جستجوی مقالات مرتبط با کلیدواژه « Sensitivity analysis » در نشریات گروه « آب و خاک »

Runoff is the main variable for the hydrological analysis of the watershed, and due to its importance, for several decades, hydrological research has focused on the simulation of rainfall-runoff relationships, which has led to the presentation of many models. Due to the multiplicity of hydrological models, choosing an optimal model among various models is not a simple process. For this purpose, in the present research, after selecting the Galikash watershed from the most flood-prone basins in Golestan province, the performance of three hydrological models AWBM, Tank, and IHACRES were evaluated and the parameters of the models were also analyzed for sensitivity and finally the efficiency of the models in wet and dry periods was examined. The amount of daily runoff from the watershed for a period of 30 years (1989-2019) was simulated using each of the mentioned models and using four criteria Nash-Sutcliffe evaluation coefficient, root mean square error, coefficient of determination, and mean absolute percentage error, the performance of each model has been checked in two periods of calibration and validation. After optimizing the values of all the parameters, the sensitivity of the parameters of each model has been analyzed. Finally, after specifying the drought condition with the SPI index, the performance of each model for two wet and dry periods has been investigated and evaluated. The results indicate that two rainfall-runoff models, IHACRES and AWBM, have almost similar performance. IHACRES model with Nash-Sutcliffe coefficients of 0.73 and 0.75 and RMSE of 2.97 and 2.94, respectively, in two calibration and validation periods and AWBM model with Nash-Sutcliffe coefficients of 0.74 and 0.69 and RMSE of 2.92 and 3.24 for the calibration and validation periods have shown good performance, but the Tank model was not successful in simulating the watershed runoff and its performance is lower than the two other models. The sensitivity analysis of the model parameters also showed that Kbase, H11, and f parameters are the most sensitive to the change of their values in AWBM, Tank, and IHACRES models, respectively. Finally, the comparison of the performance of the models in wet and dry periods showed that all the models have succeeded in simulating the watershed runoff with high accuracy in the wet period, so that the Nash-Sutcliffe coefficient is 0.79, 0.74 and 0.78 for the three AWBM, Tank and IHACRES models, respectively, shows the acceptable performance of the models in simulating the runoff in wet period. While the evaluation of the results has shown the poor performance of all models in dry period, and the Nash-Sutcliffe coefficient obtained for the models is -0.05, -0.45, and 0.12 respectively, which shows the weakness of the models in simulation of the low flow. In the evaluation of the three hydrological models AWBM, Tank, and IHACRES in daily runoff simulation, it was found that in general, with a small difference, the IHACRES model shows better results than the AWBM model. Also, in wet periods, according to the evaluations, the AWBM model led to good accuracy, while the IHACRES model has shown better performance than other models in dry period. Considering this issue, it can be said that the models performed weaker in simulating low flows that occur during dry periods, while the knowledge of streamflow conditions during dry periods can play an effective role in managing water resources. Therefore, to increase their accuracy, a solution should be found.

Simulation of runoff and hydrograph production is widely used in analyzing the behavior of basin against precipitation, calculating the volume and peak of floods, the amount of losses and the possibility of designing the dimensions of structures. HEC-HMS is one of the most common simulator models In this study, with the aim of analyzing the sensitivity of flood hydrograph routing parameters in the Merk River basin, first, the physiographic characteristics of the basin were constructed with the HEC-GeoHMS plugin in the Arc GIS environment and the DEM map of the area and they were called in the HEC-HMS model. then by entering the three flood event data and its corresponding precipitation, computational hydrograph is created.  For calibration, at first by trial and error, the model was placed in a suitable range which can be optimized using differential evolution algorithm. In optimizing the target function to make the average at least of the sum of squares of error, the K and X Muskingum parameters, which are particularly sensitive to hydrograph production, placed the model optimization and its performance in the excellent category so that for the event of November 2015 floods and a stop of 50 iteration (NSE=0.871, PBias=25.52, RMSE=0.4, NRMSE=2.63), for the event of March 2016 floods (NSE=0.731, PBias=28.82, RMSE=0.5, NRMSE=1.01) and in February 2020 (NSE=0.834, PBias=7.96, RMSE=0.4, NRMSE=0.95) which indicate an excellent performance of the model after optimization of the Muskingum coefficients by differential evolution algorithm (DE).

