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http://dx.doi.org/10.17663/JWR.2021.23.4.287

Estimation of sediment deposition rate in collapsed reservoirs(wetlands) using empirical formulas and multiple regression models  

Kim, Donghyun (Program in Smart City Engineering, Inha University)
Lee, Haneul (Program in Smart City Engineering, Inha University)
Bae, Younghye (Program in Smart City Engineering, Inha University)
Joo, Hongjun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Deokhwan (Construction Technology Safety Department, Korea Institute of Civil Engineering and Building Technology)
Kim, Hung Soo (Department of Civil Engineering, Inha University)
Publication Information
Journal of Wetlands Research / v.23, no.4, 2021 , pp. 287-295 More about this Journal
Abstract
As facilities such as dam reservoir wetlands and agricultural irrigation reservoir wetlands are built, sedimentation occurs over time through erosion, sedimentation transport, and sediment deposition. Sedimentation issues are very important for the maintenance of reservoir wetlands because long-term sedimentation of sediments affects flood and drought control functions. However, research on resignation has been estimated mainly by empirical formulas due to the lack of available data. The purpose of this study was to calculate and compare the sediment deposition rate by developing a multiple regression model along with actual data and empirical formulas. In addition, it was attempted to identify potential causes of collapse by applying it to 64 reservoir wetlands that suffered flood damage due to the long rainy season in 2020 due to reservoir wetland sedimentation and aging. For the target reservoir, 10 locations including the GaGog reservoir located in Miryang city, Gyeongsangnam province in South Korea, where there is actual survey information, were selected. A multiple regression model was developed in consideration of physical and climatic characteristics, and a total of four empirical formulas and sediment deposition rate were calculated. Using this, the error of the sediment deposition rate was compared. As a result of calculating the sediment deposition rate using the multiple regression model, the error was the lowest from 0.21(m3km2/yr) to 2.13(m3km2/yr). Therefore, based on the sediment deposition rate estimated by the multi-regression model, the change in the available capacity of reservoir wetlands was analyzed, and the effective storage capacity was found to have decreased from 0.21(%) to 16.56(%). In addition, the sediment deposition rate of the reservoir where the overflow damage occurred was relatively higher than that of the reservoir where the piping damage occurred. In other words, accumulating sediment deposition rate at the bottom of the reservoir would result in a lack of acceptable effective water capacity and reduced reservoir flood and drought control capabilities, resulting in reservoir collapse damage.
Keywords
Irrigation reservoir wetland; Sediment deposition rate; Empirical formula; Multiple regression models;
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  • Reference
1 Ackers, P. and White, W.R. (1973) Sediment Transport : A New Approach and Analysis J. of the Hydraulics Division, ASCE, vol. 99, no. HY11, Nov.[https://doi.org/10.1061/JYCEAJ.0003791]   DOI
2 Brune, G. N. (1953), Trap efficiency of reservoirs. Transactions of American Geophysical Union. Vol.34, No.3.[https://doi.org/10.1029/TR034i003p00407]   DOI
3 Choi, C. H., Kim, J. S., Kim, J. H., Kim, H. Y., Lee, W. J., and Kim, H.S. (2017). Development of heavy rain damage prediction function using statistical methodology. J. Korean Soc. Hazard Mitig., Vol. 17, No. 3, pp. 331-338.[https://doi.org/10.9798/KOSHAM.2017.17.3.331]   DOI
4 Choi, G. W., Kim, K. N., Han, M. S. and Yun, Y. J. (2011) The Analysis of Sediment Reduction Effect by Installing Check Dams at Domestic Multi-Purpose Dams. J. Korean Soc. Vol. 11, No. 3, pp. 183-189.[https://doi.org/10.9798/KOSHAM.2011.11.3.183]   DOI
5 Churchill, M. A. (1948) Discussion of analysis and use of reservoir sedimentation data. by L. C. Gottschalk, Federal Inter-Agency Sedimentation Conference. pp. 139-140.
6 Burns, M., and MacArthur, R(1996). Sediment deposition in Jennings Randolph Reservoir, Maryland and West Virginia", Proc., 6th Federal interagency Sedimentation Conf., Lasvegas, 10.16-1021.
