Skip to main content
Log in

A case study: bivariate drought identification on the Andong dam, South Korea

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Identifying hydrological drought in reservoirs is a significant challenge in water resource management. In South Korea, drought in reservoirs has historically been identified and managed through measuring storage amount. However, drought identification in accordance with storage amount has a possibility of failure to secure enough time for proper drought countermeasures. This study aims to suggest supplementary criteria for quantitatively identifying drought based on storage amount and water balance—specifically to allow more time to implement countermeasures against drought on the Andong dam. The standardized balanced index (SBI) was newly suggested to consider dry/wet conditions of dam, and the Standardized storage volume index (SSVI) was employed to consider storage amount. The proper duration of each index was estimated to be 4-month for SBI and 9-month for SSVI. A bivariate drought identification diagram using both index and copula function was derived and applied to the Andong dam. The diagram identified drought to occur 3.7 months earlier than the existing criteria which used storage amount to identify drought, and showed applicability for supplementary criteria.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Alley WM (1984) The Palmer drought severity index: limitations and assumptions. J Clim Appl Meteorol 23(7):1100–1109

    Google Scholar 

  • Beguería S, Vicente-Serrano SM, Reig F, Latorre B (2013) Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol 34(10):3001–3023

    Google Scholar 

  • Chang FJ, Chen L, Chang LC (2005) Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol Process 19(11):2277–2289

    Google Scholar 

  • Chen L, Singh VP, Guo S, Mishra AK, Guo J (2012) Drought analysis using copulas. J Hydrol Eng 18(7):797–808

    Google Scholar 

  • Cho J, Jung IW, Kim CG, Kim TG (2016) One-month lead dam inflow forecast using climate indices based on tele-connection. J Korea Water Resour Assoc. https://doi.org/10.3741/JKWRA.2016.49.5.361

    Article  Google Scholar 

  • Chou FNF, Wu CW, Lin CH (2006) Simulating multi-reservoir operation rules by network flow model. In: Operating reservoirs in changing conditions, 1st edn. ASCE, Virginia, pp. 335–344

  • Davidson J, Savic D, Walters G (2003) Symbolic and numerical regression: experiments and applications. Inf Sci 150(1–2):95–117

  • Dong X, Dohmen-Janssen CM, Booij M, Hulscher S (2006) Effect of flow forecasting quality on benefits of reservoir operation—a case study for the Geheyan reservoir (China). Hydrol Earth Syst Sci Dis 3:3771–3814

    Google Scholar 

  • El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam. Water Resour Manag 21(3):533–556

    Google Scholar 

  • El-Shafie A, Mukhlisin M, Najah AA, Taha MR (2011) Performance of artificial neural network and regression techniques for rainfall-runoff prediction. Int J Phys Sci 6(8):1997–2003

    Google Scholar 

  • Eslamian S, Eslamian FA (eds) (2017) Handbook of drought and water scarcity: environmental impacts and analysis of drought and water scarcity. CRC Press, Boca Raton

    Google Scholar 

  • Galeati G (1990) A comparison of para- metric and non-parametric methods for runoff forecastingA comparison of para- metric and non-parametric methods for runoff forecasting. Hydrol Sci J 35:79–94

    Google Scholar 

  • Gusyev MA, Hasegawa A, Magome J, Umino H, Sawano H (2015) Drought assessment in the Pampanga River basin, the Philippines. III: evaluating climate change impacts on dam infrastructure with standardized indices. In: Proceedings of the 21st international conference on modelling and simulation (MODSIM 2015), Modelling and Simulation Society of Australia and New Zealand, Inc., Canberra

  • Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm. J Am Water Resour Assoc 35(2):311–322

    Google Scholar 

  • Hao Z, AghaKouchak A, Nakhjiri N, Farahmand A (2014) Global integrated drought monitoring and prediction system. Sci Data 1(1):1–10

    Google Scholar 

  • Hao Z, Singh VP (2015) Drought characterization from a multivariate perspective: a review. J Hydrol 527:668–678

    Google Scholar 

  • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Google Scholar 

  • Huang WC, Chou CC (2008) Risk-based drought early warning system in reservoir operation. Adv Water Resour 31(4):649–660

    Google Scholar 

  • Jang TI, Park SW, Kim SM (2006) A methodology for the optimal dredge amount estimation to maintain the water supply capacity. In: ASAE annual meeting (1). American Society of Agricultural and Biological Engineers

  • Jeong DM, Bae DH (2004) Monthly dam inflow forecasts by using weather forecasting information. J Korea Water Resour Assoc. https://doi.org/10.3741/JKWRA.2004.37.6.449

    Article  Google Scholar 

  • Jung YH (2018) Dam inflow evaluation using hydrograph analysis. J Korean Soc Agric Eng. https://doi.org/10.5389/KSAE.2018.60.3.095

