Validation and reconstruction of rain gauge–based daily time series for the entire Amazon basin
Véronique Michot
(1)
,
Damien Arvor
(1)
,
Josyane Ronchail
(2, 3)
,
Thomas Corpetti
(1)
,
Nicolas Jegou
(4)
,
Paulo Sérgio Lucio
(5)
,
Vincent Dubreuil
(1)
Véronique Michot
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Damien Arvor
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- ORCID : 0000-0002-3017-9625
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Thomas Corpetti
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Vincent Dubreuil
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Résumé
Monitoring the spatio-temporal variability of rainfall regimes in the Amazon basin is difficult because (1) time series of remote sensing–based rainfall estimates are still too short for long-time variability analysis and (2) rain gauge time series are not fully reliable and operational in their current state due to frequent gaps and zero values. The objective of this paper is to introduce a quality control and reconstruction procedure designed to produce a robust database of rain gauge–based daily rainfall in the Amazon basin. Despite the low density and heterogeneous spatial distribution of the rain gauges network, we eliminated unexpected values and produced accurate estimates using spatial and mathematical relationships with neighboring rain gauges. Three reconstruction methods were tested: the nearest neighbor approach (NN), the arithmetic mean with neighboring stations (AM), and the multiple imputation by chained equations used with the predictive mean matching procedure (MICE). The quality of the reconstruction has been assessed through themean annual rainfall and themean annual number of rainy days.We concluded that theAMapproach performed better at the scale of the whole Amazon basin. This method has then been preferred to reconstruct the whole database of rainfall time series.
Format du dépôt | Notice |
---|---|
Type de dépôt | Article dans une revue |
Titre |
en
Validation and reconstruction of rain gauge–based daily time series for the entire Amazon basin
|
Résumé |
en
Monitoring the spatio-temporal variability of rainfall regimes in the Amazon basin is difficult because (1) time series of remote sensing–based rainfall estimates are still too short for long-time variability analysis and (2) rain gauge time series are not fully reliable and operational in their current state due to frequent gaps and zero values. The objective of this paper is to introduce a quality control and reconstruction procedure designed to produce a robust database of rain gauge–based daily rainfall in the Amazon basin. Despite the low density and heterogeneous spatial distribution of the rain gauges network, we eliminated unexpected values and produced accurate estimates using spatial and mathematical relationships with neighboring rain gauges. Three reconstruction methods were tested: the nearest neighbor approach (NN), the arithmetic mean with neighboring stations (AM), and the multiple imputation by chained equations used with the predictive mean matching procedure (MICE). The quality of the reconstruction has been assessed through themean annual rainfall and themean annual number of rainy days.We concluded that theAMapproach performed better at the scale of the whole Amazon basin. This method has then been preferred to reconstruct the whole database of rainfall time series.
|
Auteur(s) |
Véronique Michot
1
, Damien Arvor
1
, Josyane Ronchail
2, 3
, Thomas Corpetti
1
, Nicolas Jegou
4
, Paulo Sérgio Lucio
5
, Vincent Dubreuil
1
1
LETG - Rennes -
Littoral, Environnement, Télédétection, Géomatique
( 3177 )
- Maison de la Recherche Place du Recteur Henri Le Moal 35043 RENNES CEDEX
- France
2
VARCLIM -
Océan et variabilité du climat
( 541822 )
- France
3
UPD7 -
Université Paris Diderot - Paris 7
( 300301 )
- 5 rue Thomas-Mann - 75205 Paris cedex 13
- France
4
IRMAR -
Institut de Recherche Mathématique de Rennes
( 75 )
- Campus de Beaulieu, bâtiments 22 et 23,
263 avenue du Général Leclerc, CS 74205
35042 RENNES Cédex
- France
5
CCET -
Centro de ciencias exatas e da terra
( 444449 )
- Brésil
|
Audience |
Internationale
|
Public visé |
Scientifique
|
Nom de la revue |
|
Page/Identifiant |
759–775
|
Volume |
138
|
Langue du document |
Anglais
|
Vulgarisation |
Non
|
Comité de lecture |
Oui
|
Date de publication |
2019-04-13
|
Date de publication électronique |
2019-04-13
|
Numéro |
1-2
|
Domaine(s) |
|
Projet(s) ANR |
|
Mots-clés |
en
rainfall, Amazon, database, validation, Climate
|
DOI | 10.1007/s00704-019-02832-w |
UT key WOS | 000491945900051 |
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