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Valipour M.,Islamic Azad University at Kermanshah
Water Resources Management | Year: 2014

Evapotranspiration has a highlighted role in agricultural and forest meteorology researches, hydrological cycle, irrigation scheduling, and water resources management. There are many models to estimate the evapotranspiration including mass transfer, radiation, temperature, and pan evaporation-based models. This study aims to compare temperature-based models to detect the best model under different weather conditions. For this purpose, weather data were gathered from 181 synoptic stations in 31 provinces of Iran. The evapotranspiration was estimated using 11 temperature-based models and was compared with the FAO Penman- Monteith model. The results showed that the Modified Hargreaves-Samani models estimate the evapotranspiration better than other models in the most provinces of Iran (25 provinces). However, the values of R2 were less than 0.98 for 15 provinces of Iran. Therefore, the models were calibrated and preciseness of estimation was increased. However, the estimation was improved only in 14 provinces. The new temperature-based models estimated the evapotranspiration in the eastern (RK, NK, SB, and KE) provinces of Iran (with a various temperature range 14–20 °C) better than other provinces. The best precise methods were the Modified Hargreaves-Samani 1 method for AL (before calibration) and the Modified Hargreaves- Samani 3 method for KE (after calibration). Finally, a list of the best performance of each model has been presented to use other regions and next researches according to values of mean, maximum, and minimum temperature, elevation, minimum and mean relative humidity, sunshine, precipitation, and wind speed. The results are also useful for selecting the best model when we must apply temperature-based models because of type of available data. © Springer Science+Business Media Dordrecht 2014. Source


Valipour M.,Islamic Azad University at Kermanshah
Journal of Hydrologic Engineering | Year: 2014

Evapotranspiration has a major role in agricultural and forest meteorology researches, hydrological cycle, irrigation scheduling, and water resources management. There are many methods to estimate the potential evapotranspiration including mass transfer, radiation, temperature, and pan evaporation-based methods. The present study aims to compare radiation-based methods to determine the best method under different weather conditions. The results discussed in this paper are from the data collected in the study area, but the method can be used in other similar regions. For this purpose, weather data was collected from 181 synoptic stations in 31 provinces of Iran. The potential evapotranspiration was estimated using 22 radiation-based methods and compared with the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method. The results show that the Stephens method estimates the potential evapotranspiration better than other methods for provinces of Iran. However, the values of R2 varied from 0.93 to 0.98 for 15 provinces of Iran. Therefore, the methods were calibrated and precision of estimation was increased (the values of R2 were less than 0.99 for 4 provinces in the modified methods). The radiation-based methods estimated the potential evapotranspiration in the central provinces of Iran (solar radiation between 24.0 and 25.0 MJ · m-2 · day-1, annual relative humidity less than 50%, and sunshine more than 250 h · month-1) better than other provinces. The most precise methods were the Berengena-Gavilan method for Esfahan (ES) (before calibration), the Stephens method for Zanjan (ZA) and Lorestan (LO), and the Stephens-Stewart method for Semnan (SE) (after calibration). Finally, a list of the best performances of each method is presented to use in other region studies according to mean, maximum, and minimum temperature, relative humidity, solar radiation, elevation, sunshine, and wind speed. The best temperatures to use radiation-based equations are 10-26°C, 16.5-24.0°C (with the exception of Jones-Ritchie), and 5-13°C for mean, maximum, and minimum temperature, respectively. The results are also useful for selecting the best model when radiation-based models must be applied based on available data. © 2014 American Society of Civil Engineers. Source


Valipour M.,Islamic Azad University at Kermanshah
Meteorological Applications | Year: 2015

In this study, the ability of the seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models was investigated for long-term runoff forecasting in the United States. In the first stage, the amount of runoff is forecasted for 2011 in each US state using the data from 1901 to 2010 (mean of all stations in each state). The results show that the accuracy of the SARIMA model is better than that of the ARIMA model. The relative error of the SARIMA model for all states is <5%. In the second stage, the runoff is forecasted for 2001 to 2011 by using the average annual runoff data from 1901 to 2000. The SARIMA model with periodic term equal to 20, R2=0.91, and mean bias error (MBE)=1.29mm is the best model in this stage. According to the obtained results, a trend is observed between annual runoff data in the United States every 20 years or almost a quarter century. © 2015 Royal Meteorological Society. Source


Valipour M.,Islamic Azad University at Kermanshah
Meteorological Applications | Year: 2015

Evapotranspiration has a significant role in agricultural and forest meteorology research, the hydrological cycle, irrigation scheduling and water resources management. Several models are available to estimate evapotranspiration, including mass transfer-based, radiation-based, temperature-based and pan evaporation-based models. This study aims to assess temperature-based models versus the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith model to detect the best one using linear regression under different weather conditions. For this purpose, weather data were gathered from 181 synoptic stations in 31 provinces of Iran. Evapotranspiration was estimated using 11 temperature-based models and was compared with the FAO Penman-Monteith model. The results showed that the modified Hargreaves-Samani 1 estimates the evapotranspiration better than other models in most provinces of Iran. However, the R2 values were <0.9930 for 20 provinces of Iran. The best precise method was the modified Hargreaves-Samani 4 for Alborz province (AL). Finally, a list of the best performances of each model was presented to use in other regions according to mean, maximum and minimum temperature elevation, minimum and mean relative humidity, sunshine, precipitation and wind speed. The best weather conditions for use in temperature-based equations (based on the performance of all methods) are 12-18°C, 18.0-22.5°C, 5-13°C, 40-55%, 2.00-3.25m s-1 and 230-260h month-1 for mean, maximum and minimum temperatures, relative humidity, wind speed and sunshine respectively. Results are also useful for selecting the best model when researchers must apply temperature-based models on the basis of available data. © 2014 Royal Meteorological Society. Source


Valipour M.,Islamic Azad University at Kermanshah
Archives of Agronomy and Soil Science | Year: 2015

This study aims to compare two form of Valiantzas’ evapotranspiration methods (one of the newest models) to detect the best method under different weather conditions. For this purpose, weather data were gathered from 181 synoptic stations in 31 provinces of Iran. The reference evapotranspiration was compared with the Food and Agriculture Organization Penman-Monteith method. The results showed that they are not suitable for provinces of Iran. Therefore, the methods were calibrated and preciseness of estimation was increased (the values of R2 were less than 0.99 for only five provinces in the modified methods). The new formulas estimated the reference evapotranspira- tion in the southeast of Iran and near the Persian Gulf (solar radiation >24.4 MJ m −2 day −1 and wind speed 2.50-3.50 ms−1) better than other provinces. The more precise methods were ETo = 0.446T + 0.104RH+0.753u−0.0209Rs −7.812 for SK (east of Iran) and ETo = 0.0869T− 40.0484RH+2.1398u+0.0338Rs+1.444 for GO (north of Iran). Finally, a list of the best performances of each method was presented (using sensitive analysis) to use other regions and next studies according to temperature, relative humidity, solar radiation, and wind speed. The results are also useful for selecting the best model when we must apply the new formulas based on available data. © 2014 Taylor & Francis. Source

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