    Article  Google Scholar 

  • Kallis G (2008) Droughts. Annu Rev Environ Resour 33(1):85–118

    Google Scholar 

  • Kawade V, Kote H, Gangaji V, Kote AS (2019) Univariate time series prediction of reservoir inflow using artificial neural network. Training 80:75

    Google Scholar 

  • Kelman J, Stedinger JR, Cooper LA, Hsu E, Yuan SQ (1990) Sampling stochastic dynamic programming applied to reservoir operation. Water Resour Res 26:447–445

    Google Scholar 

  • Khorshidi MS, Nikoo MR, Sadegh M, Nematollahi B (2019) A multi-objective risk-based game theoretic approach to reservoir operation policy in potential future drought condition. Water Resour Manag 33(6):1999–2014

    Google Scholar 

  • Kim HS, Kim HS, Jeon GI, Gang SU (2016) Evaluation of the drought from 2014 to 2015 of Korea. Water Future 49(7):61–75. https://www.koreascience.or.kr/article/JAKO201628142625896.page

    CAS  Google Scholar 

  • Kim Y-O, Palmer RN (1997) Value of seasonal flow forecasts in Bayesian stochastic programming. J Water Resour Plan Manag 123(6):327–335

    Google Scholar 

  • Kim GS, Yim TK, Park CH (2009) Analysis of the secular trend of the annual and monthly precipitation amount of South Korea. Journal of the Korean Society of Hazard Mitig 9(6):17–30. https://www.koreascience.or.kr/article/JAKO200911764897686.do

    Google Scholar 

  • Kwak J, Kim D, Kim S, Singh VP, Kim H (2014) Hydrological drought analysis in Namhan river basin, Korea. J Hydrol Eng 19(8):05014001

    Google Scholar 

  • K-Water (2019) Working Reference on Dam Operations, Daejeon, Korea (in Korean)

  • K-Water (2020) Specification of the Andong Dam, K-Water.http://water.or.kr/realtime/sub01/sub01/dam/hydr.do?s_mid=1323&seq=1408&p_group_seq=1407&menu_mode=3&kwipHydrDamcd=2001110#tab-contentArea (in Korean)

  • Labadie JW, Bode DA, Pineda AM (1986) Network model for decision support in municipal raw water supply. J Am Water Resour Assoc 22(6):927–940

    Google Scholar 

  • Lall U, Bosworth K (1994) Multivariate kernel estimation of functions of space and time hydrologic data. In: Stochastic and statistical methods in hydrology and environmental engineering, 1st edn, Springer, Dordrecht, pp 301–315

  • Lange NT (1999) New mathematical approaches in hydrological modeling—an application of artificial neural networks. Phys Chem Earth Part B 24(1–2):31–35

    Google Scholar 

  • Liu S, Yan D, Wang H, Li C, Weng B, Qin T (2016) Standardized water budget index and validation in drought estimation of Haihe River Basin, North China. In: Advances in meteorology. https://doi.org/10.1155/2016/9159532

  • Lee JH, Jung JS, Han UW, Hwang BK (2010) River engineering. Goomi Library Corp, Seoul

  • Massey FJ Jr (1951) The Kolmogorov–Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78

    Google Scholar 

  • McKee TB, Doesken NJ, Kleist J (1985) Drought monitoring with multiple time scales. In: Proceedings of the 9th conference on applied climatology, Dallas, Texas, pp 233–236

  • Ministry of the Environment (2019) Adjustment regulation for water supplies in dams. Ministry of the Environment, Korea

    Google Scholar 

  • Mirabbasi R, Fakheri-Fard A, Dinpashoh Y (2012) Bivariate drought frequency analysis using the copula method. Theor Appl Climatol 108(1–2):191–206

    Google Scholar 

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216

    Google Scholar 

  • Moeeni H, Bonakdari H, Ebtehaj I (2017) Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. J Earth Syst Sci 126(2):18

    Google Scholar 

  • Mohammadi K, Eslami HR, Dardashti SD (2005) Comparison of regression, Arima and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj). J Agric Sci Technol 7:17–30

    Google Scholar 

  • Momiyama S, Sagehashi M, Akiba M (2018) Assessment of the climate change risks for inflow into Sagami Dam reservoir using a hydrological model. J Water Clim Change 11:367–379

    Google Scholar 

  • Nam WH, Choi JY, Jang MW, Hong EM (2013) Agricultural drought risk assessment using reservoir drought index. J Korean Soc Agric Eng. https://doi.org/10.5389/KSAE.2013.55.3.041

    Article  Google Scholar 

  • National Environment Information Network System (NEINS) Center (2020) The effect of climate change in South Korea. http://www.neins.go.kr/etr/climatechange/doc04b.asp. Accessed 2 Aug 2020 (in Korean)

  • Nelsen RB (2007) An introduction to copulas. Springer, Berlin

    Google Scholar 

  • Nohara D, Hori T, Sato Y (2018) Real-time reservoir operation for drought management considering operational ensemble predictions of precipitation in Japan. In: Advances in Hydroinformatics. Springer, Singapore, pp 331–345

