Journal
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
Volume 43, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.ejrh.2022.101203
Keywords
Satellite rainfall; Caribbean region; Haiti; KGE; Heavy rainfall; Seasonality rainfall; Rainfall statistics; Extreme rainfall, Hispaniola, hydrology, tropical cyclone, hurricane, flood
Categories
Funding
- French Embassy in Haiti
- Institut de Recherche pour le Developpement (French National Research Institute for Sustainable Development)
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This study evaluates the performance of several rainfall datasets and recommends their usage in different hydro-meteorological research fields. MSWEP is the preferred choice for most studies, CHIRPS and PERSIANN-CDR are suitable for water resources management research, and ERA-5 and GPM IMERG are applicable in climatic and atmospheric science research. However, bias reduction methods are needed for ERA-5 and GPM IMERG.
Study region: The studied region is the Greater Antilles (Cuba, Hispaniola, Jamaica and Puerto Rico) and the Lesser Antilles (Southern part of the Caribbean arc). Study focus: The performance of MSWEP, CHIRPS, PERSIANN-CDR, ERA-5 and GPM IMERG were evaluated to highlight their qualities and shortcomings and to guide researchers in the choice of these rainfall datasets to use for hydro-meteorological applications in this study area. Five quantitative (RMSE, KGE and his three components) and three qualitative (POD, FAR and CSI) statistical metrics are used to evaluate the amount and detection capacity of the rainfall datasets. Heavy rainfall percentiles are calculated to assess the ability of rainfall datasets to estimate rare and extreme rainfall. New hydrological insights for the region: MSWEP performs well for most statistical metrics and is recommended for most hydro-meteorological research. CHIRPS and PERSIANN-CDR performs well in estimating the annual rainfall seasonality and are recommended for research on water resources management (irrigation, energy production, etc.). CHIRPS also performs well in esti-mating heavy rainfall percentiles and is also recommended for statistical research of heavy rainfall events. ERA-5 and GPM IMERG have a good ability to capture wet and dry days and is recommended for determination of climatic research or atmospheric sciences applications. However, bias reduction methods for these rainfall gridded datasets are advised before applica-tions due to their low KGE and high RMSE.
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