과제정보
본 논문은 서울기술연구원(2022-AC-003, 서울특별시 물 재이용 관리계획 수립 용역)의 지원을 받아 수행된 연구입니다.
참고문헌
- Bharat, S., and Mishra, V. (2021). "Runoff sensitivity of Indian subcontinental river basins." Science of The Total Environment, Vol. 766, 142642.
- 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. https://doi.org/10.1016/j.ejor.2011.02.026
- 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.
- 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. https://doi.org/10.5194/hess-24-397-2020
- 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.
- 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.
- Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- 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.
- 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.
- 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. https://doi.org/10.3741/JKWRA.2021.54.10.795
- 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. https://doi.org/10.17663/JWR.2019.21.s-1.51
- 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.
- 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. https://doi.org/10.9798/KOSHAM.2017.17.1.365
- 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. https://doi.org/10.1029/94JD00483
- 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. https://doi.org/10.3402/tellusa.v48i5.12200
- 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. https://doi.org/10.1016/0022-1694(70)90255-6
- 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. https://doi.org/10.1023/A:1010616428763
- 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. https://doi.org/10.3741/JKWRA.2014.47.2.135
- 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.
- 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. https://doi.org/10.5194/hess-16-231-2012
- 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.
- 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. https://doi.org/10.1002/2015WR017394
- 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. https://doi.org/10.1016/j.jhydrol.2018.08.050