Journal of Wetlands Research (한국습지학회지)

Korean Wetlands Society (한국습지학회)
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An Optimization of distributed Hydrologic Model using Multi-Objective Optimization Method

다중최적화기법을 이용한 분포형 수문모형의 최적화

  • Kim, Jungho (Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University) ;
  • Kim, Taegyun (Department of Landscape Architecture, Gyeongnam National Univ. of Science & Tech.)
  • Received : 2018.11.02
  • Accepted : 2019.01.15
  • Published : 2019.02.28
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Abstract

In this study, the multi-objective optimization method is attemped to optimize the hydrological model to estimate the runoff through two hydrological processes. HL-RDHM, a distributed hydrological model that can simultaneously estimate the amount of snowfall and runoff, was used as the distributed hydrological model. The Durango River basin in Colorado, USA, was selected as the watershed. MOSCEM was used as a multi-objective optimization method and parameter calibration and hydrologic model optimization were tried by selecting 5 parameters related to snow melting and 13 parameters related to runoff. Data from 2004 to 2005 were used to optimize the model and verified using data from 2001 to 2004. By optimizing both the amount of snow and the amount of runoff, the RMSE error can be reduced from 7% to 40% of the simulation value based on the initial solution at three SNOTEL points based on the RMSE. The USGS observation point of the outflow is improved about 40%.

본 연구에서는 다중최적화기법을 이용하여 2가지 수문학적 과정을 통하여 유출량을 산정하는 수문모형의 모형 최적화를 시도하였으며, 수문모형으로는 융설량과 유출량을 동시에 산정할 수 있는 분포형 수문모형인 HL-RDHM을 이용하였다. 대상유역으로는 융설량 자료를 수집할 수 있는 미국 콜로라도의 Durango River 유역을 선정하였다. 다중최적화기법으로는 MOSCEM을 활용하였으며, 융설과 관련된 매개변수 5개와 유출에 관련된 매개변수 13개를 선정하여 매개변수 보정과 수문모형 최적화를 시도하였다. 모형 최적화를 위해 2004 - 2005년의 자료가 활용되었고, 2001 - 2004년 자료를 이용하여 검증하였다. 융설량과 유출량을 동시에 최적화함으로써 RMSE 기준으로, 3개의 SNOTEL 지점에서 초기해에 의한 모의치 보다 7% - 40%까지 RMSE 오차를 줄일 수 있었고, 유출구의 USGS 관측점에서 초기해에 비해 약 40% 값이 개선됨을 확인하였다.

Keywords

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Fig. 1. The Durango River Basin, Colorado and grids

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Fig. 2. Example of MOSCEM Algorithm by 2 variabes

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Fig. 3. The parameterization for model parameters using soil type and land use

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Fig. 4. The Paretosets, the Best Paretoset, Compromised Solution

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Fig. 5. The parameter estimations for snow and water balance components

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Fig. 6. The snow melt time-series measured at 3 SNOTEL stations for model calibration

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Fig. 7. The streamflow time-series measured at USGS station for model calibration

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Fig. 8. The snow melt time-series measured at USGS station for model validation

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Fig. 9. The streamflow time-series measured at USGS station for model validation

Table 1. The parameters of SNOW 17 Model to be calibrated in HL-RDHM

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Table 2. The parameters of SAC-SEA Model to be calibrated in HL-RDHM

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