Статии с изисквания за обществен достъп - Shang GaoНаучете повече
Не е налице никъде: 2
CREST-iMAP v1. 0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction
Z Li, M Chen, S Gao, X Luo, JJ Gourley, P Kirstetter, T Yang, R Kolar, ...
Environmental Modelling & Software 141, 105051, 2021
Изисквания: US National Science Foundation
Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach
L Zhang, S Gao, T Yang
Journal of Hydrology 631, 130643, 2024
Изисквания: US National Science Foundation, US Department of Defense, US National …
Налице някъде: 27
Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four …
T Kim, T Yang, S Gao, L Zhang, Z Ding, X Wen, JJ Gourley, Y Hong
Journal of Hydrology 598, 126423, 2021
Изисквания: US National Science Foundation, US Department of Energy, US National Oceanic …
The conterminous United States are projected to become more prone to flash floods in a high-end emissions scenario
Z Li, S Gao, M Chen, JJ Gourley, C Liu, AF Prein, Y Hong
Communications Earth & Environment 3 (1), 86, 2022
Изисквания: US National Oceanic and Atmospheric Administration
Cross-examination of similarity, difference and deficiency of gauge, radar and satellite precipitation measuring uncertainties for extreme events using conventional metrics and …
Z Li, M Chen, S Gao, Z Hong, G Tang, Y Wen, JJ Gourley, Y Hong
Remote Sensing 12 (8), 1258, 2020
Изисквания: US National Oceanic and Atmospheric Administration
Evaluation of GPM IMERG and its constellations in extreme events over the conterminous United States
Z Li, G Tang, P Kirstetter, S Gao, JLF Li, Y Wen, Y Hong
Journal of Hydrology 606, 127357, 2022
Изисквания: US National Aeronautics and Space Administration
Two-decades of GPM IMERG early and final run products intercomparison: Similarity and difference in climatology, rates, and extremes
Z Li, G Tang, Z Hong, M Chen, S Gao, P Kirstetter, JJ Gourley, Y Wen, ...
Journal of Hydrology 594, 125975, 2021
Изисквания: US National Oceanic and Atmospheric Administration
Understanding the re-infiltration process to simulating streamflow in North Central Texas using the WRF-hydro modeling system
J Zhang, P Lin, S Gao, Z Fang
Journal of Hydrology 587, 124902, 2020
Изисквания: US National Science Foundation, US Department of Defense
Can remote sensing technologies capture the extreme precipitation event and its cascading hydrological response? A case study of Hurricane Harvey using EF5 modeling framework
M Chen, S Nabih, NS Brauer, S Gao, JJ Gourley, Z Hong, RL Kolar, ...
Remote Sensing 12 (3), 445, 2020
Изисквания: US National Science Foundation, US National Aeronautics and Space …
A comprehensive flood inundation mapping for Hurricane Harvey using an integrated hydrological and hydraulic model
M Chen, Z Li, S Gao, X Luo, OEJ Wing, X Shen, JJ Gourley, RL Kolar, ...
Journal of Hydrometeorology 22 (7), 1713-1726, 2021
Изисквания: US National Oceanic and Atmospheric Administration
Spatiotemporal characteristics of US floods: Current status and forecast under a future warmer climate
Z Li, S Gao, M Chen, JJ Gourley, Y Hong
Earth's Future 10 (10), e2022EF002700, 2022
Изисквания: US National Oceanic and Atmospheric Administration
Mapping dynamic non-perennial stream networks using high-resolution distributed hydrologic simulation: A case study in the upper blue river basin
S Gao, M Chen, Z Li, S Cook, D Allen, T Neeson, T Yang, T Yami, Y Hong
Journal of Hydrology 600, 126522, 2021
Изисквания: US National Science Foundation
Spatiotemporal variability of global river extent and the natural driving factors revealed by decades of Landsat observations, GRACE gravimetry observations, and land surface …
S Gao, Z Li, M Chen, P Lin, Z Hong, D Allen, T Neeson, Y Hong
Remote Sensing of Environment 267, 112725, 2021
Изисквания: US National Science Foundation
Improving Subseasonal-to-Seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous US using machine learning models
L Zhang, T Yang, S Gao, Y Hong, Q Zhang, X Wen, C Cheng
Atmospheric Research 281, 106502, 2023
Изисквания: US National Science Foundation, US Department of Defense
A multi-source 120-year US flood database with a unified common format and public access
Z Li, M Chen, S Gao, JJ Gourley, T Yang, X Shen, R Kolar, Y Hong
Earth System Science Data Discussions 2021, 1-25, 2021
Изисквания: US National Oceanic and Atmospheric Administration
A flood predictability study for Hurricane Harvey with the CREST-iMAP model using high-resolution quantitative precipitation forecasts and U-Net deep learning precipitation …
M Chen, Z Li, S Gao, M Xue, JJ Gourley, RL Kolar, Y Hong
Journal of Hydrology 612, 128168, 2022
Изисквания: US National Oceanic and Atmospheric Administration
Small increases in stream drying can dramatically reduce ecosystem connectivity
MC Malish, S Gao, D Kopp, Y Hong, DC Allen, T Neeson
Ecosphere 14 (3), e4450, 2023
Изисквания: US National Science Foundation
Evaluation of multiradar multisensor and stage IV quantitative precipitation estimates during Hurricane Harvey
S Gao, J Zhang, D Li, H Jiang, ZN Fang
Natural Hazards Review 22 (1), 04020057, 2021
Изисквания: US National Science Foundation, US Department of Defense
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Z Li, S Gao, M Chen, J Gourley, N Mizukami, Y Hong
Geoscientific Model Development Discussions 2022, 1-30, 2022
Изисквания: US National Oceanic and Atmospheric Administration
Introducing Flashiness‐Intensity‐Duration‐Frequency (F‐IDF): A New Metric to Quantify Flash Flood Intensity
Z Li, S Gao, M Chen, J Zhang, JJ Gourley, Y Wen, T Yang, Y Hong
Geophysical Research Letters 50 (23), e2023GL104992, 2023
Изисквания: US National Oceanic and Atmospheric Administration
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