H
Hyosang Lee
Researcher at Chungbuk National University
Publications - 26
Citations - 736
Hyosang Lee is an academic researcher from Chungbuk National University. The author has contributed to research in topics: Runoff model & Regionalisation. The author has an hindex of 9, co-authored 26 publications receiving 651 citations. Previous affiliations of Hyosang Lee include Imperial College London & Helmholtz Centre for Environmental Research - UFZ.
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Ensemble predictions of runoff in ungauged catchments
TL;DR: In this paper, a new approach to regionalization of conceptual rainfall-runoff models is presented on the basis of ensemble modeling and model averaging, which represents an improvement on the established procedure of regressing parameter values against numeric catchment descriptors.
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Metabolomic unveiling of a diverse range of green tea (Camellia sinensis) metabolites dependent on geography.
Jang-Eun Lee,Bum-Jin Lee,Jin-Oh Chung,Hak-Nam Kim,Eun-Hee Kim,Sungheuk Jung,Hyosang Lee,Sang Jun Lee,Young-Shick Hong +8 more
TL;DR: A new approach for non-targeted or global analysis, which is highly reproducible and statistically effective in analysing a diverse range of compounds, was used to better understand the metabolome of Camellia sinensis and determine the influence of environmental factors, including geography, climate, and cultural practices, on tea-making.
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Selection of conceptual models for regionalisation of the rainfall-runoff relationship
TL;DR: In this paper, the authors identify relationships between suitable conceptual rainfall runoff model structures and catchment types, and demonstrate an objective procedure for selection of model structures for use in model regionalisation studies.
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A comparison of two event-based flood models (ReFH-rainfall runoff model and HEC-HMS) at two Korean catchments, Bukil and Jeungpyeong
TL;DR: In this article, the Revitalized Flood Hydrograph (ReFH) rainfall runoff model is compared with HEC-HMS model in two Korean catchments (Bukil and Jeungpyeong).
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Predicting runoff in ungauged UK catchments
TL;DR: In this article, the authors developed regression relationships between known catchment characteristics and parameters of a conceptual rainfall-runoff model, which allowed predictive models to be specified without calibration, and the best regression model improved the capability of predicting flow time-series in ungauged catchments.