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 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.

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Main Authors: Siqueira,Vinícius Alencar, Sorribas,Mino Viana, Bravo,Juan Martin, Collischonn,Walter, Lisboa,Auder Machado Vieira, Trinidad,Giovanni Gomes Villa
Format: Digital revista
Language:English
Published: Associação Brasileira de Recursos Hídricos 2016
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312016000400855
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spelling oai:scielo:S2318-033120160004008552017-02-15Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithmSiqueira,Vinícius AlencarSorribas,Mino VianaBravo,Juan MartinCollischonn,WalterLisboa,Auder Machado VieiraTrinidad,Giovanni Gomes Villa River flow forecasting HEC-RAS Real-time updating SCE-UA 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.info:eu-repo/semantics/openAccessAssociação Brasileira de Recursos HídricosRBRH v.21 n.4 20162016-12-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312016000400855en10.1590/2318-0331.011616086
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country Brasil
countrycode BR
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region America del Sur
libraryname SciELO
language English
format Digital
author Siqueira,Vinícius Alencar
Sorribas,Mino Viana
Bravo,Juan Martin
Collischonn,Walter
Lisboa,Auder Machado Vieira
Trinidad,Giovanni Gomes Villa
spellingShingle Siqueira,Vinícius Alencar
Sorribas,Mino Viana
Bravo,Juan Martin
Collischonn,Walter
Lisboa,Auder Machado Vieira
Trinidad,Giovanni Gomes Villa
Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm
author_facet Siqueira,Vinícius Alencar
Sorribas,Mino Viana
Bravo,Juan Martin
Collischonn,Walter
Lisboa,Auder Machado Vieira
Trinidad,Giovanni Gomes Villa
author_sort Siqueira,Vinícius Alencar
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
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.
publisher Associação Brasileira de Recursos Hídricos
publishDate 2016
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312016000400855
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