Skip to main content

Advertisement

SpringerLink
Log in

The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models

  • ORIGINAL PAPER
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Physically-based hydrologic models can accurately simulate flow discharge in natural environment, but they cannot precisely consider the anthropogenic disturbance caused by the operation of large-scale dams in a watershed. This study tried to overcome this issue by developing a hybrid modeling framework, consisting of physically-based models for simulating upstream natural watersheds and deep-learning-based models for simulating dam operation. The model was developed for the Paldang Dam watershed, a major water source for Seoul metropolitan area, where the importance of stable water supply has increased due to the increase of population and water use per capita. The prediction performance of the hybrid model was compared with that of models built based only on the physically-based hydrologic model, namely the Variable Infiltration Capacity model (VIC) model, with single and cascaded structure. For the validation period, Nash–Sutcliffe Efficiency from the developed hybrid model, the single model, and the cascaded model were 0.6410,  − 0.1054, and 0.2564, respectively, suggesting that the consideration of dam operation aided by the machine learning algorithm is essential for accurate assessment of river flow discharge and the subsequent water resources vulnerability. In order to evaluate the impact of climate change, future meteorological data under RCP4.5 scenario was used as an input for the hybrid model simulation, of which result revealed that the drought flow value (the 10th lowest daily flow over a year) with the return period of 10-year, 20-year, 50-year, 100-year, and 200-year in the far future (2071–2100) were projected to decrease by 22%, 28%, 37%, 43%, and 50%, respectively, compared to the near future (2021–2040), which calls for a proper drought mitigation measures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Abbaspour KC, Faramarzi M, Ghasemi SS et al (2009) Assessing the impact of climate change on water resources in Iran. Water Resour Res. https://doi.org/10.1029/2008WR007615

    Article  Google Scholar 

  • Bae D, Jung I, Lettenmaier DP (2011) Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju Basin, Korea. J Hydrol 401(1–2):90–105

    Article  Google Scholar 

  • Bae D, Koike T, Awan JA et al (2015) Climate change impact assessment on water resources and susceptible zones identification in the Asian monsoon region. Water Resour Manage 29(14):5377–5393

    Article  Google Scholar 

  • Bai Y, Chen Z, Xie J et al (2016) Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J Hydrol 532:193–206

    Article  Google Scholar 

  • Batisani N, Yarnal B (2010) Rainfall variability and trends in semi-arid Botswana: implications for climate change adaptation policy. Appl Geogr 30(4):483–489

    Article  Google Scholar 

  • Birkinshaw SJ, Guerreiro SB, Nicholson A et al (2017) Climate change impacts on Yangtze River discharge at the three Gorges Dam. Hydrol Earth Syst Sci 21(4):1911–1927

    Article  CAS  Google Scholar 

  • Bristow KL, Campbell GS (1984) On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric for Meteorol 31(2):159–166

    Article  Google Scholar 

  • Burke M, Jorde K, Buffington JM (2009) Application of a hierarchical framework for assessing environmental impacts of dam operation: changes in streamflow, bed mobility and recruitment of riparian trees in a western North American river. J Environ Manage 90:S224–S236

    Article  Google Scholar 

  • Buser CM, Künsch HR, Lüthi D et al (2009) Bayesian multi-model projection of climate: bias assumptions and interannual variability. Clim Dyn 33(6):849–868

    Article  Google Scholar 

  • Byun K, Hamlet AF (2020) A risk-based analytical framework for quantifying non-stationary flood risks and establishing infrastructure design standards in a changing environment. J Hydrol 584:124575

    Article  Google Scholar 

  • Byun K, Chiu C, Hamlet AF (2019) Effects of 21st century climate change on seasonal flow regimes and hydrologic extremes over the Midwest and Great Lakes region of the US. Sci Total Environ 650:1261–1277

    Article  CAS  Google Scholar 

  • Chen M, Papadikis K, Jun C (2021) An investigation on the non-stationarity of flood frequency across the UK. J Hydrol 597:126309

    Article  Google Scholar 

  • Cho H, Kim D, Olivera F et al (2011) Enhanced speciation in particle swarm optimization for multi-modal problems. Eur J Oper Res 213(1):15–23

    Article  Google Scholar 

  • Cho H, Park J, Kim D (2019) Evaluation of four GLUE likelihood measures and behavior of large parameter samples in ISPSO-GLUE for TOPMODEL. Water 11(3):447

    Article  Google Scholar 

  • Christian JI, Basara JB, Hunt ED et al (2021) Global distribution, trends, and drivers of flash drought occurrence. Nat Commun 12(1):1–11

    Article  Google Scholar 

  • Dan L, Ji J, Xie Z et al (2012) Hydrological projections of climate change scenarios over the 3H region of China: a VIC model assessment. J Geophys Res Atmosph. https://doi.org/10.1029/2011JD017131

