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http://dx.doi.org/10.3741/JKWRA.2022.55.12.1115

Prospect of future water resources in the basins of Chungju Dam and Soyang-gang Dam using a physics-based distributed hydrological model and a deep-learning-based LSTM model  

Kim, Yongchan (Department of Civil Engineering, Hongik University)
Kim, Youngran (Technology Development Headquarter, Seoul Institute of Technology)
Hwang, Seonghwan (Technology Development Headquarter, Seoul Institute of Technology)
Kim, Dongkyun (Department of Civil Engineering, Hongik University)
Publication Information
Journal of Korea Water Resources Association / v.55, no.12, 2022 , pp. 1115-1124 More about this Journal
Abstract
The impact of climate change on water resources was evaluated for Chungju Dam and Soyang-gang Dam basins by constructing an integrated modeling framework consisting of a dam inflow prediction model based on the Variable Infiltration Capacity (VIC) model, a distributed hydrologic model, and an LSTM based dam outflow prediction model. Considering the uncertainty of future climate data, four models of CMIP6 GCM were used as input data of VIC model for future period (2021-2100). As a result of applying future climate data, the average inflow for period increased as the future progressed, and the inflow in the far future (2070-2100) increased by up to 22% compared to that of the observation period (1986-2020). The minimum value of dam discharge lasting 4~50 days was significantly lower than the observed value. This indicates that droughts may occur over a longer period than observed in the past, meaning that citizens of Seoul metropolitan areas may experience severe water shortages due to future droughts. In addition, compared to the near and middle futures, the change in water storage has occurred rapidly in the far future, suggesting that the difficulties of water resource management may increase.
Keywords
Climate change; VIC model; LSTM; GCM; Water resources;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Yang, T., Gao, X., Sorooshian, S., and Li, X. (2016). "Simulating California reservoir operation using the classification and regression tree algorithm combined with a shuffled cross validation scheme." Water Resources Research, Vol. 52, No. 3, pp. 1626-1651.   DOI
2 Zhang, D., Lin, J., Peng, Q., Wang, D., Yang, T., Sorooshian, S., Liu, X., and Zhuang, J. (2018). "Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm." Journal of Hydrology, Vol. 565, pp. 720-736.   DOI
3 Bharat, S., and Mishra, V. (2021). "Runoff sensitivity of Indian subcontinental river basins." Science of The Total Environment, Vol. 766, 142642.
4 Cho, H., Kim, D., Olivera, F., and Guikema, S.D. (2011). "Enhanced speciation in particle swarm optimization for multi-modal problems." European Journal of Operational Research, Vol. 213, No. 1, pp. 15-23.   DOI
5 Cho, H., Park, J., and Kim, D. (2019). "Evaluation of four GLUE likelihood measures and behavior of large parameter samples in ISPSO-GLUE for TOPMODEL." Water, Vol. 11, No. 3, 447.
6 Dang, T.D., Chowdhury, A.F.M., and Galelli, S. (2020). "On the representation of water reservoir storage and operations in large-scale hydrological models: Implications on model parameterization and climate change impact assessments." Hydrology and Earth System Sciences, Vol. 24, No. 1, pp. 397-416.   DOI
7 Do, Y., and Kim, G. (2018). "Analysis of the change of dam inflow and evapotranspiration in the Soyanggang Dam basin according to the AR5 climate change scenarios." Journal of the Korean Society of Agricultural Engineers, Vol. 60, No. 1, pp. 89-99.
8 Gohari, A., Mirchi, A., and Madani, K. (2017). "System dynamics evaluation of climate change adaptation strategies for water resources management in central Iran." Water Resources Management, Vol. 31, No. 5, pp. 1413-1434.
9 Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780.   DOI
10 Intergovernmental Panel on Climate Change (IPCC) (2021). Climate change 2021: The physical science basis. contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK and New York, NY, U.S., pp. 3-32.
11 Kim, C.G., Park, J.H., and Cho, J. (2018). "Future climate change impact assessment of chungju dam inflow considering selection of GCMs and downscaling technique." Journal of Climate Change Research, Vol. 9, No. 1, pp. 47-58.
12 Kim, D., and Kang, S. (2021). "Data collection strategy for building rainfall-runoff LSTM model predicting daily runoff." Journal of Korea Water Resources Association, Vol, 54, No. 10, pp. 795-805.   DOI
13 Kim, S., Kim, H., Jung, Y., and Heo, J.H. (2019). "Assessment of frequency analysis using daily rainfall data of HadGEM3-RA climate model." Journal of Wetlands Research, Vol. 21, No. spc, pp. 51-60.   DOI
14 Lee, M.H., and Bae, D.H. (2015). "Climate change impact assessment on green and blue water over Asian monsoon region." Water Resources Management, Vol. 29, No. 7, pp. 2407-2427.
15 Nash, J.E., and Sutcliffe, J.V. (1970). "River flow forecasting through conceptual models part I - A discussion of principles." Journal of Hydrology, Vol. 10, No. 3, pp. 282-290.   DOI
16 Lee, O.J., Jo, D.J., and Kim, S.D. (2017). "Future PMP estimation of Chungjudam watershed under KMA climate change scenarios." Journal of the Korean Society of Hazard Mitigation, Vol. 17, No. 1, pp. 365-373.   DOI
17 Liang, X., Lettenmaier, D.P., Wood, E.F., and Burges, S.J. (1994). "A simple hydrologically based model of land surface water and energy fluxes for general circulation models." Journal of Geophysical Research: Atmospheres, Vol. 99, No. D7, pp. 14415-14428.   DOI
18 Lohmann, D.A.G., Nolte-Holube, R.A.L.P.H., and Raschke, E. (1996). "A large-scale horizontal routing model to be coupled to land surface parametrization schemes." Tellus A, Vol. 48, No. 5, pp. 708-721.   DOI
19 Nijssen, B., O'Donnell, G.M., Hamlet, A.F., and Lettenmaier, D.P. (2001). "Hydrologic sensitivity of global rivers to climate change." Climatic Change, Vol. 50, No. 1, pp. 143-175.   DOI
20 Park, J., Kwon, J.H., Kim, T., and Heo, J.H. (2014). "Future inflow simulation considering the uncertainties of TFN model and GCMs on Chungju Dam basin." Journal of Korea Water Resources Association, Vol. 47, No. 2, pp. 135-143.   DOI
21 Song, Y.H., Chung, E.S., and Sung, J.H. (2019). "Selection framework of representative general circulation models using the selected best bias correction method." Journal of Korea Water Resources Association, Vol. 52, No. 5, pp. 337-347.
22 Wang, G.Q., Zhang, J.Y., Jin, J.L., Pagano, T.C., Calow, R., Bao, Z.X., Liu, C.S., Liu, Y.L. and Yan, X.L (2012). "Assessing water resources in China using PRECIS projections and a VIC model." Hydrology and Earth System Sciences, Vol. 16, No. 1, pp. 231-240.   DOI
23 Yang, S., Yang, D., Chen, J., Santisirisomboon, J., Lu, W., and Zhao, B. (2020). "A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data." Journal of Hydrology, Vol. 590, 125206.