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Sensors (Basel). 2017 Jun 16;17(6). pii: E1413. doi: 10.3390/s17061413.

A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield.

Author information

1
Université de Guyane, UMR ESPACE-DEV, IRD, Université de La Réunion, Université de Montpellier, F-97300 Cayenne, French Guiana. justine.ringard@ird.fr.
2
IRD, UMR ESPACE-DEV, Université de Guyane, Université de La Réunion, Université de Montpellier, Maison de la Télédétection, 500 rue Jean-François Breton, F-34093 Montpellier CEDEX 5, France. frederique.seyler@ird.fr.
3
Université de Guyane, UMR ESPACE-DEV, IRD, Université de La Réunion, Université de Montpellier, F-97300 Cayenne, French Guiana. linguetlaur@gmail.com.

Abstract

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.

KEYWORDS:

Guiana Shield; TRMM-TMPA 3B42V7; hydroclimatic area; quantile mapping bias correction; temporal distribution

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