flow simulation

Considering the importance of knowing and awareness of the watersheds water balance status, and analyzing the hydrological behavior of watersheds for planning and implementing water-related projects, the need to use new technologies in predicting water balance components is more evident than ever. Based on this, in the Kashkan basin by utilizing the TOPKAPI-X hydrological model, the water balance components of the basin were simulated according to the cellular network design. Digital maps of the basin, land use, outlet point, soil texture, elevation, and continuous time series of temperature, precipitation, and discharge in the daily time step are the main inputs of the model. The model in each cell network balances the water balance of the entire period. Model calibration was done for the 15 years of the statistical period (1999 to 2014) and model validation for the 6-year period (2014 to 2020). The results showed that 27.02 and 28.43 percent of the total precipitation of the Kashkan basin was discharged from the basin as total runoff (respectively for calibration and validation periods), which is consistent with the observation data at the outlet hydrometry station. Next, to evaluate the efficiency of the model, the simulated values in both statistical periods were compared to the observational discharge. Statistical methods such as the Nash-Sutcliffe evaluation criteria showed that the TOPKAPI-X model predicted the water balance components such as actual and potential evapotranspiration, infiltration, and the amount of runoff, especially the total runoff, in this basin with relatively good accuracy (coefficient above 60%).

River's flow prediction and its flood are the important subjects related to the flood projects, hydroelectric power production and water allocation for agricultural, industry and drinking uses. In the present study, wavelet analysis combined with Artificial Neural Network (ANN) was performed to simulate Gamasiyab River flow, located in Nahavand.The analysis was done in daily and monthly scale. For this purpose, the original time series, using wavelet theory, decomposed to multi time sub-signals. Then, these sub-signals were used as input data to the ANN to simulate flow and then the outputs compared with the ANN’s results. In this study, we used the precipitation, temperature, evaporation and flow data of Varayeneh station during the period of 1969 to 2011.Then, 75% of the data was used for training and 25% was considered for simulating data.
In this research, the amount of N and, L were determined 516 and 2 recpectivly. Also, 1 to 4 decomposition degrees were examined to be more precise. The number of neurons in the first layer depends on the degree of wavelet decomposition. The number of input neurons to the network is “m*(j )” in which “j” is the wavelet decomposition degree and “m” is the number of input parameters. For example, for j=1, according to the input parameters which in this study is 4 (precipitation, flow, temperature and evaporation), the number of input neurons is equal to 8. The output layer also has one neuron. The number of the middle layer neurons is variable and is obtained by trial and error. However, in this study, the number of neurons analyzed in the middle layer was varied from 3 to 20. In this case, modeling was done using different training functions and transfer functions for different neurons of the hidden layers. For daily period, input parameters were also decomposed with wavelet function with the levels of 5 to 9 and used as the input data of ANN. Also, the results concluded that 5 architectures had the best performance and it was also observed that the number of neurons of the middle layer was less than 10 in the superior structures. This means that the optimal solution can be reached with a lower number of neurons. Different training functions were examined, but useing all training functions is not suggested because it is time-consuming. Therefore, as proposed in this article, the three models of “Levenberg-Marquardt”, “BFGS Quasi-Newton” and “Bayesian Regularization” due to their better performance are recommended for future studies. After examining different stimulus functions, it is concluded that four types of “Tansig”, “Logsig”, “Satlin” and “Poslin” stimulus functions are suggested to be used for future studies.
Signal decomposition with wavelet increased correlation between observation and simulation data compared to Artificial Neural Network model, so the R2 was increased by 3 percent during the daily period and 24 percent during the monthly period. Two Artificial Neural Network and Wavelet – Artificial Neural Network (WANN) models in the daily period had almost similar performance but Wavelet – Artificial Neural Network model had a better performance in predicting minimum points. Moreover, after examining different structures, is the results determined that 5 architectures, functioned better in the various models and in the daily and monthly periods of the architecture. It means that using the data of evaporation and temperature makes the performance of the models better than the architecture in which these data are not used. Accordingly, the use of temperature and evaporation parameters is suggested in further studies in addition to flow and precipitation parameters.
The results showed that hybrid model of Wavelet-Artificial Neural Network (WANN) outperformed the Artificial Neural Network model. The reason of this preference of the WANN hybrid model is that the separation affects the time series inputs before interring to the network and primary signal decomposed to the various sub-signals. By doing this, we can use the analysis that contain short term and long term effects.
Besides, wavelet function of “Db4” has a better performance than other wavelet functions in each time period. It has the best performance in the daily period in the 5 level and in the monthly period in the second level. Also, the results showed hybrid model outperformed to estimate extent points than artificial neural network model.

