Abstract
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.
Highlights
Stochastic modeling of hydrologic time series has been widely used for planning and management of water resources systems (Fortin et al, 2004)
A Periodic Auto Regressive Moving Average (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
The historical and generated periodic mean and periodic standard deviation using the PARMA(1,0) model are shown in Fig. 3 and 4 respectively which clearly demonstrates that the model is capable of reproducing the basic periodic statistics of the historical data
Summary
Stochastic modeling of hydrologic time series has been widely used for planning and management of water resources systems (Fortin et al, 2004). Stochastic models are used in operational hydrology to generate synthetic time series which exhibit similar statistical characteristics as the observed data. 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, 1980 to 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. It was concluded that the model is incapable of predicting the exact monthly rainfalls
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