Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea

Abstract 2) over the last decade (2011–2020). Seasonal weather forecast data (including precipitation, temperature and evapotranspiration) from the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) are used to drive the Tank model (conceptual hydrological model) to generate the flow ensemble forecasts. We assess the contribution of each weather variable to the performance of flow forecasting by isolating individual variables. In addition, we quantitatively evaluate the “overall skill” of SFFs, representing the probability of outperforming the benchmark (ESP), using the continuous ranked probability skill score (CRPSS). Our results highlight that precipitation is the most important variable in determining the performance of SFFs and that temperature also plays a key role during the dry season in snow-affected catchments. Given the coarse resolution of seasonal weather forecasts, a linear scaling method to adjust the forecasts is applied, and it is found that bias correction is highly effective in enhancing the overall skill. Furthermore, bias-corrected SFFs have skill with respect to ESP up to 3 months ahead, this being particularly evident during abnormally dry years. To facilitate future applications in other regions, the code developed for this analysis has been made available as an open-source Python package.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea ; volume:28 ; number:14 ; year:2024 ; pages:3261-3279 ; extent:19 ; Hydrology and earth system sciences ; 28, Heft 14 (2024), 3261-3279 (gesamt 19)

Contributors, Places and Time

• Creator: Lee, Yongshin ; Pianosi, Francesca ; Peñuela, Andres ; Rico-Ramirez, Miguel Angel

Further information

• DOI: 10.5194/hess-28-3261-2024
• URN: urn:nbn:de:101:1-2408061449549.492964694067
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:54 AM CEST