Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 5
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 , with lead times up to , 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.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
Financial assistance for this research was provided by FINEP/CT-Hidro (Financiadora de Estudos e Projetos) from the Brazilian Ministry of Science and Technology (MCT) and by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) which supported the first two writers. C.B.U. acknowledges financial support by the Swedish International Development Cooperation Agency (SIDA) and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT). Special thanks go to Robin Clarke who reviewed the manuscript.
References
Agência Nacional de Águas (ANA). (2005). “HidroWeb—Sistema de Informações Hidrológicas.” ⟨http://hidroweb.ana.gov.br⟩ (August 2005).
Agência Nacional de Energia Elétrica (ANEEL). (2005). Atlas de energia elétrica do Brasil, 2nd Ed., Brasília, Brazil.
Allasia, D. G., Collischonn, W., Silva, B. C., and Tucci, C. E. M. (2006). “Large basin simulation experience in South America.” Predictions in ungauged basins: Promises and progress, M. Sivapalan, T. Wagener, S. Uhlenbrook, E. Zehe, V. Lashmi, X. Liang, Y. Tachikawa and P. Kumar, IAHS Publication No. 303, IAHS Press, Wallingford, U.K., 360–370.
Anderson, M. L., Chen, Z. Q., Kavvas, M., and Feldman, A. (2002). “Coupling HEC-HMS with atmospheric models for prediction of watershed runoff.” J. Hydrol. Eng., 7, 312–318.
Bastidas, L. A., Gupta, H. V., and Sorooshian, S. (2002). “Emerging paradigms in the calibration of hydrologic models.” Mathematical models of large watershed hydrology, V. Singh and D. K. Frevert, eds., Water Resources Publications, Littleton, Colo., 25–66.
Bell, V. A., and Moore, R. J. (1998). “A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies.” Hydrology Earth Syst. Sci., 2(3), 283–298.
Birikundavyi, S., Labib, R., Trung, H. T., and Rousselle, J. (2002). “Performance of neural networks in daily streamflow forecasting.” J. Hydrol. Eng., 7(5), 392–398.
Bishop, C. M. (1995). Neural networks for pattern recognition, Clarendon, Oxford, U.K.
Black, T. L. (1994). “The new NMC mesoscale ETA model: Description and forecast examples.” Weather Forecast., 9, 265–278.
Bowden, G. J., Dandy, G. C., and Maier, H. R. (2005). “Input determination for neural network models in water resources applications. Part 1—Background and methodology.” J. Hydrol., 301, 75–92.
Bravo, J. M. (2006). “Optimization of multipurpose reservoir operation based on streamflow forecast.” MSc dissertation, Univ. Federal do Rio Grande do Sul—Instituto de Pesquisas Hidráulicas, Porto Alegre, Portugal (in Portuguese).
Bustamante, J. F. F., Gomes, J. L., Chou, S. C., and Rozante, J. R. (1999). “Evaluation of April 1999 rainfall forecasts over South America using the ETA model.” Climanálise, São José dos Campos, Brazil.
CEPEL. (2004). “Streamflow forecasting model Previvaz applicated to Brazilian major water reservoirs.” References manual, Electric Energy Research Center, Rio de Janeiro.
Chou, S. (1996). “The ETA regional model.” Climanálise, Special Edition, INPE, São José dos Campos, Brazil (in Portuguese).
Chou, S. C., Bustamante, J. F. F., and Gomes, J. L. (2005) “Evaluation of eta model seasonal precipitation forecasts over South America.” Nonlinear Processes Geophys., 12, 537–555.
Chou, S. C., and Justi da Silva, M. G. A. (1999). “Assessment of the precipitation forecasts of the regional ETA model.” Climanálise, Vol. 1, INPE, São José dos Campos, Brazil (in Portuguese).
Chou, S. C., Nunes, A., and Cavalcanti, I. (2000). “Extended range forecasts over South America using the regional ETA model.” J. Geophys. Res., 105(D8), 10147–10160.
Chou, S. C., Tanajura, C. A. S., Xue, Y., and Nobre, C. A. (2002). “Validation of the coupled ETA/SSiB model over South America.” J. Geophys. Res., 107, D20.
