한국습지학회지 (Journal of Wetlands Research)

  • 제18권4호
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  • Pages.404-412
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  • 2016
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  • 1229-6031(pISSN)
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  • 2384-0056(eISSN)
한국습지학회 (Korean Wetlands Society)
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관측소간의 상관관계를 고려한 수위관측망 최적화 연구

A Study on Optimal Stage Gauge Network Considering Correlation of Individual Stage Gauge Station

  • 주홍준 (인하대학교 사회인프라공학과) ;
  • 김덕환 (인하대학교 사회인프라공학과) ;
  • 김정욱 (인하대학교 사회인프라공학과) ;
  • 최창현 (인하대학교 사회인프라공학과) ;
  • 한대건 (인하대학교 사회인프라공학과) ;
  • 이지호 (서울과학기술대학교 건설시스템공학과) ;
  • 김형수 (인하대학교 사회인프라공학과)
  • Joo, Hong jun (Department of Civil Engineering, Inha University) ;
  • Kim, Duck hwan (Department of Civil Engineering, Inha University) ;
  • Kim, Jung wook (Department of Civil Engineering, Inha University) ;
  • Choi, Chang hyun (Department of Civil Engineering, Inha University) ;
  • Han, Dae gun (Department of Civil Engineering, Inha University) ;
  • Lee, Ji ho (Department of Civil Engineering, Seoul National University of Science and Technology) ;
  • Kim, Hung soo (Department of Civil Engineering, Inha University)
  • 투고 : 2016.08.03
  • 심사 : 2016.11.02
  • 발행 : 2016.11.30
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초록

본 연구는 제한된 인력과 비용을 활용하여 습지 지대에서의 일관되고 적절한 수위자료를 획득하기 위한 방안 수립을 목표로 하였다. 이를 위해 기존의 수위관측소 설치 기준에 입각한 상 하류간의 유기적인 상관관계를 파악하여 관측소간의 최적의 수위관측망의 선정 기술을 개발함으로서 유역을 대표할 수 있는 일관된 수위자료 획득에 중점을 두었다. 우선 기존에 습지 유역을 포함한 충주댐 유역을 대상으로 하천을 중심으로 설치되어 있는 수위관측소 현황을 파악한 후, 유출 특성을 나타내는 대표단위도를 산정한 후 확률밀도함수로 변환하였으며, 대상 유역내에서 엔트로피 이론에 의한 정보 전달량을 산정하였다. 마지막으로 각 관측소 간의 공간적인 상관관계를 분석하고, 정보 전달량과 각 관측소의 상관관계를 고려해 수위관측망을 최적화하였다. 즉, 정보 전달량으로 수위관측소의 개수에 따른 조합을 고려하되, 수위관측소간의 상관분석을 적용하여 수위관측소 설치위치와 개수에 대하여 최적화된 수위관측망을 제시할 수 있었다.

This paper not only aims to establish a plan to acquire the water stage data in a constant and proper manner by using limited manpower and costs, but also establishes the fundamental technology for acquiring the water level observation data or the stage data. For this, this paper focuses on how to acquire the stage data, in a uniform manner, that can represent each basin by developing the technology for establishing the optimal observational network. For that, this paper identifies the current status of the stage gauge stations installed in the ChungJu dam including wetland basin mainly along the national rivers. Then, thus obtained factors are used to develop the representative unit hydrograph. After that, the data are converted into the probability density function. Then, the stations are calculated information transfer amount. As a last step, we establish the optimized stage gauge network by the location of the stage station and space impact that takes into account for the combinations of the number of the stations. In other words, we consider the combination of the stage gauge station with information transfer amount and spatial correlation analysis for estimation.

