@article{fdi:010090021,
  title = {{E}nsemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across {M}adagascar},
  author = {{O}llivier, {C}. {C}. and {C}arriere, {S}. {D}. and {H}eath, {T}. and {O}lioso, {A}. and {R}abefitia, {Z}. and {R}akoto, {H}. and {O}udin, {L}. and {S}atg{\'e}, {F}r{\'e}d{\'e}ric},
  editor = {},
  language = {{ENG}},
  abstract = {{S}tudy region: this study focuses on {M}adagascar. {T}his island is characterized by a great diversity of climate, due to trade winds and the varying topography. {T}his country is also undergoing extreme rainfall events such as droughts and cyclones. {S}tudy focus: the rain gauge network of {M}adagascar is limited (about 30 stations). {C}onsequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. {W}e assessed the reliability of 21 satellite-based and reanalysis precipitation products ({P}-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: {K}ling-{G}upta {E}fficiency ({KGE}), {C}orrelation {C}oefficient ({CC}), {R}oot {M}ean {S}quare {E}rror ({RMSE}), and {B}ias. {B}ased on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. {N}ew hydrological insights for the region: based on the {KGE} and the {CC} scores, {WFDEI} ({WATCH} {F}orcing {D}ata methodology applied to {ERA}-{I}nterim), {CMORPH}-{BLD} ({C}limate {P}rediction {C}enter {MORPH}ing satellite-gauge merged) and {MSWEP} ({M}ulti-{S}ource {W}eighted {E}nsemble {P}recipitation) are the most accurate for estimating rainfall at the national scale. {A}dditionally, the results reveal a high discrepancy between bio-climatic regions. {T}he merged dataset reveals higher performance than the other products in all situations. {T}hese results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity.},
  keywords = {{P}recipitation products ; {R}emote sensing ; {E}nsemble approach ; {H}ydrology ; {M}adagascar ; {MADAGASCAR}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrology : {R}egional {S}tudies},
  volume = {47},
  numero = {},
  pages = {101400 [20 p.]},
  year = {2023},
  DOI = {10.1016/j.ejrh.2023.101400},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090021},
}