    Article  Google Scholar 

  • Dang TD, Chowdhury A, 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. Hydrol Earth Syst Sci 24(1):397–416

    Article  Google Scholar 

  • Feizi H, Apaydin H, Sattari MT et al (2022) Improving reservoir inflow prediction via rolling window and deep learning-based multi-model approach: case study from Ermenek Dam, Turkey. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-022-02185-3

    Article  Google Scholar 

  • Fowler HJ, Lenderink G, Prein AF et al (2021) Anthropogenic intensification of short-duration rainfall extremes. Nat Rev Earth Environ 2(2):107–122

    Article  Google Scholar 

  • Greimel F, Schülting L, Graf W et al (2018) Hydropeaking impacts and mitigation. Riverine Ecosys Manag 8:91–110

    Article  Google Scholar 

  • Gutenson JL, Tavakoly AA, Wahl MD et al (2020) Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps. Hydrol Earth Syst Sci 24(5):2711–2729

    Article  Google Scholar 

  • Haddeland I, Skaugen T, Lettenmaier DP (2006) Anthropogenic impacts on continental surface water fluxes. Geophys Res Lett. https://doi.org/10.1029/2006GL026047

    Article  Google Scholar 

  • Hamlet AF, Elsner MM, Mauger GS et al (2013) An overview of the columbia basin climate change scenarios project: approach, methods, and summary of key results. Atmos Ocean 51(4):392–415

    Article  CAS  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  CAS  Google Scholar 

  • Hunt KM, Matthews GR, Pappenberger F et al (2022) Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States. Hydrology and Earth System Sciences Discussions, pp 1–30

  • 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.

  • Jiang S, Zhou L, Ren L et al (2021) Development of a comprehensive framework for quantifying the impacts of climate change and human activities on river hydrological health variation. J Hydrol 600:126566

    Article  Google Scholar 

  • Jiongxin X (1996) Underlying gravel layers in a large sand bed river and their influence on downstream-dam channel adjustment. Geomorphology 17(4):351–359

    Article  Google Scholar 

  • Kim D, Kang S (2021) Data collection strategy for building rainfall-runoff LSTM model predicting daily runoff. J Korea Water Res Associat 54(10):795–805

    Google Scholar 

  • Kim B, Kim B, Kwon H (2011) Assessment of the impact of climate change on the flow regime of the Han River basin using indicators of hydrologic alteration. Hydrol Process 25(5):691–704

    Article  Google Scholar 

  • Kim NW, Lee JE, Kim JT (2012) Assessment of flow regulation effects by dams in the Han River, Korea, on the downstream flow regimes using SWAT. J Water Resour Plann Manage 138(1):24–35

    Article  Google Scholar 

  • Kimball JS, Running SW, Nemani R (1997) An improved method for estimating surface humidity from daily minimum temperature. Agric for Meteorol 85(1–2):87–98

    Article  Google Scholar 

  • Kratzert F, Klotz D, Brenner C et al (2018) Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrol Earth Syst Sci 22(11):6005–6022

    Article  Google Scholar 

  • Krause P, Boyle DP, Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97

    Article  Google Scholar 

  • Lee M, Bae D, Im E (2019) Effect of the horizontal resolution of climate simulations on the hydrological representation of extreme low and high flows. Water Resour Manage 33(13):4653–4666

    Article  Google Scholar 

  • Lee M, Qiu L, Ha S et al (2022) Future projection of low flows in the Chungju basin, Korea and their uncertainty decomposition. Int J Climatol 42(1):157–174

    Article  Google Scholar 

  • Liang X, Lettenmaier DP, Wood EF et al (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res: Atmosph 99(D7):14415–14428

    Article  Google Scholar 

  • Liu J, Yang H, Gosling SN et al (2017) Water scarcity assessments in the past, present, and future. Earth’s Future 5(6):545–559

    Article  Google Scholar 

  • Lohmann D, Nolte-Holube R, Raschke E (1996) A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus A 48(5):708–721

    Article  Google Scholar 

  • Murray FW (1966) On the computation of saturation vapor pressure. Rand Corp Santa Monica Calif

  • Myhre G, Alterskjær K, Stjern CW et al (2019) Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci Rep 9(1):1–10

    Article  CAS  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Nijssen B, Lettenmaier DP, Liang X et al (1997) Streamflow simulation for continental-scale river basins. Water Resour Res 33(4):711–724

    Article  Google Scholar 

  • Nijssen B, O’Donnell GM, Hamlet AF et al (2001) Hydrologic sensitivity of global rivers to climate change. Clim Change 50(1):143–175

    Article  CAS  Google Scholar 

  • Ortiz-Bobea A, Ault TR, Carrillo CM et al (2021) Anthropogenic climate change has slowed global agricultural productivity growth. Nat Clim Chang 11(4):306–312

