Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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High-resolution medium-range streamflow prediction using distributed hydrological model WRF-Hydro and numerical weather forecast GDAPS

분포형 수문모형 WRF-Hydro와 기상수치예보모형 GDAPS를 활용한 고해상도 중기 유량 예측

  • 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)
  • 김소현 (금오공과대학교 토목공학과) ;
  • 김보미 (금오공과대학교 토목공학과) ;
  • 이가림 (금오공과대학교 토목공학과) ;
  • 이예원 (금오공과대학교 토목공학과) ;
  • 노성진 (금오공과대학교 토목공학과)
  • Received : 2024.02.06
  • Accepted : 2024.05.03
  • Published : 2024.05.31
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Abstract

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.

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.

Keywords

Acknowledgement

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.

References

  1. Baik, J., and Choi, M. (2015). "Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea." International Journal of Remote Sensing, Vol. 36, No. 1, pp. 375-402.  https://doi.org/10.1080/01431161.2014.998349
  2. Cerbelaud, A., Lefevre, J., Genthon, P., and Menkes, C. (2022). "Assessment of the WRF-Hydro uncoupled hydro-meteorological model on flashy watersheds of the Grande Terre tropical island of New Caledonia (South-West Pacific)." Journal of Hydrology: Regional Studies, Vol. 40, 101003. 
  3. Cha, W., Lee, O., Kim, S., and Park, Y. (2017). "Analysis of indicators of hydrological alteration on the Geumho River Basin under AR5 RCP scenarios." Journal of the Korean Society of Hazard Mitigation, Vol. 17, No. 4, pp. 317-326.  https://doi.org/10.9798/KOSHAM.2017.17.4.317
  4. Cho, K., and Kim, Y. (2022). "Improving streamflow prediction in the WRF-Hydro model with LSTM networks." Journal of Hydrology, Vol. 605, 127297. 
  5. Doherty, J. (2010). PEST, Model-independent parameter estimation - User manual, (5th ed., with slight additions), Watermark Numerical Computing, Brisbane, Australia. 
  6. Gochis, D.J., Barlage, M., Cabell, R., Casali, M., Dugger, A., Fitz-Gerald, K., McAllister, M., McCreight, J., RafieeiNasab, A., Read, L., Sampson, K., Yates, D., and Zhang, Y. (2020). The WRF-Hydro® modeling system technical description, (Version 5.1.1), NCAR Technical Note, National Center for Atmospheric Research, Boulder, CO, U.S. 
  7. Kerandi, N., Arnault, J., Laux, P., Wagner, S., Kitheka, J., and Kunstmann, H. (2018). "Joint atmospheric-terrestrial water balances for East Africa: A WRF-Hydro case study for the upper Tana River basin." Theoretical and Applied Climatology, Vol. 131, No. 3, pp. 1337-1355.  https://doi.org/10.1007/s00704-017-2050-8
  8. Kim, B., Lee, G., Lee, Y., Kim, S., and Noh, S.J. (2024). "Assessment of the impact of spatial variability on streamflow predictions using high-resolution modeling and parameter estimation: Case study of Geumho River Catchment, South Korea." Water, Vol. 16, No. 4, 591. 
  9. Kim, J.H., and Bae, D.H. (2006). "Probabilistic Medium- and Long-Term Reservoir Inflow Forecasts (II) Use of GDAPS for Ensemble Reservoir Inflow Forecasts." Journal of Korea Water Resources Association, Vol. 39, No. 3, pp. 275-288.  https://doi.org/10.3741/JKWRA.2006.39.3.275
  10. Kim, S. (2024). High-resolution streamflow prediction using distributed hydrological model WRF-Hydro and mid-range numerical forecast GDAPS. Master Thesis, Kumoh National Institute of Technology. 
  11. Kim, S., Shen, H., Noh, S. J., Seo, D.-J., Welles, E., Pelgrim, E., Weerts, A., Lyons, E., and Philips, B. (2021). "High-resolution modeling and prediction of urban floods using WRF-Hydro and data assimilation." Journal of Hydrology, Vol. 598, 126236. 
  12. Lahmers, T.M., Gupta, H., Castro, C.L., Gochis, D.J., Yates, D., Dugger, A., Goodrich, D., and Hazenberg, P. (2019). "Enhancing the structure of the WRF-Hydro hydrologic model for semiarid environments." Journal of Hydrometeorology, American Meteorological Society, Vol. 20, No. 4, pp. 691-714.  https://doi.org/10.1175/JHM-D-18-0064.1
  13. Lee, D.G., and Ahn, K.H. (2021). "A stacking ensemble model for hydrological post-processing to improve streamflow forecasts at medium-range timescales over South Korea." Journal of Hydrology, Vol. 600, 126681. 
  14. Lee, J., Kim, Y., and Wang, D. (2022). "Assessing the characteristics of recent drought events in South Korea using WRF-Hydro." Journal of Hydrology, Vol. 607, 127459. 
  15. Lee, J.K., Kim, Y.O., and Jeong, D.I. (2006). "Use of climate information for improving extended streamflow prediction in Korea." Journal of Korea Water Resources Association, Vol. 39, No. 9, pp. 755-766.  https://doi.org/10.3741/JKWRA.2006.39.9.755
  16. Naabil, E., Kouadio, K., Lamptey, B., Annor, T., and Achugbu, I.C. (2023). "Tono basin climate modeling, the potential advantage of fully coupled WRF/WRF-Hydro modeling System." Modeling Earth Systems and Environment, Vol. 9, No. 2, pp. 1669-1679.  https://doi.org/10.1007/s40808-022-01574-5
  17. Niu, G.Y., Yang, Z.L., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y. (2011). "The community Noah land surface model with multiparameterization options (Noah-MP): 1. model description and evaluation with local-scale measurements." Journal of Geophysical Research: Atmospheres, Vol. 116, No. 12, D12109. 
  18. Rodell, M., Houser, P.R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J.K., Walker, J.P., Lohmann, D., and Toll, D. (2004). "The global land data assimilation system." Bulletin of the American Meteorological Society, Vol. 85, No. 3, pp. 381-394.  https://doi.org/10.1175/BAMS-85-3-381
  19. Rummler, T., Arnault, J., Gochis, D., Kunstmann, H. (2019). "Role of lateral terrestrial water flow on the regional water cycle in a complex terrain region: Investigation with a fully coupled model system." Journal of Geophysical Research: Atmospheres, Vol. 124, No. 2, pp. 507-529.  https://doi.org/10.1029/2018JD029004
  20. Senatore, A., Mendicino, G., Gochis, D.J., Yu, W., Yates, D.N., and Kunstmann, H. (2015). "Fully coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales." Journal of Advances in Modeling Earth Systems, Vol. 7, No. 4, pp. 1693-1715.  https://doi.org/10.1002/2015MS000510
  21. Wang, W., Liu, J., Li, C., Liu, Y., Yu, F. and Yu, E. (2020). "An evaluation study of the fully coupled WRF/WRF-Hydro Modeling System for simulation of storm events with different rainfall evenness in space and time." Water, Vol. 12, No. 4, 1209. 
  22. Zhang, J., Lin, P., Gao, S. and Fang, Z. (2020). "Understanding the re-infiltration process to simulating streamflow in North Central Texas using the WRF-hydro modeling system." Journal of Hydrology, Vol. 587, 124902.