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참고문헌

  1. Al-Zahrani, M and Husain, T (1998). An algorithm for designing a precipitation network in the south-western region of Saudi Arabia, J. of Hydrology, Vol. 205, pp. 205-216. https://doi.org/10.1016/S0022-1694(97)00153-4
  2. Caselton, WF and Husain T (1980). Hydrologic networks: Information transmission, J. of Water Resources Planning and Management Division, Vol. 106, pp. 503-529.
  3. Chapman, TG (1986). Entropy as a measurement of hydrologic data uncertainty and model performance, J. of Hydraulic Engineering, Vol. 85, pp. 307-324.
  4. Faber, BA (2000). Reservoir optimization using sampling stochastic dynamic programming (SSDP) with ensemble streamflow prediction (ESP) forecasts, Ph.D. dissertation, University of Cornell, America.
  5. Franz, KJ, Hartmann, HC, Sorooshian, S and Bales, R (2003). Verification of national weather service ensemble streamflow predictions for water supply forecasting in the Colorado river basin, J. of Hydrometeorology, Vol. 4, pp. 1105-1118. https://doi.org/10.1175/1525-7541(2003)004<1105:VONWSE>2.0.CO;2
  6. Jin, YK, Jung, TH, Lee, SH and Kang, SU (2016). Reservoir operations of Hapcheon dam applying a discrete hedging rule and ensemble streamflow prediction to cope with droughts, J. of Korea Society of Hazard Mitigation, KOSHM, Vol. 16, No. 1, pp. 93-101. https://doi.org/10.9798/KOSHAM.2016.16.1.93
  7. Jung, DI (2002). Forecasting monthly inflow to Chungju Dam using ensemble streamflow prediction, MS. Thesis, University of Seoul, Korea. [Korean Literature]
  8. Kang, MS, Yu, MS, Yi, JE (2014). Prediction of Andong reservoir inflow using ensemble technique, J. of Korean Society of Civil Engineers, KSCE, Vol. 34, No. 3, pp. 795-804. [Korean Literature] https://doi.org/10.12652/Ksce.2014.34.3.0795
  9. Kim, JH and Joo, JG (2015). Characteristics of daily precipitation data based on the detailed climate change ensemble scenario depending on the regional climate models and the calibration, J. of Korea Society of Hazard Mitigation, KOSHM, Vol. 15, No. 4, pp. 261-272. [Korean Literature] https://doi.org/10.9798/KOSHAM.2015.15.4.261
  10. Kim, SJ, Joo, HJ, Lee, JH, Jun, HD and Kim, HS (2014). Evaluation of stream gauge network considered discharge characteristics between upstream and downstream of the river, J. of Korea Society of Hazard Mitigation, KOSHM, Vol. 14, No. 4, pp. 309-319. [Korean Literature]
  11. Krstanovic, PF and Singh, VP (1992). Evaluation of rainfall networks using entropy: II, Application, Water Resources Management, Vol. 6, pp. 295-314. https://doi.org/10.1007/BF00872282
  12. Olsson, J and Lindstrom, G (2008). Evaluation and calibration of operational hydrological ensemble forecasts in Sweden, J. of Hydrology, Vol. 350, pp. 14-24. https://doi.org/10.1016/j.jhydrol.2007.11.010
  13. Ozkul, S, Harmancioglu, NB and Singh, VP (2000). Entropy based assessment of water quality monitoring networks, J. of Hydraulic Engineering, Vol. 5, No. 1, pp. 90-100.
  14. Seo L, Jeon, MH, Kim TW and Kim SD (2012). Ensemble prediction of future design rainfalls considering climate change, J. of Korea Society of Hazard Mitigation, KOSHM, Vol. 12, No. 2, pp. 159-171. [Korean Literature]
  15. Shannon, CE and Weaver, W (1963). The mathematical theory of communication, The University of Illinois Press, Urbana, Illinois, USA.
  16. Smith, JA, Day, GN. and Kane, MD (1992). Nonparametric framework for long-range streamflow forecasting, J. of Water Resources Planning and Management, ASCE, Vol. 118, pp. 82-92. https://doi.org/10.1061/(ASCE)0733-9496(1992)118:1(82)
  17. Wahl, KL and Crippen, JR (1984). A programmatic approach to evaluating a multi-purpose stream-gauaing network, U.S. Geological Survey Water Resources Investigation report.
  18. WMO(1994). Guide to hydrological practice, Fifth edition.
  19. Won, TY and Jung, SW (2011). SPSS 18.0 statistical research&analysis
  20. Yang, Y and Burn, DH (1994). An entropy approach to data collection network design, J. of Hydraulic Engineering, Vol. 157, pp. 307-324.
  21. Yoo, CS and Jung, GS (2002). Evaluation of a raingauge network using entropy theory: Comparison between Mixed and Continuous Distributions. J. of Korean Society of Civil Engineers, KSCE, Vol. 22, No. 4-B, pp. 447-457. [Korean Literature]
  22. Yoo, CS and Kim, IB (2003). Optimization of stream gauge network using the entropy theory, J. of Korean Water Resources, KWRA, Vol. 36, No. 2, pp. 161-172. [Korean Literature] https://doi.org/10.3741/JKWRA.2003.36.2.161
  23. Yoon, Y.N.(2007) Hydrology, Chungmoongak Publisher. [Korean Literature]