watershed, the simulated discharge under the RCP 2.6 and RCP 8.5 scenarios was equal to 3.85 and 3.72 m3/s, which reveals a decline compared to the observed discharge of 4.10 m3/s. In the Babol station of the Babolrood watershed, however, the discharge under the RCP 2.6 scenario was predicted to be equal to 11.40 m3/s, which has increased compared to the discharge (11.34 m3/s) during the observation period. Under the RCP 8.5 scenario, it was simulated with a value of 11.22 m3/s, less than the observed discharge.

In general, the impact of climate change on water resources is one of the major challenges facing water resources planners and can have serious consequences for ecosystems and communities. Based on the findings of the research and the importance of climate change effects on the hydrological conditions of the watersheds in the study area, it is necessary to apply appropriate policies for the sustainable management of water resources.

Satellite products are the only available data source with adequate spatial coverage, however, their data do not match the observed values ​​and have biases, although this discrepancy cannot be fixed precisely, however, a solution to reduce the bias is data recalibration. Currently, machine learning techniques are used to improve the accuracy of forecasting various types of weather phenomena, so regression solving such problems through methods based on machine learning and deep learning is very efficient. The daily precipitation of 19 rain gauge stations of the Ministry of Energy between 2010 and 2021 was extracted and compared to the average values ​​of their corresponding daily precipitation pixels in the ERA5 database. To measure the data, three algorithms D-Tree, KNN, and MLP were used. The range of changes of correlation coefficient in MLP, D-Tree, and KNN is equal to [0.87, 0.98], [0.75, 0.97], and [0.4, 0.87], respectively. In addition, the range of changes for RMSE in MLP varies from 0.7 to 2.4 mm per day, and these changes for D-Tree and KNN are calculated between 0.8 to 2.2 and 1.2 to 2.5, respectively. In 75% of stations, RMSE in MLP, D-Tree, and KNN algorithms is less than 1.5, 1.9, and 2.2 mm per day, respectively. The range of bias changes in MLP is [0.18, -0.6 mm per day] and this range of changes for D-Tree and KNN is respectively [0.16, 0.5 mm per day] and [0.6, -0.8 mm per day] have been calculated. The bias of corrected data and observed values ​​in MLP, D-Tree, and KNN algorithms for the middle of the stations is -0.09, -0.11, and -0.16 mm per day, respectively. The evaluation of the performance of three machine learning algorithms (MLP, D-Tree, and KNN) in correcting the daily precipitation of the ERA5 database and the comparison of CC, RMSE, and bias statistical indices for the reproduced data compared to ground values ​​showed that in all three statistical indices, the MLP algorithm works better than the others and has good accuracy for correcting the daily precipitation.

Due to the large volume of human and commercial activities, one of the most important goals of infrastructure is to collect and transfer urban runoff, control floods and prevent flooding in cities. To correctly estimate the runoff and characteristics of hydrological units and channels, it is important to use appropriate hydrological and water models. Considering the importance of this topic, this research was conducted with the aim of estimating the urban runoff of the Malayer to determine the potential flooding points using a storm water management model (SWMM).

On the basis of available technical reports, the area of the sub-watersheds, nodes and main channels of the city were first determined. Flow direction and slope of the area was determined using a digital elevation model and 11 sub-watersheds were identified. In addition, 11 nodes were determined, regardless of the initial water depth and water level. Using the daily rainfall data of the Malayer synoptic station during the 1992–2020 statistical period, a nine-hour rainfall with a return period of 2 years was calculated and entered into the model. The geometrical shape of the channels was an open rectangle and the flow trend in the channels was determined using the kinetic wave method. The size of the maximum flow of water through the channels was calculated using the relationship between the cross-section of the flow and the speed of the flow a 9-hour the rainfall with a return period of 2 years. To calibrate the model, the variables of percentage of impervious areas, pond storage and roughness coefficient of impervious areas were used in the change range of the allowed modification range. In order to evaluate the model, Nash-Sutcliffe efficiency coefficient and the root mean square error were used.