Water is becoming a scarce and precious resource all over the world. Nowadays, irrigation has allocated 69 percent of the 3240 cubic kilometers of human consumed water and 87 percent of the total consumed water and transformation of dry lands into irrigated areas with correct operation, reasonable utilization and good maintenance could be greatly effective on increasing of human needed products. Accomplished fact all over the world is that most of operating irrigation and drainage projects don’t redound to objective points and not only they couldn’t successfully increase efficiency, but also they cause reduction in production levels by harmful effect on aforesaid resources. Reasons have almost been incorrect design and operation, unsuitable and insufficient utilization and maintenance and generally poor management in different Sketches. Ghoorichay reservoir dam is located in Ardabil province. This earth dam has clay core and storage capacity of this structure and usage volumes are respectively 18.07 and 17.71 cubic million meter. Ghoorichay irrigation and drainage network is situated downstream of the dam, which amount to 2200 ha area. Water is drawn from the reservoir and distributed through the main canal and many secondary channels to farms. Main canal is established along the kilometers, 13� to 20�, to convey and distribute water into 4 secondary channel named LC1, LC2, LC3 and LC4. This canal has 6.97-kilometer length, after LC1 turnout from kilometer 13� to 15폝 it is generated with trapezoidal cross section and 0.001 slope and 2.25 cubic meter per second discharge. This section of main canal has 1.2 meter width, 1 meter height and bank slope 1:1.5 (V:H). In this section of main canal, normal depth is considered 0.75 meter, free height 0.25 meter and normal flow velocity is 1.3 meter per hour.
In this study, first step to calibrate the gates is to choose the suitable place for flow measuring. So LC2 channel with 6- kilometer length and 20 turnouts considered the most important secondary channel in Ghoorichay irrigation and drainage network, is chosen and one of its slide gates with 1.15 meter width is assessed. It is necessary to notice that chosen point for measurement should be in a location with minimum swing and wave ruffling to decrease measurement errors in hydraulic characteristics such as water surface width and water depth. In addition, it shouldn’t be any other turnout close by measuring location in order to ignore water losses through the distance. Then, needed condition for operation should be created. So, water depth behind regulation structures should rise up to FSL or Full Supply Level and keep constant by proper regulation while measuring duration. Under this situation flow measurement in LC2 channel had done. Present structure in entry point of channel for measuring discharge is 3-foot Parshal flume. Having measured depth and discharge parameters in specialized points of channel and considering channel dimensions, turnout and regulator structures, flow condition simulates by hydrodynamic models Root canal and ICSS and results have gotten with 10 different turnout opening.
Considering amount of off-taking in upstream of LC2 channel and downstream demand of this turnout which is 600 liter per second, also with considering network efficiency equal to 80 percent and evaporation and head losses equal to 50 liter per second. After inputting data in models, simulation is done for one hour duration and discharge and depth amount is determined. In order to assess introduced hydrodynamic models, and after calibration of two models, this part is allocated to comparison of output results with measured results along studied reach. This comparison is included water depth in turnout and regulator location and simulated water depth along the studied reach. Comparison is accomplished by drawing figures and statistics survey of errors in different points. Finally comparing the best coincidence of results obtained from each model, with observed condition, the proper model is chosen. Result shows that in both simulation dates, 1391/3/20 with entry discharge equal to 0.95 cubic meters per second and 1391/3/21 with entry discharge equal to 0.79 cubic meter per second, calculated error in hydrodynamic model Rootcanal is fewer and better coincidence with observed data is seen. Although results shows that for Icss hydrodynamic model the parameters are equal 5.14 and 4.71 centimeter respectively and for Rootcanal hydrodynamic model they are equal 2.12 and 1.61 centimeter respectively. These results show better performance of Rootcanal model than Icss model for simulation and estimation of flow parameters in irrigation canals.

In this paper climate change impacts on hydrological regime of a mountainous river basin is assessed. In order to do that, scenarios of global climate models are downscaled by using change factor method. The climate scenarios are used as inputs of a rainfall-runoff model, which is well calibrated for the basin, and daily stream-flow series for present condition and future scenarios (2067-93) are simulated. By comparison of river-flow characteristics for present condition and future scenarios, the climate change impacts on hydrologic regime of the basin are assessed. for analysis of the emission scenarios uncertainty, scenarios of A2, A1B, and B1, which relevant to high, medium, and low emission scenarios, respectively. Based on the results, basin temperature will increase between 3 to 5 Celsius degrees and potential evapotranspiration will increase for all month of the year. Despite uncertainty of emission scenarios, under all emission scenarios, annual average of rainfall and stream flow will raise; however, seasonal cycle of rainfall and river flow will change, too. Average river flow in the autumn and winter will increase, while the average river flow in spring and summer will decrease. Decreasing of the river flow in the second half of the water year, Implies the importance of considering of the climate change impacts on the river-flow for designing of dam’s reservoir.

Simulation of rainfall-runoff process in the watershed has a significant importance from various points of view، such as better understanding of hydrological issues، water resources management، river engineering، flood control structures and flood storage. Therefore in this study، the river flow and surface runoff are simulated using the distributed hydrological model، WetSpa. In the WetSpa model runoff process of the basin is simulated using diffusive wave approximation method based on gradient، flow rate and distributed features along the flow routes. Atrak watershed with about 11639 km2 area is one of the largest watersheds of Iran and average annual precipitation is about 283mm. Meteorological data from 1383 to 1390 consisting of rainfall in 25 stations، temperature and evaporation measurements in 5 stations were used as model input data. To run the model three base maps including DEM، land use and soil type with cell size of 100m were provided. Simulation results show a relatively good agreement between calculated hydrographs and measurements at the basin outlet. The model estimates daily hydrographs، with an accuracy of over 60% and 53% based on Nash-Sutcliff criterion، for calibration and validation periods، respectively. And based on Nash-Sutcliff criterion adapted for the maximum flow rate، the model accuracy was evaluated as 77%. According to model output and hydrological factors with spatial distribution at each time step، the model has the ability to analyze topographic effects، soil texture and land use in hydrological behavior of basin.