Estimation and prediction of scouring around the piers play a significant role to design these structures since with increasing dimensions of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers is estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. To do this, firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Next, the superior ANFIS-FCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Lastly, a computer code was introduced so as to simulate the scour hole around the twin and three piers.

Due to the existence of various uncertainties in the materials and modeling of these structures, the quantification of these uncertainties requires sensitivity analysis and reducing the number of variable parameters to reduce cost and time in modeling. In this research, numerical analysis of Maku dam located in West Azerbaijan province which is considered as a case study is performed using FLAC2D software and the static response is obtained. The effect of construction layers and the amount and shape of contours of stress and displacement were compared and validated with previous researches. The 49 random variables (RVs) (of materials properties in the zones of the core, shell, filter, drainage, alluvium and bedrock) was considered. By performing sensitivity analysis and using the Tornado diagram, the number of variable parameters is reduced and 18 important RVs were identified for the modeling and analysis of the earth dam. The results showed that altering the dry density (γd) of the shell causes a change of about 8% and altering the Poisson's ratio (ν) and also the modulus of elasticity (E) of the core leads to a change of about 7% in earth dams’ response. Finally, parameters of dry density (γd), Poisson's ratio (ν), modulus of elasticity (E) and internal friction angle (ϕ) were identified as more sensitive parameters that have the greatest impact on the response of earth dams.

Soil conservation requires a suitable model and framework to assess soil erosion and sediment yield based on land use scenarios. Therefore, the present study was conducted with the aim of investigating the sensitivity of the G2 model to the soil erodibility factor in the Kasilian representative watershed in the period of 2001-2021. Points such as the use of global soilgrid database and comparison with soil sampling data, removal of soil erodibility in rocky areas, and removal of outlier data created by systematic error were included in the current research. The results showed that qualitatively, the rate of soil erosion estimated based on each of the mentioned points was completely in the minimum category. The initial results of the soil erosion rate obtained from the global soilgrid database and the IDW method were 4.31 and 4.33 t ha-1 y-1, respectively, which were not significantly different based on the independent t test. But the effect of other changes, including removal of soil erodibility in rocky areas and removal of outlier data on the change of soil erosion estimates using one-way ANOVA and Duncan's tests were significant (P<0.01). The estimated average rate of soil erosion in the watershed after following the mentioned points was reduced by more than 65% compared to the initial results and became closer to the data of erosion plot.

The purpose of this research was to investigate the sensitivity of reference evapotranspiration to meteorological variables and to introduce the most accurate database in providing these variables in the Urmia Lake basin. For this purpose, 24 synoptic stations were selected and their meteorological data was prepared on a daily basis between 2010 and 2019. Then, the reference evapotranspiration was calculated using the FAO-Penman-Monteith equation, and the effect of changes in meteorological variables on ET0 was investigated individually in the range of ±20%. In the next step, the accuracy of ERA5, CFSv2 and MERRA2 meteorological data was evaluated and the most accurate one was introduced. The ten-year average of reference evapotranspiration of the meteorological stations was obtained 3.1 mm d-1, and the results showed that the maximum temperature is the most influential meteorological variable on reference evapotranspiration changes. After that, wind speed and minimum temperature had the greatest effect, respectively. The value of sensitivity coefficient for maximum temperature, wind speed and minimum temperature was obtained 0.4, 0.2 and 0.1 respectively. In the review of meteorological data of ERA5, CFSv2 and MERRA2, statistical indicators showed that the ERA5 dataset has the most accuracy. Based on the results, the 10-year average of reference evapotranspiration of the ERA5 was obtained 2.86 mm d-1 and according to the CRM index, this value was 8% underestimated compared to the value obtained from meteorological stations. Finally, the values of EF indices equal to 0.92 and nRMSE equal to 0.17 put the reference evapotranspiration of the ERA5 in a suitable and reliable rank.

