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

Due to the heterogeneity in watersheds and the non-linearity of hydrological behaviors, it is very complicated and difficult to fully understand the relationships within watersheds. Therefore, in evaluating these systems, a modeling process is necessary. Over the last few decades, hydrological/hydraulic models have become essential in hydrology studies due to the development of programming languages and the provision of optimal and efficient algorithms for solving differential problems. The application of rainfall-runoff simulation models for flood events has been extensively studied by researchers in the field of water and soil protection, leading to the development of various models to simulate rainfall-runoff processes. One of the successful models in this field is the TOPKAPI-X model. This model was created in the 1990s at the University of Bologna by Professor Todini as a distributed rainfall-runoff model in watersheds. An important feature of distributed models is their ability to simulate components at any point of the watershed, allowing results to be extracted at any required point. Unlike lumped models that consider the entire watershed as a single unit, distributed models allow spatial distribution at any point in the watershed. Therefore, in this research, after calibrating and validating the TOPKAPI-X physical-distributed model in the studied basin, the model was optimized for flood estimation.

The Gamasiab basin is located in the west of Iran, in the northern region of the Zagros mountain ranges, to the north of the Karkheh dam basin, and primarily within the territories of Hamadan and Kermanshah provinces. The mountainous regions of this basin are mainly concentrated in the northern and southern parts, while its lowlands and plains are mostly located in the middle and southeastern parts of the basin (Ministry of Energy, 2014). In this research, the TOPKAPI-X model was used to simulate floods in the Gamasiab watershed. First, the watershed boundary was delineated using a digital elevation model (DEM) with a resolution of 30 meters. Land use maps, soil texture, watershed network, and climatic components were entered into the TOPKAPI-X model. The outlet location of the basin (hydrometric station) was used to simulate the flow using the TOPKAPI-X distributed hydrological model. Continuous time series data on a daily time step were used in this rainfall-runoff model. Specifically, daily rainfall data from 13 rain gauge stations and temperature data from 4 synoptic stations during the statistical period (1999 to 2020) were used to simulate the flow. After running the model several times, the general parameters were manually adjusted each time until the optimal values of the general parameters were obtained by considering the appropriate values of the evaluation criteria (NS and Bias) for the basin.

This research was conducted to analyze the flood discharge of one of the main sub-basins of the Karkheh dam basin using the TOPKAPI-X model on a daily time scale. In the TOPKAPI-X software environment, simulations were performed during the calibration period using input maps and observational rainfall, temperature, and discharge data. A visual comparison of the observed and simulated hydrographs allows for a general and quick evaluation of the model's accuracy. The graphical results of the comparison between the discharge generated by the TOPKAPI-X model with the calibrated parameters and the measured discharge in the Gamasiab basin were presented. The TOPKAPI-X model has the ability to estimate the maximum daily flow rates of the Gamasiab basin; however, some of the simulated flow rates are higher than the observed flow rates. Four criteria—NSE, R, BIAS, and RMSE—were used to evaluate the model. The evaluation results of the TOPKAPI-X model indicate the accuracy of flow simulation, with a Nash-Sutcliffe criterion of 0.697 during the calibration period (1999-2014) and 0.660 during the validation period (2015-2020) for the Gamasiab basin. Therefore, it can be concluded that this model has good performance for flow simulation.

The importance and usefulness of hydrological models for water resources management, understanding hydrological processes, and conducting impact assessment studies is clear. Hydrological models are crucial tools that enable scientists and policymakers to make informed decisions based on simulations of watershed behavior. Considering the increasing demand for water and the impact of climate change, hydrological simulation will be one of the essential methods for future water management. The results of this study showed that the TOPKAPI-X model has potential in simulating runoff in the selected basin. Due to the capabilities of the TOPKAPI-X distributed hydrological model, this software is recommended as a modeling tool for other basins.

