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Advances in Meteorology
Volume 2013 (2013), Article ID 363029, 14 pages
http://dx.doi.org/10.1155/2013/363029
Research Article

Rainfall-Altitude Relationship in Saudi Arabia

1King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia
2Taibah University, P.O. Box 344, Madinah 30001, Saudi Arabia

Received 24 December 2012; Revised 23 February 2013; Accepted 26 February 2013

Academic Editor: Harry D. Kambezidis

Copyright © 2013 Khalid Al-Ahmadi and Sharaf Al-Ahmadi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Relations between rainfall and the altitude of the terrain can have significant implications for rainfall-runoff studies in hydrology. The aim of this paper is to report on a study of relationships between annual and seasonal rainfall and the altitude of the terrain in Saudi Arabia (SA) using global ordinary least square (OLS) and local geographically weighted regression (GWR) methods. Results using the OLS method showed a significant correlation between altitude and spring rainfall, with a coefficient of determination of , but no significant correlation for the annual and other seasons’ rainfalls. The relationships were more pronounced when GWR local analysis was performed with coefficients of determination of , 0.64, 0.83, 0.82, and 0.71 for annual, winter, spring, summer, and fall rainfalls, respectively. There was some variation in the parameter estimates derived with GWR, but the majority of the estimates indicated a positive association. Results from this study corroborate those of selected other studies in which rainfall and altitude were found to be correlated spatially. The authors concluded that the use of a nonstationary local model such as GWR enabled them to provide a deeper explanation of relations between rainfall and the altitude of the terrain than a global model such as OLS in terms of spatial estimation and prediction.