The results showed that after optimizing the sizes of the variables, the Nash-Sutcliffe efficiency coefficient and the root mean square error were 0.73 and 0.02, respectively. Of the total rainfall of 14.54 mm, 5.53 mm was related to infiltration losses, 7.55 mm was related to surface runoff, and 1.45 mm was related to pond storage. The results showed that the sub-watersheds that were in the north of the city and overlooking the heights and leading to node number 10 had more volume and drainage and it is necessary to revise the design and expansion of the channels in this area. In addition, the sub-watersheds of the western part of the city (sub-watersheds no. 4) and the southwestern part (sub-watersheds no. 8) had the highest and lowest flood potential of 0.651 and 0.547, respectively.

In this research, the results showed that almost half of the city would be affected by flood risks in the event of rains. Therefore, the current drainage network does not have the necessary efficiency to discharge urban runoff in the northern part of the city, and it is necessary to determine the optimal dimensions of the channels. Because the eastern elevations of the studied area are snow-covered and considering the geology of the area, in future research, models capable of calculating the runoff caused by snow melting should be used.

Today, the use of hydrological models is mainly necessary to simulate changes in water source and flow (runoff and evaporation). Proper modeling of hydrological processes requires the determination of model parameters. In calibration processes, the values ​​of the model parameters are estimated so that the model can simulate a natural system well. It is generally impossible to estimate the parameters of such models directly due to the large number of parameters and it is necessary to estimate them with the help of optimization tools (model calibration). In the present study, the parameters of the daily Hymod rainfall-runoff model (a simple conceptual rainfall-runoff model) were calibrated using the Whale algorithm (WOA), which is derived from the way whale food is searched. The evaluation of the mentioned calibration method was performed using daily precipitation, evapotranspiration and transpiration data for 5 years and its validation was performed in 5 years in the Leaf River Basin of the United States. The simulated and observed flow rates were compared using correlation coefficient (R2), root mean square error (RMSE) and Nash-Sutcliffe coefficient (NS). The values ​​of error measurement criteria were 0.91, 1.2 and 0.8 for the calibration period and 0.91, 2.5 and 0.83 for the validation period, respectively. Also, the parameters calculated using the whale algorithm, the maximum moisture storage in the area of 216.95 mm, spatial variation of soil moisture storage 0.38, the distribution factor between the two moisture tanks 0.98, the shelf life in the laminar tank 0.08 days and Shelf life in fast flow tank is 0.47 days. Examination of error values ​​showed that the Whale Optimization Algorithm has high efficiency in calibrating rainfall-runoff models.

The amount of Total Dissolved Solids (TDS) is an important factor in determining the water quality of rivers. The purposes of this paper was to investigate the efficiency of four intelligent models including, SVM-IWO, SVM-PSO, SVM-ABC, LS-SVM and Bayesian Neural Network, in predicting water quality of Babolrood and Sefidrood rivers, north  of Iran. To achieve this aim, unpublished measured monthly data including; Ca, Mg, HCO3, Na, SO4, EC, pH, and TDS from Quran-Talar station of Babolrood river and Prorijabad station of Sefidrood river were analyzed in the periods of 1966-2015 and 2002-2015, respectively. Evaluation of the four above-mentioned models, based on the correlation coefficient (R2), Root Mean Square Error (RMSE) and the Nash-Sutcliff coefficient (NSE) showed that the SVM-ABC model in both Babolrood and Sefidrood rivers with the highest R2s equal to 0.985 and 0.989 and the least amounts of RMSE equal to 10.493mg/l and 5.289 mg/l and the highest Nash-Sutcliffe equal to 0.983 and 0.992, respectively, has better and faster performance compared to others models in assessing water quality.

