predicting the effect of temperature changes on reference evapotranspiration by means of time series modeling (case study: khorramabad basin)

Global warming phenomenon has affected the hydrologic balance, especially in the arid and semi-arid regions of the world. therefore, it seems necessary to study these effects to achieve better water resources management system. in this study, maximum and minimum temperature information at the period of 1992-2017 of khorramabad synoptic station were assessed. the changes of these two characteristics and reference evapotranspiration of khorramabad plain were investigated by time series analysis. mse, rmse and r2 indices were used to validate the models. the results has shown that both maximum and minimum temperature series are static and abnormal, so for normalization, the square root for the minimum data and the squared conversion for the maximum temperature data were used. the acf chart of both series reaches its local peak at time intervals of multiples of 12, indicating a seasonal trend with a period of 12 months. finally, the arima model (0,0,4) (0,1,1) for the minimum temperature and the arima model (0,0,1) (0,1,1) for the maximum temperature were the best chosen models. the values of r2, rmse and mse for the selected maximum temperature model were 0.971, 1.656 and 0.991, respectively, and for the minimum temperature model 0.965, 1.304 and 0.991, respectively, which indicates the acceptable accuracy of the proposed models. forecasts indicate an increase in the minimum and maximum temperatures in the whole future period compared to the base period. the peak of this increase occurs in june, july and august for the minimum and maximum temperatures respectively for tmin: 2.03, 1.54, 1.75, and for tmax: 1.91, 2.03, 1.77 celsius. in the next period, the reference evapotranspiration will increase on average compared to the base period, with most of this increase occurring in march, april, and may.

predicting the effect of temperature changes on reference evapotranspiration by means of time series modeling (case study: khorramabad basin)

global warming phenomenon has affected the hydrologic balance, especially in the arid and semi-arid regions of the world. therefore, it seems necessary to study these effects to achieve better water resources management system. in this study, maximum and minimum temperature information at the period of 1992-2017 of khorramabad synoptic station were assessed. the changes of these two characteristics and reference evapotranspiration of khorramabad plain were investigated by time series analysis. mse, rmse and r2 indices were used to validate the models. the results has shown that both maximum and minimum temperature series are static and abnormal, so for normalization, the square root for the minimum data and the squared conversion for the maximum temperature data were used. the acf chart of both series reaches its local peak at time intervals of multiples of 12, indicating a seasonal trend with a period of 12 months. finally, the arima model (0,0,4) (0,1,1) for the minimum temperature and the arima model (0,0,1) (0,1,1) for the maximum temperature were the best chosen models. the values of r2, rmse and mse for the selected maximum temperature model were 0.971, 1.656 and 0.991, respectively, and for the minimum temperature model 0.965, 1.304 and 0.991, respectively, which indicates the acceptable accuracy of the proposed models. forecasts indicate an increase in the minimum and maximum temperatures in the whole future period compared to the base period. the peak of this increase occurs in june, july and august for the minimum and maximum temperatures respectively for tmin: 2.03, 1.54, 1.75, and for tmax: 1.91, 2.03, 1.77 celsius. in the next period, the reference evapotranspiration will increase on average compared to the base period, with most of this increase occurring in march, april, and may.