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

被引：0

作者：

Gomes, Weslley Brito ^{[1
]}

Satyamurty, Prakki ^{[1
]}

Correia, Francis Wagner Silva ^{[2
]}

Chou, Sin Chan ^{[3
]}

Fleischmann, Ayan Santos ^{[4
]}

Papa, Fabrice ^{[5
,6
]}

Vergasta, Leonardo Alves ^{[1
]}

Lyra, Andre de Arruda ^{[3
]}

机构：

[1] INPA UEA, Climate & Environm CLIAMB, Av Andre Araujo 2936,Campus II,Aleixo, BR-69060001 Manaus, AM, Brazil

[2] Amazonas State Univ, Super Sch Technol, Av Darcy Vargas,1200 Parque 10 Novembro, BR-69065020 Manaus, AM, Brazil

[3] Natl Inst Space Res, Ctr Weather Forecasting & Climate Res, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos, SP, Brazil

[4] Mamiraua Inst Sustainable Dev, Tefe, AM, Brazil

[5] Univ Toulouse, Lab Etud Geophys & Oceanog Spatiales LEGOS, IRD, CNRS,CNES,USP, Toulouse, France

[6] Univ Brasilia UnB, Inst Geosci, Inst Rech Dev IRD, Brasilia, Brazil

来源：

JOURNAL OF WATER AND CLIMATE CHANGE | 2024年 / 15卷 / 08期

关键词：

bias correction; downscaling; ensemble intraseasonal prediction; Madeira River basin; MGB model; FLOOD ALERT SYSTEM; PRECIPITATION FORECASTS; ATLANTIC REGION; ETA-MODEL; WEATHER; SKILL; BASIN; PART; PARAMETERIZATION; VARIABILITY;

D O I：

10.2166/wcc.2024.262

中图分类号：

TV21 [水资源调查与水利规划];

学科分类号：

081501 ;

摘要：

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.

引用

页码：4076 / 4098

页数：23

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