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Spatio-temporal evaluation of remote sensing rainfall data of TRMM satellite over the Kingdom of Saudi Arabia

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Abstract

Rainfall estimation is the most important parameter for many water resource simulations and practices; therefore, precise and long-term data are required for trustworthy precipitation depiction. Recent advancements in remote sensing applications enabled researchers to estimate rainfall with greater geographical and temporal precision. The goal of this study was to evaluate the performance of a climatological satellite, the Tropical Rainfall Measuring Mission (TRMM) in estimating rainfall, with ground-based gauge data for five years (2008–2012) across the entire Kingdom of Saudi Arabia (KSA). In regional and station-based evaluations, many statistical performance metrics such as R-square (R2), root-mean-squared error (RMSE), mean absolute error (MAE), relative BIAS (R.B.), and correlation coefficient (CC) have been utilized. The southern, north-western, and south-western areas performed very well in the regression and correlation analyses. The problem of under and overestimating satellite data, according to R.B. analysis, exists across the Kingdom, with the southern, eastern, and north-western areas dominating (maximum over is R.B. = 94.6% and minimum over is 7.5%, while maximum under R.B. =  − 52.8% and minimum under R.B. =  − 5.9%). The RMSE and MAE were higher in the Qassim, Jazan, and Makkah regions, whereas they were the lowest in the northwestern. In general, TRMM prominently identified rainfall in comparison with the ground-based data and performed moderately for the majority of stations and regions during the research period.

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Data availability

All data generated or analyzed during this study are included in this published article.

Code availability

All codes for data cleaning and analysis associated with the current submission are available at the local authorities in Saudi Arabia.

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Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant No. (G-51-155-1442). The authors, therefore, acknowledge with thanks, DSR technical, and financial support.

Funding

This research was funded by The Chinese Academy of Sciences (CAS), President’s International Fellowship Initiative (PIFI), grant number 2021VEA0007.

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Authors and Affiliations

  1. Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Sajjad Hussain, Amro M. Elfeki, Anis Chaabani, Esubalew Adem Yibrie & Mohamed Elhag

  2. Irrigation and Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Amro M. Elfeki

  3. Department of Geoinformation in Environmental Management, CI-HEAM/Mediterranean Agronomic Institute of Chania, 73100, Chania, Greece

    Mohamed Elhag

  4. The State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Science (CAS), Beijing, 100101, China

    Mohamed Elhag

  5. Department of Applied Geosciences, Faculty of Science, the German University of Technology in Oman, Muscat, 1816, Oman

    Mohamed Elhag

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  1. Sajjad Hussain
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  2. Amro M. Elfeki
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  3. Anis Chaabani
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  4. Esubalew Adem Yibrie
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  5. Mohamed Elhag
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Contributions

Conceptualization, A. E. and M. E.; methodology, S. H. and A. C.; validation, E. A., A. E., and S. H.; formal analysis, S. H. and M. E.; writing—original draft preparation, A. E., M. E., and A. C.; writing—review and editing, A. E., M. E., and S. H.

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Correspondence to Mohamed Elhag.

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Hussain, S., Elfeki, A.M., Chaabani, A. et al. Spatio-temporal evaluation of remote sensing rainfall data of TRMM satellite over the Kingdom of Saudi Arabia. Theor Appl Climatol 150, 363–377 (2022). https://doi.org/10.1007/s00704-022-04148-8

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