Precipitation variability and Annual Rainfall Prediction for Central and Southeast (SE) Iran using Stochastic Time Series Modeling
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
The present study sought to investigate precipitation variability and prediction the annual rainfall in central and southeast (SE) Iran using stochastic modeling (time series analysis). For this, the annual rainfall data gathered from 15 synoptic stations in the center and SE of Iran were obtained from Iran Meteorological Organization (IMO). Then, different autoregressive integrated moving average (ARIMA) models were used to fit the time series of annual rainfall in the studied stations. Based on the results, ARMA (2, 1) was found to be the best model fitted to the annual rainfall time series of the stations under study. Excluding the Ferdows and Iranshahr stations with an uptick in the rate of precipitation, the trend of predicted annual precipitation until 2027 was slipping for all the studied stations. The validation indices including MAD (mean absolute deviation), MSE (mean square error), RMSE (root mean square error), and MAE (mean absolute error) were employed to measure the accuracy of annual rainfall forecasts until 2027. According to these indices, the forecasts had substantial errors and were of low accuracy. Such a high rate of errors may be due to the short-term statistics obtained from synoptic stations in these regions. Hence, for modeling in central and SE Iran, future studies are recommended to use different networks of rainfall databases with long-term statistics.
Keywords:
Language:
Persian
Published:
Journal of Climate Change Research, Volume:6 Issue: 21, 2025
Pages:
1 to 22
https://www.magiran.com/p2839297
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اطلاعات نویسنده(گان) توسط ایشان ثبت و تکمیل شدهاست. برای مشاهده مشخصات و فهرست همه مطالب، صفحه رزومه را ببینید.
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