The spatial quality of the Digital Elevation Model (DEM) has a great effect on the Soil and Water Assessment Tool (SWAT) semi-distributed model. The purpose of this study was to evaluate the effect of spatial accuracy of three DEMs with spatial resolutions of 10, 50 and 200 m on the results of daily discharge simulation in the Arazkuseh subwatershed located in Gorganroud watershed, Golestan province, Iran. To do this, after-mentioned DEMs was obtained.  First, the model was run using a DEM with a spatial resolution of 10 m and climate data from 1998 to 2013. So that 1998 to 2000 were used to warm-up the model, 2001 to 2009 to calibrate and 2010 to 2013 to validate the model. In the validation period, the results of coefficient of determination ranged from 0.40 to 0.60 and Nash-Sutcliffe efficiency criteria ranged from 0.37 to 0.58. Both criteria had a decreasing value in the validation phase compared to the calibration phase. The PBIAS criteria was negative for DEMs with 10 and 50 m spatial resolution, respectively which indicates overestimation, and positive value ​​for DEM with 200 m spatial resolution, which represents the overestimation of the model. The results showed that the spatial resolution of 50m is the best spatial resolutions to simulate the discharge on a daily time scale for Arazkuseh watershed.

The SCS-curve number is one of the common methods to estimate drirect runoff in the watershed scale. There is often an uncertainty in predicting runoff in many areas due to to the model’s assumption on the initial abstraction ratio (l= 0.20). This study was conducted to evaluate the accuracy of SCS-CN method in estimating runoff in some semi-arid subbasins of the Aras river consits of the Livar, Kalaleh and Shekaralichay in northwest of Iran. Runoff values were obtained from hydrometery stations for a 30-year period from 1987-2017) and compared with the estimated values which were determined using the maximum retention capacity (S). Based on the results, mean annual runoff in the Livar, Kalaleh and Shekaralichay subbasins is is 6.4, 1.45 and 9.5 mm, respectively. The model accuracy of the Livar, Kalaleh and Shekaralichay was -0.44, -0.25 and -0.27 percent, respectively. It showed over-estimations for small runoff values and down-estimation for large values. The model was calibrated usingthe revision of the initial abstraction ratio value (l= 0.08) and the model accuracy improved to 95, 96 and 93 percent, respectively. As a conclution, the calibration of l is essential to acuurate prediction of runoff in the the Aras river and similar basins.

Groundwater resources are one of the main natural resources that support socio-economic development of countries (Siebert et al., 2010). Management and protection of groundwater resources is of great importance in countries such as Iran, which are located in arid and semi-arid regions without surface water resources. Therefore, resource conservation, in addition to short-term and long-term planning, is essential to optimizing their productivity (Abu-Khalaf et al., 2013). These natural resources face a variety of issues that threaten their sustainability, such as the effects of climate change, human activities and natural processes (Alabjah et al., 2018; Baghvand et al., 2010; Burri et al., 2019; El Asri et al., 2019; Houemenou et al., 2020; Mountadar et al., 2018). Considering the great importance of recognizing the quality characteristics of groundwater in desert areas and the need for its proper management, predicting groundwater quality for the management and exploitation of water resources in the Yazd-Ardakan plain seems urgent. Therefore, the aim of this study was to evaluate and compare the efficiency of artificial neural network and random forest models in predicting EC, SAR, SO4- and TDS values. In this research, modeling will be done based on the relationship between environmental (auxiliary) data and groundwater quality parameters.

The study area is spread over ​​482900 ha of land, which in terms of position is 53° 08´ 36˝ and 54° 85´ 32˝ E longitude, is located in the central plateau of Iran and the central part of Yazd province. The maximum height of the area is 2677 m and the minimum height is 997 m from the sea level. To evaluate the quality of groundwater sample, 201 wells of the monitoring network of Yazd Regional Water Company in 2016, the measured parameters EC, SAR, SO4- and TDS were used. The factors of environmental (auxiliary) data in this research include geological data, land use, vegetation indices, soil salinity indices, derivation of digital elevation model, distance from mines, distance from road, distance from river, distance from residential areas, rainfall and population.After preparing the groundwater quality parameters and environmental data, the values of EC, SAR, SO4- and TDS were predicted using artificial neural network and random forest models. In order to validate the random forest model, the cross-validation method (10-fold) was used in R statistical software. In this method, the data is divided into 10 parts. 9 parts of the data are used for modeling and the remaining part is used to validate the obtained model. MAE, RMSE and R2 statistical indices were used to evaluate the efficiency of artificial neural network and random forest models for groundwater parameters.