Aquifers, as one of the most vulnerable water sources, are exposed to various contaminants in different forms, which detecting and controlling pollution in these resources are more difficult and costly than surface water. The best way to prevent their contamination is to identify polluting sources and vulnerable areas, prepare vulnerable zoning maps and adopt appropriate management policies. In this study, the DRASTIC and GODS methods are used to assess the Khezri area. Khezri area is one of the sub-basins of Khaf-Petergan Playa Iran, based on the political divisions of the country, and located in the southern Khorasan province near Qayen city, between the longitudes of 35 58 to 17 59 east and latitudes 41 33 to 6 34. The results of the DRASTIC for this area showed that a small part of the area has very low vulnerability, a very wide area of the plain has low vulnerability (69.68%), and the northwest and west of this plain have medium, high and very high vulnerability. Sensitivity analysis is also done in this area with two methods of eliminating the parameter and single parameter. In the single parameter sensitivity analysis, the aquifer environment parameter has the most effective weight and the parameter of the topographic slope percentage has the least effective weight in calculating the vulnerability index. In the map zoned by both methods, the points with high vulnerability are in the area of Khezri city and steel factory.

Groundwater is one of the main sources for supplying needs, including drinking water and agriculture.With increasing human and climate pressures on groundwater resources, accurate forecasts of groundwater flow and quality for sustainable management are essential. Underground water quality modeling is a useful tool for identifying how to transfer pollutants in a the porous aquifer. These models include several parameters that are often estimated based on previous studies or expert judgment, Or in the best cases, based on Measurements in plaeses . In conclusion, the input data to the simulation models are not accurate and are accompanied by errors, so it is necessary to first define the effective and sensitive parameters relative to the outputs of the model.In this paper, a problem with the MODFLOW-2010 and MT3DMS programs was first simulated and the desired pollutant concentrations in wells were estimated according to the parameters given over a two-year period. Sensitivity analysis for time outs and Maximum concentration has been done during these two years. According to the statistical characteristics of the outputs, effective parameters are estimated on the maximum concentration and time. The results show that the effective parameters on the concentration of solute pollutants in the wells are 1 ) Reduction coefficient 2) Distribution coefficient 3) Longitudinal diffusion 4) Hydraulic conductivity 5) Transverse diffusion coefficient 6) Porosity Respectively.effective parameters on the time associated with the maximum concentration in the well operation are 1) Hydraulic conductivity 2) Distribution coefficient 3) Porosity 4) Transverse distribution coefficient 5) Reduction coefficient 6) Longitudinal diffusion.

While having economic advantages, non-linear weirs have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM), gene expression programming (GEP), software (QNET) and artificial intelligence network (ANN) in predicting the discharge coefficient of non-linear Weirs of 318 data series for the first scenario And the second scenario includes the number of 363 data series and the third scenario includes data integration (the sum of the first and second scenario) which includes 681 data series. The difference between the first and second scenarios is in the shape of the quarter-circle and semi-circle weir crown. The geome tric and hydraulic lines used in this research include total water load ratio (H_T/p), magnification) L_C/W), cycle wall angle (α) and discharge coefficient (Cd). The results of artificial intelligence showed that the combinations (Cd, H_T/p, α, L_C/W) in QNET, ANN, GEP, SVM algorithms in the training stage related to the superior scenario are equal to the evaluation indicators respectively (R2=0.9960), (RMSE=0.0080), (DC=0.9961), (R2=0.9980), (RMSE=0.0057), (DC=0.9980), (R2=0.9837), (RMSE=0.0207), (DC=0.9838) and (R2=0.9902), (RMSE=0.0186), (DC=0.9830). Which has led to the most optimal output compared to other combinations, which indicates a very favorable accuracy in all four methods, namely ANN, QNET, SVM and GEP in predicting the weir discharge coefficient is non-linear. The results of the sensitivity analysis showed that the effective parameter in determining the nonlinear weir discharge coefficient in all methods is the total water load ratio parameter (H_T/p).

