• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.333-346
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• 2024

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.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.275-288
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• 2006

This study develops ESP (Ensemble Streamflow Prediction) system by using medium-term numerical weather prediction model which is GDAPS(T213) of KMA. The developed system forecasts medium- and long-range exceedance Probability for streamflow and RPSS evaluation scheme is used to analyze the accuracy of probability forecasts. It can be seen that the daily probability forecast results contain high uncertainties. A sensitivity analysis with respect to forecast time resolution shows that uncertainties decrease and accuracy generally improves as the forecast time step increase. Weekly ESP results by using the GDAPS output with a lead time of up to 28 days are more accurately predicted than traditional ESP results because conditional probabilities are stably distributed and uncertainties can be reduced. Therefore, it can be concluded that the developed system will be useful tool for medium- and long-term reservoir inflow forecasts in order to manage water resources.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.571-583
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• 2024

Forecasting dam inflows in the medium to long term is crucial for effective dam operation and the prevention of water-related disasters such as floods and droughts. However, the increasing frequency of extreme weather events due to climate change has made hydrological forecasting more challenging. Since 2000, seasonal weather forecasts, which provide predictions for weather variables up to about seven months ahead, and their hydrological interpretation, known as Seasonal Flow Forecasts (SFFs) have gained significant global interest. This study utilises seasonal weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), converting them into inflow forecasts using a hydrological model for 12 multipurpose dams in South Korea from 2011 to 2020. We then compare the performance of these SFFs with the Ensemble Streamflow Prediction (ESP). Our results indicate that while SFFs are more effective for short-term predictions of 1-2 months, ESP outperforms SFFs for long-term predictions. Seasonally, the performance of SFFs is higher in October-November but lower from December to February. Moreover, our findings demonstrate that SFFs are highly effective in quantitatively predicting dry conditions, although they tend to underestimate inflows under wet conditions.