7 Korea Water Resources Corporation. Construction methods of sand storage dam for reservoir sedimentation reduction, (2004).
8 Hiroshi Ishigai. "On the Amount of Bottom Sediments in Reservoirs" Journal of the Japan Society of Engineering Geology. Vol. 7, No. 4, pp. 173-190.[https://doi.org/10.5110/jjseg.7.173]   DOI
9 Jang, S. W., Hwang, P. S., Kim, K. H and Shin, Y. H. (2012) A Study on Estimation Method of Sediment Deposition Rate of Reservoir. Proceedings of the Korea Water Resources Association Conference. Korea Water Resources Association, 2012.
10 Kim, J. S., Choi, C. H., Kim, D. H., Lee, M. J., and Kim, H.S. (2017). Development of heavy rain damage prediction function using artificial neural network and multiple regression model. J. Korean Soc. Hazard Mitig., Vol. 17, No. 6, pp. 73-80.[https://doi.org/10.9798/KOSHAM.2017.17.6.73]   DOI
11 Lee, J. W., Paik, K. R. and Yoo, C. S (2016) Empirical equation for estimating specific sediment of the multipurpose dams in Korea. Proceedings of the Korea Water Resources Association Conference. Korea Water Resources Association, 2016.
12 Rubey, W. W. (1933). Settling Velocities of Gravel, Sand and Silt Particles, American J. of Science, 5th series, vol. 25, no. 148.
13 Ryu, H. J. and Kim, S. W. (1976) Study on Sedimentation in Reservoir. The Korea Water Resources Association. Vol. 9, No. 2, pp. 67-75.
14 Ghose, D. K., & Samantaray, S. (2019). Sedimentation process and its assessment through integrated sensor networks and machine learning process. In Computational intelligence in sensor networks (pp. 473-488). Springer, Berlin, Heidelberg.[DOI: 10.1007/978-3-662-57277-1_20]
15 Suh, S. D., Lim, H. I., Cheon, M. B. and Yoon, K. D. (1988) Regression Analysis Between Specific Sediments of Reservoirs and Physiographic Factors of Watersheds. Journal of the Korean Society of Agricultural Engineers. Vol. 30, No. 4, pp. 45-61.
16 Yoon, Y. N. (1981) Estimation of Silting Load and Capacity Loss Rate of Irrigation Reservoirs. Korean society of civil engineers magazine. Vol. 1, No. 1, pp. 69-76.
17 You, S. C. and Min, B. H. (1975) A Study for Sedimentation in Reservoir -on district of Chin Young-. Journal of the Korean Society of Agricultural Engineers. Vol. 17, No. 3, pp. 3840-3847.
18 Strand, R.I. and Pemberton, E.L.(1987) "Reservoir Sedimentation", In Design of Small Dams. U.S. Bureau of Reclamation, Denver.
19 Maatooq, J., Omran, H., & Aliwe, H. (2016). Empirical Formula for Estimation the Sediment Load in Shat AL-Gharaf River. Basrah Journal for Engineering Sciences, 16, 38-41.   DOI
20 Bhattacharya, B., & Solomatine, D. P. (2006). Machine learning in sedimentation modelling. Neural Networks, 19(2), 208-214.[DOI:10.1016/j.neunet.2006.01.007]   DOI
21 Morris, G.L. and Fan, J.(2009) Reservoir Sedimentation Handbook.
22 Kim, D. H., Choi, C. H., Kim, J. S., Joo, H. J., Kim, J. W., & Kim, H. S. (2018). Development of a heavy rain damage prediction function by risk classification. Journal of the Korean Society of Hazard Mitigation, 18(7), 503-512.[https://doi.org/10.9798/KOSHAM.2018.18.7.503]   DOI
23 Hair, Joseph F., William C. Black, Barry J. Babin, and Rolph E. Anderson. (2010) Multivariate Data Analysis, Englewood Cliffs, NJ: Prentice Hall.
24 Ahn, J. H., Jang, S. H., Choi, W. S. and Yoon, Y. N. (2006) An Efficient Management of Sediment Deposit for Reservoir Long-Term Operation (1) - Reservoir Sediment Estimation. Journal of Korean Society on Water Quality. Vol. 22, No. 6, pp. 1088-1093.