@article{fdi:010085144,
  title = {{A} novel high-resolution gridded precipitation dataset for {P}eruvian and {E}cuadorian watersheds : development and hydrological evaluation},
  author = {{F}ernandez-{P}alomino, {C}. {A}. and {H}attermann, {F}. {F}. and {K}rysanova, {V}. and {L}obanova, {A}. and {V}ega-{J}acome, {F}. and {L}avado, {W}. and {S}antini, {W}illiam and {A}ybar, {C}. and {B}ronstert, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A} novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called {RAIN}4{PE} ({R}ain for {P}eru and {E}cuador), at 0.1 degrees spatial resolution for the period 1981-2015 covering {P}eru and {E}cuador. {I}t is based on the application of 1) the random forest method to merge multisource precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and 2) observed and modeled streamflow data to first detect biases and second further adjust gridded precipitation by inversely applying the simulated results of the ecohydrological model {SWAT} ({S}oil and {W}ater {A}ssessment {T}ool). {H}ydrological results using {RAIN}4{PE} as input for the {P}eruvian and {E}cuadorian catchments were compared against the ones when feeding other uncorrected ({CHIRP} and {ERA}5) and gauge-corrected ({CHIRPS}, {MSWEP}, and {PISCO}) precipitation datasets into the model. {F}or that, {SWAT} was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. {R}esults showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. {H}owever, {CHIRPS}, {MSWEP}, and {PISCO} showed limitations for streamflow simulation in several catchments draining into the {P}acific {O}cean and the {A}mazon {R}iver. {RAIN}4{PE} provided the best overall performance for streamflow simulation, including flow variability (low, high, and peak flows) and water budget closure. {T}he overall good performance of {RAIN}4{PE} as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods. {S}ignificance {S}tatement{W}e developed a novel precipitation dataset {RAIN}4{PE} for {P}eru and {E}cuador by merging multisource precipitation data (satellite, reanalysis, and ground-based precipitation) with terrain elevation using the random forest method. {F}urthermore, {RAIN}4{PE} was hydrologically corrected using streamflow data in watersheds with precipitation underestimation through reverse hydrology. {T}he results of a comprehensive hydrological evaluation showed that {RAIN}4{PE} outperformed state-of-the-art precipitation datasets such as {CHIRP}, {ERA}5, {CHIRPS}, {MSWEP}, and {PISCO} in terms of daily and monthly streamflow simulations, including extremely low and high flows in almost all {P}eruvian and {E}cuadorian catchments. {T}his underlines the suitability of {RAIN}4{PE} for hydrometeorological applications in this region. {F}urthermore, our approach for the generation of {RAIN}4{PE} can be used in other data-scarce regions.},
  keywords = {{A}mazon region ; {C}omplex terrain ; {S}outh {A}merica ; {S}treamflow ; {P}recipitation ; {H}ydrology ; {W}ater budget balance ; {I}nverse methods ; {M}ountain meteorology ; {M}achine learning ; {PEROU} ; {EQUATEUR} ; {AMAZONIE}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrometeorology},
  volume = {23},
  numero = {3},
  pages = {309--336},
  ISSN = {1525-755{X}},
  year = {2022},
  DOI = {10.1175/jhm-d-20-0285.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085144},
}