Ensemble hydrological predictions at an intraseasonal scale through a statistical–dynamical downscaling approach over southwestern Amazonia-Reference-Cited by-同舟云学术

Ensemble hydrological predictions at an intraseasonal scale through a statistical–dynamical downscaling approach over southwestern Amazonia

Published:2024-07-11 Issue:8 Volume:15 Page:4076-4098
ISSN:2040-2244
Container-title:Journal of Water and Climate Change
language:en
Short-container-title:

Author:

Gomes Weslley Brito¹^ORCID,Satyamurty Prakki¹^ORCID,Correia Francis Wagner Silva²^ORCID,Chou Sin Chan³^ORCID,Fleischmann Ayan Santos⁴^ORCID,Papa Fabrice⁵⁶^ORCID,Vergasta Leonardo Alves¹^ORCID,Lyra André de Arruda³^ORCID

Affiliation:

1. a Climate and Environment (CLIAMB, INPA/UEA), Av. André Araújo, 2936, Campus II, Aleixo, Manaus, AM 69060-001, Brazil

2. b Superior School of Technology, Amazonas State University, Av. Darcy Vargas, 1200, Parque 10 de Novembro, Manaus, AM 69065-020, Brazil

3. c Center for Weather Forecasting and Climate Research, National Institute for Space Research, Av. dos Astronautas, 1758, São José dos Campos, SP 12227-010, Brazil

4. d Mamirauá Institute for Sustainable Development, Tefé, AM, Brazil

5. e Laboratoire d'Etudes em Géophysique et Océanographie Spatiales (LEGOS), Université Toulouse, IRD, CNRS, CNES, USP, Toulouse, France

6. f Institut de Recherche pour le Développement (IRD), Institute of Geosciences, University of Brasilia (UnB), Brasília, Brazil

Abstract

ABSTRACT We developed and analyzed the performance of an ensemble forecasting system for the Madeira River basin, the largest sub-basin of the Amazon, with forecasts up to 30 days under different hydrometeorological conditions. We used outputs from the regional Eta model of precipitation and global climatological data as inputs to a large-scale hydrological model. Bias correction of precipitation through quantile mapping significantly improved the results, achieving a hit rate >70%. The system demonstrated the ability to discriminate between high, medium, and low flow conditions. Forecast performance is better for larger catchment areas. This system is expected to increase decision-making efficiency for flood and drought situations in the largest Amazon tributary.

Publisher

IWA Publishing

Link

https://iwaponline.com/jwcc/article-pdf/15/8/4076/1473929/jwc2024262.pdf

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