Collischonn, W., et al. (2007a). “Medium-range reservoir inflow predictions based on quantitative precipitation forecasts.” J. Hydrol., 344, 112–122.
Collischonn, W., Allasia, D., Silva, B. C., and Tucci, C. E. M. (2007b). “The MGB-IPH model for large scale rainfall runoff modeling.” Hydrol. Sci. J., 52(5), 878–895.
Collischonn, W., Haas, R., Andreolli, I., and Tucci, C. E. M. (2005). “Forecasting River Uruguay flow using rainfall forecasts from a regional weather-prediction model.” J. Hydrol., 205, 87–98.
Collischonn, W., and Tucci, C. E. M. (2001). “Large scale rainfall runoff modeling.” Brazilian J. Water Resour., 6(1), 950–118 (in Portuguese).
Dawson, C. W., Harpham, C., Wilby, R. L., and Chen, Y. (2002). “Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China.” Hydrology Earth Syst. Sci., 6(4), 619–926.
Dawson, C. W., and Wilby, R. L. (2001). “Hydrological modeling using artificial neural networks.” Prog. Phys. Geogr., 25(1), 80–108.
Food and Agriculture Organization (FAO). (1974). Soil map of the world, Vols. 1–10, United Nations and UNESCO, Paris.
Food and Agriculture Organization (FAO). (1988). Soil map of the world: Revised legend, United Nations, Rome.
Guilhon, L. G. F. (2002). “Heuristic natural weekly forecast inflows model apply to Foz do Areia plant.” MSc dissertation, Univ. Federal do Rio de Janeiro, Rio de Janeiro, Brazil (in Portuguese).
Guilhon, L. G. F., Rocha, V. F., and Moreira, J. C. (2007). “Skill comparation of streamflow forecasting models.” Brazilian J. of Water Resour., 12(3), 13–20 (in Portuguese).
Guo, S., Zhan, G. H., Chen, H., Peng, D., Liu, P., and Pang, B. (2004). “A reservoir flood forecasting and control system for China.” Hydrol. Sci. J., 49(6), 959–972.
Hamlet, A. F., Huppert, D., and Lettenmaier, D. P. (2002). “Economic values of long-lead streamflow forecasts for Columbia River hydropower.” J. Water Resour. Plann. Manage., 128(2), 91–101.
Jasper, K., Gurtz, J., and Lang, H. (2002). “Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model.” J. Hydrol., 267, 40–52.
Kouwen, N., Soulis, E. D., Pietroniro, A., Donald, J., and Harrington, R. A. (1993). “Grouped response units for distributed hydrologic modeling.” J. Water Resour. Plann. Manage., 119(3), 289–305.
Lettenmaier, D. P., and Wood, E. F. (1993). Hydrologic forecasting, handbook of hydrology, D. R. Maidment, ed., McGraw-Hill, New York, 26.
Liang, X, Lettenmaier, D. P., Wood, E. F., and Burges, S. J. (1994). “A simple hydrologically based model of land surface water and energy fluxes for general circulation models.” J. Geophys. Res., 99(7), 14415–14428.
Ludwig, K., and Bremicker, M., eds. (2006). “The water balance model LARSIM—Design, content and applications.” Freiburger Schriften zur Hydrologie, 22, Institut für Hydrologie Universität, Freiburg, Germany.
Maceira, M. E. P., and Damázio, J. M. (2005). “Periodic auto-regressive streamflow models applied to operation planning for the Brazilian hydroelectric system.” Regional hydrological impacts of climatic change—Impact assessment and decision making, IAHS Publication No. 295, Proc., Symp. S6 Seventh IAHS Scientific Assembly, Foz do Iguaçu, Brazil, April 2005, 239–247.
Maceira, M. E. P., Damázio, J. M., Ghirardi, A. O., and Dantas, H. M. (1997). “Using ARMA(p,q) models to streamflow forecasting.” XII Brazilian National Symp. on Water Resources, Vitória, Brazil.
