Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Effects of Hydro-Climate Conditions on Calibrating Conceptual Hydrologic Partitioning Model

개념적 수문분할모형의 보정에 미치는 수문기후학적 조건의 영향

  • Choi, Jeonghyeon (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Seo, Jiyu (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Won, Jeongeun (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Lee, Okjeong (School of Integrated Science for Sustainable Earth & Environmental Disaster, Pukyong National University) ;
  • Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
  • 최정현 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 서지유 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 원정은 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 이옥정 (부경대학교 지구환경교육연구단) ;
  • 김상단 (부경대학교 환경공학과)
  • Received : 2020.09.29
  • Accepted : 2020.11.30
  • Published : 2020.11.30
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Abstract

Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.

Keywords

Acknowledgement

본 결과물은 환경부의 재원으로 한국환경산업기술원의 지능형 도시 수자원 관리사업의 지원을 받아 연구되었습니다.(2019002950004)

References

  1. Abebe, N., Ogden, F., and Pradhan, N. (2010). Sensitivity and uncertainty analysis of the conceptual HBV rainfall-runoff model: implications for parameter estimation, Journal of Hydrology, 389, 301-310. https://doi.org/10.1016/j.jhydrol.2010.06.007
  2. Anctil, F., Perrin, C., and Andreassian, V. (2004). Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models, Environmental Modelling and Software, 19, 357-368. https://doi.org/10.1016/S1364-8152(03)00135-X
  3. Allen, R., Pereira, L., Raes, D., and Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56, Rome, Italy: Food and Agriculture Organization of the United Nations, ISBN 978-92-5-104219-9.
  4. Bardossy, A. and Singh, S. (2008). Robust estimation of hydrological model parameters, Hydrology Earth System Sciences, 12, 1273-1283. https://doi.org/10.5194/hess-12-1273-2008
  5. Box, G. and Cox, D. (1964). An analysis of transformations, Journal of the Royal Statistical Society: Series B (Methodological), 26, 211-243. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
  6. Budyko, M. (1974). Climate and Life, Academic Press, New York, USA.
  7. Choi, J., Jang, S., and Kim, S. (2020). Parameter and modeling uncertainty analysis of semi-distributed hydrologic model using Markov-chain Monte Carlo technique, Journal of Korean Society on Water Environment, 36(5), 373-384. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.5.373
  8. Choi, J., Kim, R., and Kim, S. (2020). Development of parsimonious semi-distributed hydrolgic partitioning model based on soil moisture storages, Journal of Korean Society on Water Environment, 36(3), 229-244. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.3.229
  9. Dakhlaoui, H., Ruelland, D., Tramblay, Y., and Bargaoui, Z. (2017). Evaluating the robustness of conceptual rainfall-runoff models under climate variability in northern Tunisia, Journal of Hydrology, 550, 201-217. https://doi.org/10.1016/j.jhydrol.2017.04.032
  10. Fowler, K., Peel, M., Western, A., Zhang, L., and Peterson, T. (2016). Simulating runoff under changing climatic conditions: revisiting an apparent deficiency of conceptual rainfall runoff models, Water Resources Research, 52, 1820-1846. https://doi.org/10.1002/2015WR018068
  11. Gan, T., Dlamini, E., and Biftu, G. (1997). Effects of model complexity and structure, data quality, and objective functions on hydrologic modeling, Journal of Hydrology, 192, 81-103. https://doi.org/10.1016/S0022-1694(96)03114-9
  12. Gupta, H., Kling, H., Yilmaz, K., and Martinez, G. (2009). Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling, Journal of Hydrology, 377, 80-91. https://doi.org/10.1016/j.jhydrol.2009.08.003
  13. Hartmann, G. and Bardossy, A. (2005). Investigation of the transferability of hydrological models and a method to improve model calibration, Advances in Geosciences, 5, 83-87. https://doi.org/10.5194/adgeo-5-83-2005
  14. Kavetski, D. and Fenicia, F. (2011). Elements of a flexible approach for conceptual hydrological modeling: 2. Applications experimental insights, Water Resources Research, 47, 1-19. https://doi.org/10.1029/2010WR009138
  15. Kim, R., Won, J., Choi, J., Lee, O., and Kim, S. (2020). Application of bayesian approach to parameter estimation of TANK model: comparison of MCMC and GLUE methods, Journal of Korean Society on Water Environment, 36(4), 300-313. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.4.300
  16. Klemes, V. (1986). Operational testing of hydrological simulation models, Hydrological Sciences Journal, 31, 13-24. https://doi.org/10.1080/02626668609491024
  17. Korea Meteorological Administration (KMA). (2020). Weather Data Open Portal, http://data.kma.go.kr (accessed Sept. 2020).
  18. Kuczera, G. (1983). Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty, Water Resources Research, 19, 1151-1162. https://doi.org/10.1029/WR019i005p01151
  19. Lee, O., Kim, H., and Kim, S. (2020). Hydrological simple water balance modeling for increasing geographically isolated doline wetland functions and its application to climate change, Ecological Engineering, 149, 105812. https://doi.org/10.1016/j.ecoleng.2020.105812
