Articles | Volume 29, issue 5
https://doi.org/10.5194/hess-29-1429-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-1429-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Exploring the value of seasonal flow forecasts for drought management in South Korea
Yongshin Lee
CORRESPONDING AUTHOR
School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, BS8 1TR, United Kingdom
Andres Peñuela
Department of Agronomy, Unidad de Excelencia María de Maeztu, University of Cordoba, 14071 Cordoba, Spain
Francesca Pianosi
School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, BS8 1TR, United Kingdom
Miguel Angel Rico-Ramirez
School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, BS8 1TR, United Kingdom
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Cited articles
Afshar, A., Mariño, M. A., Saadatpour, M., and Afshar, A.: Fuzzy TOPSIS multi-criteria decision analysis applied to Karun reservoirs system, Water Resour. Manage., 25, 545–563, https://doi.org/10.1007/s11269-010-9713-x, 2011.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration: Guidelines for computing crop water requirements, Irrigation and drainage paper 56, United Nations Food and Agriculture Organization, Rome, Italy, ISBN 92-5-104219-5, 1998.
Alley, R. B., Emanuel, K. A., and Zhang, F.: Advances in weather prediction, Science, 363, 342–344, https://doi.org/10.1126/science.aav7274, 2019.
Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over Europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, 2018.
Arsyah, U. I., Jalinus, N., Syahril, Ambiyar, Arsyah, R. H., and Pratiwi, M.: Analysis of the Simple Additive Weighting method in educational aid decision making, Turkish Journal of Computer and Mathematics Education, 12, 2389–2396, 2021.
Baker, S. A., Rajagopalan, B., and Wood, A. W.: Enhancing ensemble seasonal streamflow forecasts in the upper Colorado river basin using multi-model climate forecasts, J. Am. Water Resour. As., 57, 906–922, https://doi.org/10.1111/1752-1688.12960, 2021.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015.
Block, P.: Tailoring seasonal climate forecasts for hydropower operations, Hydrol. Earth Syst. Sci., 15, 1355–1368, https://doi.org/10.5194/hess-15-1355-2011, 2011.
Chen, C.-T.: Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Set. Syst., 114, 1–9, https://doi.org/10.1016/s0165-0114(97)00377-1, 2000.
Chiew, F. H. S., Zhou, S. L., and McMahon, T. A.: Use of seasonal streamflow forecasts in water resources management, J. Hydrol., 270, 135–144, https://doi.org/10.1016/s0022-1694(02)00292-5, 2003.
Chiu, W.-Y., Yen, G. G., and Juan, T.-K.: Minimum Manhattan distance approach to multiple criteria decision making in multiobjective optimization problems, IEEE T. Evolut. Comput., 20, 972–985, https://doi.org/10.1109/tevc.2016.2564158, 2016.
Copernicus: Climate Data Store, https://cds.climate.copernicus.eu/ (last access: 19 May 2024), 2024.
Crippa, N., Grillakis, M. G., Tsilimigkras, A., Yang, G., Giuliani, M., and Koutroulis, A. G.: Seasonal forecast-informed reservoir operation, Potential benefits for a water-stressed Mediterranean basin, Climate Services, 32, 100406–100406, https://doi.org/10.1016/j.cliser.2023.100406, 2023.
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016.
Das, I.: On characterizing the `knee' of the Pareto curve based on normal-boundary intersection, Struct. Optimization, 18, 107–115, 1999.
Day, G. N.: Extended streamflow forecasting using NWSRFS, J. Water Res. Pl., 111, 157–170, 1985.
Ehsani, N., Vörösmarty, C. J., Fekete, B. M., and Stakhiv, E. Z.: Reservoir operations under climate change: Storage capacity options to mitigate risk, J. Hydrol., 555, 435–446, https://doi.org/10.1016/j.jhydrol.2017.09.008, 2017.