Scour around bridge piers is an important challenging issue in hydraulic engineering and one of the primary causes of bridge failures (Singh et al., 2022). To date, a large number of empirical equations have been proposed by researches for estimating local scour depth at bridge piers. However, their applicability and results are very different from each other, in such a way, calculated local scour depths using these equations, for a 1 m diameter pier in fine and coarse sand beds, could vary almost from 0.2 m to 3 m, and 0.25 m to 5 m, respectively (Sheppard et al. 2014). In these conditions, evaluation of the performance and accuracy of pier scour empirical equations is very important. In fact, it is necessary to assess the effect of errors in measurements of input properties on estimation of local scour at piers in order to specify the equations that are mostly affected by propagation of input data errors. Sensitivity analysis of these equations and determining the effect of data uncertainties on them could result in precise computation of pier scour that leads in safe designs and reduction in further damages. Variables that are used in local scour equations at bridge piers, are flow and sediment properties, such as flow velocity and depth, and sediment grain sizes. Computation of local scour depth at bridge piers using empirical equations is based on the measurements of these properties. Errors in these measurements, that are common in engineering applications, affect the accuracy of estimations of pier scour. In the present research, propagation of input data errors and its effect on the pier scour estimation analyzed by Monte Carlo numerical method, that determines the sensitivity level, and relative strengths and weaknesses of the equations. In this research, sensitivity analysis carried out for four empirical equations of local scour depth around cylindrical piers in non-cohesive sediment beds and in both clear-water and live-bed scour conditions. Evaluated equations are: Froehlich (1991), Gao et al. (1993), S/M (2014), and HEC-18 (2016) equations. Monte Carlo method is based on generation of random numbers. The goal of the Monte Carlo method is to simulate an existing model (in the present study, an empirical equation to estimate the local scour at bridge piers) by randomly sampling from the input physical properties distribution using a large number of samples and finally predicting the output response (Pinto et al., 2006). The physical properties selected for the error analysis include the depth-averaged flow velocity (v ̅_1), flow depth (y_1), and sediment median grain diameter (d_50). Bridge pier width (w) is a property in the equations that is supposed to be known precisely, therefore it is excluded from the analysis. In this research, pier’s reference width is supposed to be constant in the value of 1 m. The densities of water (ρ) and sediment (ρ_s) are variables that could be the error source in the computation of pier scour. However, they are both excluded from sensitivity analysis of pier scour equations, because their values are usually known and supposed to be constant. In order to analyze the results, four output quantities are defined: 1- local scour depth around bridge pier that is computed based on the mean values of physical properties (z_mp=z(v ̅_1,y ̅_1,d ̅_50)), 2- pier scour depths in each Monte Carlo simulation (z_i=z(v_1i,y_1i,d_50i );i=1,…,N), the number of computations (N) set to 10 000 in all simulations, 3- Mean pier scour depth (z_m=z ̅_i), and 4- standard deviation of pier scour depths (σ_z). The error analysis is based on the ratio between fourth and first quantities, that is defined as r ratio (r=σ_z/z_mp). In evaluating velocity errors on local scour depth at piers, in all velocities, except in v ̅_1=0.5 m/s acceptable values of r (r≤0.3) obtained for all equations. In relative comparison of the equations, two equations, Gao et al. and HEC-18 show most and least sensitivity to the velocity variations, respectively. For flow depth (y_1) variations, small values of r obtained for all equations. In all cases, values of r were below 0.1. comparison of equations reveals that HEC-18 and S/M equations are the most and least sensitive to the flow depth errors, respectively. In analysis of sediment median grain size errors on pier scour depth, acceptable values of r observed for all the cases simulated. In all simulations, values of r were smaller than 0.07, except for Gao et al. equation, with d_50 = 0.2 mm and 0.5 mm and with α = 0.4, that values of r reached to 1.3 and 0.17, respectively.Among four evaluated equations, Gao et al. and HEC-18 equations show most and least sensitivity to the sediment grain size variation, respectively. Therefore, Gao et al. equation is more sensitive to physical properties than the other equations analyzed. Results also show that among three input variables, errors in the measurements of flow depth and velocity have minimum and maximum effect on the estimation of pier scour depth, respectively.

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

 In order to simulate the yield and water productivity of cucumber plant (Cucumis sativus L.), an experiment was conducted in the form of a completely randomized block design with three irrigation levels of 100, 85 and 75% of the water requirement in two growing seasons during 2017 and 2018 and using perceptron neural networks (MLP) and support vector machine (SVM) methods were used and finally, to select the appropriate and optimal model, the indices of explanatory coefficient, mean squared error and normalized mean squared error were used. The amount of irrigation water, number of leaves on the plant, temperature, evaporation rate and relative humidity were selected as input data and 60%, 20% and 20% of the total data were allocated for training, validation and testing of the model, respectively. The results showed that the MLP neural network with the inputs of irrigation water and number of leaves was more accurate in simulating fruit yield and water productivity in cucumber plants with an explanation coefficient of 0.92 and 0.86, respectively. The results of the sensitivity analysis indicated that the irrigation water input parameters are the most important effective parameters on the water consumption efficiency model and cucumber fruit yield with sensitivity coefficients of 0.9 and 0.86, respectively.

