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

Saada, Nidhal

doi:10.3844/ajessp.2014.277.282

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…Comparison of the time-series models performance in annual and monthly time scales at all four stations indicated that the monthly PARMA model with a higher acceptance ratio of the performance evaluation indices appears to be better than the annual AR model in both training and test stages (table 4). This result is consistent with Saada (2014) who reported a better performance in monthly PARMA compared to annual time-series model.…”

Section: Modelling With Time-series Approachsupporting

confidence: 92%

“…In the case of wettest years (1982, 1993, 1994 and 2004), the conditions between normal to very wet were Table 5. Trend analysis of ZSI and SPI values in historical (1960-2014) and prediction (1960-2024 Table 6. Validation of predicted yearly drought conditions in the worst drought and wettest years with previous studies (Osmani 2009;Salahi and Faridpour 2016;Javanmard et al 2017).…”

Section: Drought Prediction Using the Time-series Modelmentioning

confidence: 99%

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Evaluation and prediction of meteorological drought conditions using time-series and genetic programming models

Omidvar

Tahroodi

2019

J Earth Syst Sci

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Over the years, a number of prediction methods have been proposed for the evaluation of probability of hydrological-meteorological variables or drought indices. In this study, the precipitation data recorded in four stations of northwestern Iran over the period 1960-2014 were used to develop the time-series and genetic programming (GP) models. Comparison of the observed and predicted data showed that although both models have acceptable accuracy in predicting precipitation, the time-series models had lower errors than the GP models. So, the autoregressive and periodic autoregressive moving average models were chosen as the superior models for annual and monthly series, respectively. Therefore, the Standard Precipitation Index (SPI) and Z-Score Index (ZSI) were used to assess the drought conditions. According to the results, the SPI recognised a higher percentage of historical and prediction periods as drought conditions than ZSI. The validation of indices showed that the ZSI was more capable for detecting the drought and wetness conditions. The trend analysis of SPI and ZSI showed significant decreasing trends in different stations at all-time scales, except yearly in Urmia and all-time scales in Zanjan, which statically had no significant trend. In conclusion, given the current precipitation trends, the droughts are increasing in both severity and numbers.

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Section: Modelling With Time-series Approachsupporting

confidence: 92%

Section: Drought Prediction Using the Time-series Modelmentioning

confidence: 99%

Evaluation and prediction of meteorological drought conditions using time-series and genetic programming models

Omidvar

Tahroodi

2019

J Earth Syst Sci

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“…Statistical comparison of historic and generated data revealed that the models were capable of preserving the statistics of historic data such as mean, standard deviation and serial correlation structure [6]. SAMS was also used for modeling and simulation of PARMA models to the monthly rainfall data for Surat Obeida, Saudi Arabia [7]. Similarly, the temporal disaggregation model was also used for modeling and simulation purposes for Surat Obeida, Saudi Arabia [7].…”

Section: Resultsmentioning

confidence: 99%

“…SAMS was also used for modeling and simulation of PARMA models to the monthly rainfall data for Surat Obeida, Saudi Arabia [7]. Similarly, the temporal disaggregation model was also used for modeling and simulation purposes for Surat Obeida, Saudi Arabia [7]. Results indicate that both PARMA and disaggregation model were capable of preserving the seasonal statistics of the data [7].…”

Section: Resultsmentioning

confidence: 99%

Application of Stochastic Analysis, Modeling and Simulation (SAMS) to Selected Hydrologic Data in the Middle East

Saada

Abdullah

Hamaideh

et al. 2019

Eng. Technol. Appl. Sci. Res.

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Water resources in the Middle East are very scarce and the management of these resources is a challenge. In this paper, the use of stochastic analysis, modeling, and simulation (SAMS) software package to selected hydrologic data in the Middle East (namely Jordan and Saudi Arabia) are explored. Modeling and simulation experiments were conducted to test the capabilities of SAMS to be used for stochastic modeling and simulation in the Middle East region. The hydrologic data used in this study consist of historic observed rainfall data of different lengths at various sites in Jordan and Saudi Arabia. The models used in this study include: autoregressive moving average (ARMA) models, periodic autoregressive moving average (PARMA) models, multi-site contemporaneous autoregressive moving average (CARMA) models, and temporal disaggregation models. Results indicate that SAMS can be used as a tool for stochastic modeling and simulation of hydrologic data in Jordan and Saudi Arabia. It is important for managers and decision makers of water resources in these countries to be able to use sophisticated tools such as SAMS while deciding water management policies in these countries.

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“…The annual streamflow data have no autocorrelation and follow an AR(0) process (see for example, Modarres & de Paulo Rodrigues da Silva, 2007;Saada, 2014), which is unsurprising given the climate of the region. Also, the first difference of flow data sequence showed that the resulting series are indeed white noise, that is, ε t~i id(0, σ 2 ).…”

Section: Correlation Of Hydrological Variablesmentioning

confidence: 99%

Understanding spatio‐temporal rainfall‐runoff changes in a semi‐arid region.

Aryal

Silberstein

et al. 2020

Hydrological Processes

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The rainfall-runoff response in arid and semi-arid regions is very different from that in humid or temperate regions due to factors including the relative difference in magnitude of rainfall and potential evaporation and the absence of sustained base flow.Despite being infrequent and of short duration, rainfall and runoff events sustain regional ecosystems through flooding and groundwater recharge. Understanding spatial and temporal hydrological patterns and their changes together with interrelations of hydrological variables can provide insight into the current and future water resource in these regions. However, scarcity of data, small numbers of flow events and access difficulties make study of arid zone hydrology difficult. This study carried out quantitative hydrological analysis using regional-scale data from a semiarid region in northern Western Australia and compared the findings with those from other similar regions of the world. We found that the regional runoff showed a spatial pattern that does not correlate closely with the rainfall across the region. The mean peak annual floods per unit catchment area were similar to those from many other arid and semi-arid regions of the world, despite cyclonic rainfall. Furthermore, the annual runoff showed high temporal and spatial variations with little to no carry-over from the past at an annual time scale. We found statistically significant covariance between the hydrological variables such as mean annual runoff, number of flow days, high flows and drought-like conditions. The mean annual runoff and runoff ratio however showed no significant correlation with catchment area. Despite increasing trends in annual and seasonal rainfall in recent years, the annual and seasonal flow trends were quite small and exhibited much lower rainfall elasticity of runoff than in other semi-arid regions across the world. However, part of the region had a 44% increase in rainfall during a 7-year period, which nearly doubled the runoff. Key findings• Annual and seasonal runoff trends can be considerably different from corresponding rainfall trends for a variety of reasons.• Runoff spatial variability patterns may not follow the rainfall spatial variability.• Mean annual runoff can be uncorrelated to catchment area or mean annual rainfall in semi-arid regions.

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

Cited by 6 publications

References 9 publications

Evaluation and prediction of meteorological drought conditions using time-series and genetic programming models

Evaluation and prediction of meteorological drought conditions using time-series and genetic programming models

Application of Stochastic Analysis, Modeling and Simulation (SAMS) to Selected Hydrologic Data in the Middle East

Understanding spatio‐temporal rainfall‐runoff changes in a semi‐arid region.

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