Fan, F. M., Schwanenberg, D., Alvarado, R., Reis, A. A., Collischonn. W., and Naumman. S.: Performance of deterministic and probabilistic hydrological forecasts for the short-term optimization of a tropical hydropower reservoir, Water Resour. Manage., 30, 3609–3625, https://doi.org/10.1007/s11269-016-1377-8, 2016.
Ficchì, A., Raso, L., D Dorchies, Pianosi, F., Malaterre, P-O., van Overloop, P-J., and Jay-Allemand, M.: Optimal operation of the multireservoir system in the Seine River basin using deterministic and ensemble forecasts, J. Water Res. Pl., 142, 1–12, https://doi.org/10.1061/(asce)wr.1943-5452.0000571, 2016.
Fishburn, P. C.: Additive utilities with finite sets: Applications in the management sciences, Nav. Res. Logist. Q., 14, 1–13, https://doi.org/10.1002/nav.3800140102, 1967.
Giagkiozis, I. and Fleming, P. J.: Pareto front estimation for decision making, Evol. Comput., 22, 651–678, https://doi.org/10.1162/evco_a_00128, 2014.
Goldsmith, E. and Hildyard, N.: The Social and Environmental Effects of Large Dams, Random House, NY, ISBN 978-0871568489, 1984.
Goodarzi, M., Jabbarian Amiri, B., Azarneyvand, H., Khazaee, M., and Mahdianzadeh, N.: Assessing the performance of a hydrological Tank model at various spatial scales, Journal of Water Management Modeling, 29, 1–8, https://doi.org/10.14796/jwmm.c472, 2020.
Greuell, W., Franssen, W. H. P., Biemans, H., and Hutjes, R. W. A.: Seasonal streamflow forecasts for Europe – Part I: Hindcast verification with pseudo- and real observations, Hydrol. Earth Syst. Sci., 22, 3453–3472, https://doi.org/10.5194/hess-22-3453-2018, 2018.
Hurkmans, R. T. W. L., Hurk, B., Schmeits, M., Wetterhall, F., and Pechlivanidis, I. G.: Seasonal streamflow forecasting for fresh water reservoir management in the Netherlands: An assessment of multiple prediction systems, J. Hydrometeorol., 24, 1275–1290, https://doi.org/10.1175/jhm-d-22-0107.1, 2023.
Hwang, C. L. and Yoon, K.: Multiple attribute decision making: methods and applications, A state-of-the-art survey, Springer-Verlag, New York, ISBN 978-3540105589, 1981.
Jackson-Blake, L. A., Clayer, F., Haande, S., Sample, J. E., and Moe, S. J.: Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network, Hydrol. Earth Syst. Sci., 26, 3103–3124, https://doi.org/10.5194/hess-26-3103-2022, 2022.
Johnson, F. and Sharma, A.: A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations, Water Resour. Res., 48, 1–16, https://doi.org/10.1029/2011wr010464, 2012.
Jung, Y., Nam, W. S., Shin, H., and Heo, J.-H.: A study on low-flow frequency analysis using dam inflow, Journal of The Korean Society of Civil Engineers, 32, 363–371, https://doi.org/10.12652/ksce.2012.32.6b.363, 2012.
K-water: 2013–2018 Sustained drought analysis and assessment report, Korea Water Resources Corporation, South Korea, 2018.
K-water – Korea Water Resources Corporation: My water, http://water.or.kr (last access: 10 May 2023), 2023.
Lee, Y., Peñuela, A., Pianosi, F., and Rico-Ramirez, M. A.: Catchment-scale skill assessment of seasonal precipitation forecasts across South Korea, Int. J. Climatol., 43, 5092–5111, https://doi.org/10.1002/joc.8134, 2023.
Lee, Y., Pianosi, F., Peñuela, A., and Rico-Ramirez, M. A.: Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea, Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, 2024.
Li, W., Zhang, G., Zhang, T., and Huang, S.: Knee point-guided multiobjective optimization algorithm for microgrid dynamic energy management, Complexity, 2020, 1–11, https://doi.org/10.1155/2020/8877008, 2020.
