Journal of Arid Regions Geographic Studies

Journal of Arid Regions Geographic Studies

Stochastic Monthly Rainfall Time Series Analysis, Modeling and Forecasting ( A cas study: Ardebilcity

Authors
Rsol Imani Imani
Reza Ghazavi
Abazar Esmali Ouri
Abstract
Rainfall is the main source of the available water for human. Predicting the amount of the future rainfall is useful for informed policies, planning and decision making that will help potentially make optimal and sustainable use of available water resources. The main aim of this study was to investigate the trend and forecast monthly rainfall of selected synoptic station in Ardabil province using the best models of stochastic time series models. In this study, monthly rainfall for the next 5 years (2020 to 2024 AD) in the study area was predicted using different models of ARIMA family time series. Non-parametric Kendall- test was used to ensure the existence of the trend and the correlation diagram (ACF) was used to ensure the existence of seasonal changes in the time series. The best precipitation forecasting model in each of the 5 methods used for stabilization, was selected based on the values ​​of the model parameters, AIC criteria and correlation coefficient. The best static method and the best predictor model were used to predicte the next 5 year monthly rainfall. The results of man -Kendal test showed that the monthly rainfall data of Ardabil Synoptic Station had a decreasing trend (Z = 0.6119), but this trend was not significant at 95% confidence level. Study of the monthly rainfall data showed that there was a significant correlation between 12, 24, 36 and 48 month delays. The results of the monthly rainfall forecasting for the next five years (2020 to 2024) using the best static method and the best time series model in Ardabil Synoptic Station showed that the annual rainfall should decrease in 4 years of the next 5 years compared to the average of the 20 past years by 3 to 17 percent, the biggest drop since 2022. Rainfall will increase by 0.3% only in 2023.
 
Keywords
City Rainfall time series
Self-correlation
ARIMA models
Ardabi
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  • Receive Date 23 November 2022
  • Publish Date 22 June 2021