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

Yongchan Kim; Youngran Kim; Seonghwan Hwang; Dongkyun Kim

doi:10.3741/JKWRA.2022.55.12.1115

All Issue

2022 Vol.55, Issue 12 Preview Page Next Page

Research Article

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 물리기반 분포형 수문 모형과 딥러닝 기반 LSTM 모형을 활용한 충주댐 및 소양강댐 유역의 미래 수자원 전망

31 December 2022. pp. 1115-1124

PDF XML

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

본 연구는 충주댐과 소양강댐 유역을 대상으로 분포형 수문모형인 Variable Infiltration Capacity (VIC) 모형 기반의 댐 유입량 예측 모형과 딥러닝 모형의 일종인 LSTM 기반의 댐 방류량 예측 모형으로 구성된 통합 모델링 프레임워크를 구성하여 미래 기후변화가 수자원에 미치는 영향을 평가하였다. 기후모델에 따른 미래 기후자료의 불확실성을 고려하여 4개의 CMIP6 GCM 모델의 기후자료를 미래기간(2021-2100)에 대한 VIC 모형의 기상자료로 입력하였다. 미래기후자료를 적용한 결과, 미래가 진행됨에 따라 기간별 평균 유입량이 증가하였으며, 먼 미래(2070-2100)에는 관측기간(1986-2020)에 비해 유입량이 최대 22% 증가하였다. 갈수량 분석 결과, 임의의 4일~50일에 대한 댐 방류량의 최소값은 관측치에 비해 현저히 낮은 것으로 나타났다. 이는 가뭄이 과거에 관측된 것보다 더 장기간에 걸쳐 발생할 수 있음을 나타내며, 수도권 시민들이 미래의 가뭄으로 인해 심각한 물 부족을 겪을 수 있다는 것을 의미한다. 또한, 단기 및 중기 미래에 비하여 장기미래에 저수량의 변화가 급격하게 이루어졌으며 이는 수자원 관리의 어려움이 증대될 수 있음을 시사한다.

키워드

기후변화

VIC 모형

LSTM

GCM

수자원

References

Bharat, S., and Mishra, V. (2021). "Runoff sensitivity of Indian sub-continental river basins." Science of The Total Environment, Vol. 766, 142642. 10.1016/j.scitotenv.2020.14264233059900

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. 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. 10.3390/w11030447

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. 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. 10.1007/s11269-017-1575-z

Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780. 10.1162/neco.1997.9.8.17359377276

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. 10.15531/ksccr.2018.9.1.47

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.

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.

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. 10.1007/s11269-015-0949-3

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. 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. 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. 10.1034/j.1600-0870.1996.t01-3-00009.x

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. 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. 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. 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. 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. 10.1016/j.jhydrol.2020.125206

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. 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. 10.1016/j.jhydrol.2018.08.050

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 55
No :12
Pages :1115-1124
Received Date : 2022-10-28
Revised Date : 2022-11-24
Accepted Date : 2022-11-25
DOI :https://doi.org/10.3741/JKWRA.2022.55.12.1115

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

All Issue