Spatial and temporal analysis of climatological data in Jordan

Freiwan, Muwaffaq; Kadıoğlu, Mikdat

doi:10.1002/joc.1562

Cited by 38 publications

(22 citation statements)

References 3 publications

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“…Mean annual rainfall and mean maximum daily rainfall are increasing but statistically not significant in two stations (RAS and JOU) in the subhumid zone and this is consistent with the findings with similar nearby subhumid regions such as central western parts of the Jordan river (Alpert et al, 2002;Wettenberg et al, 2007;Yusef et al, 2009). Mean annual rainfall in the semi-arid zone shows a general nonsignificant decreasing trend and this agrees with some earlier studies which reported nonsignificant decrease in mean daily and monthly rainfall (Freiwan and Kadioglu, 2008a;2008b;Ghanem, 2011). But, this study shows that maximum daily rainfall is a more sensitive rainfall index to variability than other rainfall indices, especially in the semi arid regions.…”

Section: Discussionsupporting

confidence: 91%

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Trends in Maximum Daily Rainfall in Marginal Desert Environment: Signs of Climate Change

Al-Qudah¹

2011

American Journal of Environmental Sciences

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Problem statement: Change in intense precipitation has been used as an indicator for anticipated climate change. The trend in maximum daily rainfall in 10 rainfall stations distributed in the north and north east of Jordan has been investigated. Based on mean annual rainfall and geographic location, rainfall stations were divided into subhumid, semi-arid and arid stations. Approach: The regression approach T-test and Mann-Kendall test were used to investigate significant trends in maximum daily rainfall. Logistic regression model used to test the trends in the frequency of heavy rainfall. Based on p-vales, significant trends were determined. Results: The results show that pronounced significant decreasing trend is observed in three out of four semi-arid stations used in this study. In arid stations, only one station out of three show significant increasing trend. However, none of the three subhumid stations used in the study show any significant trend. Logistic regression test for readings above 90th percentile among the maximum daily rainfall shows significant downward trend only in the semi-arid stations. Conclusion: The results of this study confirm that semi-arid zone is more vulnerable to climate change. Serious actions to mitigate and adapt to climate changes should be given a priority in the semi-arid zone. Redistribution of daily rainfall intensity will have a significant impact on water resources management.

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Section: Discussionsupporting

confidence: 91%

“…In general, no statistically significant trend has been found (Freiwan and Kadioglu, 2008a;2008b);Ghanem, 2011). Decline in the total rainfall and number of rain days in the second half of the past century has been reported in Jordan (Smadi and Zghoul, 2006).…”

Section: Introductionmentioning

confidence: 94%

Trends in Maximum Daily Rainfall in Marginal Desert Environment: Signs of Climate Change

Al-Qudah¹

2011

American Journal of Environmental Sciences

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“…Spatial variability in the precipitation data of Jordan was analyzed by Shehadeh (1976) and very recently by Freiwan and Kadioglu (2008a), periodicity by Tarawneh and Kadioglu (2003), and structural characteristics by Dahamsheh and Aksoy (2007) and Freiwan and Kadioglu (2008b).…”

Section: Study Area and Datamentioning

confidence: 99%

Artificial neural network models for forecasting monthly precipitation in Jordan

Aksoy

Dahamsheh²

2008

Stoch Environ Res Risk Assess

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Forecasting precipitation in arid and semi-arid regions, in Jordan in the Middle East for example, has particular importance since precipitation is the unique source of water in such regions. In this study, 1-month ahead precipitation forecasts are made using artificial neural network (ANN) models. Feed forward back propagation (FFBP), radial basis function (RBF) and generalized regression type ANNs are used and compared with a simple multiple linear regression (MLR) model. The models are tested on monthly total precipitation recorded at three meteorological stations (Baqura, Amman and Safawi) from different climatological regions in Jordan. For the three stations, it is found that the best calibrated model is FFBP with respect to all performance criteria used in the study, including determination coefficient, mean square error, mean absolute error, the slope and the intercept in the best-fit linear line of the scatter diagram. In the validation stage, FFBP is again the best model in Baqura and Amman. However, in Safawi, the driest station, not only FFBP but also RBF and MLR perform equally well depending on the performance criterion under consideration.

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“…Israel in the Middle East has been studied widely (Ben-Gai et al, 1993;1994;1998;Steinberger and Gazit-Yaari, 1996;Krichak et al, 2000) while studies devoted to the analysis of precipitation data in Jordan are rare. Variability in the precipitation data of Jordan was analysed by Shehadeh (1976) and Freiwan and Kadioglu (2007a), periodicity by Tarawneh and Kadioglu (2003), and the structural characteristics by Dahamsheh and Aksoy (2007) and Freiwan and Kadioglu (2007b).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Dahamsheh

Aksoy

2009

Meteorological Applications

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Forecasting monthly precipitation in arid regions is investigated by means of feed forward back propagation (FFBP) artificial neural networks (ANNs) and compared to the linear regression technique with multiple inputs (MLR). Four meteorological stations from different geographical regions in Jordan are selected. The ANNs and MLR processes are analysed based on the mean square error, relative/absolute error, determination coefficient as well as the central statistical moments such as mean, standard deviation, and minimum and maximum values. It is found that whilst on one hand the ANNs are slightly better than the MLR in forecasting the monthly total precipitation, on the other hand, both are found with to have limitations which should be improved by means of either changing the type and architecture of the ANNs or incorporating modelling tools such as Markov chains into the forecast model.

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Spatial and temporal analysis of climatological data in Jordan

Cited by 38 publications

References 3 publications

Trends in Maximum Daily Rainfall in Marginal Desert Environment: Signs of Climate Change

Trends in Maximum Daily Rainfall in Marginal Desert Environment: Signs of Climate Change

Artificial neural network models for forecasting monthly precipitation in Jordan

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

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