Publications des scientifiques de l'IRD

Ringard Justine, Seyler Frédérique, Linguet L. (2017). A quantile mapping bias correction method based on hydroclimatic classification of the Guiana shield. Sensors, 17 (6), art. no 1413 [17 p. en ligne]. ISSN 1424-8220.

Titre du document
A quantile mapping bias correction method based on hydroclimatic classification of the Guiana shield
Année de publication
2017
Type de document
Article référencé dans le Web of Science WOS:000404553900229
Auteurs
Ringard Justine, Seyler Frédérique, Linguet L.
Source
Sensors, 2017, 17 (6), art. no 1413 [17 p. en ligne] ISSN 1424-8220
Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.
Plan de classement
Séries temporelles [020STAT04] ; Modélisation [062MECEAU06] ; Météorologie / Climatologie [126TELAPP06]
Descripteurs
HYDROCLIMAT ; PRECIPITATION ; ZONE COTIERE ; TELEDETECTION SPATIALE ; DONNEES SATELLITE ; DISTRIBUTION STATISTIQUE ; MODELISATION ; DEBIT ; TRAITEMENT D'IMAGE ; METHODOLOGIE ; CHANGEMENT CLIMATIQUE ; CORRECTION DE DONNEES
Description Géographique
GUYANE FRANCAISE ; AMAZONIE ; BRESIL ; COLOMBIE ; GUYANA ; SURINAME ; VENEZUELA
Localisation
Fonds IRD [F B010070438]
Identifiant IRD
fdi:010070438
Contact