@article{fdi:010088755,
  title = {{R}ainfall erosivity in {P}eru : a new gridded dataset based on {GPM}-{IMERG} and comprehensive assessment (2000-2020)},
  author = {{G}utierrez, {L}. and {H}uerta, {A}. and {S}abino, {E}. and {B}ourrel, {L}uc and {F}rappart, {F}. and {L}avado-{C}asimiro, {W}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n soil erosion estimation models, the variables with the greatest impact are rainfall erosivity ({RE}), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density ({ED}), which relates {RE} to precipitation. {T}he {RE} requires high temporal resolution records for its estimation. {H}owever, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for {RE} estimation. {T}his study evaluates the performance of a new gridded dataset of {RE} and {ED} in {P}eru ({PISCO}_reed) by merging data from the {IMERG} v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000-2020. {B}y using this method, a correlation of 0.94 was found between {PISCO}_reed and {RE} obtained by the observed data. {A}n average annual {RE} for {P}eru of 7840 {MJ} center dot mm center dot ha-1 center dot h-1 was estimated with a general increase towards the lowland {A}mazon regions, and high values were found on the {N}orth {P}acific {C}oast area of {P}eru. {T}he spatial identification of the most at risk areas of erosion was evaluated through a relationship between the {ED} and rainfall. {B}oth erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.},
  keywords = {rainfall erosivity ; erosivity density ; satellite rainfall product ; {IMERG} ; hourly observed rainfall ; {P}eru ; {A}ndes ; {PEROU} ; {ANDES}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {15},
  numero = {22},
  pages = {5432 [28 p.]},
  year = {2023},
  DOI = {10.3390/rs15225432},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088755},
}