Soil organic carbon is very important in increasing the production of agricultural products and reducing soil erosion and greenhouse gas emissions. A better understanding of the amount and storage of soil organic carbon is essential to the use of soil and maintaining its productivity. The extended pastures of Fandoqloo region in Ardabil province potentially could support and sequestrate considerable amounts of carbon but they are subjected to land use change, high population and tourism. Such tensions led to land degradation and low carbon sequestration in some parts. This study aimed to find effective natural variables on soil carbon density and sequestration. Such variables could be applied for remediation and resilience of degraded pastures and croplands in Fandoghloo region. By application of Latin Hypercube techniques and spatial variables like soil, land use and geological maps the location of sample points was determined. For each land use unit (pasture or cropland) 70 composite surface soil samples (in total 140 samples) were collected from zero to 15 cm deep. After pretreatments of soil samples, soil's physical and chemical properties were measured according to standard protocols. A digital elevation model of the study area was formed using digital isolines of topographic maps and primary and secondary derivates were constructed. Landsat 8 bands were extracted from the Geology Department site of the USA and Normalized difference vegetation index and thermal map of the study area were calculated. Multiple linear regression and artificial neural network models were applied and soil data, digital elevation model and its derivates, normalized difference vegetation index and land surface temperature map have entered the models as independent variables for the prediction of soil organic carbon density. To evaluate the results of the models, the coefficients of determination and root mean square error was used. Application of multiple linear regression model indicated that total soil nitrogen, normalized difference vegetation index and mean weight diameter of aggregates justified 53% of soil organic carbon density in the pasture while 46% of soil organic carbon density in croplands land use was explained by total soil nitrogen and plane curvature. The results of the paired t-test showed a significant difference (P< 0.01) between soil organic carbon density of the pasture and cropland land use. An artificial neural network with an arrangement of 1-10-24 and tan-sigmoid transfer function in the hidden layer justified 71% and 82% of the soil organic carbon density in the pasture and croplands, respectively. Root mean square error of multiple linear regression models for predicting soil organic carbon density in the pasture and croplands were 1.07 and 1.04, respectively but of artificial neural network model was decreased to 0.85 and 0.58 for pasture and cropland, respectively. Sensitivity analysis revealed that the artificial neural network model was severely sensed to digital elevation model and soil moisture content at wilting point. The result of this study indicated the significance of natural vegetation cover and the mean weight diameter of soil aggregates for carbon sequestration in the pastures. Artificial neural networks performed to simulate changes in soil organic carbon density showed that these networks are successful in tracking an important part of soil organic carbon density changes at pasture and cropland land uses. In other words, artificial neural networks were able to identify the effects of these variables on soil organic carbon density using tracing and identifying the effects and interactions between an independent variable series with organic carbon density status. The results showed that transmission functions can be used by the data of primary and secondary topographic parameters for estimating soil organic carbon density in the region.

This research aimed to select essential features for modeling the cost of pressurized irrigation systems using the data of 515 drip irrigation projects in four parts, including the cost of pumping station and central control system (TCP), cost of on-farm equipment (TCF), cost of installation and operation on-farm and pumping station (TCI), and total cost (TCT). In the first stage, a database including 39 features influencing the cost of the mentioned sectors was prepared and the price of all projects (2006 to 2019) was updated for the base year of 2021. Then, feature selection was done with different algorithms in MATLAB environment and in two parts including (1) all features (39 features before and after the design stage) and (2) 18 features before the design phase (BD). The results showed that the amounts of RMSE and R2 for all the features were equal to 0.007 and 0.92, respectively, and for the BD section, they were equal to 0.003 and 0.89, respectively. Among the different algorithms for feature selection, support vector machine (SVM) and optimization algorithms (Wrapper) were identified as the best learner and feature selection method, respectively. The results of the evaluation criteria showed that the two LCA and FOA algorithms achieved the best estimation, and their error criterion in all the features were 0.0020 and 0.0018, respectively, while their correlations were 0.94 and 0.94. In the BD features, these criteria were 0.0006 and 0.95 for both algorithms, respectively. Finally, in the all features section, 10 out of 39 features and for BD section, 8 out of 18 were selected as the most effective features. The results of choosing the most effective features that affect the cost of different parts of the drip irrigation system can make the cost modeling of the systems simpler and faster and, while being useful for research works, it facilitates estimation and management of costs before implementation of each project.