Extended AbstractIntroductionHydrological modeling is an essential tool in water resources management, and its accuracy and reliability are critical to successful design, planning, and decision-making processes. Calibration and validation are two essential processes used to evaluate the performance of hydrological models. The warm-up period is a crucial component of hydrological modeling that allows the model to reach an equilibrium state by representing the initial system conditions accurately.This study aimed to investigate the impact of the length of the warm-up period on the performance of four different hydrological models, namely AWBM, Sacramento, SimHyd, and TANK, in the Kashkan watershed. The study used different optimization methods in the RRL software package during the calibration and validation periods. The proposed warm-up periods of 5%, 7%, and 10% of the initial data length were used without considering drought and wet conditions.The findings of this study provide valuable insights into the impact of the warm-up period on hydrological modeling performance. The study showed that the length of the warm-up period does have a significant impact on model performance, with the best results obtained when the warm-up period was set to 5% or 7% of the initial data length. These findings have important implications for the design and implementation of hydrological models, as they highlight the importance of carefully selecting the warm-up period length to ensure accurate and reliable modeling results. Overall, this study adds to the body of knowledge on hydrological modeling and provides useful guidance for future research and practical applications.Materials and methodsThe Kashkan River watershed, with an area of over 9,000 hectares, was selected as the study area for this research. The Kashkan River is an important sub-watershed of the Karkheh River watershed, and daily rainfall, potential evapotranspiration, and potential evapotranspiration for the Kashkan watershed were used in this study, with a statistical period of 29 years (1988-2018). Since the rainfall-runoff process was investigated for the entire watershed, the Thiessen polygon method was used to obtain the weighted average of rainfall and evapotranspiration for the entire study area. Additionally, the Hargreaves-Samani (H-S) method was used to obtain potential evapotranspiration data.The data used in this study were divided into two parts, training and testing, based on trial and error and a review of sources. The training data accounted for 70% of the total data, while the remaining 30% was used for testing. The AWBM, Sacramento, SimHyd, and TANK models in the RRL software package were investigated, along with seven optimization methods using the Nash-Sutcliffe objective function.The findings of this study provide insights into the application of different hydrological models and optimization methods in the Kashkan River watershed. The study highlights the importance of accurately representing initial system conditions during modeling and the impact of the length of the warm-up period on model performance. These findings have important implications for water resource management, particularly in the design and implementation of hydrological models for the Kashkan River watershed and other similar regions.Results and DiscussionThis study examined the influence of different durations for the warm-up period on the calibration and validation of RRL software package models. Seven optimization methods and the Nash-Sutcliffe criterion were utilized in the analysis. Specifically, the warm-up phase of the software, which constitutes the initial segment of the statistical period, was investigated during the calibration and validation processes. Durations of 5%, 7%, and 10% were selected from the onset of the statistical period. The study involved conducting over 4000 iterations for all the examined models and optimizers.Given the characteristics of the optimizers, up to 5 iterations were performed for each optimizer in each model. The resulting average NSE value (Nash-Sutcliffe Efficiency) was analyzed and examined.The findings indicate that, on average, configuring the warm-up period to account for 5% and 7% of the complete dataset in the calibration and validation processes enhances the efficiency of the model compared to the recommended period suggested by the software. However, it is important to note that the outcomes may vary depending on the specific problem and prevailing conditions. Therefore, these results should be interpreted cautiously and in conjunction with other factors. Overall, this study offers a practical guideline for selecting an appropriate warm-up duration in the calibration and validation of RRL software package models.

In recent years, river flow forecasting is one of the most important issues for water resources management in Iran. This prediction requires statistics and information, unfortunately, most of the basins of the country lack data of the desired quantity and quality.

Therefore, hydrological modelling and the use of artificial intelligence are examples of solutions that are used to solve this challenge in hydrology. The criteria for selecting the appropriate model for this process are to evaluate the performance of the models according to the hydrological conditions of each region. In this research, IHACRES model and Artificial Neural Network (ANN) were used to predict the streamflow in Bakhtiary basin. The data from 1984 to 1994 were used as calibration period and the data from 1995 to 2006 were used for validation.

The evaluation results of the hydrological model and the artificial neural network were evaluated using Kling-Gupta, Nash-Sutcliffe indices, coefficient of determination, mean squared error and absolute mean error. Results showed that the artificial neural network had better results in the simulation in all the evaluated evaluation criteria.

According to the results of the methods used in the research, the artificial neural network method has a more accurate prediction of the Bakhtiary river flow than the hydrological model.

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.

