Abstract
Flow simulation and forecasting accuracy using rainfall-runoff models is one of the main challenges in hydrological modelling, especially when focused on the flow simulation at a short time scale. These uncertainties are typically dependent on the model design, the technique used for estimating model parameters, the process of considering rainfall variability and errors in the discharge data used for the calibration. Indeed, the uncertainty associated with the discharge derived from the rating curve is ignored in many earlier rainfall-runoff models. In this paper, we provide a quantitative approach to rigorously investigate the effect that the rating curve uncertainty model has on the auto-calibration of Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model at hourly time scale. The multisegment BaRatin rating curve, based on the Bayesian analysis, was used to construct the most probable (MaxPost) rating curve with the bounds uncertainty for hydrometric station of Allala watershed. This allows establishing a new discharge hydrograph with its uncertainty bounds that are subsequently used in HEC-HMS calibration, to provide model parameters with confidence interval and to evaluate the model prediction accuracy. In HEC-HMS model, soil conservation service-curve number (SCS-CN) was applied to compute the runoff losses, while the SCS unit hydrograph (SCS-UH) method was used to estimate the direct runoff at the basin outlet. The model calibration process was carried out for the flood event of 2002 using four objective functions and validated for three independent events. We found that the calibration of the initial abstraction values (IA) varied between −15.16% and 20% when assessing the uncertainties associated with the rating curve, whereas the calibrated curve number values (CN) varied between −5.18% and 7.8%. The confidence interval for the CN and IA were extended from 65.71 to 74.81 and from 26.95 to 19.08, respectively. Results highlighted that the rating curve uncertainty has significant impact on the HEC-HMS model calibration parameters. Rigorous consideration of this uncertainty can improve considerably the model ability to predict the hourly discharge hydrographs.
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References
Andréassian V, Oddos A, Michel C, Anctil F, Perrin C, Loumagne C (2004) Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall-runoff models: A theoretical study using chimera watersheds. Water Resources Research 40(5), DOI: https://doi.org/10.1029/2003WR002854
Aronica GT, Candela A, Viola F, Cannarozzo M (2006) Influence of rating curve uncertainty on daily rainfall-runoff model predictions. In: Predictions in ungauged basins: promise and progress. IAHS Press, Wallingford, UK
Cunderlik J, Simonovic SP (2004) Calibration, verification and sensitivity analysis of the HEC-HMS hydrologic model. The University of Western Ontario, London, ON, Canada
Dariane AB, Javadianzadeh MM, James LD (2016) Developing an efficient auto-calibration algorithm for HEC-HMS program. Water Resources Management 30(6):1923–1937, DOI: https://doi.org/10.1007/s11269-016-1260-7
Di Baldassarre G, Montanari A (2009) Uncertainty in river discharge observations: A quantitative analysis. Hydrology & Earth System Sciences 13(6), DOI: https://doi.org/10.5194/hessd-6-39-2009
Dukić V, Erić R (2021) SHETRAN and HEC HMS model evaluation for runoff and soil moisture simulation in the Jičinka River Catchment (Czech Republic). Water 13(6):872, DOI: https://doi.org/10.3390/w13060872
Garcia R Costa V, Silva F (2020) Bayesian rating curve modeling: Alternative error model to improve low-flow uncertainty estimation. Journal of Hydrologic Engineering 25(5):04020012, DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0001903
Hossain F, Anagnostou EN, Dinku T, Borga M (2004) Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty. Hydrological Processes 18(17):3277–3291, DOI: https://doi.org/10.1002/hyp.5659
ISO/TS 25377 (2007) Hydrometric uncertainty guidance (HUG). ISO/TS 25377, International Organization for Standardization, Geneva, Switzerland
Kastali A, Zeroual A, Remaoun M, Serrano-Notivoli R Moramarco T (2021) Design flood and flood-prone areas under rating curve uncertainty: Area of Vieux-Ténès, Algeria. Journal of Hydrologic Engineering 26(3):05020054, DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0002049
Kiang JE, Gazoorian C, McMillan H, Coxon G, Le Coz J, Westerberg IK, Reitan T (2018) A comparison of methods for streamflow uncertainty estimation. Water Resources Research 54(10):7149–7176, DOI: https://doi.org/10.1029/2018WR022708
Knebl MR, Yang ZL, Hutchison K, Maidment DR (2005) Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: A case study for the San Antonio River Basin Summer 2002 storm event. Journal of Environmental Management 75(4): 325–336, DOI: https://doi.org/10.1016/j.jenvman.2004.11.024