Liu, P.: Multi-attribute decision-making method research based on interval vague set and TOPSIS method, Technol. Econ. Dev. Eco., 15, 453–463, https://doi.org/10.3846/1392-8619.2009.15.453-463, 2009.
Lu, L. Anderson-Cook, C. M., and Robinson, T. J.: Optimization of designed experiments based on multiple criteria utilizing a Pareto frontier, Technometrics, 53, 353–365, https://doi.org/10.1198/tech.2011.10087, 2011.
Lucatero, D., Madsen, H., Refsgaard, J. C., Kidmose, J., and Jensen, K. H.: Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency, Hydrol. Earth Syst. Sci., 22, 3601–3617, https://doi.org/10.5194/hess-22-3601-2018, 2018.
Malekmohammadi, B., Zahraie, B., and Kerachian, R.: Ranking solutions of multi-objective reservoir operation optimization models using multi-criteria decision analysis, Expert Syst. Appl., 38, 7851–7863, https://doi.org/10.1016/j.eswa.2010.12.119, 2011.
Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability distributions, Manage. Sci., 22, 1087–1096, https://doi.org/10.1287/mnsc.22.10.1087, 1976.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, 1–34, https://doi.org/10.1029/2009rg000314, 2010.
Millner, A. and Washington, R.: What determines perceived value of seasonal climate forecasts? A theoretical analysis, Global Environ. Chang., 21, 209–218, https://doi.org/10.1016/j.gloenvcha.2010.08.001, 2011.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010.
Ni, X., Dong, Z., Jiang, Y., Xie, W., Yao, H., and Chen, M.: A subjective-objective integrated multi-objective decision-making method for reservoir operation featuring trade-offs among non-inferior solutions themselves, J. Hydrol., 613, 128430, https://doi.org/10.1016/j.jhydrol.2022.128430, 2022.
Ou, X., Gharabaghi, B., McBean, E., and Doherty, C.: Investigation of the Tank model for urban storm water management, Journal of Water Management Modeling, 25, 1–5, https://doi.org/10.14796/jwmm.c421, 2017.
Park, J. Y. and Kim, S. J.: Potential impacts of climate change on the reliability of water and hydropower supply from a multipurpose dam in South Korea, J. Am. Water Resour. As., 50, 1273–1288, https://doi.org/10.1111/jawr.12190, 2014.
Park, M., Lee, J. H., Lim, Y. K., and Kwon, H. H.: Estimation of evaporation from water surface in Yongdam dam using the empirical evaporation equation, Journal of Korea Water Resources Association, 2, 139–150, 2024.
Pechlivanidis, I. G., Crochemore, L., Rosberg, J., and Bosshard, T.: What are the key drivers controlling the quality of seasonal streamflow forecasts?, Water Resour. Res., 56, 1–19, https://doi.org/10.1029/2019wr026987, 2020.
Peñuela, A. and Pianosi, F.: iRONS (interactive Reservoir Operation Notebooks and Software), Zenodo [code], https://doi.org/10.5281/zenodo.4277645, 2020.
Peñuela, A., Hutton, C., and Pianosi, F.: Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK, Hydrol. Earth Syst. Sci., 24, 6059–6073, https://doi.org/10.5194/hess-24-6059-2020, 2020.
Peñuela, A., Hutton, C., and Pianosi, F.: An open-source package with interactive Jupyter Notebooks to enhance the accessibility of reservoir operations simulation and optimization, Environ. Model. Softw., 145, 105188, https://doi.org/10.1016/j.envsoft.2021.105188, 2021.
Prudhomme, C., Hannaford, J., Harrigan, S., Boorman, D., Knight, J., Bell, V., Jackson, C., Svensson, C., Parry, S., Bachiller-Jareno, N., Davies, H., Davis, R., Mackay, J., McKenzie, A., Rudd, A., Smith, K., Bloomfield, J., Ward, R., and Jenkins, A.: Hydrological Outlook UK: an operational streamflow and groundwater level forecasting system at monthly to seasonal time scales, Hydrolog. Sci. J., 62, 2753–2768, https://doi.org/10.1080/02626667.2017.1395032, 2017.
