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

Fan, Fernando Mainardi; Pontes, Paulo Rógenes Monteiro; Buarque, Diogo Costa; Collischonn, Walter

doi:10.1590/2318-0331.0217160027

Cited by 9 publications

(10 citation statements)

References 28 publications

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“…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.…”

Section: Model Descriptionmentioning

confidence: 99%

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

Fan

Siqueira

Fleischmann

et al. 2021

RBRH

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The discretization of river networks is a critical step for computing flow routing in hydrological models. However, when it comes to more complex hydrologic-hydrodynamic models, adaptations in the spatial representation of model calculation units are further required to allow cost-effective simulations, especially for large scale applications. The objective of this paper is to assess the impacts of river discretization on simulated discharge, water levels and numerical stability of a catchment-based hydrologic-hydrodynamic model, using a fixed river length (Δx) segmentation method. The case study was the Purus river basin, a sub-basin of the Amazon, which covers an area that accounts for rapid response upstream reaches to downstream floodplain rivers. Results indicate that the maximum and minimum discharges are less affected by the adopted Δx (reach-length), whereas water levels are more influenced by this selection. It is showed that for the explicit local inertial one-dimensional routing, Δx and the α parameter of CFL (Courant-Friedrichs-Lewy) condition must be carefully chosen to avoid mass balance errors. Additionally, a simple Froude number-based flow limiter to avoid numerical issues is proposed and tested.

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Section: Model Descriptionmentioning

confidence: 99%

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

Fan

Siqueira

Fleischmann

et al. 2021

RBRH

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“…Modelos hidrológicos em grande escala são ferramentas úteis para diferentes aplicações de recursos hídricos, incluindo avaliações de possíveis impactos das mudanças climáticas (Fan et al, 2017); previsão hidrológica em grande escala (Alfieri et al, 2013;Fan et al, 2015a;Kauffeldt et al, 2016;Sorribas et al, 2020); como base para a qualidade da água e modelos biogeoquímicos (Fan et al, 2015c); avaliação de risco de inundação (Trigg et al, 2016); e para uso da terra e outras analises de cenários antrópicos (Bayer & Collischonn, 2013), além de gestão de bacias e uso de água.…”

Section: Introductionunclassified

Modelo hidrológico de grande escala aplicado a Bacia do Rio Uruguai

Cardoso,

Reginatto

2023

Cad. Pedagógico

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O uso de ferramentas computacionais para representar o ciclo hidrológico, e analisar suas características para entender o comportamento por trás de suas mudanças tem sido uma tarefa difícil, pois o meio ambiente está sempre mudando, seja por ações antrópicas ou por novos períodos climáticos. As diversas variáveis a serem inseridas em um modelo que possa representar, entender e prever o funcionamento hidrológico de uma grande bacia hidrográfica é um desafio que só pode ser superado com o apoio de modelos de simulação hidrológico-hidrodinâmica, que leva em consideração os vários fatores que influenciam uma determinada região hidrográfica e podem representar adequadamente o ambiente em questão. Assim o presente trabalho tem como objetivo avaliar e prever o funcionamento hidrológico de grandes bacias hidrográficas, suportado por modelos de simulação hidrológico-hidrodinâmica. Foi utilizado o modelo MGB-IPH para teste de aplicação na bacia Hidrográfica do Rio Uruguai. O banco de dados SIG para o estudo foi elaborado com os dados disponibilizados para a região da bacia hidrográfica, e depois realizado o pré-processamento de informações geográficas preliminares para então realizar a aplicação para a bacia do rio Uruguai. Os resultados mostram que os parâmetros mais importantes para os trechos de escoamento superficial foram os ligados a vegetação e uso do solo, e para escoamento sub-superficial e infiltração conforme o tipo de solo. Foram encontrados por calibração manual, valores para os coeficientes de nash acima de 0,7 e erros volumétricos dentro da faixa de amplitude de cinco a menos cinco, tendo ajustados os parâmetros calibráveis em 65 sub-bacias do rio Uruguai. O modelo utilizado apresentou bons resultados para previsão de comportamento na bacia, porém exige do usuário um conhecimento prévio da bacia e do funcionamento do modelo. Assim o modelo pode ser uma boa opção para previsão de comportamento de grandes bacias hidrográficas.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets.

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

Cited by 9 publications

References 28 publications

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

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

Modelo hidrológico de grande escala aplicado a Bacia do Rio Uruguai

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

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