In this research, in simulating EC, SAR, SO4- and TDS parameters, the best structure obtained from 100 repetitions of artificial neural network learning has 2 hidden layers and 10 hidden neurons in each layer. The results of random forest sensitivity analysis show the high importance of the parameters extracted from the DEM. The use of satellite image data to investigate the groundwater quality parameters is also a convenient and cost-effective method. Furthermore, combining satellite data with DEM to investigate groundwater quality parameters and their zoning makes the results more efficient and increases its accuracy. A very important point in the analysis of parameters is the important role of parameters such as distance from the road and distance from the mines. This indicates the direct effect of man-made environmental factors on the groundwater quality of the study area. The results of modeling the EC, SAR, SO4- and TDS parameters using artificial neural network and random forest models show the appropriate accuracy of the artificial neural network model in predicting these parameters. Accordingly, the neural network model has predicted the parameters of EC, SAR, SO4- and TDS of groundwater with coefficient of determination of 0.82, 0.92, 0.92 and 0.97, respectively, while the model Stochastic forest has predicted EC, SAR, SO4- and TDS parameters of groundwater with coefficients of determination of 0.37, 0.65, 0.80 and 0.68, respectively. Based on the obtained results, it can be concluded that the artificial neural network model has a higher accuracy than the random forest model in predicting groundwater quality parameters. EC, SAR, SO4- and TDS parameters have the highest values in the north, center and southwest, and the lowest values in the southeast and southwest.

It can be said that the technique is highly efficient for estimating EC, SAR, SO4 and TDS parameters in Yazd-Ardakan plain as long as a proper and a sufficient number of input elements, a proper and compatible artificial neural network, and an appropriate calibration are used.

Accelerated soil erosion and high sediment production are important issues in many parts of Iran. The identification of high erosion risk regions is necessary for the planning of soil and water conservation measures in order to reduce erosion damages. Empirical models are required for estimating soil erosion and sediment yield in the absence of gauged small watersheds. The Erosion Potential Method (EPM), originally developed in Croatia, is a popular erosion estimating model in Iran; however, its efficiency across the country has not been thoroughly assessed. Therefore, a calibration study was carried out by comparison between the model the estimated and measured sediment yields through sediment survey for 63 reservoirs at the outlet of small catchments in the Semnan, Markazi, Isfahan, Chaharmahal va Bakhtiari, West Azarbaijan, Lorestan, Fars, Golestan, and the Khorasan Razavi Provinces. All required maps and information of the study basins for estimating the EPM sediment yield were produced via field survey as well as data analysis. Comparison of the results showed that in most cases, the model estimates (0.19 to 9.71 tons per square kilometer per year) are lower than the measured sediment yields ​​at the reservoir inlets (0.44 to 459.64 tons per square kilometer per year). Thus, we calibrated the EPM by modifying its original tables and evaluated the results using the Nash‐Sutcliffe efficiency coefficient in two scenarios, a, all basins, and b, after dividing into three geographical zones, which significantly improved the estimates. The Nash ‐ Sutcliffe efficiency coefficient for scenario a was 0.66, and 0.68, 0.50 and 0.89 for the Central, Zagros, and Northeast zones, for scenario b respectively. Based on the results, it is strongly recommended to use the modified EPM instead of the original model.