Rougé, C., Penuela-Fernandez, A., Pianosi, F.: Forecast families: A new method to systematically evaluate the benefits of improving the skill of an existing forecast, J. Water Res. Pl., 149, 1–24, https://doi.org/10.1061/JWRMD5.WRENG-5934, 2023.
Ryoo, K.-S., Lee, H.-G., Park, J.-H., and Hur, Y.-T.: Improvement of estimation method on the low flow frequency inflow for the optimal reservoir operation, J. Water Res. Pl., 42, 1287–1291, 2009.
Sanchez-Gomez, J., Vega-Rodríguez, M. A., and Pérez, C.: Comparison of automatic methods for reducing the Pareto front to a single solution applied to multi-document text summarization, Knowledge-Based Systems journal, 174, 123–136, https://doi.org/10.1016/j.knosys.2019.03.002, 2019.
Schwalm, C. R.. Anderegg, W. R. L., Michalak, A. M., Fisher, J. B., Biondi, F., Koch, G., Litvak, M., Ogle, K., Shaw, J. D., Wolf, A., Huntzinger, D. N., Schaefer, K., Cook, R., Wei, Y., Fang, Y., Hayes, D., Huang, M., Jain, A., and Tian, H.: Global patterns of drought recovery, Nature, 548, 202–205, https://doi.org/10.1038/nature23021, 2017.
Shah, D., Zhao, G., Li, Y., Singh, V. P., and Gao, H.: Assessing global reservoir-based hydrological droughts by fusing storage and evaporation, Geophys. Res. Lett., 51, e2023GL106159, https://doi.org/10.1029/2023gl106159, 2024.
Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought over the past 60 years, Nature, 491, 435–438, https://doi.org/10.1038/nature11575, 2012.
Shiau, J.-T.: Risk-aversion optimal hedging scenarios during droughts, Applied Water Science, 13, 1–14, https://doi.org/10.1007/s13201-022-01817-x, 2022.
Shrestha, M., Acharya, S. C., and Shrestha, P. K.: Bias correction of climate models for hydrological modelling - are simple methods still useful?, Meteorol. Appl., 24, 531–539, https://doi.org/10.1002/met.1655, 2017.
Soares, B. M. and Dessai, S.: Barriers and enablers to the use of seasonal climate forecasts amongst organisations in Europe, Climatic Change, 137, 89–103, https://doi.org/10.1007/s10584-016-1671-8, 2016.
Sugawara, M.: Tank model, in: Computer models of watershed hydrology, edited by: Singh, V. P., Water Resources Publications, Highlands Ranch, Colorado, ISBN 978-1-887201-74-2, 1995.
Sugawara, M., Watanabe, I., Ozaki, E., and Katsuyama, Y.: Tank model programs for personal computer and the way to use, National Research Centre for Disaster Prevention, Japan, https://dil-opac.bosai.go.jp/publication/nrcdp/nrcdp_report/PDF/37/37sugawara.pdf (last access: 11 October 2022), 1986.
Tian, F., Li, Y., Zhao, T., Hu, H., Pappenberger, F., Jiang, Y., and Lu, H.: Evaluation of the ECMWF system 4 climate forecasts for streamflow forecasting in the Upper Hanjiang River basin, Hydrol. Res., 49, 1864–1879, https://doi.org/10.2166/nh.2018.176, 2018.
Turner, S. W. D., Bennett, J. C., Robertson, D. E., and Galelli, S.: Complex relationship between seasonal streamflow forecast skill and value in reservoir operations, Hydrol. Earth Syst. Sci., 21, 4841–4859, https://doi.org/10.5194/hess-21-4841-2017, 2017.
Tzeng, G.H and Huang, J.: Multiple attribute decision making: methods and applications, CRC Press, Boca Raton, Florida, ISBN 978-1439861578, 2011.
University of Bristol: SEAFORM, Zenodo [code], https://doi.org/10.5281/zenodo.12800811, 2023a.