One of the most critical issues that have always been the concern of researchers in water engineering and hydrology is the simulation of runoff or river discharge to plan, prevent damages, and solve the water shortage problem, and soil erosion. In addition, due to the ever-increasing limitations of extractable freshwater resources, it is very important to predict the river discharge and its changes as accurately as possible. The prediction of runoff, this important hydrological variable, significantly impacts the sustainable management of water resources, engineering designs, environmental protection, water supply planning, water quality management, irrigation systems, and electricity generation worldwide. Besides, for data accuracy and to modify and complete the data, the results of these models can be used. The rainfall-runoff process is one of the most complex, dynamic, and non-linear hydrological phenomena, which is influenced by various factors such as temporal and spatial changes, geomorphology, and climatic characteristics of the catchment area. Knowing the connection between precipitation and runoff is one of the important issues of hydrology because precipitation data are used in flood prediction, and a good prediction is made when a suitable relationship is defined. By increasing the accuracy of river runoff prediction, more efficient management and planning are done. Therefore, improving runoff prediction modeling seems essential.

So far, complex and diverse relationships have been presented to predict the extent of river floodings, such as conceptual rainfall-runoff models, time series linear models, and hybrid models. However, due to the lack of accurate knowledge and the complexity of factors affecting river flooding, the values ​​calculated from various relationships have significantly differed in many cases. In the meantime, hydrological models, with their potential, are considered efficient tools, especially in climate change conditions. One of the models that researchers use to model rainfall and runoff is the RRL (Rainfall Runoff Library) hydrological model. This software package includes integrated and conceptual models (such as AWBM, Sacramento, SMAR, SimHyd, and Tank). In this research, the comparative evaluation of the Sacramento, SimHyd, and SMAR models of this tool has been done in predicting the long-term runoff of the Galikesh watershed and also investigating the effect of parameters on the performance of each model.

In this research, after preparing the input data, the models were calibrated and validated for 1989-2010 and 2010-2019. The simulated and observation runoff results were analyzed to check the potential of the models. Furthermore, after evaluating the model using the optimized parameters, the sensitivity of each of the parameters of the three Sacramento, SimHyd, and SMAR models was investigated in the simulation of runoff from the Galikesh watershed so that the sensitivity of the models to the change of parameters and the effect of each parameter in the simulation makes it clear. To be the results of the evaluations indicate the optimal performance of all models in runoff simulation. However, the Sacramento model with a Nash Sutcliffe coefficient of 0.82 for the calibration period and 0.7 for the validation period, and then the SimHyd model with a Nash Sutcliffe coefficient of 0.71 and 0.76 for the calibration and validation period has the best performance in the runoff simulation of the Galikesh watershed. The SMAR model has shown weaker results than other models in runoff simulation. Finally, the sensitivity of all parameters was checked. The results showed some parameters such as LZTWM (Lower zone tension water capacity) and Zperc (Maximum percolation rate coefficient) in the Sacramento model, impervious threshold parameters and infiltration coefficient in the SimHyd model, and the evaporation conversion (T) parameter in the SMAR model was the most sensitive to reducing their values. Also, increasing the value of the Rexp (Percolation equation exponent) parameter in the Sacramento model and the proportion direct runoff (H) parameter in the SMAR model has the greatest impact on the simulation runoff compared to other parameters.

Different models have been proposed to explain these complexities, considering the importance of runoff forecasting and the non-linearity of converting precipitation into the runoff. Different structures and approaches of studied models have led to their different predictions, which has led to the importance of the comparative evaluation of the models for various purposes. For this purpose, in this research, three hydrological models, Sacramento, SimHyd, and SMAR have been used to simulate the runoff of the Galikash catchment area. The investigations showed that all three models could simulate the outflow of the watershed, and all the models have successfully simulated high amounts of runoff. However, the Sacramento model has performed better than the others. Models parameters sensitivity analysis has been investigated considering the importance and effect on runoff simulation. Finally, The sensitivity analysis showed that some parameters are more sensitive than others in the runoff simulation. The optimal amount of these parameters should be considered during the simulation due to their high sensitivity.