Today population growth, inaccurate management practices and land degradation processes have caused many watersheds to be severely degraded, leading to increased runoff and sediment. In this study, the aim of the study was to investigate the effect of land use change on surface water fluctuations in the Doiraj watershed. Landsat satellite imagery was used for three periods of 1995, 2006, and 2015, it was found that the major changes occurred with decreasing forest and rangelang  levels and increasing land barren. For modeling rainfall-runoff, a SWAT computer model was used. Then, using the SWAT_CUP model, The performance of the model was evaluated using statistics, NS, R2, P_FACTOR and R_FACTOR. After calibration and validation, the values of these coefficients were 0.58, 0.60, 0.81, 0.87 for the calibration period and 0.56, 0.57, 0.81 and 89 for the validation period of the model, respectively was obtained. Annual runoff estimation indicated that changes in forest, pasture and wasteland land use from 1995 to 2015 increased the outflow runoff the basin from 3.91 to 5.85 m3 / s.

Hydrological models are simplified representation of the real basin system, which helps to assess basin function in response to different inputs and better understanding of hydrological processes. The HEC-HMS model is one of the most important hydrological models for Flood estimating volumes and discharge in watersheds. In this research, the HEC-HMS hydrologic model was used to simulate the runoff flow in the Sarbaz river basin in Sistan and Baluchestan province. For this, basin and sub-basins models were prepared using the Digital Elevation Model (DEM) map and using Arc Hydro and HEC-geo HMS extensions. Next, through integration of land use maps and soil hydrologic groups in the Arc GIS, the curve number map for the studied basin was obtained. The results suggest that this basin has low permeability and high runoff volume. The results showed that there is a good consistency between observed and simulated hydrographs at the basin outlet and values of model efficiency index at the validation stage with Nash-Sutcliffe efficiency and were 0.90 and 16.37respectively which indicates the high efficiency of the model in estimating peak flow in basins and sub-basins. Also, according to the results, in this basin, as returns period increases, the runoff volume and basin discharge will be increased.

In this study SWMM software has been calibrated with real meteorological and hydrometric data at the North of Tehran basins and simulation parameters have been obtained. For this purpose, five rainfall events and runoff data related to these rainfalls, recorded at the outlet of Zargandeh catchment were used. This model is calibrated with three events and verified with two other events. Also, in this simulation the peaks of flood, outflow hydrographs, runoff volumes and peak flood times is obtained. The root mean square error are obtained for outlet hydrographs for the first to fifth events were 0.05, 0.22, 0.4, 0.37 and 0.16, and the Nash-Sutcliffe coefficient are obtained 0.91, 0.94, 0.93, 0.9 and 0.94, respectively. Also, the percentage of difference of the flood discharge peak modeling and observations for first to fifth events are calculated 7.33%, 9.69%, 5.8%, 5.6% and 9.93% and for runoff volume, this percentage difference are calculated -8.82%, -3.08%, 8.8%, -19.43% and 5.11%, respectively. Based on these results, the performance and application of this model to simulate runoff in this area is acceptable and can be used to manage and control urban runoff.

Over the past few decades due to population growth and urban development, urban runoff has increased and led to different problems such as inundation of urban pathways, dissemination of environmental pollutions and flood hazards. In order to urban runoff management, it is necessary to estimate runoff rate correctly. SWMM is one of the most widely used models in estimating urban runoff. The goal of this research is to evaluate the performance of SWMM model in simulating flow rate in an urban watershed in District 22 of Tehran. At first, model required parameters were calculated. For model evaluation and validation, in three events, runoff was measured in the watershed outlet and was compared with simulated runoff. The model validation results showed that the simulated flow rates had good adaptation with the observed ones. The validation results were used for estimating optimum values of model input parameters. The results of SWMM model evaluation confirm model accuracy with NS= 0.72 and RSR= 0.53 and indicate the model ability in simulating urban runoff. So, SWMM model can be used for urban runoff management plans and designing urban runoff drainage networks in this area.

