한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제57권5호
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  • Pages.333-346
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  • 2024
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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분포형 수문모형 WRF-Hydro와 기상수치예보모형 GDAPS를 활용한 고해상도 중기 유량 예측

High-resolution medium-range streamflow prediction using distributed hydrological model WRF-Hydro and numerical weather forecast GDAPS

  • 김소현 (금오공과대학교 토목공학과) ;
  • 김보미 (금오공과대학교 토목공학과) ;
  • 이가림 (금오공과대학교 토목공학과) ;
  • 이예원 (금오공과대학교 토목공학과) ;
  • 노성진 (금오공과대학교 토목공학과)
  • Kim, Sohyun (Department of Civil Engineering, Kumoh National Institute of Technology) ;
  • Kim, Bomi (Department of Civil Engineering, Kumoh National Institute of Technology) ;
  • Lee, Garim (Department of Civil Engineering, Kumoh National Institute of Technology) ;
  • Lee, Yaewon (Department of Civil Engineering, Kumoh National Institute of Technology) ;
  • Noh, Seong Jin (Department of Civil Engineering, Kumoh National Institute of Technology)
  • 투고 : 2024.02.06
  • 심사 : 2024.05.03
  • 발행 : 2024.05.31
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초록

수량과 수질 및 수생태를 동시에 고려한 수자원 관리를 위해서는 신뢰도 높은 중기 유량 예측 기술이 필수적이다. 이를 위해서는 기상자료의 특성에 대한 이해와 더불어, 시공간 해상도가 낮은 기상예측 정보를 고해상도 분포형 수문모형에서 효과적으로 활용하는 기술이 중요하다. 본 연구에서는 분포형 수문모형 WRF-Hydro와 선행시간 288시간까지의 기상정보를 제공하는 Global Data Assimilation and Prediction System (GDAPS)를 활용해 고해상도 중기 유량 예측을 수행하고 적용성을 검토하였다. 이를 위해 대상 유역인 낙동강 지류 금호강 유역에 대해 100 m 공간해상도의 WRF-Hydro모형을 구축하고 기상지상관측자료 Automatic Weather Stations (AWS)& Automated Synoptic Observing Systems (ASOS), 기상수치예보모형 GDAPS, 기상재분석자료 Global Land Data Assimilation System (GLDAS)를 입력자료로 적용한 유량 예측 모의 결과를 비교하였다. 2020~2022년 기간 3개의 강우사상에 대해 유역 평균 누적 강우량을 분석 결과, AWS&ASOS대비 GDAPS는 36%~234%, GLDAS 재분석자료는 80%~153% 범위의 과소 및 과대 산정되었음을 확인하였다. AWS&ASOS입력자료로 한 유량 예측 결과는 KGE, NSE지표가 유역 말단 강창교 지점 기준 0.6이상이었으나, GDAPS 기반 유량 모의는 강우 사상에 따라 KGE 값이 0.871~-0.131로 큰 변동성이 확인되었다. 한편, 첨두 유량 오차는 GDAPS가 GLDAS보다 크거나 비슷했지만, 첨두 홍수 발생시간의 오차는 AWS&ASOS, GDAPS, GLDAS가 각각 평균 3.7시간, 8.4시간, 70.1시간으로, 첨두 발생시간 측면에서는 GDAPS의 오차가 GLDAS보다 적었다. GDAPS를 입력자료로 한 WRF-Hydro 고해상도 중기 유량 예측은 첨두 유량의 불확실성은 크지만, 첨두 유량 발생시점에 대한 정확도는 상대적으로 높아 수자원 시설 운영에 효과적으로 활용될 수 있을 것으로 판단된다.

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.

키워드

과제정보

본 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단(No. 2022R1A4A5028840, RS-2023-00246532)과 정부(환경부)의 재원으로 한국환경산업기술원(No. RS-2023-00218973)의 지원을 받아 수행되었습니다. 원고의 수준이 향상되도록 고견을 주신익명의 심사위원과 편집위원께감사드립니다.

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