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Impact of satellite precipitation estimation methods on the hydrological response: case study Wadi Nu’man basin, Saudi Arabia

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Abstract

Arid regions have a strong lack of observational datasets, specifically in rainfall and runoff, which plays a crucial role in the effective management of water resources and the assessment of hydrological systems. Therefore, the objective of this study was to investigate the impact of different precipitation estimation methods on the hydrological response, specifically the runoff, in the Wadi Nu'man basin located in Saudi Arabia. To achieve this, rainfall data were collected over 13 years (2006–2018) from ground observations and satellite rainfall products. The remote sensing-based precipitation data utilized in this study includes Tropical Rainfall Measuring Mission (TRMM-3B42), TRMM-Realtime (TRMM-3B42RT), and Climate Hazards Group InfraRed Precipitation (CHRIPS) datasets. The performance of these datasets was evaluated using various statistical metrics. Additionally, a linear scaling bias correction method was performed to align the satellite and ground-based station data. Finally, the HEC-HMS model was utilized to simulate the runoff using all available bias-corrected precipitation datasets. This article primarily focuses on comparing and assessing two categories of data: rainfall and runoff. In terms of daily rainfall data, a lower correlation (R2 < 0.5) was observed when comparing the datasets. However, a moderate regression (R2 = 0.52) was found between the average values derived from the two gauges and the corresponding satellite data. Similarly, a moderate relationship (R2 ≥ 0.43) was found on a monthly scale between ground observations and satellite precipitation. The average gauge data, along with the corresponding satellite data, were utilized for runoff modeling in HEC-HMS. The runoff model showed a high regression (R2) value of 0.75 between peak discharges generated by the gauges and CHRIPS data.  However, a lower (R2) value of 0.3 was observed between gauge rainfall and TRMM rainfall. This research emphasizes the importance of utilizing the satellite rainfall dataset for runoff estimation in arid regions of Saudi Arabia where gauge data may be unavailable. Furthermore, future studies should focus on implementing more sophisticated bias correction techniques, testing diverse satellite datasets, and integrating advanced modeling tools such as machine learning for enhanced predictions of the hydrologic response.

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All data generated or analyzed during this study are included in this published article and public data are used for the satellite rainfall data.

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Authors and Affiliations

  1. Department of Water Resources, Faculty of Environmental Sciences, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

    Esubalew Adem, Amro Elfeki, Anis Chaabani, Abdullah Alwegdani, Sajjad Hussain & Mohamed Elhag

  2. Department of Geology, College of Natural and Computational Science, Arba Minch University, Arba Minch, Ethiopia

    Esubalew Adem

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

    Amro Elfeki

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

    Mohamed Elhag

  5. Department of Geoinformation in Environmental Management, CIHEAM/Mediterranean Agronomic Institute of Chania, 73100, Chania, Greece

    Mohamed Elhag

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

    Mohamed Elhag

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  1. Esubalew Adem
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Contributions

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

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Correspondence to Esubalew Adem.

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Adem, E., Elfeki, A., Chaabani, A. et al. Impact of satellite precipitation estimation methods on the hydrological response: case study Wadi Nu’man basin, Saudi Arabia. Theor Appl Climatol 155, 3907–3925 (2024). https://doi.org/10.1007/s00704-024-04855-4

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