Flood hazard assessment is an important topic that can reduce flood-related losses. Rainfall-runoff modeling plays a key role in the management of water resources in addition to protecting from flood hazards. The use of hydrological models to simulate the runoff necessitates the proper calibration of the different parameters. Therefore, In the present study, the Watershed Modeling System (WMS11.0) was evaluated to simulate peak discharge and volume of floods of Babolrood catchment. WMS model calibrated and validated using 3 and 2 rainfall events, respectively. Afterwards, design precipitation (DP) for 2, 5, 10, 25, 50, 100 and 500-year return periods was determined and flood resulting from DPs simulated. The results showed that the WMS model could accurately estimate the peak discharge (the error was about 5%) and the and flood volume (the error was less than 26%). But the model was not able to simulate properly the shape of the hydrograph. It also revealed that peak discharge and flood volume arising from 2 to 500-year return periods of rainfall vary between 50 to 300 m3/s and 6.6 to 32.4 Mm3, respectively.

One of the most important challenges of researchers in rainfall-runoff modelling is the estimation losses and extracting excess rainfall, which can affect on accuracy of the model and hydrograph characteristics. In the present study, at first the infiltration index of was estimated based on the depth of runoff and rainfall and also effective rainfall time. Then, instantaneous unit hydrograph was computed through Russo Method and the dimensions of direct runoff hydrograph were determined for 20 rainfall- runoff events. Then in order to increase the precision in estimating the dimensions of obtained hydrograph, the value of penetration index was estimated based on bivariate distribution resulted from infiltration index and one of rainfall characteristics which obtained from Copula function. For this purpose, at first the correlation between infiltration index of , characteristics of rainfall hyetograph and runoff hydrograph was calculated, then the best- fitted marginal distribution on each variable was specified. Finally, Galambos function was chosen as the best copula function for creating bivariate distribution for pairs of infiltration index of and rainfall height, infiltration index of and maximum velocity, infiltration index of and average flow and also infiltration index of and velocity. Therefore, the hydrograph dimensions were obtained for each event. Comparing the various dimensions of computed and observed hydrograph by copula function in Russo method showed that the infiltration index of can be estimated more accurately by using the copula function.

Estimating runoff from atmospheric precipitation and its quantitative understanding of various production processes is considered as one of the important issues in hydrology knowledge. Since runoff in the watershed, due to several factors influencing it, is a complex issue, therefore, it is estimated that many researches, studies and various plans for development, use of water resources and hydrostructure. According to the above mentioned it was used, rain and discharge daily data of the Bar-Ariyeh area of Razavi Korasan province were used to estimate runoff from the extracted number curve method from the Natural Soil Conservation Service (SCS-CN). For this purpose, rainfall and discharge data of Bar and Ariyeh stations during the period of 56 years and in the daily scale, the value of the curve number in the three conditions of previous moisture in low, medium and high antecedent moisture conditions of soil was evaluated. The results showed that the maximum, minimum and average values of the curve number in summer and autumn were 93, 91, 92 and 85, 81, 83 percent, and in the spring and summer were 81, 79, 80 and 78, 76 and 77 percent in The domain was studied. The highest and lowest monthly average number of curve number for April and January were 93 and 76 respectively. In addition, the maximum and minimum amount of runoff was calculated for April and September with a numerical value of 12.45 and 1.29 mm in the statistical period.

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.

The conversion of rainfall to runoff in basins includes nonlinear relations between the complex interactions of various hydrological processes. In this study, without considering of predetermined structure, relationship between input and output system was derived individually from the nature of the data recorded. Also, the phase difference occurred between rainfall and runoff signals using cross-wavelet transform analysis. Then phase difference diagram was plotted for the single and compound events of Lighvan basin. By applying these phases at the time of the rainfall signals, all errors resulting from considering average losses in basin was minimized that in this study was introduced as "minimum error time position"(METP). Also, discharge forecasting for basin was carried out by Kalman filter model and optimization in Lighvan basin state space, using calibration step events. By applying this phase difference to effective rainfall components, the error resulting from the considering of average infiltration losses and φ index decreases to the minimum. Also, using the linear programming optimization method, the unit hydrograph for Lighvan basin was used as Kalman filter measurement model. The results showed that by applying the phases difference between rainfall and runoff signals and integration with Kalman filtering and linear programming (KF-LP-CW), the corrected Nash-Sutcliff coefficient were obtained 0.94 in both Calibration and verification steps. These values were obtained 0.63 and 0.68 in calibration and validation steps respectively, as compared to the state that phase difference was not applied (KF-LP method). Therefore, in this study, significant improvement was observed with the application of phase differences in effective precipitation components.