Climate Change Research

Climate Change Research

Precipitation variability and Annual Rainfall Prediction for Central and Southeast (SE) Iran using Stochastic Time Series Modeling

Document Type : Original Article

Authors
Faezeh Nazeri 1
Peyman Mahmoudi 1
Seyed Mahdi Amir Jahanshani 2
1 Department of Physical Geography, Faculty of Geography and Environmental Planning, University of Sistan and Baluchestan, Zahedan, Iran
2 Department of Statistics, Faculty of Mathematics, Statistics and Computer Science, University of Sistan and Baluchestan, Zahedan, Iran
10.30488/ccr.2024.458098.1215
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
coefficient of variation
coefficient of skewness
stationarity of variance
stationarity of mean
autocorrelation function
partial autocorrelation function
Subjects
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