    Article  Google Scholar 

  • Oubeidillah AA, Kao S, Ashfaq M et al (2014) A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US. Hydrol Earth Syst Sci 18(1):67–84

    Article  Google Scholar 

  • Pascale S, Kapnick SB, Delworth TL et al (2020) Increasing risk of another Cape Town “Day Zero” drought in the 21st century. Proc Natl Acad Sci 117(47):29495–29503

    Article  CAS  Google Scholar 

  • Rahimzad M, Moghaddam Nia A, Zolfonoon H et al (2021) Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting. Water Resour Manage 35(12):4167–4187

    Article  Google Scholar 

  • Salathé EP Jr (2003) Comparison of various precipitation downscaling methods for the simulation of streamflow in a rainshadow river basin. Int J Climatol: A J Royal Meteorol Soci 23(8):887–901

    Article  Google Scholar 

  • Salathé EP Jr, Hamlet AF, Mass CF et al (2014) Estimates of twenty-first-century flood risk in the Pacific Northwest based on regional climate model simulations. J Hydrometeorol 15(5):1881–1899

    Article  Google Scholar 

  • Shon T, Lee S, Kim S et al (2010) An analysis of the effect of climate change on flow in Nakdong river basin using watershed-based model. J Korea Wat Resour Associat 43(10):865–881

    Article  Google Scholar 

  • Silva DF, Simonovic SP, Schardong A et al (2021) Assessment of non-stationary IDF curves under a changing climate: case study of different climatic zones in Canada. J Hydrol: Reg Stud 36:100870

    Google Scholar 

  • Song YH, Chung E, Shahid S (2021) Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation model s. Int J Climatol 41(13):5899–5919

    Article  Google Scholar 

  • Song YH, Chung E, Shahid S (2022a) Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios. Sci Total Environ 838:156162

    Article  CAS  Google Scholar 

  • Song YH, Chung E, Shahid S (2022b) Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea. Sci Total Environ 825:153953

    Article  CAS  Google Scholar 

  • Tennessee Valley Authority (1972) Heat and mass transfer between a water surface and the atmosphere. Water resources research laboratory report 14, Report No. 0–6803. Norris-Tennessee

  • Thornton PE, Running SW (1999) An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric for Meteorol 93(4):211–228

    Article  Google Scholar 

  • Tian D, He X, Srivastava P et al (2022) A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information. Stoch Env Res Risk Assess 36(8):2353–2375

    Article  Google Scholar 

  • UN office for disaster risk reduction (2020) The human cost of disasters: an overview of the Last 20 Years (2000–2019)

  • Wang GQ, Zhang JY, Jin JL et al (2012) Assessing water resources in China using PRECIS projections and a VIC model. Hydrol Earth Syst Sci 16(1):231–240

    Article  Google Scholar 

  • Wu CH, Huang GR, Yu HJ (2015) Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China. Hydrol Earth Syst Sci 19(3):1385–1399

    Article  Google Scholar 

  • WWAP U (2020) The United Nations world water development report 2020: Water and climate change

  • Zhao Y, Xu K, Dong N et al (2022) Projection of climate change impacts on hydropower in the source region of the Yangtze River based on CMIP6. J Hydrol 606:127453

    Article  Google Scholar 

Download references

Funding

This research was supported by the Basic Research Laboratory Program (Grant No. 2022R1A4A3032838) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hongik University, Seoul, South Korea

    Yongchan Kim & Dongkyun Kim

  2. Department of Civil Engineering, Seoul National University of Science and Technology, Seoul, South Korea

    Eun-Sung Chung

  3. Department of Civil Engineering, New Mexico State University, Las Cruces, NM, 88003, USA

    Huidae Cho

  4. Department of Environmental Engineering, Incheon National University, Incheon, South Korea

    Kyuhyun Byun

Authors
  1. Yongchan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eun-Sung Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huidae Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyuhyun Byun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dongkyun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YK Software, Validation, Formal analysis, Investigation, Data Curation, Writing – Original Draft, Visualization; ESC Validation, Investigation, Data Curation, Formal analysis, Investigation, Writing—Review & Editing; HC Conceptualization, Methodology, Formal analysis, Investigation, Writing—Review & Editing; KB Conceptualization, Methodology, Formal analysis, Investigation, Writing—Review & Editing; DK Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing—Review & Editing, Visualization, Supervision, Project administration, Funding acquisition.

Corresponding authors

Correspondence to Kyuhyun Byun or Dongkyun Kim.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, Y., Chung, ES., Cho, H. et al. The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models. Stoch Environ Res Risk Assess 37, 1777–1798 (2023). https://doi.org/10.1007/s00477-022-02366-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-022-02366-0

Keywords

Search

Navigation