Environmental assessment of semi-humid and humid regions based on modeling and forecasting of changes in monthly temperature

Abstract

Investigation of air temperature is obligatory in order to monitor drought, especially in semiarid regions. This study was conducted to consider this parameter in north and northwest of Iran. Initially, using different parametric or nonparametric statistical techniques, the monthly average temperature trend for the period of 1965–2014 at 15 synoptic meteorological stations was analyzed. The results of trend analysis showed the significant increasing trend at 20% of stations. Then, using autoregressive integrated moving average models, monthly average temperature time series modeling was done, and to evaluate the fitness of these models different measures of goodness of fit were used. Results indicated that in most cases (66.7%), the moving average model, in 20% of cases, the autoregressive moving average model and in 13.3% of cases, the autoregressive model had the best performance in monthly average temperature time series modeling and forecasting. Finally, using the best models fitted to monthly average temperature time series, the values for 2015–2019 period were forecasted. Results indicated that in 67% of cases the forecasted values show a rise in temperature that will affect the management of water resources and the environment.