Le Coz J, Chaléon C, Bonnifait L, Le Boursicaud R, Renard B, Branger F, Valente M (2013) Analyse bayésienne des courbes de tarage et de leurs incertitudes: La méthode BaRatin. La Houille Blanche (6):31–41
Le Coz J, Renard B, Bonnifait L, Branger F, Le Boursicaud R (2014) Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: A Bayesian approach. Journal of Hydrology 509:573–587, DOI: https://doi.org/10.1016/j.jhydrol.2013.11.016
Lobligeois F, Andréassian V, Perrin C, Tabary P, Loumagne C (2014) When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events. Hydrology and Earth System Sciences 18(2):575–594, DOI: https://doi.org/10.5194/hess-18-575-2014
McMillan H, Freer J, Pappenberger F, Krueger T, Clark M (2010) Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions. Hydrological Processes: An International Journal 24(10):1270–1284, DOI: https://doi.org/10.1002/hyp.7587
McMillan HK, Westerberg IK, Krueger T (2018) Hydrological data uncertainty and its implications. Wiley Interdisciplinary Reviews: Water 5(6):e1319, DOI: https://doi.org/10.1002/wat2.1319
Mishra SK, Singh VP, Singh PK (2018) Revisiting the soil conservation service curve number method. In: Singh V, Yadav S, Yadava R (eds) Hydrologic modeling. Springer, Singapore, 667–693
Montanari A (2004) An attempt to quantify uncertainty in observed river flows: Effect on parameterisation and performance evaluation of rainfall-runoff models. 2004 AGU fall meeting, December 13–17, San Francisco, CA, USA
Moyeed RA, Clarke RT (2005) The use of Bayesian methods for fitting rating curves, with case studies. Advances in Water Resources 28(8): 807–818, DOI: https://doi.org/10.1016/j.advwatres.2005.02.005
Petersen-Øverleir A, Soot A, Reitan T (2009) Bayesian rating curve inference as a streamflow data quality assessment tool. Water Resources Management 23(9):1835–1842
Piotrowski AP, Napiorkowski MJ, Napiorkowski JJ, Osuch M, Kundzewicz ZW (2017) Are modern metaheuristics successful in calibrating simple conceptual rainfall-runoff models? Hydrological Sciences Journal 62(4):606–625, DOI: https://doi.org/10.1080/02626667.2016.1234712
Rahman KU, Balkhair KS, Almazroui M, Masood A (2017) Sub-catchments flow losses computation using Muskingum-Cunge routing method and HEC-HMS GIS based techniques, case study of Wadi Al-Lith, Saudi Arabia. Modeling Earth Systems and Environment 3(1):4, DOI: https://doi.org/10.1007/s40808-017-0268-1
Schmidt A (2002) Analysis of stage-discharge relations for open-channel flows and their associated uncertainties. PhD Thesis, University of Illinois at Urbana-Champaign, Champaign, IL, USA
Sharafati A, Khazaei MR, Nashwan MS, Al-Ansari N, Yaseen ZM, Shahid S (2020) Assessing the uncertainty associated with flood features due to variability of rainfall and hydrological parameters. Advances in Civil Engineering 2020, DOI: https://doi.org/10.1155/2020/7948902
Sharma VC, Regonda SK (2021) Multi-spatial resolution rainfall-runoff modelling — A case study of Sabari river basin, India. Water 13(9): 1224, DOI: https://doi.org/10.3390/w13091224
Shrestha R Tachikawa Y, Takara K (2006) Input data resolution analysis for distributed hydrological modeling. Journal of Hydrology 319:36–50, DOI: https://doi.org/10.1016/j.jhydrol.2005.04.025
Sikorska AE, Renard B (2017) Calibrating a hydrological model in stage space to account for rating curve uncertainties: General framework and key challenges. Advances in Water Resources 105:51–66, DOI: https://doi.org/10.1016/j.advwatres.2017.04.011
US Army Corps of Engineers (2018) Hydrologic modeling system HEC-HMS, user’s manual, version 4.3. US Army Corps of Engineers Hydrologic Engineering Center, Davis, CA, USA
Wheater H, Sorooshian S, Sharma KD (2007) Hydrological modelling in arid and semi-arid areas. Cambridge University Press, Cambridge, UK
Zeroual A, Meddi M, Assani AA (2016) Artificial neural network rainfall-discharge model assessment under rating curve uncertainty and monthly discharge volume predictions. Water Resources Management 30(9):3191–3205, DOI: https://doi.org/10.1007/s11269-016-1340-8
Acknowledgments
The authors acknowledge the computational support of BaRatin software provided by the National Research Institute of Science and Technology for Environment and Agriculture (Irstea) at Villeurbanne, France. The authors appreciate the support from the National Agency of Water Resources Algeria by facilitating access to data required in this study. In addition, comments on the manuscript from anonymous referees are greatly acknowledged.
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Auto-calibration of HEC-HMS Model for Historic Flood Event under Rating Curve Uncertainty. Case Study: Allala Watershed, Algeria
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Kastali, A., Zeroual, A., Zeroual, S. et al. Auto-calibration of HEC-HMS Model for Historic Flood Event under Rating Curve Uncertainty. Case Study: Allala Watershed, Algeria. KSCE J Civ Eng 26, 482–493 (2022). https://doi.org/10.1007/s12205-021-1051-4
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DOI: https://doi.org/10.1007/s12205-021-1051-4