Evaluation of upper Uruguay river basin (Brazil) operational flood forecasts

Abstract

System for hydrological forecasting and alert running in an operational way are important tools for floods impacts reduction. The present study describes the development and results evaluation of an operational discharge forecasting system of the upper Uruguay River basin, sited in Southern Brazil. Developed system was operated every day to provide experimental forecasts with special interest for Barra Grande and Campos Novos hydroelectric power plants reservoirs inflow, with 10 days in advance. We present results of inflow forecasted for floods occurred between July 2013 to July 2016, the period which the system was operated. Forecasts results by visual and performance metrics analysis showed a good fit with observations in most cases, with possibility of floods occurrence being well predicted with antecedence of 2 to 3 days. Comparing the locations, it was noted that the sub-basin of Campos Novos, being slower in rainfall-runoff transformation, is easier forecasted. The difference in predictability between the two basins can be observed by the coefficient of persistence, which is positive from 12h in Barra Grande and from 24h to Campos Novos. These coefficient values also show the value of the rainfall-runoff modeling for forecast horizons of more than one day in the basins.

On the discretization of river networks for large scale hydrologic-hydrodynamic models

“…MGB is a conceptual, semi-distributed, hydrological model which has been widely used for large-scale modeling in South America from rapid-response basins to markedly seasonal and often slow response basins (Collischonn et al, 2007;Fan et al, 2015Fan et al, , 2017Paiva et al, 2013;Siqueira et al, 2018;Fleischmann et al, 2019). In its most recent version, the basin is divided into unit-catchments (Paiva et al, 2011;Pontes et al, 2017), each one containing a single river reach with an associated floodplain and hydrological vertical water and energy balance.…”

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

“…Additional data pre-processing, such as bias-correction and downscaling, can further increase the quality of forecasted hydrometeorological variables [19,20]. Even if important advancements have been achieved at the sub-seasonal time horizon (i.e., a week to a month of forecast) [21][22][23][24][25], challenges remain for the seasonal lead-times (i.e., one to six months of forecast) [26][27][28], particularly in highly variable rainfall dominated catchments [29].…”

Estimativa de cheias em bacias hidrográficas com base em previsões de precipitação por conjunto

“…De acordo com os mesmos autores, a previsão pode ser realizada em curto prazo, com Martins, C. M. S.; Silva, B. C.; Pons, N. A. D. antecedência de poucas horas até aproximadamente 14 dias, e em longo prazo, com antecedência de meses. No Brasil o setor elétrico é o que mais utiliza a previsão de vazão, como recurso para o planejamento e operação de reservatórios para geração de energia (Santos e Oliveira, 2016;Fan et al, 2017;Silveira et al, 2017). Quando se trata de alerta de enchente, o uso da previsão de vazão ainda está no início, devido a disponibilidade limitada dos dados de vazão e precipitação em tempo real com resolução espacial e temporal adequadas (Alfieri e Thielen, 2015;Reis et al, 2016;Artinyan et al 2016;Azam et al, 2017;Negrão et al, 2017;Rodriguez et al, 2017;Souza et al, 2017).…”