University of Bristol: SEAFLOW, Zenodo [code], https://doi.org/10.5281/zenodo.12800917, 2023b.
Vassoney, E., Mammoliti Mochet, A., Erika, D., Negro, G., Pilloni, M. G., and Comoglio, C.: Comparing multi-criteria decision-making methods for the assessment of flow release scenarios from small hydropower plants in the alpine area, Frontiers in Environmental Science, 9, 1–20, https://doi.org/10.3389/fenvs.2021.635100, 2021.
Velasquez, M. and Hester, P. T.: An analysis of multi-criteria decision-making methods, International Journal of Operations Research, 2, 56–66, 2013.
Wang, Z. and Rangaiah, G. P.: Application and analysis of methods for selecting an optimal solution from the Pareto-optimal front obtained by multiobjective optimization, Ind. Eng. Chem. Res., 56, 560–574, https://doi.org/10.1021/acs.iecr.6b03453, 2017.
While, L., Hingston, P., Barone, L., and Huband, S.: A faster algorithm for calculating hypervolume, IEEE T. Evolut. Comput., 10, 29–38, https://doi.org/10.1109/tevc.2005.851275, 2006.
Wurbs, R. A. and Ayala, R. A.: Reservoir evaporation in Texas, USA, J. Hydrol., 510, 1–9, https://doi.org/10.1016/j.jhydrol.2013.12.011, 2014.
Yang, G., Guo, S., Liu, P., and Block, P.: Sensitivity of forecast value in multiobjective reservoir operation to forecast lead time and reservoir characteristics, J. Water Res. Pl., 147, 1–16, https://doi.org/10.1061/(asce)wr.1943-5452.0001384, 2021.
Yoe, C. E.: Principles of risk analysis: decision making under uncertainty, CRC Press, Taylor And Francis, Boca Raton, Fl, ISBN 978-1439857496, 2019.
You, G. J.-Y.: Hedging Rules for the operation of lake Okeechobee in Southern Florida, J. Am. Water Resour. As., 49, 1179–1197, https://doi.org/10.1111/jawr.12078, 2013.
Yossef, N. C., Winsemius, H., Weerts, A., van Beek, R., and Bierkens, M. F. P.: Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing, Water Resour. Res., 49, 4687–4699, https://doi.org/10.1002/wrcr.20350, 2013.
Zhang, X., Hao, Z., Singh, V. P., Zhang, Y., Feng, S., Xu, Y., and Hao, F.: Drought propagation under global warming: Characteristics, approaches, processes and controlling factors, Sci. Total Environ., 838, 156021, https://doi.org/10.1016/j.scitotenv.2022.156021, 2022.
Zhao, T., Cai, X. and Yang, D.: Effect of streamflow forecast uncertainty on real-time reservoir operation, Adv. Water Resour., 34, 495–504, https://doi.org/10.1016/j.advwatres.2011.01.004, 2011.
Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., and Zhang, Q.: Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm Evol. Comput., 1, 32–49, https://doi.org/10.1016/j.swevo.2011.03.001, 2011.
Zhu, F., Zhong, P., Xu, B., Wu, Y., and Zhang, Y.: A multi-criteria decision-making model dealing with correlation among criteria for reservoir flood control operation, J. Hydroinform., 18, 531–543, https://doi.org/10.2166/hydro.2015.055, 2015.
Zhu, F., Zhong, P., Sun, Y., and Xu, B.: Selection of criteria for multi-criteria decision making of reservoir flood control operation, J. Hydroinform., 19, 558–571, https://doi.org/10.2166/hydro.2017.059, 2017.
Short summary
This study assesses the value of seasonal flow forecasts (SFFs) in informing decision-making for drought management in South Korea and introduces a novel method for assessing values benchmarked against historical operations. Our results showed the importance of considering flow forecast uncertainty in reservoir operations. There was no significant correlation between the forecast accuracy and value. The method for selecting a compromise release schedule was a key control of the value.
This study assesses the value of seasonal flow forecasts (SFFs) in informing decision-making for...