All Issue

2025 Vol.58, Issue 3 Preview Page

Research Article

High-resolution ensemble streamflow predictions using WRF-Hydro and LDAPS: a case study of the Geumho River catchment

WRF-Hydro와 LDAPS를 활용한 고해상도 앙상블 유량 예측: 금호강 유역 사례

Yaewon Leea
,
Bomi Kimb
,
Seong Jin Nohc*
이 예원a
,
김 보미b
,
노 성진c*
aMaster Student, Department of Civil Engineering, Kumoh National Institute of Technology, Gumi, Korea
bPh.D. Student, Department of Civil Engineering, Kumoh National Institute of Technology, Gumi, Korea
cAssociate Professor, Department of Civil Engineering, Kumoh National Institute of Technology, Gumi, Korea
a금오공과대학교 토목공학과 석사과정
b금오공과대학교 토목공학과 박사과정
c금오공과대학교 토목공학과 부교수
*Corresponding Author
31 March 2025. pp. 263-275
PDF XML Citation
Abstract

Climate change is expected to intensify the frequency of extreme rainfall events with greater spatial coverage, exacerbating socioeconomic damages. Therefore, accurate short-range (up to 48 hours) streamflow predictions are increasingly essential. This study aims to evaluate ensemble short-term streamflow predictions using Weather Research and Forecasting Hydrological modeling system (WRF-Hydro), a physics-based distributed hydrological model, combined with the Local Data Assimilation and Prediction System (LDAPS) numerical weather prediction system. To achieve this, a 100-meter resolution WRF-Hydro model was configured for the Geumho River catchment, a tributary of the Nakdong River. Hydrological simulations were conducted for the August 2020 heavy rainfall event (Event 2020) and Typhoon Hinnamnor in 2022 (Event 2022) using LDAPS forecast data and Automatic Weather System (AWS) & Automated Synoptic Observing System (ASOS) ground observation data. A streamflow ensemble was generated by incorporating past deterministic hydrological simulations conducted at 6-hour intervals over a 48-hour lead time, and improvements of prediction accuracy were evaluated. The analysis showed that the performance of the ensemble predictions based on LDAPS and WRF-Hydro yielded NSE values of 0.93 and 0.84 for Event 2020 and Event 2022, respectively, indicating robust performance. The ensemble approach reduced the interquartile range of NSE values by 71.1% and 82.3% for the two events, respectively, compared to the individual ensemble which exhibited sudden performance degradation depending on the prediction’s initialization time. The application of a physics-based ensemble method proposed in this study is expected to reduce the impact of meteorological forecast uncertainty on hydrological processes, enhance the accuracy of short-term streamflow predictions, and extend lead times, thereby contributing to improved water resource management.

Keywords
LDAPS
Numerical weather prediction model
WRF-Hydro
Hydrologic modeling
Ensemble predictions

기후변화로 인해 극한 강우와 홍수 발생의 빈도와 규모가 증가하면서 신뢰성 높은 단기 유량 예측 기술의 확보가 중요해지고 있다. 본 연구에서는 물리기반 공간분포형 수문모형인 Weather Research and Forecasting Hydrological modeling system (WRF-Hydro)와 기상수치예보모형 Local Data Assimilation and Prediction System (LDAPS)를 연계한 단기 유량 예측을 수행하고, 앙상블(ensemble)을 생성하여 확률론적 접근이 예측 신뢰도 향상에 미치는 영향을 분석한다. 이를 위해 낙동강 지류인 금호강 유역을 대상으로 100 m 공간해상도 WRF-Hydro 모형을 구축하고, 2020년 8월 집중호우(Event 2020)와 2022년 태풍 힌남노 사상(Event 2020)에 대해 LDAPS 예측자료와 Automatic Weather System (AWS) & Automated Synoptic Observing System (ASOS)지상 관측자료를 기상자료로 적용하여 수문 해석을 수행하였다. 또한, 6시간 간격 48시간 선행 결정론적 수문 모의의 과거 시간대 예측을 동시에 활용하여 앙상블을 구성하고 예측 안정성의 개선 여부를 평가하였다. 분석 결과, LDAPS와 WRF-Hydro 기반 앙상블 예측의 성능은 Event 2020과 Event 2022에 대해 NSE가 각각 0.93, 0.84로 비교적 양호하였다. 결정론적 단일 모의는 6시간 간격의 유량 예측 성능이 급감하는 경우가 있었으나, 앙상블 기법 적용시 NSE의 1/4-3/4 분위 범위가 두 사상에 대해 각각 71.1%, 82.3% 감소하여 예측 성능의 변동성이 완화되었다. 본 연구에서 제안한 기상수치예보와 분포형 수문 모형을 연계한 물리 기반 앙상블 기법 적용으로 기상 예측 불확실성이 수문과정에 미치는 영향을 완화하고 단기 유량 예측의 정확도와 선행시간을 추가 확보하여 수자원 관리의 안정성 향상에 기여할 수 있을 것으로 기대된다.

키워드
LDAPS
수치예보모형
WRF-Hydro
수문모형
앙상블 예측
References
1

Abaza, M., Anctil, F., Fortin, V., and Turcotte, R. (2014). "Sequential streamflow assimilation for short-term hydrological ensemble forecasting." Journal of Hydrology, Vol. 519, pp. 2692-2706.

10.1016/j.jhydrol.2014.08.038
2

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.

