Výsledky vyhledávání - "Suhyung Jang"

Akademický článek

Effect of mountainous rainfall on uncertainty in flood model parameter estimation

Autor: Jeonghoon Lee, Jeonghyeon Choi, Suhyung Jang, Sangdan Kim

Publikováno v: Hydrology Research, Vol 55, Iss 2, Pp 221-236 (2024)

Explaining the significant variability of rainfall in orographically complex mountainous regions remains a challenging task even for modern raingauge networks. To address this issue, a real-time spatial rainfall field estimation model, called WREPN (

Externí odkaz: https://doaj.org/article/76f6182eb8ee4ee09f9af9c89dadc858

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Akademický článek

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

Autor: Jiyu Seo, Jeongeun Won, Jeonghyeon Choi, Jungmin Lee, Suhyung Jang, Okjeong Lee, Sangdan Kim

Publikováno v: Atmosphere, Vol 12, Iss 2, p 227 (2021)

Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study,

Externí odkaz: https://doaj.org/article/bc76bfdc35f74da090f1e23276a2b810

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Estimation of Real-Time Rainfall Fields Reflecting the Mountain Effect of Rainfall Explained by the WRF Rainfall Fields

Autor: Jeonghoon Lee, Okjeong Lee, Jeonghyeon Choi, Jiyu Seo, Jeongeun Won, Suhyung Jang, Sangdan Kim

Publikováno v: Water; Volume 15; Issue 9; Pages: 1794

The effect of mountainous regions with high elevation on hourly timescale rainfall presents great difficulties in flood forecasting and warning in mountainous areas. In this study, the hourly rainfall–elevation relationship of the regional scale is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c103bc15e70d500426374a12115dbd82

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Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction

Autor: Jeonghyeon Choi, Jeongeun Won, Suhyung Jang, Sangdan Kim

Publikováno v: Water; Volume 14; Issue 18; Pages: 2910

Many studies have applied the Long Short-Term Memory (LSTM), one of the Recurrent Neural Networks (RNNs), to rainfall-runoff modeling. These data-driven modeling approaches learn the patterns observed from input and output data. It is widely known th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5259ec965847b31606b21ef152f23402

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Reconstruction of Historical Atmospheric-Hydrologic Data by a Hydro-climate Model for a Sparsely Gauged Watershed

Autor: Michael L. Anderson, M. Levent Kavvas, Noriaki Ohara, Shuichi Kure, Z. Q. Chen, Suhyung Jang, K. J. Carr

A lack of observations within watersheds can make the production of streamflow data via hydrologic models a big challenge. Authors propose a methodology to reproduce historical atmospheric-hydrologic data at the desired fine time-space scales for a s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::80a9cedb2eeca7d93d04dde8270eca84
https://doi.org/10.9734/bpi/nicst/v11/7688d

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Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

Autor: Jeonghyeon Choi, Sangdan Kim, Okjeong Lee, Jeongeun Won, Jiyu Seo, Jungmin Lee, Suhyung Jang

Publikováno v: Atmosphere
Volume 12
Issue 2
Atmosphere, Vol 12, Iss 227, p 227 (2021)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c77cdee8b846030a8adbabf9303eb9b

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