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Water Resources Management

, Volume 30, Issue 15, pp 5671–5685 | Cite as

Hydrologic Modelling Calibration for Operational Flood Forecasting

  • Juliana Mendes
  • Rodrigo Maia
Article

Abstract

Runoff prediction in flood forecasting depends on the use of hydrological simulation models and on the input of accurate precipitation forecasts. Reliability of predictions thus obtained hinges on proper calibration of the model. Moreover, when the model is intended to be used systematically in operational forecasting of streamflows, the calibration process must take into account the variation of the model parameters over time, namely in response to changing weather and hydrological conditions in the basin. The goal of the study was to build a process to adjust, on a daily basis, the simulation model parameters to the current hydrological conditions of the river basin, in order for the model to be run operationally for prediction of the streamflow for the next 10-days period, and, thereby, to forecast the occurrence of flood events. Towards this end, hydrological simulations using the HEC-HMS model were performed, using a 3 h period time step. The present communication focuses on the hydrological model calibration and verification processes and on the evaluation of forecasts’ accuracy. The procedure was applied to a part of the largest (full) Portuguese river basin, the Mondego river basin, corresponding to the Aguieira dam section watershed, which comprises an area of 3070 km2. Four wet periods, associated with the occurrence of flooding, were selected for the calibration and verification of the model, by adjustment of the model parameters. The results of the study aim to define the optimal calibration parameters values to model the observed streamflow for various hydro-meteorological states, thus enabling adequate prediction of flow in flooding situations and proper application of the model in operational flood forecasting.

Keywords

Hydrologic modelling Calibration and verification Accuracy Runoff Flood forecasting 

Notes

Acknowledgments

The first author is indebted to the Portuguese Foundation for Science and Technology (PhD grant reference SFRH/BD/65905/2009, supported by “Programa Operacional Potencial Humano do Fundo Social Europeu” (POPH/FSE) funding). Moreover, the authors also thank EDP-Produção for providing the data used in this study, and Pedro Pinto de Sousa for linguistic revision.

