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Showing 3 results for Dinpazhoh

R. Mirabbasi Najafabadi, Y. Dinpazhoh , A. Fakheri-Fard,
Volume 15, Issue 58 (winter 2012)
Abstract

Accurate estimation of runoff for a watershed is a very important issue in water resources management. In this study, the monthly runoff was estimated using the rainfall information and conditional probability distribution model based on the principle of maximum entropy. The information of monthly rainfall and runoff data of Kasilian River basin from 1960 to 2006 were used for the development of model. The model parameters were estimated using the prior information of the watershed such as mean of rainfall, runoff and their covariance. Using the developed model, monthly runoff was estimated for different values of runoff coefficient, , return period, , at different probability levels of rainfall for the basin under study. Results showed that the developed model estimates runoff for all return periods satisfactorily if the runoff coefficient value is taken 0.6. Also, it is observed that at a particular probability level and runoff coefficient, the estimated runoff decreases as return period increases. However, the rate of change of runoff decreases slightly as return period increases.
O. Babamiri, Y. Dinpazhoh,
Volume 20, Issue 77 (Fall 2016)
Abstract

Accurate estimation of ET0 in any region is very important. The aim of this study is to compare and calibrate the 20 empirical methods of estimating evapotranspiration (ET0) based on three categories in monthly timescale at the Urmia Lake watershed. These categories are: 1) temperature-based models (Hargreaves (HG), Thornthwaite (TW), Blaney-Criddle (BC), Linacre (Lin)), 2) radiation-based model (the Doorenbos-Pruitt (DP), Priestly-Taylor (PT), Makkink (Mak), Jensen-Haise (JH), Turc (T), Abtew (A), McGuinness-Bordne (MB)) and 3) mass transfer-based model (Meyer (M), Dalton (D), Rohwer (R), Penman (P), Brockamp-Wenner (BW), Mahringer (Ma), Trabert (Tr), WMO and Albrecht (AL)). For this purpose, the information of 10 synoptic meteorological stations during the period of 1986-2010 was used. Results from the above mentioned methods were compared with the output of the FAO Penman-Monteith (PMF-56) method. Performance of the methods evaluated using the R2, RMSE, MBE and MAE statistics. The best and worst methods of each category were determined for the study area. The best methods of each category were calibrated for the area under study. Results indicated that there is a significant difference between the results of selected methods of each category and the PMF-56 method. Performance of the selected methods remarkably increased after calibration. Among the temperature-based group, the HG method having the median R2 value of 0.9597 was recognized as the best method. After calibration the medians of RMSE, MBE, and MAE were 72.09, 3.14 and 10.70 mm/ month, respectively. After HG, the Lin and BC found to be the best second and third methods in the study area. The TW showed Large error, therefore, it was not a suitable method for ET0 estimation in study area. Among the radiation-based group, the DP model was selected as the best method in the study area. Furthermore, the median of R2 values was 0.982. In this method, the medians of RMSE, MBE and MAE after calibration were 7.89. -0.62 and 6.03 mm/month, respectively. Following DP, the PT method was recognized as the 2nd best one. The methods namely M, JH, T, A and MB were put in the 3rd to seventh rank of the radiation category. Finally, among the mass transfer-based group, having R2=0.8945, the Meyer method was selected as the best method of this group for the study area. In the mentioned method (after calibration) the medians of RMSE, MBE, and MAE were 21.8, -8.7 and 17.3 mm per month, respectively. From mass transfer based group, the D method was found as the second best method in the study area. The methods namely R, P, BW, Ma, Tr, WMO and A were ranked 3rd to 7th, respectively. In general, the performance of radiation based methods was superior than others in Urmia Lake basin. Temperature based methods and mass transfer based methods were ranked second and third, respectively. Further examination of the performance resulted in the following rank of accuracy as compared with the PMF-56: DP (Radiation based), HG (Temperature based) and Meyer (Mass transfer). In general, it can be concluded that after calibration the DP method is suitable to estimate reference crop evapotranspiration among 20 selected methods in the Urmia Lake basin.


M. Isazadeh, P. Mohammadi, Y. Dinpazhoh,
Volume 21, Issue 4 (Winter 2018)
Abstract

Statistical analysis and forecast discharge data play an important role in management and development of water systems. The most fundamental issues of statistical analysis and forecast discharge in Iran are lack of data in long term period and lack of stream flow data in gauging stations. Considering the issues mentioned in this study, we tried to estimate the daily data flow (runoff) of Santeh gauging station in Kordestan province using the nearby hydrometric and meteorological stations data. This estimation occurred based on the sixteen different input combinations, including data of daily flow of hydrometric stations Safakhaneh and Polanian and daily runoff in Santeh precipitation gauging station. In this research, the daily flow estimation of the Santeh station in each of the months of the year was evaluated for sixteen different combinations and artificial neural network models and multiple linear regressions. The performance of each model was evaluated with the indicators RMSE, CC, NS and t-student statistic. The results showed good performance of both models but the performance of the artificial neural network model was better than the regression model in estimation of the daily runoff in the most months of the year. Mean error of artificial neural network and multiple linear regression models was respectively estimated as 6.31 and 8.07 m3/s in the months of the year. It should be noted that the artificial neural network, for each sixteen combination used, had better result than the regression model.


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