Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm
ABSTRACT Real-time updating of channel flow routing models is essential for error reduction in hydrological forecasting. Recent updating techniques found in scientific literature, although very promising, are complex and often applied in models that demand much time and expert knowledge for their de...
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doaj-21e72feddc48473cb5c05c7eda54ce922020-11-25T03:28:51ZengAssociação Brasileira de Recursos HídricosRevista Brasileira de Recursos Hídricos2318-033121485587010.1590/2318-0331.011616086S2318-03312016000400855Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithmVinícius Alencar SiqueiraMino Viana SorribasJuan Martin BravoWalter CollischonnAuder Machado Vieira LisboaGiovanni Gomes Villa TrinidadABSTRACT Real-time updating of channel flow routing models is essential for error reduction in hydrological forecasting. Recent updating techniques found in scientific literature, although very promising, are complex and often applied in models that demand much time and expert knowledge for their development, posing challenges for using in an operational context. Since powerful and well-known computational tools are currently available, which provide easy-to-use and less time-consuming platforms for preparation of hydrodynamic models, it becomes interesting to develop updating techniques adaptable to such tools, taking full advantage of previously calibrated models as well as the experience of the users. In this work, we present a real-time updating procedure for streamflow forecasting in HEC-RAS model, using the Shuffled Complex Evolution - University of Arizona (SCE-UA) optimization algorithm. The procedure consists in a simultaneous correction of boundary conditions and model parameters through: (i) generation of a lateral inflow, based on Soil Conservation Service (SCS) dimensionless unit hydrograph and; (ii) estimation of Manning roughness in the river channel. The algorithm works in an optimization window in order to minimize an objective function, given by the weighted sum of squared errors between simulated and observed flows where differences in later intervals (start of forecast) are more penalized. As a case study, the procedure was applied in a river reach between Salto Caxias dam and Hotel Cataratas stream gauge, located in the Lower Iguazu Basin. Results showed that, with a small population of candidate solutions in the optimization algorithm, it is possible to efficiently improve the model performance for streamflow forecasting and reduce negative effects caused by lag errors in simulation. An advantage of the developed procedure is the reduction of both excessive handling of external files and manual adjustments of HEC-RAS model, which is important when operational decisions must be taken in relatively short times.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312016000400855&lng=en&tlng=enprevisão de vazõeshec-rasatualização em tempo realsce-ua |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vinícius Alencar Siqueira Mino Viana Sorribas Juan Martin Bravo Walter Collischonn Auder Machado Vieira Lisboa Giovanni Gomes Villa Trinidad |
spellingShingle |
Vinícius Alencar Siqueira Mino Viana Sorribas Juan Martin Bravo Walter Collischonn Auder Machado Vieira Lisboa Giovanni Gomes Villa Trinidad Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm Revista Brasileira de Recursos Hídricos previsão de vazões hec-ras atualização em tempo real sce-ua |
author_facet |
Vinícius Alencar Siqueira Mino Viana Sorribas Juan Martin Bravo Walter Collischonn Auder Machado Vieira Lisboa Giovanni Gomes Villa Trinidad |
author_sort |
Vinícius Alencar Siqueira |
title |
Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm |
title_short |
Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm |
title_full |
Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm |
title_fullStr |
Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm |
title_full_unstemmed |
Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm |
title_sort |
real-time updating of hec-ras model for streamflow forecasting using an optimization algorithm |
publisher |
Associação Brasileira de Recursos Hídricos |
series |
Revista Brasileira de Recursos Hídricos |
issn |
2318-0331 |
description |
ABSTRACT Real-time updating of channel flow routing models is essential for error reduction in hydrological forecasting. Recent updating techniques found in scientific literature, although very promising, are complex and often applied in models that demand much time and expert knowledge for their development, posing challenges for using in an operational context. Since powerful and well-known computational tools are currently available, which provide easy-to-use and less time-consuming platforms for preparation of hydrodynamic models, it becomes interesting to develop updating techniques adaptable to such tools, taking full advantage of previously calibrated models as well as the experience of the users. In this work, we present a real-time updating procedure for streamflow forecasting in HEC-RAS model, using the Shuffled Complex Evolution - University of Arizona (SCE-UA) optimization algorithm. The procedure consists in a simultaneous correction of boundary conditions and model parameters through: (i) generation of a lateral inflow, based on Soil Conservation Service (SCS) dimensionless unit hydrograph and; (ii) estimation of Manning roughness in the river channel. The algorithm works in an optimization window in order to minimize an objective function, given by the weighted sum of squared errors between simulated and observed flows where differences in later intervals (start of forecast) are more penalized. As a case study, the procedure was applied in a river reach between Salto Caxias dam and Hotel Cataratas stream gauge, located in the Lower Iguazu Basin. Results showed that, with a small population of candidate solutions in the optimization algorithm, it is possible to efficiently improve the model performance for streamflow forecasting and reduce negative effects caused by lag errors in simulation. An advantage of the developed procedure is the reduction of both excessive handling of external files and manual adjustments of HEC-RAS model, which is important when operational decisions must be taken in relatively short times. |
topic |
previsão de vazões hec-ras atualização em tempo real sce-ua |
url |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312016000400855&lng=en&tlng=en |
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