Class A pan evaporation method as one of the most common methods for reference evapotranspiration (ET0) estimation has been widely used in the world due to its simplicity, relatively low cost, and ability to estimate daily ET. In this study, the performance of 8 empirical methods consisting of Allen and Pruitt (1991), Cuenca (1989), Snyder (1992), modified Snyder, Pereira, et al. (1995), Orang (1998), Raghuwanshi and Wallender (1998), and FAO/56 were analyzed to estimate class A pan coefficient and ET0 at Fasa synoptic station located in Fars province. The calculated pan evaporation coefficients from the above equations were compared with measured pan evaporation coefficients which were obtained from the ratio of evapotranspiration calculated by the FAO-Penman-Monteith method to the rate of evaporation from the pan. The results showed that all empirical methods did not predict pan coefficient values well (R2 < 0.3 and NRMSE > 0.25). The comparison results between ET0 from empirical methods and ET0 obtained from FAO-Penman–Monteith indicated that the FAO/56 method had the best performance (R2 = 0.72 and NRMSE = 0.3). To increase the accuracy of empirical pan coefficient equations, these equations were modified with eight years (2007-2015) of meteorological data from the Fasa synoptic station and validated using two years of independent data (2015-2017). The results showed that the accuracy of all empirical models was improved and the Cuenca equation with NRMSE = 0.16 and R2= 0.63 was selected as the best equation for pan coefficient estimation and ET0 (R2 =0.85; NRMSE =0.18) in Fasa region. The sensitivity analysis revealed that the estimated pan coefficient is more sensitive to wind speed, followed by relative humidity, fetch distance, the slope of the saturation vapor pressure curve, sunshine hours, and air pressure. According to statistical results and sensitivity analysis, an equation was expanded for the Fasa region and other areas with the same climate.

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

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

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

The decrease in rainfall, the increase in drought and the development of agriculture in recent years have led to an increase in the consumption of underground water and an increasing decline in water levels. In this study, using version 10 of GMS software, the condition of Qazvin Plain aquifer was modeled in two stable states for October 2017 and unstable from October 2017 to September 2018. The sensitivity analysis of the model was performed with respect to the parameters of hydraulic conductivity and specific yield, and these parameters were determined after calibration and validation of the model. The sensitivity analysis of the model with respect to the two mentioned parameters shows that the model is more sensitive to changes in hydraulic conductivity compared to changes in specific yield. The root mean square error in stable and unstable state was 0.84 and 1.06 m, respectively, and the absolute mean error was 0.66 and 0.75 m, respectively, which shows the high accuracy of the model simulation. The average value of hydraulic conductivity in the aquifer area was estimated as 14.06 m/day and specific drainage was 0.064. Then the underground water level in 6 scenarios of changing the irrigation systems (maintaining the existing situation, increasing the irrigation level under pressure, changing the systems to surface, changing the systems to rain, changing the systems to drip and irrigation with 100% efficiency) was simulated. The results showed that assuming that the underground water extraction is constant, in the surface irrigation scenario, the underground water level will be at its highest level with 30% return water and in drip irrigation conditions at its lowest level with 3.6% return water.

Evaporation losses from free surface water in reservoir dams are one of the important processes in meteorology and hydrology. Each experimental methods estimates the evaporation based on the climatic conditions of each region. For this reason, calibration of the relationship between evaporation estimates in different regions are required (Vanzyl et al., 1989). experimental methods are common in engineering sciences. Most scholars, by modifying experimental methods and discovering new relationships, try to find a simple and high-level relationship to replace with the field methods. More than 50 experimental relationships have been proposed for estimating evapotranspiration by various researchers (Naorem and Devi, 2014). Despite the importance of evaporation in hydrology, less attention has been paid to it. However, with the correct estimation, it can be applied in water resource planning with fewer errors (Saadatkhah et al., 2002).In this research, the experimental methods of Mayer, Marciano, Shahthin, Henfer, Ivanov and USBR have been used to study the evaporation from the free surface of the Choghakhor lake, located in Chaharmahal and Bakhtiari province of Iran. The results have been compared with actual evaporation values from the free surface.