Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis

Momani, P. E. N. M.

doi:10.3844/ajessp.2009.599.604

Cited by 63 publications

(32 citation statements)

References 7 publications

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“…Sampson et al (2013) used a seasonal ARIMA model to model the monthly rainfall amounts for the Navrongo meteorological service station from the period January, 1980to December, 2010 and concluded that the model was adequate. Naill and Momani (2009) used time series analysis to model monthly rainfall data at Amman Airport Station in Jordan. A seasonal ARIMA model was used in that study.…”

Section: Introductionmentioning

confidence: 99%

Time Series Modeling of Monthly Rainfall in Arid Areas: Case Study for Saudi Arabia

Saada¹

2014

American Journal of Environmental Sciences

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Stochastic techniques are essential in planning and management of water resources systems especially in arid and semi-arid areas where water is scarce. The forecasting of future events requires identifying proper stochastic models to be used in this process. For this purpose, a Periodic ARMA (PARMA) model and a temporal disaggregation models were used in this study to investigate weather they are appropriate for modeling the monthly rainfall data in Saudi Arabia. Results showed PARMA and temporal disaggregation models performed well in modeling the monthly rainfalls in Saudi Arabia. These models were able to preserve the basic seasonal statistics of the observed data well as preserving the seasonal correlation structure observed in the historical data. However, the PARMA model did not perform well at the annual level. In contrast, the disaggregation model performed well in preserving the correlation structure of the historical data at the annual level. Thus, these models can be used in modeling and forecasting of monthly rainfall in Arid and semi-arid areas.

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Section: Introductionmentioning

confidence: 99%

Time Series Modeling of Monthly Rainfall in Arid Areas: Case Study for Saudi Arabia

Saada¹

2014

American Journal of Environmental Sciences

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“…The ACF and Fig. 1 show a seasonal fluctuation occur every 12 month, resulting in s = 12 (Wang, 2008;Momani and Naill, 2009). Concentrating on the ACF of original data, we note a slow decreasing trend in the ACF peaks at seasonal lags, h = 1s, 2s, 3s, 4s, where s = 12.…”

Section: Resultsmentioning

confidence: 99%

Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series

Chang

Gao²,

Yan

et al. 2012

Journal of Mathematics and Statistics

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Predicting the trend of precipitation is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for precipitation prediction. The ARIMA model incorporating seasonal characteristics, which is referred to as seasonal ARIMA model was presented. The time series data is the monthly precipitation data in Yantai, China and the period is from 1961 to 2011. The model was denoted as SARIMA (1, 0, 1) (0, 1, 1) 12 in this study. We first analyzed the stability and correlation of the time series. Then we predicted the monthly precipitation for the coming three yesrs. The results showed that the model fitted the data well and the stochastic seasonal fluctuation was sucessfuly modeled. Seasonal ARIMA model was a proper method for modeling and predicting the time series of monthly percipitation.

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“…However, Figure 8 A closer look at the results showed that the model was successful in predicting the overall statistics with a given period at an annual scale. Using monthly time series, Momani (2009), found that the model was not appropriate to predict the exact monthly rainfall data. An intervention time series analysis could be used to forecast the peak value of rainfall.…”

Section: Arima Forecastingmentioning

confidence: 99%

“…The ARIMA model, which is used in this paper, possesses many appealing features. It allows a researcher who has data only on past years (e.g., rainfall) to forecast future events without having to search for other related time series data such as temperature [27]. A comparison of six rainfall-runoff modeling approaches was conducted to simulate daily, monthly and annual flows in eight unregulated catchments in Australia [28].…”

Section: Introductionmentioning

confidence: 99%

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Characterizing, Monitoring and Forecasting of Drought in Jordan River Basin

Shatanawi¹,

Rahbeh²,

Shatanawi³

2013

JWARP

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Jordan is very vulnerable to drought because of its location in the arid to semi-arid part of the Middle East. Droughts coupled with water scarcity are becoming a serious threat to the economic growth, social cohesion and political stability. Rainfall time series from four rain stations covering the Jordan River Basin were analyzed for drought characterization and forecasting using standardized precipitation index (SPI), Markov chain and autoregressive integrated moving average (ARIMA) model. The 7-year moving average of Amman data showed a decreasing trend while data from the other three stations were stable or showed an increasing trend. The frequency analysis indicated 2-year return period for near zero SPI values while the return period for moderate drought was 7 years. Successive droughts had occurred at least three times during the past 40 years. Severe droughts are expected once every 20 -25 year period at all rain stations. The extreme droughts were rare events with return periods between 80 and 115 years. There are equal occurrence probabilities for drought and wet conditions in any given year, irrespective, of the condition in the previous year. The results showed that ARIMA model was successful in predicting the overall statistics with a given period at annual scales. The overall number of predicted/observed droughts during the validation periods were 2/2 severe droughts for Amman station and, 0/1, 1/1, 0/1 extreme droughts for Amman, Irbid and Mafraq stations, respectively. In addition, the ARIMA model also predicted 3 out of 4 actual moderate droughts for Amman and Mafraq stations. It was concluded that early warning of developing droughts can be deduced form the monthly Markov transitional probabilities. ARIMA models can be used as a forecasting tool of the future drought trends. Using the first and second order Markov probabilities can complement the ARIMA predictions.

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Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis

Cited by 63 publications

References 7 publications

Time Series Modeling of Monthly Rainfall in Arid Areas: Case Study for Saudi Arabia

Time Series Modeling of Monthly Rainfall in Arid Areas: Case Study for Saudi Arabia

Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series

Characterizing, Monitoring and Forecasting of Drought in Jordan River Basin

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