干旱区生态环境知识资源中心(Arid): Spatiotemporal analysis of the annual rainfall in the Kingdom of Saudi Arabia: predictions to 2030 with different confidence levels

Arid

DOI	10.1007/s00704-021-03778-8
	Spatiotemporal analysis of the annual rainfall in the Kingdom of Saudi Arabia: predictions to 2030 with different confidence levels
	Bahrawi, Jarbou; Alqarawy, Abdulaziz; Chabaani, Anis; Elfeki, Amro; Elhag, Mohamed
通讯作者	Elhag, M (corresponding author), King Abdulaziz Univ, Fac Meteorol Environm & Arid Land Agr, Dept Hydrol & Water Resources Management, POB 80208, Jeddah 21589, Saudi Arabia. ; Elhag, M (corresponding author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, Beijing 100094, Peoples R China. ; Elhag, M (corresponding author), German Univ Technol Oman, Fac Sci, Dept Appl Geosci, Muscat 1816, Oman.
来源期刊	THEORETICAL AND APPLIED CLIMATOLOGY
ISSN	0177-798X
EISSN	1434-4483
出版年	2021
英文摘要	Predictions of future water resources are essential for the strategic plans of any country especially in arid regions such as Saudi Arabia (SA). This paper presents a modeling study of the temporal annual rainfall variability over SA, evaluating the best model for future rainfall predictions and mapping spatiotemporal rainfall over SA. Common time series models of orders p and q such as autoregressive, AR(p), moving average, MA(q), and the combined autoregressive-moving average, ARMA(p,q), models are utilized. The models are applied to 28 metrological stations distributed over SA. Spatiotemporal statistical analysis of rainfall data is performed over a period between 1970 and 2012. The minimum and maximum fitted parameters of the models are phi 1 = - 0.55, 0.46 for ARMA (1,0), theta 1 = - 0.66, 0.17 for ARMA (0,1) and phi 1 = - 0.84, 0.94, theta 1 = - 0.87, 0.78 for ARMA (1,1), respectively. It has been shown that ARMA (1,0) is the best to model the temporal variability based on the Akaike information criterion (AIC), the correlation coefficient (R), and the root mean square error (RMSE). The Monte Carlo method is used to make future predictions (100 realizations) with the confidence levels (CIs) based on ARMA (1,0). Spatial distribution of the ensemble predictions and their CIs are presented graphically at the upper limit of 95%, 97.5%, and 99% and the lower limit of 5%, 2.5%, and 1% confidence, respectively, for the year 2030 to help decision-makers for future water resources planning of the country. Abha city has the highest annual rainfall prediction in 2030 (221 mm) with upper confidences (436, 533, and 643 mm) for 95%, 97.5%, and 99%, respectively. The prediction results indicate that the high mountainous areas (Asir and Taif) are expected to have more rainfall in the future than the rest of the regions in SA. The use of non-traditional water resources is the solution to future challenges.
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000705715300001
WOS关键词	CURVES ; REGION
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364790
作者单位	[Bahrawi, Jarbou; Alqarawy, Abdulaziz; Chabaani, Anis; Elfeki, Amro; Elhag, Mohamed] King Abdulaziz Univ, Fac Meteorol Environm & Arid Land Agr, Dept Hydrol & Water Resources Management, POB 80208, Jeddah 21589, Saudi Arabia; [Elhag, Mohamed] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, Beijing 100094, Peoples R China; [Elhag, Mohamed] German Univ Technol Oman, Fac Sci, Dept Appl Geosci, Muscat 1816, Oman
推荐引用方式 GB/T 7714	Bahrawi, Jarbou,Alqarawy, Abdulaziz,Chabaani, Anis,et al. Spatiotemporal analysis of the annual rainfall in the Kingdom of Saudi Arabia: predictions to 2030 with different confidence levels[J],2021.
APA	Bahrawi, Jarbou,Alqarawy, Abdulaziz,Chabaani, Anis,Elfeki, Amro,&Elhag, Mohamed.(2021).Spatiotemporal analysis of the annual rainfall in the Kingdom of Saudi Arabia: predictions to 2030 with different confidence levels.THEORETICAL AND APPLIED CLIMATOLOGY.
MLA	Bahrawi, Jarbou,et al."Spatiotemporal analysis of the annual rainfall in the Kingdom of Saudi Arabia: predictions to 2030 with different confidence levels".THEORETICAL AND APPLIED CLIMATOLOGY (2021).