Different models have been designed to simulate incoming floods into urban areas from rural areas, one of the models is the EPASWMM. Mashhad urban watershed with 8 inde-pendent subbasins and area of 2,472 hectares, located in south of Mashhad city . Study area is the residential area of Mashhad Paydari township (Area= 95.6 ha) that located below the M2, M3 and M4 subbasins. Due to high peak flood events in M2, M3 and M4 subbasins, the capacity of urban transmission channels in the Paydari township is’nt suf-ficient and after every rain, a lot of runoff enters the residential areas. The purpose of this research is evaluate the efficiency of EPASWMM model in simulating runoff. The study area was divided into 12 hydrological units (OUT1 to OUT12) and after determine of input parameters of the model, the runoff gathering network in the form of nodes-channels was introduced into the model. Four rainfall events were used to evaluate and calibrate the model with (NS), (RMSE) and (R2) coefficients. In the calibration section of the model, the value of the (NS) coefficient for discharge in the first and third events was 0.94 and 0.92 respectively and for runoff height, 0.9 and 0.86, respectively and indicates simulation accuracy of EPASWMM model in both parameters. The results of the model evaluation section for the second and fourth events shows that the NS and R2 coefficients for three parameters of discharge, height and runoff were from 0.78 to 0.9 and 0.73 to 0.89 and indicates a large correlation between observational and simulated data. Flood reduction scenarios in hydrological units have been investigated according to the simulation results.

Accelerated soil erosion and the production and transfer of large amounts of sediment are considered as one of the most important issues in many parts of Iran. Having enough knowledge on soil erosion and sediment production as well as identification of critical areas are necessary in order to reduce the adverse effects of this problem through soil and water conservation measures. Due to lack of gauge stations in the outlets’ of small watersheds, empirical models are considered as the proper tools for estimating soil erosion and sedimentation. One of these methods which was developed based on Meditranean sediment data, is the Factorial Scoring Model (FSM). Towards this, the main objective of this research is to compare estimated vs. observed sediment yields in some selected regions of Iran. For this purpose, 58 small chatchments equipped by reservoirs in their outlets were selected in Semnan, Central, Isfahan, Chaharmahal and Bakhtiari, West Azarbaijan, Lorestan, Fars, Golestan and Khorasan Razavi Provinces. The comparison of the results showed that all estimates of the original model (before calibration) are several times bigger than observed values obtained by surveying (0.44 to 459.44 t.km-2.y-1). In order to correct the model coefficients for Iran, calibration was performed in two scenarios; once using all data and in the second secenario after allocating data into three geographic regions, which in both cases resulted in significant improvement of estimates. The Nash‐Sutcliffe Efficiency coefficient for all catchments was 0.39, and for Central, Zagros and Northeast regions were 51.1, 0.66 and 0.78, respectively. Based on obtained findings, the application of original FSM must be avoided in study areas. Instead, we strongly recommend using calibrated models based on observational values for different geographic regions.

In recent decades, due to the importance of watershed management programs and the need for adequate information and correct estimation of rainfall and runoff, many conceptual models have been proposed. These models have parameters that must be estimated according to observational data. However, finding the optimal values for the parameters of simulation models has always faced uncertainty. One of the main methods for evaluating and predicting floods is the soil conservation methods provided by the Soil Conservation Service (SCS). This study aims to calibrate model parameters for three modified SCS models, i.e. 1- the Mishra and Singh model, 2- curve number model, and 3- modified curve number (MCN) model, in a basin in southern Chile using the evolutionary PSO algorithm. Comparison of the results was carried out using performance criteria such as Kling Gupta Efficiency (KGE), Nash-Sutcliff coefficient (NS), and mean square error (RMSE). Results revealed that simulated runoff is more efficient in MCN and Mishra and Singh models with KGE = 0.91 in rainfall-runoff simulations.

