Evaluating the Impact of Land Cover and Topography on Meteorological Parameters’ Relations and Similarities in the Alberta Oil Sands Region

• Published in: Applied Sciences
• Main Authors: Dhananjay Deshmukh, M. Razu Ahmed, John Albino Dominic, Mohamed S. Zaghloul, Anil Gupta, Gopal Achari, Quazi K. Hassan
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: Athabasca River Basin; network optimization; redundancy analysis; weather parameters; Technology; T; Engineering (General). Civil engineering (General); TA1-2040; Biology (General); QH301-705.5; Physics; QC1-999; Chemistry; QD1-999; Athabasca River
• Online Access: https://doi.org/10.3390/app122312004; https://doaj.org/article/af4c82dae1f64876874374bdd7e26ddf

Herein, the focus was on the identification of similarities in the weather parameters collected within 19 stations, consisting of 3 weather networks located in the Lower Athabasca River Basin operated under the Oil Sands Monitoring program. These stations were then categorised into seven distinct groups based on comparable topography and land cover. With regard to weather parameters, these were air temperature (AT), precipitation (PR), relative humidity (RH), solar radiation (SR), atmospheric/barometric pressure (BP), snowfall depth (SD), and wind speed/direction (WSD). For all seven groups, relational analysis was conducted for every station pair using Pearson’s coefficient (r) and average absolute error (AAE), except for wind direction and wind speed. Similarity analysis was also performed for each station pair across all seven groups using percentage of similarity (PS) measures. Our similarity analysis revealed that there were no similarities (i.e., PS value < 75%) for: (i) SR, PR, and WSD for all groups; (ii) AT for all groups except group G3; (iii) RH for group G7; and (iv) BP for group G1. This study could potentially be decisive in optimizing or rationalising existing weather networks. Furthermore, it could be constructive in the development of meteorological prediction models for any place and that requires input from surrounding stations.