10.9798/KOSHAM.2017.17.4.317
3

Cloke, H.L., and Pappenberger, F. (2009). "Ensemble flood forecasting: A review." Journal of Hydrology, Vol. 375, No. 3, pp. 613-626.

10.1016/j.jhydrol.2009.06.005
4

Dutta, D., Welsh, W.D., Vaze, J., Kim, S.S.H., and Nicholls, D. (2012). "A comparative evaluation of short-term streamflow forecasting using time series analysis and rainfall-runoff models in eWater source." Water Resources Management, Vol. 26, No. 15, pp. 4397-4415.

10.1007/s11269-012-0151-9
5

Gochis, D.J., Barlage, M., Cabell, R., Casali, M., Dugger, A., FitzGerald, K., McAllister, M., McCreight, J., RafieeiNasab, A., and Read, L., et al. (2020). The WRF-Hydro® modeling system technical description (Version 5.1.1). NCAR Technical Note, National Center for Atmospheric Research, Boulder, CO, U.S.

6

Harrigan, S., Prudhomme, C., Parry, S., Smith, K., and Tanguy, M. (2018). "Benchmarking ensemble streamflow prediction skill in the UK." Hydrology and Earth System Sciences, Vol. 22, No. 3, pp. 2023-2039.

10.5194/hess-22-2023-2018
7

He, S., Guo, S., Zhang, J., Liu, Z., Cui, Z., Zhang, Y., and Zheng, Y. (2022). "Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction." Journal of Hydrology, Vol. 610, 127936.

10.1016/j.jhydrol.2022.127936
8

Jie, W., Wu, T., Wang, J., Li, W., and Liu, X. (2014). "Improvement of 6-15 day precipitation forecasts using a time-lagged ensemble method." Advances in Atmospheric Sciences, Vol. 31, No. 2, pp. 293-304.

10.1007/s00376-013-3037-8
9

Kim, B., Lee, G., Lee, Y., Kim, S., and Noh, S.J. (2024a). "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.

10.3390/w16040591
10

Kim, D.-J., Kang, G., Kim, D.-Y., and Kim, J.-J. (2020). "Characteristics of LDAPS-Predicted surface wind speed and temperature at automated weather stations with different surrounding land cover and topography in Korea." Atmosphere, Vol. 11, No. 11, 1224.

10.3390/atmos11111224
11

Kim, K.-J., Kim, Y.-O., and Kang, T.-H. (2017). "Application of time-lagged ensemble approach with auto-regressive processors to reduce uncertainties in peak discharge and timing." Journal of Hydrology: Regional Studies, Vol. 9, pp. 140-148.

10.1016/j.ejrh.2016.12.081
12

Kim, S., Kim, B., Lee, G., Lee, Y., and Noh, S.J. (2024b). "High- resolution medium-range streamflow prediction using distributed hydrological model WRF-Hydro and numerical weather forecast GDAPS." Journal of Korea Water Resources Association, Vol. 57, No. 5, pp. 333-346.

13

Kim, S., Kwon, J.-H., Om, J.-S., Lee, T., Kim, G., Kim, H., and Heo, J.-H. (2023). "Increasing extreme flood risk under future climate change scenarios in South Korea." Weather and Climate Extremes, Vol. 39, 100552.

10.1016/j.wace.2023.100552
14

Kim, S., Shen, H., Noh, S., 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.

10.1016/j.jhydrol.2021.126236
15

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, Vol. 20, No. 4, pp. 675-698.

10.1175/JHM-D-18-0064.1
16

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.

10.1016/j.jhydrol.2021.126681
17

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.

10.1016/j.jhydrol.2022.127459
18

Ministry of the Interior and Safety (MOIS) (2021). 2020 Yearbook of disasters. pp. 108-112.

19

Ministry of the Interior and Safety (MOIS) (2023). 2022 Yearbook of disasters. pp. 113-122.

20

Muhammad, A., Stadnyk, T.A., Unduche, F., and Coulibaly, P. (2018). "Multi-model approaches for improving seasonal ensemble streamflow prediction scheme with various statistical post- processing techniques in the Canadian prairie region." Water, Vol. 10, No. 11, 1604.

10.3390/w10111604
21

Sahai, A.K., Kaur, M., Joseph, S., Dey, A., Phani, R., Mandal, R., and Chattopadhyay, R. (2021). "Multi-model multi-physics ensemble: A futuristic way to extended range prediction system." Frontiers in Climate, Vol. 3, Article 655919.

10.3389/fclim.2021.655919
22

Shen, M., Chen, J., Zhuan, M., Chen, H., Xu, C.-Y., and Xiong, L. (2018). "Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology." Journal of Hydrology, Vol. 556, pp. 10-24.

10.1016/j.jhydrol.2017.11.004
23

Yi, C., Shin, Y., and Roh, J.-W. (2018). "Development of an urban high-resolution air temperature forecast system for local weather information services based on statistical downscaling." Atmosphere, Vol. 9, No. 5, 164.

10.3390/atmos9050164
24

Zafer, D. (2021). Hurricane Florence numerical modeling. U.S. Geological survey, accessed 26 November 2024, <https://www.usgs.gov/media/images/hurricane-florence-numerical-modeling-0>.

Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 58
  • No :3
  • Pages :263-275
  • Received Date : 2025-01-08
  • Revised Date : 2025-01-31
  • Accepted Date : 2025-02-04
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.3.263
Journal Informaiton Journal of Korea Water Resources Association Journal of Korea Water Resources Association
Online Submission
Archives
  • scopus
  • KCI-수자원
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close