Leveraging Python and Google Earth Engine for Spatiotemporal Analysis to Develop a Synthetic Rainfall Distribution Pattern Using GSMaP Data

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Nezar Farrag, Ashraf M. El-Moustafa, Morad H. Abdelsalheen

10.14445/23488352/IJCE-V12I4P110

How to Cite?

Nezar Farrag, Ashraf M. El-Moustafa, Morad H. Abdelsalheen, "Leveraging Python and Google Earth Engine for Spatiotemporal Analysis to Develop a Synthetic Rainfall Distribution Pattern Using GSMaP Data," SSRG International Journal of Civil Engineering, vol. 12,  no. 4, pp. 100-112, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I4P110

Abstract:

Accurate representation of rainfall patterns is crucial for water resource management and flood mitigation, particularly in arid regions susceptible to flash floods. This study presents a novel methodology for generating synthetic rainfall distributions utilizing the GSMaP dataset to address the limitations of conventional hyetograph representations. The proposed algorithm comprises two stages: 1) Preprocessing hourly GSMaP precipitation data and employing the DBSCAN algorithm to identify individual storms; 2) Applying the Alternating Block Method to reorganize rainfall depths and generate a dimensionless synthetic distribution. A Python algorithm was developed to automate this entire process. The methodology was evaluated using a four-day storm in South Sinai, showing a strong similarity to the SCS Type-II distribution but revealing significant differences in hydrological modeling for Wadi Fieran. Specifically, the GSMaP distribution reduced peak discharge by 12% and increased flood volume by 0.27%. These findings highlight the utility of satellite-based precipitation data in enhancing hydrological simulations.

Keywords:

Flood Hazard, Remote Sensing, Cloud Computing, Arid Climate, Precipitation, Synthetic Hyetograph, Sinai.

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