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Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting

Bravo, J. M.; Paz, A. R.; Collischonn, W.; Bertacchi Uvo, Cintia LU ; Pedrollo, O. C. and Chou, S. C. (2009) In Journal of Hydrologic Engineering 14(5). p.435-445
Abstract
This study reports on the performance of two medium-range streamflow forecast models: (1) a multilayer feed-forward artificial neural network; and (2) a distributed hydrologic model. Quantitative precipitation forecasts were used as input to both models. The Furnas Reservoir on the Rio Grande River was selected as a case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generating system. Streamflow forecasts were calculated for a drainage area of about 51,900 km(2), with lead times up to 12 days, at daily intervals. The Nash-Sutcliffe efficiency index, the root-mean-square error, the... (More)
This study reports on the performance of two medium-range streamflow forecast models: (1) a multilayer feed-forward artificial neural network; and (2) a distributed hydrologic model. Quantitative precipitation forecasts were used as input to both models. The Furnas Reservoir on the Rio Grande River was selected as a case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generating system. Streamflow forecasts were calculated for a drainage area of about 51,900 km(2), with lead times up to 12 days, at daily intervals. The Nash-Sutcliffe efficiency index, the root-mean-square error, the mean absolute error, and the mean relative error were used to assess the relative performance of the models. Results showed that the performance of streamflow forecasts was strongly dependent on the quality of quantitative precipitation forecasts used. The artificial neural network (ANN) method seemed to be less sensitive to precipitation forecast error relative to the distributed hydrological model. Hence, the latter presented a better skill in flow forecasting when using the more accurate perfect precipitation forecast. The conceptual hydrological model also demonstrates better forecast skill than ANN models for longer lead times, when the representation of the rainfall-runoff process and of the water storage in the watershed becomes more important than the flow routing along the drainage network. In addition, results obtained by incorporating a quantitative precipitation forecast in both models performed better than the current streamflow obtained by the Brazilian national electric system operator using statistical models which do not utilize information on precipitation, whether observed or forecast. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Hydrologic Engineering
volume
14
issue
5
pages
435 - 445
publisher
American Society of Civil Engineers (ASCE)
external identifiers
  • wos:000265236200001
  • scopus:65149104448
ISSN
1084-0699
DOI
10.1061/(ASCE)HE.1943-5584.0000014
language
English
LU publication?
yes
id
37a34693-36df-4538-991f-7dcc9ccbb2a8 (old id 1400230)
date added to LUP
2009-06-15 14:45:34
date last changed
2017-08-13 03:49:14
@article{37a34693-36df-4538-991f-7dcc9ccbb2a8,
  abstract     = {This study reports on the performance of two medium-range streamflow forecast models: (1) a multilayer feed-forward artificial neural network; and (2) a distributed hydrologic model. Quantitative precipitation forecasts were used as input to both models. The Furnas Reservoir on the Rio Grande River was selected as a case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generating system. Streamflow forecasts were calculated for a drainage area of about 51,900 km(2), with lead times up to 12 days, at daily intervals. The Nash-Sutcliffe efficiency index, the root-mean-square error, the mean absolute error, and the mean relative error were used to assess the relative performance of the models. Results showed that the performance of streamflow forecasts was strongly dependent on the quality of quantitative precipitation forecasts used. The artificial neural network (ANN) method seemed to be less sensitive to precipitation forecast error relative to the distributed hydrological model. Hence, the latter presented a better skill in flow forecasting when using the more accurate perfect precipitation forecast. The conceptual hydrological model also demonstrates better forecast skill than ANN models for longer lead times, when the representation of the rainfall-runoff process and of the water storage in the watershed becomes more important than the flow routing along the drainage network. In addition, results obtained by incorporating a quantitative precipitation forecast in both models performed better than the current streamflow obtained by the Brazilian national electric system operator using statistical models which do not utilize information on precipitation, whether observed or forecast.},
  author       = {Bravo, J. M. and Paz, A. R. and Collischonn, W. and Bertacchi Uvo, Cintia and Pedrollo, O. C. and Chou, S. C.},
  issn         = {1084-0699},
  language     = {eng},
  number       = {5},
  pages        = {435--445},
  publisher    = {American Society of Civil Engineers (ASCE)},
  series       = {Journal of Hydrologic Engineering},
  title        = {Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting},
  url          = {http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000014},
  volume       = {14},
  year         = {2009},
}