Selection of Observed Gridded Rainfall Data for different Analysis Purposes in Cambodia
    1. Water and Environmental Research Unit, Research and Innovation Center, Institute of Technology of Cambodia, Russian Federation Blvd., P.O. Box 86, Phnom Penh, Cambodia

Received: April 04,2023 / Revised: Accepted: October 05,2023 / Published: December 31,2023

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 For Hydrological and Meteorological research over Cambodia with sparsed rainfall gauges, reliable rainfall is essential. In this study,12 gridded rainfall datasets with a reasonable spatial resolution including Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE), Gridded rainfall Observational Dataset for precipitation and temperature Southeast Asia (SA-OBS), Integrated Multi-gridded rainfall Retrieval for the global precipitation mission (GPMIMERG), Tropical Rainfall Measuring Mission Project (TRMM), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS-V2), Bias-Corrected Climate Prediction Center (CPC) Morphing Technique (CMORPH), JAXA Global Gridded rainfall Mapping Precipitation (GSMaP), Precipitation Estimation Remotely Sensed Information Artificial Neural Network PERSIANN), PERSIANN Dynamic Infrared Rain Rate Near Real-Time (PERSIANN-PDIRNOW), PERSIANN Cloud Classification System (PERSIANN-CCS), PERSIANN Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), were properly evaluated during 2000-2014 by using statistical metric and categorical metrics and comparing with 58 local rainfall station data. At the same time, this study also set out to find which product could detect historical extreme rainfall events. The result shows that APHRODITE and GPM-IMERG are the better rainfall product reflecting the local rainfall in Cambodia. For the overall performance, APHRODITE is seen to be underestimated but has the highest correlation with station data. Meanwhile, GPM-IMERG shows a lower correlation than APHRODITE, but lower biases in variation magnitude. Well-known extreme indices, namely Consecutive Dry Day (CDD) and Consecutive Wet Day (CWD) of the Expert Team on Climate Change Detection and Indices (ETCCDI) were investigated as a showcase of extreme event detection. GPM-IMERG with an average bias of 29.87, and APHRODITE with an average bias of 31.85, in comparison to rainfall station data which indicates that GPM-IMERG is good at detecting extreme rainfall events compared to APHRODITE. Observabally, the following conclusions can be drawn from the analysis 1) APHRODITE product can be utilized for gauged rainfall estimations in some sort of relative analysis application, like rainfall index transformation. 2) GPM-IMERG is recommended for the study of extreme rainfall since it is capable of detecting light and heavy rainfall event magnitude.