References

  1. Abbaspour KC (2005) Calibration of Hydrologic Models: When is a Model Calibrated? In: Zerger A and Argent RM (eds) MODSIM 2005 International Congress on Modelling and Simulation Modelling and Simulation Society of Australia and New Zealand, pp. 2249–2455. ISBN: 0–9758400–2-9Google Scholar
  2. Anderson M, Chen Z, Kavvas M, Feldman A (2002) Coupling HEC-HMS with atmospheric models for prediction of watershed runoff. J Hydrol Eng 7(4):312–318. doi: 10.1061/(ASCE)1084-0699(2002)7:4(312) CrossRefGoogle Scholar
  3. Boyle DP, Gupta H, Sorooshian S (2000) Toward improved calibration of hydrologic models: combining the strengths of manual and automatic methods. Water Resour Res 36(12):3663–3674. doi: 10.1029/2000WR900207 CrossRefGoogle Scholar
  4. Chu X, Steinman A (2009) Event and Continuous Hydrologic Modeling with HEC-HMS. J Irrig Drain E-ASCE 135(1):119–124. doi: 10.1061/(ASCE)0733-9437(2009)135:1(119) CrossRefGoogle Scholar
  5. Cloke H, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375(3–4):613–626. doi: 10.1016/j.jhydrol.2009.06.005 CrossRefGoogle Scholar
  6. Cunderlik J, Simonovic SP (2004) Calibration, verification and sensitivity analysis of the HEC-HMS hydrologic model. Water Resources Research Report, Book 11. ISBN (Online): 978–0–7714-2627-8. http://ir.lib.uwo.ca/wrrr/11. Accessed 20 February 2015
  7. Cuo L, Pagano T, Wang Q (2011) Review of quantitative precipitation forecasts and their use in short- to medium-range streamflow forecasting. J Hydrometeorol 12:713–728. doi: 10.1175/2011JHM1347.1 CrossRefGoogle Scholar
  8. Dariane AB, Javadianzadeh MM, James LD (2016) Developing an efficient auto-calibration algorithm for HEC-HMS program. Water Resour Manag 30:1923–1937. doi: 10.1007/s11269-016-1260-7 CrossRefGoogle Scholar
  9. Datorani MT, Khodaparast R, Talebi A, Vafakhah M, Dashti J (2011) Determination of the ability of HEC-HMS model components in rainfall-runoff simulation. Res J Environ Sci 5(10):790–797. doi: 10.3923/rjes.2011.790.797 CrossRefGoogle Scholar
  10. Deckers D, Booij M, Rientjes T, Krol M (2010) Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall–runoff model. Water Resour Manag 24(14):3961–3985. doi: 10.1007/s11269-010-9642-8 CrossRefGoogle Scholar
  11. Doherty J, Johnston JM (2003) Methodologies for calibration and predictive analysis of a watershed model. J Am Water Resour As 39(2):251–265. doi: 10.1111/j.1752–1688.2003.tb04381.x CrossRefGoogle Scholar
  12. Feldman, A, (2000). Hydrologic Modeling System HEC-HMS - Technical Reference Manual (CPD-74B). Hydrologic Engineering Center of U.S. Army Corps of Engineers. http://www.hec.usace.army.mil/software/hec-hms/documentation.aspx. Accessed 15 January 2015
  13. Fonseca A, Ames DP, Yang P, Botelho C, Boaventura R, Vilar V (2014) Watershed model parameter estimation and uncertainty in data-limited environments. Environ Model Softw 51:84–93. doi: 10.1016/j.envsoft.2013.09.023 CrossRefGoogle Scholar
  14. Gupta H, Sorooshian S (1998) Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resour Res 34(4):751–763. doi: 10.1029/97WR03495 CrossRefGoogle Scholar
  15. Gupta H, Sorooshian S, Yapo P (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143. doi: 10.1061/(ASCE)1084-0699(1999)4:2(135) CrossRefGoogle Scholar
  16. Krause P, Boyle DP, Base F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97. doi: 10.5194/adgeo-5-89-2005 CrossRefGoogle Scholar
  17. Madsen H (2003) Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Resour 26(2):205–2016. doi: 10.1016/S0309-1708(02)00092-1 CrossRefGoogle Scholar
  18. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50 (3): 885–900. http://handle.nal.usda.gov/10113/9298. Accessed 29 January 2015
  19. Neto JG, Ribeiro-Neto A, Montenegro SM (2014) Assessment of rainfall-runoff models for flood river extreme event simulations. In: Proceedings of the 6th International Conference on Flood Management (ICFM6). http://www.abrh.org.br/icfm6/proceedings/papers/PAP014409.pdf. Accessed 4 February 2015
  20. Nigussie TA, Altunkaynak A (2016) Assessing the hydrological response of Ayamama watershed from urbanization predicted under various Landuse policy scenarios. Water Resour Manag 30:3427–3441. doi: 10.1007/s11269-016-1360-4 CrossRefGoogle Scholar
  21. Nyeko M (2015) Hydrologic modelling of data Scarce Basin with SWAT model: capabilities and limitations. Water Resour Manag 29:81–94. doi: 10.1007/s11269-014-0828-3 CrossRefGoogle Scholar
  22. Refsgaard JC (1997) Parameterisation, calibration and validation of distributed hydrological models. J Hydrol 198:69–97. doi: 10.1016/S0022-1694(96)03329-X CrossRefGoogle Scholar
  23. Reshma T, Reddy KV, Pratap D, Ahmedi M, Agilan V (2015) Optimization of calibration parameters for an event based watershed model using genetic algorithm. Water Resour Manag 29:4589–4606. doi: 10.1007/s11269-015-1077-9 CrossRefGoogle Scholar
  24. Ritter A, Muñoz-Carpena R, (2013) Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol 480:33–45Google Scholar
  25. Shahid MA, Boccardo P, Usman M, Albanese A, Qamar MU (2016) Predicting peak flows in real time through event based hydrologic modeling for a trans-Boundary River catchment. Water Resour Manag:1–18. doi: 10.1007/s11269-016-1435-2
  26. Thampi SG, Raneesh KY, Surya TV (2010) Influence of scale on SWAT model calibration for streamflow in a River Basin in the humid tropics. Water Resour Manag 24(15):4567–4578. doi: 10.1007/s11269-010-9676-y CrossRefGoogle Scholar
  27. Thiessen AH (1911) Precipitation averages for large areas. Mon Weather Rev 39(7):1082–1084. doi: 10.1175/1520-0493(1911)39<1082b:PAFLA>2.0.CO;2 Google Scholar
  28. Tiwari M, Chatterjee C (2010) Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach. J Hydrol 394:458–470. doi: 10.1016/j.jhydrol.2010.10.001 CrossRefGoogle Scholar
  29. Werner M, Cranston M, Harrison T, Whitfield D, Schellekens J (2009) Recent developments in operational flood forecasting in England, Wales and Scotland. Meteorol Appl 16:13–22. doi: 10.1002/met.124 CrossRefGoogle Scholar
  30. WMO (2011) Manual on Flood Forecasting and Warning. World Meteorological Organization publication, WMO-No.1072, ISBN 978–92–63-11072-5.Google Scholar
  31. Yapo PO, Gupta HV, Sorooshian S (1996) Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J Hydrol 181:23–48. doi: 10.1016/0022-1694(95)02918-4 CrossRefGoogle Scholar
  32. Yilmaz A, Imteaz M, Ogwuda O (2010) Accuracy of HEC-HMS and LBRM Models in Simulating Snow Runoffs in Upper Euphrates Basin. J Irrig Drain E-ASCE 17(2):342–347. doi: 10.1061/(ASCE)HE.1943-5584.0000442 Google Scholar
  33. Zappa M, Jaun S, Germann U, Walser A, Fundel F (2011) Superposition of three sources of uncertainties in operational flood forecasting chains. Atmos Res 100:246–262. doi: 10.1016/j.atmosres.2010.12.005 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Water Resources and Environment Division, Civil Engineering DepartmentFaculty of Engineering of University of PortoPortoPortugal

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