  20. Li, C., Wang, H., Liu, J., Yan, D., Yu, F., and Zhang, L. (2010). Effect of calibration data series length on performance and optimal parameters of hydrological model, Water Science and Engineering, 3, 378-393. https://doi.org/10.3882/j.issn.1674-2370.2010.04.002
  21. Me, W., Abell, J. M., and Hamilton, D. P. (2015). Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand, Hydrology and Earth System Sciences, 19, 4127-4147. https://doi.org/10.5194/hess-19-4127-2015
  22. Ministry of Environment (ME). (2020). Water Resources Management Information System (WAMIS), http://www.wamis.go.kr (accessed Sept. 2020).
  23. Motavita, D., Chow, R., Guthke, A., and Nowak, W. (2019). The comprehensive differential split-sample test: A stress-test for hydrological model robustness under climate variability, Journal of Hydrology, 573, 501-515. https://doi.org/10.1016/j.jhydrol.2019.03.054
  24. Nash, J. and Sutcliffe, J. (1970). River flow forecasting through conceptual models part I - A discussion of principles, Journal of Hydrology, 10, 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  25. Patil, S. and Stieglitz, M. (2015). Comparing spatial and temporal transferability of hydrological model parameters, Journal of Hydrology, 525, 409-417. https://doi.org/10.1016/j.jhydrol.2015.04.003
  26. Perrin, C., Oudin, L., Andreassian, V., Rojas-Serna, C., Michel, C., and Mathevet, T. (2007). Impact of limited streamflow data on the efficiency and the parameters of rainfall runoff models, Hydrological Sciences Journal, 52, 131-151. https://doi.org/10.1623/hysj.52.1.131
  27. Refsgaard, J. and Knudsen, J. (1996). Operational validation and intercomparison of different types of hydrological models, Water Resources Research, 32, 2189-2202. https://doi.org/10.1029/96WR00896
  28. Ritter, A. and Munoz-Carpena, R. (2013). Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments, Journal of Hydrology, 480, 33-45. https://doi.org/10.1016/j.jhydrol.2012.12.004
  29. Ruelland, D., Hublart, P., and Tramblay, Y. (2015). Assessing uncertainties in climate change impacts on runoff in Western Mediterranean basins, Proceedings of the International Association of Hydrological Sciences, Copernicus Publications, 371, 75-81. https://doi.org/10.5194/piahs-371-75-2015
  30. Seibert, J., Rodhe, A., and Bishop, K. (2003). Simulating interactions between saturated and unsaturated storage in a conceptual runoff model, Hydrological Process, 17, 379-390. https://doi.org/10.1002/hyp.1130
  31. Seiller, G., Anctil, F., and Perrin, C. (2012). Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions, Hydrology Earth System Sciences, 16, 1171-1189. https://doi.org/10.5194/hess-16-1171-2012
  32. Sorooshian, S., Gupta, V., and Fulton, J. (1983). Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall runoff models: influence of calibration data variability and length on model credibility, Water Resources Research, 19, 251-259. https://doi.org/10.1029/WR019i001p00251
  33. Troin, M., Arsenault, R., Martel, J., and Brissette, F. (2018). Uncertainty of hydrological model components in climate change studies over two nordic quebec catchments, Journal of Hydrometeorology, 19, 27-46. https://doi.org/10.1175/JHM-D-17-0002.1
  34. Vaze, J., Post, D., Chiew, F., Perraud, J., Viney, N., and Teng, J. (2010). Climate non-stationarity: validity of calibrated rainfall runoff models for use in climate change studies, Journal of Hydrology, 394, 447-457. https://doi.org/10.1016/j.jhydrol.2010.09.018
  35. Wagener, T., McIntyre, N., Lees, M., Wheater, H., and Gupta, H. (2003). Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis, Hydrological Process, 17, 455-476. https://doi.org/10.1002/hyp.1135
  36. Won, J., Choi, J., Lee, O., and Kim, S. (2020). Copula-based joint drought index using SPI and EDDI and its application to climate change, Science of The Total Environment, 140701. https://doi.org/10.1016/j.scitotenv.2020.140701
  37. Wu, K. and Johnston, C. (2007). Hydrologic response to climatic variability in a great lakes watershed: a case study with the swat model, Journal of Hydrology, 337, 187-199. https://doi.org/10.1016/j.jhydrol.2007.01.030
  38. Xia, Y., Yang, Z., Jackson, C., Stoffa, P., and Sen, M. (2004). Impacts of data length on optimal parameter and uncertainty estimation of a land surface model, Journal of Geophysical Research - Atmospheres, 109, 1-13.
  39. Yapo, P., Gupta, H., and Sorooshian, S. (1996). Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data, Journal of Hydrology, 181, 23-48. https://doi.org/10.1016/0022-1694(95)02918-4
  40. Yen, H., Wang, X., Fontane, D., Harmel, R., and Arabi, M. (2014). A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling, Environmental Modelling and Software, 54, 211-221. https://doi.org/10.1016/j.envsoft.2014.01.004
  41. Zhang, H., Huang, G., Wang, D., and Zhang, X. (2011). Multi-period calibration of a semidistributed hydrological model based on hydroclimatic clustering, Advances in Water Resources, 34, 1292-1303. https://doi.org/10.1016/j.advwatres.2011.06.005
  42. Zhang, J., Li, Y., Huang, G., Chen, X., and Bao, A. (2016). Assessment of parameter uncertainty in hydrological model using a Markov-Chain-Monte-Carlo-based multilevelfactorial analysis method, Journal of Hydrology, 538, 471-486. https://doi.org/10.1016/j.jhydrol.2016.04.044