    Google Scholar 

  • Rodriguez JC (2007) Measuring financial contagion: a copula approach. J Empir Finance 14(3):401–423

    Google Scholar 

  • Saad C, El Adlouni S, St-Hilaire A, Gachon P (2015) A nested multivariate copula approach to hydrometeorological simulations of spring floods: The case of the Richelieu River (Québec, Canada) record flood. Stoch Env Res Risk Assess 29(1):275–294

    Google Scholar 

  • Serinaldi F, Bonaccorso B, Cancelliere A, Grimaldi S (2009) Probabilistic characterization of drought properties through copulas. Phys Chem Earth Parts A/B/C 34(10–12):596–605

    Google Scholar 

  • Shafer BA (1982) Development of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. In: Proceedings of the 50th annual western snow conference. Fort Collins, Colorado State University

  • Sheffield J, Goteti G, Wen F, Wood EF (2004) A simulated soil moisture based drought analysis for the United States. J Geophys Res 109:D24

    Google Scholar 

  • Shiau JT (2006) Fitting drought duration and severity with two-dimensional copulas. Water Resour Manage 20(5):795–815

    Google Scholar 

  • Shiau JT, Modarres R (2009) Copula-based drought severity‐duration‐frequency analysis in Iran. Meteorol Appl J Forecast Pract Appl Train Tech Model 16(4):481–489

    Google Scholar 

  • Stedinger JR, Sule BF, Loucks DP (1984) Stochastic dynamic programming models for reservoir operation optimization. Water Resour Res 20(11):1499–1505

    Google Scholar 

  • Thomas HE (1962) The meteorological phenomenon of drought in the Southwest. US Government Printing Office

  • Tiwari D, Tiwari HL, Saini R (2018) Hydrological modelling in Narmada basin using remote sensing and GIS with SWAT model and runoff prediction in Patan watershed. Int J Adv Res Ideas Innovations Technol 4(2):344–352

    Google Scholar 

  • Turgeon A (2005) Solving a stochastic reservoir management problem with multi lag autocorrelated inflows. Water Resour Res 41:W12414. https://doi.org/10.1029/2004WR003846

  • Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35(2)

  • Walker WR, Hrezo MS, Haley CJ (1991) Management of water resources for drought conditions. US Geological Survey Water-Supply Paper 2375, pp 1988–1989

  • Wang Y, Guo S, Chen H, Zhou Y (2014) Comparative study of monthly inflow prediction methods for the three Gorges Reservoir. Stoch Env Res Risk Assess 28:555–570

    Google Scholar 

  • Wilhite DA, Glantz MH (1985) Understanding the drought phenomenon: the role of definitions. Water Int 10(3):111–120

    Google Scholar 

  • Wilhite DA (2000) Drought as a natural hazard: concepts and definitions. In: Drought. A global assessment. Routledge, New York

  • Svoboda M, Hayes M, Wood D (2012) Standardized precipitation index user guide. World Meteorological Organization, Geneva

    Google Scholar 

  • Wong G, Van Lanen HAJ, Torfs PJJF (2013) Probabilistic analysis of hydrological drought characteristics using meteorological drought. Hydrol Sci J 58(2):253–270

    Google Scholar 

  • Wu J, Chen X, Yao H, Gao L, Chen Y, Liu M (2017) Non-linear relationship of hydrological drought responding to meteorological drought and impact of a large reservoir. J Hydrol 551:495–507

    Google Scholar 

  • Wurbs RA (2005) Modeling river/reservoir system management, water allocation, and supply reliability. J Hydrol 300(1–4):100–113

    Google Scholar 

  • Xu ZX, Ito K, Liao S, Wang L (1997) Incorporating inflow uncertainty into risk assessment for reservoir operation. Stochast Hydrol Hydraul 11(5):433–448

    Google Scholar 

  • Yevjevich VM (1967) Objective approach to definitions and investigations of continental hydrologic droughts. An. Hydrology papers (Colorado State University); no. 23

  • Zhao G, Gao H (2019) Towards global hydrological drought monitoring using remotely sensed reservoir surface area. Geophys Res Lett 46(22):13027–13035

    Google Scholar 

  • Zhong Y, Guo S, Liu Z, Wang Y, Yin J (2018) Quantifying differences between reservoir inflows and dam site floods using frequency and risk analysis methods. Stoch Env Res Risk Assess 32(2):419–433

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B3005695).

Funding

This study was conducted with financial support from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B3005695).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jongso Lee.

Ethics declarations

Conflict of interest

The authors declare that there are no conflict of interests regarding the publication of this paper.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kwak, J., Joo, H., Jung, J. et al. A case study: bivariate drought identification on the Andong dam, South Korea. Stoch Environ Res Risk Assess 35, 549–560 (2021). https://doi.org/10.1007/s00477-020-01917-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-020-01917-7

Keywords

Navigation