The Effects of Calibration Parameters in Muskingum Models on Flood Prediction Accuracy

Year 2023, Volume: 15 Issue: 1, 19 - 35, 03.05.2023

Olusegun Alabi , Abigail Olaoluwa , Samuel Sedara

https://doi.org/10.24107/ijeas.1204338

Abstract

Attenuation, time lag, outflow peak and storage are very essential factors required in flood risk prediction and flood pattern. However, the accurate prediction strongly depends on appropriate calibration of routine parameters of the model, such as weighting factor (x) and storage time constant (K). The weighting factor being used to determine storage time constant has not been given consideration in the previous studies and this could have led to inaccurate prediction in the past. In this work, a set of data obtained from an ungauged Awara river in Ondo State, Nigeria were used to test the effects of a weighting factor, x at levels ranging from 0.1-0.5 at interval of 0.1. The Muskingum model was used to obtain the storage and weighted discharge storage. It was observed that the correlation coefficient (R2) decreases with an increase in the weighting factor (x). This implies that there is a strong relationship between storage and weighted discharge storage at 0.1-0.3 levels of x while, the relationship is fair at 0.4-0.5 levels. It is therefore appropriate to choose a value of x ranging between 0.1 and 0.3 for attenuation prediction, while values of x ranging between 0.4 and 0.5 would be appropriate for accurate prediction of both outflow peak and storage.

Keywords

Muskingum model, Awara river, hydrograph, flood risk, Attenuation, outflow peak

References

• Sheshu, E.D., Manjunath, N., Karthik, S., Akash, U., Implementation of Flood Warning System using IoT. In 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), 445-448. 2018.
• Fernandez, D.S., Lutz, M.A., Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111, 1-4:90-98, 2010.
• Karley, N.K., Flooding and physical planning in urban areas in West Africa: situational analysis of Accra, Ghana. Theoretical and Empirical Researches in Urban Management, 4 (13), 25-41, 2009.
• Adebayo, W.O., Jegede, A.O., The Environmental Impact of Flooding on Transportation Landuse in Benin City, Nigeria. African Research Review, 390-400, 2010.
• Plate, E.J., Flood risk and flood management. Journal of hydrology, 267.1-2, 2-11, 2002.
• Sivakumar, V.K., Impacts of natural disasters in agriculture, rangeland and forestry: an overview. Natural disasters and extreme events in Agriculture, 1-22, 2005.
• Wu, H., Adler, R.F., Hong, Y., Tian, Y., Policelli, F., Evaluation of global flood detection using satellite-based rainfall and hydrologic model. Journal of Hydrometeorology, 13(4),1268-1284, 2012.
• Freer, J., Beven, K.J., Neal, J., Schumann, G., Hall, J., Bates, P., Flood Risk and Uncertainty. Risk and Uncertainty Assessment for Natural Hazards, Cambridge, UK.190-233, 2011.
• Carsell, K.M., Pingel, N.D., Ford, D.T., Quantifying the Benefit of a Flood Warning System. Natural Hazards Review, 5(3),131-140, 2004.
• Dennis, P., Tunstall, S., Wilson, T., Socio-economic benefits of flood forecasting and warning. Flood Hazard Research Centre, Middlesex University, Queensway, Enfield, London, UK, 2005.
• Yuan, L., Ryu, D., Western, A.W., Wang, Q.J., Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags. Water resources research, 49(4),1887-1900, 2013.
• Vieux, B.E., Cui, Z., Gaur, A., Evaluation of a physics-based distributed hydrologic model for flood forecasting. Journal of hydrology, 298(1-4), 155-177, 2004.
• Mendes, J., Maia, R., Hydrological Modelling Calibration for Operational Flood Forecasting. Water Resources Management, 30, 5671-5685, 2016.
• Alabi, O.O., Sedara, S.O., Adetoyinbo, A.A., Akinwande, D.D., Estimation of Outflow Discharge from an Ungauged River: Case Study of Awara in Ondo State, Southwestern Nigeria. FUTA J. of Research in Sci., 13(2), 343-349, 2017.
• Alabi, O.O., Olawale, S.O., Ogunbiyi, M.A., Sedara, S.O., Akinwande, D.D., Modeling the Flooding of Awara River in Ondo State, Southwestern Nigeria. Federal University Wukari (FUW) Trends in Sci. and Tech. J., 2(2):343-349, 2017.
• Barati, R., Parameter estimation of nonlinear Muskingum models using Nelder-Mead Simplex algorithm. J. Hydrologic Eng., 16(11), 946-954, 2011. Govinda, R., Ramana, M.K.V., Application of Muskingum Flood Routing Method for the Dhalegaon-Babli Reach of the Godavari, 512-516, 1989.
• Burgan, H.I., Icaga, Y., Flood Analysis Using Adaptive Hydraulics (ADH) Model in the Akarcay Basin, Teknik Dergi, 9029-9051, 2019, https://doi.org/10.18400/tekderg.416067.
• Pangali Sharma, T.P., Zhang, J., Khanal, N.R., Nepal, P., Pangali Sharma, B.P., Nanzad, L., Gautam, Y., Household Vulnerability to Flood Disasters among Tharu Community, Western Nepal. Sustainability, 14, 12386, 2022, https://doi.org/10.3390/su141912386.
• Mustafa, Y.M., Amin, M.S.M., Lee, T.S., Shariff, A.R.M., Evaluation of land development impact on a tropical watershed hydrology using remote sensing and GIS. Journal of spatial hydrology, 5(2), 2012.
• Zoppou, C., Reverse routing of flood hydrographs using level pool routing. J. Hydrologic Eng., 4(2),184-188, 1999.
• Ramírez, J.A., Prediction and modeling of flood hydrology and hydraulics. Inland flood hazards: Human, riparian and aquatic communities, 498, 2000.
• Chow, V.T., Maidment, D.R., Mays, L.W., Applied Hydrology. McGraw-Hill International Editions: Singapore, 1988.
• McCarthy, G.T., The unit hydrograph and flood routing, Conference of the North Atlantic Division, U.S. Army Corps of Engineers, 1938.
• Song, X., Kong, F., Zhao-Xia, Z.H.U., Application of Muskingum routing method with variable parameters in ungauged basin. Water Sci. Eng., 2011.
• UNIDO-RC-SHP, Detailed project report for Awara dam/Oyibo River Small Hydro Power Development, Ikare-Akoko, North East LGA Ondo State by UNIDO Regional Center for Small Hydro Power in Africa, Abuja, Nigeria, 2010.
• Moriasi, D. N., Arnold, J.G., Van Liew, M.W., Bingner, R. L., Harmel, R.D., Veith, T.L., Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900, 2007, doi:10.13031/2013.23153.
• Campforts, B., Vanacker, V., Vanderborght, J., Baken, S., Smolders, E., Govers, G., Simulating the mobility of meteoric 10 Be in the landscape through a coupled soil-hillslope model (Be2D). Earth and Planetary Science Letters. 439: 143–157, 2016.