Seasonal Streamflow Forecast in the Tocantins River Basin, Brazil: An Evaluation of ECMWF-SEAS5 with Multiple Conceptual Hydrological Models

被引:7
作者
avila, Leandro [1 ,2 ]
Silveira, Reinaldo [1 ]
Campos, Andre [1 ]
Rogiski, Nathalli [1 ]
Freitas, Camila [3 ]
Aver, Cassia [3 ]
Fan, Fernando [4 ]
机构
[1] Sistema Tecnol & Monitoramento Ambiental Parana SI, BR-81531980 Curitiba, Brazil
[2] Univ Fed Parana UFPR, Grad Program Water Resources & Environm Engn PPGER, BR-80230901 Curitiba, Brazil
[3] COPEL GeT, BR-81200240 Curitiba, Brazil
[4] Univ Fed Rio Grande Sur UFRGS, Inst Pesquisas Hidrol IPH, BR-91501970 Porto Alegre, Brazil
关键词
hydrological model; ECMWF-SEAS5; hydropower plant; streamflow forecast; CLIMATE-CHANGE IMPACT; PREDICTABILITY; SIMULATIONS; SKILL;
D O I
10.3390/w15091695
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The assessment of seasonal streamflow forecasting is essential for appropriate water resource management. A suitable seasonal forecasting system requires the evaluation of both numerical weather prediction (NWP) and hydrological models to represent the atmospheric and hydrological processes and conditions in a specific region. In this paper, we evaluated the ECMWF-SEAS5 precipitation product with four hydrological models to represent seasonal streamflow forecasts performed at hydropower plants in the Legal Amazon region. The adopted models included GR4J, HYMOD, HBV, and SMAP, which were calibrated on a daily scale for the period from 2014 to 2019 and validated for the period from 2005 to 2013. The seasonal streamflow forecasts were obtained for the period from 2017 to 2019 by considering a daily scale streamflow simulation comprising an ensemble with 51 members of forecasts, starting on the first day of every month up to 7 months ahead. For each forecast, the corresponding monthly streamflow time series was estimated. A post-processing procedure based on the adjustment of an autoregressive model for the residuals was applied to correct the bias of seasonal streamflow forecasts. Hence, for the calibration and validation period, the results show that the HBV model provides better results to represent the hydrological conditions at each hydropower plant, presenting NSE and NSElog values greater than 0.8 and 0.9, respectively, during the calibration stage. However, the SMAP model achieves a better performance with NSE values of up to 0.5 for the raw forecasts. In addition, the bias correction displayed a significant improvement in the forecasts for all hydrological models, specifically for the representation of streamflow during dry periods, significantly reducing the variability of the residuals.
引用
收藏
页数:20
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