ERM model analysis for adaptation to hydrological model errors

Research Article - Special Issue
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Abstract

Hydrological conditions are changed continuously and these phenomenons generate errors on flood forecasting models and will lead to get unrealistic results. Therefore, to overcome these difficulties, a concept called model updating is proposed in hydrological studies. Real-time model updating is one of the challenging processes in hydrological sciences and has not been entirely solved due to lack of knowledge about the future state of the catchment under study. Basically, in terms of flood forecasting process, errors propagated from the rainfall-runoff model are enumerated as the main source of uncertainty in the forecasting model. Hence, to dominate the exciting errors, several methods have been proposed by researchers to update the rainfall-runoff models such as parameter updating, model state updating, and correction on input data. The current study focuses on investigations about the ability of rainfall-runoff model parameters to cope with three types of existing errors, timing, shape and volume as the common errors in hydrological modelling. The new lumped model, the ERM model, has been selected for this study to evaluate its parameters for its use in model updating to cope with the stated errors. Investigation about ten events proves that the ERM model parameters can be updated to cope with the errors without the need to recalibrate the model.

Keywords

Real-time model updating Forecasting errors Concentration time Time to peak 

References

  1. Arnell NW (1999) The effect of climate change on hydrological regimes in Europe: a continental perspective. Glob Environ Change 9(1):5–23CrossRefGoogle Scholar
  2. Bartholmes J, Todini E (2005) Coupling meteorological and hydrological models for flood forecasting. Hydrol Earth Syst Sci Discuss 9(4):333–346CrossRefGoogle Scholar
  3. Baymani-Nezhad M, Han D (2013) Hydrological modeling using effective rainfall routed by the Muskingum method (ERM). J Hydroinform 15(4):1437–1455CrossRefGoogle Scholar
  4. Beven K (1993) Prophecy, reality and uncertainty in distributed hydrological modeling. Adv Water Resour 16:41–51CrossRefGoogle Scholar
  5. Brath A, Montanari A, Toth E (2002) Neural networks and nonparametric methods for improving real-time flood forecasting through conceptual hydrological models. Hydrol Earth Syst Sci Discuss 6(4):627–639CrossRefGoogle Scholar
  6. Charlton R, Fealy R, Moore S, Sweeney J, Murphy C (2006) Assessing the impact of climate change on water supply and flood hazard in Ireland using statistical downscaling and hydrological modelling techniques. Clim Change 74(4):475–491CrossRefGoogle Scholar
  7. Chiew F, Whetton P, McMahon T, Pittock A (1995) Simulation of the impacts of climate change on runoff and soil moisture in Australian catchments. J Hydrol 167(1):121–147CrossRefGoogle Scholar
  8. Dibike YB, Coulibaly P (2005) Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J Hydrol 307(1):145–163CrossRefGoogle Scholar
  9. Fang X, Thompson DB, Cleveland TG, Pradhan P, Malla R (2008) Time of concentration estimated using watershed parameters determined by automated and manual methods. J Irrig Drain Eng 134(2):202–211CrossRefGoogle Scholar
  10. Fu B, Wang J, Chen L, Qiu Y (2003) The effects of land use on soil moisture variation in the Danangou catchment of the Loess Plateau, China. Catena 54(1):197–213CrossRefGoogle Scholar
  11. Hagg W, Braun L, Kuhn M, Nesgaard T (2007) Modelling of hydrological response to climate change in glacierized Central Asian catchments. J Hydrol 332(1):40–53CrossRefGoogle Scholar
  12. Haktanir T, Sezen N (1990) Suitability of two-parameter gamma and three-parameter beta distributions as synthetic unit hydrographs in Anatolia. Hydrol Sci J 35(2):167–184CrossRefGoogle Scholar
  13. Han D (2011) Flood risk assessment and management. Bentham Science PublishersGoogle Scholar
  14. Izzard CF, Hicks W (1946) Hydraulics of runoff from developed surfaces. Highway Res Board Proc 26:129–150Google Scholar
  15. Johnstone D, Cross WP (1949) Elements of applied hydrology. Ronald Press Company, New YorkGoogle Scholar
  16. Kirpich Z (1940) Time of concentration of small agricultural watersheds. Civ Eng 10(6):362Google Scholar
  17. Lardet P, Obled C (1994) Real-time flood forecasting using a stochastic rainfall generator. J Hydrol 162(3):391–408CrossRefGoogle Scholar
  18. McCalla GR, Blackburn WH, Merrill LB (1984) “Effects of live stock grazing on infiltration rates”. Edwards Plateau of Texas. J Range Manag 37:265–269CrossRefGoogle Scholar
  19. McCuen RH, Spiess JM (1995) Assessment of kinematic wave time of concentration. J Hydraul Eng 121(3):256–266CrossRefGoogle Scholar
  20. Morgali J, Linsley RK (1965) Computer analysis of overland flow. J Hydraul Div 91(3):81–100Google Scholar
  21. Nguyen M, Sheath G, Smith C, Cooper A (1998) Impact of cattle treading on hill land: 2. Soil physical properties and contaminant runoff. N Z J Agric Res 41(2):279–290CrossRefGoogle Scholar
  22. Penning-Rowsell EC, Tunstall SM, Tapsell S, Parker DJ (2000) The benefits of flood warnings: real but elusive, and politically significant. Water Environ J 14(1):7–14CrossRefGoogle Scholar
  23. USDA SCS (US Department of Agricluture Soil Conservation Service) (1986) Urban hydrology for small watersheds, 2nd edn. Technical Release 55Google Scholar
  24. Wilby R, Greenfield B, Glenny C (1994) A coupled synoptichydrological model for climate change impact assessment. J Hydrol 153(1):265–290CrossRefGoogle Scholar
  25. Wong TS, Chen C-N (1997) Time of concentration formula for sheet flow of varying flow regime. J Hydrol Eng 2(3):136–139CrossRefGoogle Scholar
  26. Xu C-Y (1999) Climate change and hydrologic models: a review of existing gaps and recent research developments. Water Resour Manag 13(5):369–382CrossRefGoogle Scholar
  27. Yakowitz S (1985) Markov flow models and the flood warning problem. Water Resour Res 21(1):81–88CrossRefGoogle Scholar
  28. Yang X, Michel C (2000) Flood forecasting with a watershed model: a new method of parameter updating. Hydrol Sci J 45(4):537–546CrossRefGoogle Scholar
  29. Younis J, Anquetin S, Thielen J (2008) The benefit of high resolution operational weather forecasts for flash flood warning. Hydrol Earth Syst Sci Discuss Discuss 5(1):345–377CrossRefGoogle Scholar
  30. Yu P-S, Chen S-T (2005) Updating real-time flood forecasting using a fuzzy rule-based model/mise a Jour de Prevision de Crue en Temps Reel Grace a un Modele a Base de Regles Floues. Hydrol Sci J 50(2):265–278CrossRefGoogle Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringKaraj Branch Islamic Azad UniversityKarajIran
  2. 2.Water and Environmental Manager CenterUniversity of BristolBristolUK

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