1. Rainfall in the Greater and Lesser Antilles: Performance of five gridded datasets on a daily timescale
- Author
-
Bathelemy, Ralph, Brigode, Pierre, Boisson, Dominique, Tric, Emmanuel, Géoazur (GEOAZUR 7329), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), LMI CARIBACT, URGéo, and Université d’Etat d’Haïti, Faculté des Sciences (UEH, FDS)
- Subjects
KGE ,tropical cyclone ,Satellite rainfall ,hurricane ,Extreme rainfall ,[SDU.STU]Sciences of the Universe [physics]/Earth Sciences ,hydrology ,flood ,Caribbean region ,Haiti ,Seasonality rainfall ,Hispaniola ,[SDU]Sciences of the Universe [physics] ,[SDE]Environmental Sciences ,Heavy rainfall ,Rainfall statistics - Abstract
International audience; 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 estimating 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 applications due to their low KGE and high RMSE.
- Published
- 2022