During last decades hydrological models were extesively used in rainfall-runoff modeling. These models contain some constant parameters that must be optimized through appropraite mthods. In addation to model structur, the efficieny of hydrological models depend on these optimized parameters. In this study, the efficiency of three automatic optimizing algorithms including Dynamically Dimensioned Search (DDS), Shuffled Complex Evolution and Genetic algorithms in calibration lumped hydrological model HyMod in Ghorchay Ramian Catchment were investigated. For these mehods, convergence speed and variability of final optimized values were investigated. Results showed that Genetic algorithm converged faster than two other methods. Following, DDS algorithm converged faster than Shuffled Complex Evolution algorithm. Shuffled Complex Evolution and Genetic algorithms took shorter and longer time per each epock, respectively. Highest and the least variability of final results were obtained for DDS and Shuffled Complex Evolution algorithms, respectively. With respect to final results variability, Shuffled Complex Evolution algorithm was more satable and had better performance than other methods. Using analyse variance and comper means in Shuffled Complex Evolution algorithms for complexes less than 12, the model performance was increased as the number of complexe increased. As alpha value increased, the model performance decreased and model had the best performance at the value of 0.58. Conversely, model performance was increase as beta values increasd and the best perfromnce was obtained for beta equal to 1. For Genetic algorithm, the best performance was obtained when the value of values crossover, mutation and chromosome number was equal to 0.2 and 0.3 and 16, respectively.

Understanding the base flow can be useful in river flow analysis, rainfall-runoff modeling, calibration of models, water resource management in low flow conditions and determination of the amount of water storage in watershed. In this research, 22 stations were selected with the appropriate data and common period of the years of 1982-2012. The trend of flow changes during the months of the year was determined for all hydrometric stations in the study area and the driest month was determined. Then, the calibration of six recursive digital algorithms was performed using the long-term data of the driest month of the year and after obtaining optimal parameters of the models, the base flow separation for the whole period was performed. The performance evaluation of the models was done using root mean square error. The results showed that the major part of the river flow in the study area was related to the base flow and the minimum, maximum and average annual base flow index for the whole period was equal to 0.48, 0.62 and 0.56, respectively, representing more than 50% of ground water contribution to stream flow of the studied watersheds. Results of the evaluation of the models using the root mean square error showed that the mean error in the research area for all the methods ranged from 0.025 to 0.044. The minimum was related to Lynie and Holick, and the maximum related to the One-parameter digital filter. Over all, conclusion of the results of the calibration process and investing the correlation between calculated and measured data showed that there was a correlation with a coefficient of explanation of more than 80%. Calibration method with dry season data in the absence of tracer-based methods is suggested as the most suitable method for calibrating digital separation filters in the study area.

In a natural ecosystem, land exploitation and change in environmental conditions, particularly changes in vegetation and land use effects on hydrological responses such as floods and erosion and sediment and ultimately causing heavy social-economic losses. Then, prediction of effect of land use changes on flood situation in the future decades and sedimentation of watersheds will be the way to deal with this phenomenon. To simulate the hydrological behavior in future decades, HEC-HMS model was calibrated and validated for the previous period, with changes in curve number and percentage of impermeability (as a result of land use changes). Then, predicted hydrograph for coming decades (2020, 2025 and 2030) and last (2004) were compared. The results show that the peak discharge and flood volume in the 2020, 2025 and 2030, will be increased in compared to the decade of 2004 equal to (52.8 and 20/41), (70.29 and 32.51) and (87.78 and 38.05), respectively will increase. Mean peak in the 2020, 2025 and 2030 will be increase (due to land use change), 16, 25 and 38.35 percent, respectively. In the next step, sediment changes in the future was investigated using the sediment rating curve in decades of 2020, 2025 and 2030. The results showed that the amount of sediment for 2020 will be increase 12.01 % compare to 2004, for 2025 compare to 2020, 15.14 % and for 2030 compare to 2025, 36.64 %. According to the results, land use change in future leads to increase in discharge and sediment, which could affect the hydrological and morphological conditions of watershed.