@article{fdi:010065349,
  title = {{A} comparative analysis of {TRMM}-rain gauge data merging techniques at the daily time scale for distributed rainfall-runoff modeling applications},
  author = {{N}erini, {D}. and {Z}ulkafli, {Z}. and {W}ang, {L}. {P}. and {O}nof, {C}. and {B}uytaert, {W}. and {L}avado-{C}asimiro, {W}. and {G}uyot, {J}ean-{L}oup},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study compares two nonparametric rainfall data merging methodsthe mean bias correction and double-kernel smoothingwith two geostatistical methodskriging with external drift and {B}ayesian combinationfor optimizing the hydrometeorological performance of a satellite-based precipitation product over a mesoscale tropical {A}ndean watershed in {P}eru. {T}he analysis is conducted using 11 years of daily time series from the {T}ropical {R}ainfall {M}easuring {M}ission ({TRMM}) {M}ultisatellite {P}recipitation {A}nalysis ({TMPA}) research product (also {TRMM} 3{B}42) and 173 rain gauges from the national weather station network. {T}he results are assessed using 1) a cross-validation procedure and 2) a catchment water balance analysis and hydrological modeling. {I}t is found that the double-kernel smoothing method delivered the most consistent improvement over the original satellite product in both the cross-validation and hydrological evaluation. {T}he mean bias correction also improved hydrological performance scores, particularly at the subbasin scale where the rain gauge density is higher. {G}iven the spatial heterogeneity of the climate, the size of the modeled catchment, and the sparsity of data, it is concluded that nonparametric merging methods can perform as well as or better than more complex geostatistical methods, whose assumptions may not hold under the studied conditions. {B}ased on these results, a systematic approach to the selection of a satellite-rain gauge data merging technique is proposed that is based on data characteristics. {F}inally, the underperformance of an ordinary kriging interpolation of the rain gauge data, compared to {TMPA} and other merged products, supports the use of satellite-based products over gridded rain gauge products that utilize sparse data for hydrological modeling at large scales.},
  keywords = {{A}mazon region ; {P}recipitation ; {S}atellite observations ; {S}urface observations ; {S}tatistical techniques ; {H}ydrologic models ; {PEROU} ; {ANDES} ; {AMAZONE} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrometeorology},
  volume = {16},
  numero = {5},
  pages = {2153--2168},
  ISSN = {1525-755{X}},
  year = {2015},
  DOI = {10.1175/jhm-d-14-0197.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010065349},
}