Maier, H. R., and Dandy, G. C. (1997). “Modelling cyanobacteria (blue-green algae) in the River Murray using artificial neural networks.” Math. Comput. Simul., 43, 377–386.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the predictions and forecasting of water resources variables: A review of modeling issues and applications.” Environ. Modell. Software, 15(1), 101–124.
Maurer, E. P., and Lettenmaier, D. P. (2004). “Potential effects of long-lead hydrologic predictability on Missouri River main-stem reservoirs.” J. Clim., 17, 174–186.
Mesinger, F., Janjic, Z. I., Nickovic, S., Gavrilov, D., and Deaven, D. G. (1988) “The step-mountain coordinate: Model description and performance for cases of Alpine lee cyclogenesis and for a case of an Appalachian redevelopment.” Mon. Weather Rev., 116, 1493–1518.
Mishalani, N. R., and Palmer, R. N. (1988). “Forecast uncertainty in water supply reservoir operation.” Water Resour. Bull., 24(6), 1237–1245.
Moller, M. S. (1993). “A scaled conjugate gradient algorithm for fast supervised learning.” Neural Networks, 6(4), 525–534.
Nijssem, B., Lettenmaier, D. P., Liang, X., Wetzel, S. W., and Wood, E. F. (1997). “Streamflow simulation for continental-scale river basins.” Water Resour. Res., 33(4), 711–724.
Paz, A. R., and Collischonn, W. (2007). “River reach length and slope estimates for large-scale hydrological models based on relatively high-resolution digital elevation model.” J. Hydrol., 343(3–4), 127–139.
Paz, A. R., Collischonn, W., and Silveira, A. L. L. (2006). “Improvements in large scale drainage networks derived from digital elevation models.” Water Resour. Res., 42.
Paz, A. R., Collischonn, W., Tucci, C. E. M., Clarke, R. T., and Allasia, D. (2007). “Data assimilation in a large-scale distributed hydrological model for medium range flow forecasts.” Quantification and reduction of predictive uncertainty for sustainable water resources management, IAHS Publ. 313, IAHS Press, Wallingford, U.K., 471–478.
Projeto RADAMBrasil. (1982). “Programa de integração nacional.” Levantamento de Recursos Naturais, Ministério das Minas e Energia. Sheets SE-23 (Belo Horizonte) and SF-22 (Paranapanema), IBGE, Rio de Janeiro.
Stokelj, T., Paravan, D., and Golob, R. (2002). “Enhanced artificial neural network inflow forecasting algorithm for run-of-river hydropower plants.” J. Water Resour. Plann. Manage., 128(6), 415–423.
Tchaban, T., Taylor, M. J., and Griffin, J. P. (1998). “Establishing impacts of the inputs in a feedforward neural network.” Neural Comput. Appl., 7, 309–317.
Tucci, C. E. M., Clarke, R. T., Collischonn, W., Dias, Pedro L. S., and Sampaio, G. (2003). “Long term flow forecast based on climate and hydrological modeling: Uruguay River basin.” Water Resour. Res., 39(7), 1181–1190.
Tucci, C. E. M., and Collischonn, W. (2003). “Streamflow forecast.” Weather and water resources of Brazil, C.E.M. Tucci and B. Braga, Orgs. ABRH, Porto Alegre, Brazil, 281–348 (in Portuguese).
Wu, J. S., Han, J., Annambhotla, S., and Bryant, S. (2005). “Artificial neural networks for forecasting watershed runoff and stream flows.” J. Hydrol. Eng., 10(3), 216–222.
Wurbs, R. A. (1996). Modeling and analysis of reservoir system operations, Prentice-Hall, Upper Saddle River, N.J.
Yapo, P. O., Gupta, H. V., and Sorooshian, S. (1998). “Multi-objective global optimization for hydrologic models.” J. Hydrol., 204, 83–97.
Yeh, W. W.-G., Becker, L., and Zettlemoyer, R. (1982). “Worth of inflow forecast for reservoir operation.” J. Water Resour. Plng. and Mgmt. Div., 108(3), 257–269.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 5 • May 2009
Pages: 435 - 445
Copyright
© 2009 ASCE.
History
Received: Mar 26, 2008
Accepted: Aug 6, 2008
Published online: Feb 11, 2009
Published in print: May 2009
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.