Articles | Volume 21, issue 2
https://doi.org/10.5194/hess-21-1279-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-21-1279-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
02 Mar 2017
Research article |
| 02 Mar 2017
Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model
Department of Water Resources and Environment, Sun Yat-sen
University, Guangzhou, 510275, China
Department of Water Resources and Environment, Sun Yat-sen
University, Guangzhou, 510275, China
Huanyu Wang
Department of Water Resources and Environment, Sun Yat-sen
University, Guangzhou, 510275, China
Jianming Qin
Department of Water Resources and Environment, Sun Yat-sen
University, Guangzhou, 510275, China
Jie Li
Hydrology Bureau, Pearl River
Water Resources Commission, Guangzhou, 510370, China
Sen Chiao
Department of
Meteorology and Climate Science, San Jose State University, San Jose, CA
95192, USA
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53 citations as recorded by crossref.
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- Impacts of Hydrometeor Drift on Orographic Precipitation: Two Case Studies of Landfalling Atmospheric Rivers in British Columbia, Canada R. Mo et al. 10.1175/WAF-D-18-0176.1
- Flash flood modeling in the data-poor basin: A case study in Matina River Basin R. Macalalad et al. 10.1016/j.tcrr.2021.06.003
- Toward Street‐Level Nowcasting of Flash Floods Impacts Based on HPC Hydrodynamic Modeling at the Watershed Scale and High‐Resolution Weather Radar Data P. Costabile et al. 10.1029/2023WR034599
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- Future sea level rise exacerbates compound floods induced by rainstorm and storm tide during super typhoon events: A case study from Zhuhai, China Z. Zeng et al. 10.1016/j.scitotenv.2023.168799
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- Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State P. da Cunha Luz Barcellos & M. Cataldi 10.3390/atmos11080834
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- Verification Results from the 2017 HMT–WPC Flash Flood and Intense Rainfall Experiment M. Erickson et al. 10.1175/JAMC-D-19-0097.1
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- Bayesian network model for flood forecasting based on atmospheric ensemble forecasts L. Goodarzi et al. 10.5194/nhess-19-2513-2019
- Response Efficacy Perception and Taking Action to Prepare for Disasters with Different Lead Time Z. Chen & Z. Cong 10.1061/(ASCE)NH.1527-6996.0000526
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- Relating forecast and satellite precipitation to generate future skillful ensemble forecasts over the northwest Himalayas at major avalanche and glacier sites A. Singhal et al. 10.1016/j.jhydrol.2022.128795
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- A decision-making model for flood warning system based on ensemble forecasts L. Goodarzi et al. 10.1016/j.jhydrol.2019.03.040
- Real‐Time Flood Forecasting Based on a High‐Performance 2‐D Hydrodynamic Model and Numerical Weather Predictions X. Ming et al. 10.1029/2019WR025583
- An Overview of Flood Concepts, Challenges, and Future Directions A. Mishra et al. 10.1061/(ASCE)HE.1943-5584.0002164
- Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment Y. Umer et al. 10.1007/s11069-021-05117-6
- Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution L. Li et al. 10.1016/j.atmosres.2023.107133
- Evaluation and Benchmarking of Operational Short-Range Ensemble Mean and Median Streamflow Forecasts for the Ohio River Basin T. Adams III & R. Dymond 10.1175/JHM-D-18-0102.1
- Operationalizing a flood forecasting decision support system for Ayeyarwady river, Myanmar A. Agarwal et al. 10.1080/15715124.2020.1738445
- High-Resolution QPF Uncertainty and Its Implications for Flood Prediction: A Case Study for the Eastern Iowa Flood of 2016 B. Seo et al. 10.1175/JHM-D-18-0046.1
- Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances V. Kumar et al. 10.3390/hydrology10070141
- Flood forecasting in urban reservoir using hybrid recurrent neural network B. Cai & Y. Yu 10.1016/j.uclim.2022.101086
- Higher Frozen Soil Permeability Represented in a Hydrological Model Improves Spring Streamflow Prediction From River Basin to Continental Scales J. Agnihotri et al. 10.1029/2022WR033075
- Compounding effects of urbanization, climate change and sea-level rise on monetary projections of flood damage I. Pariartha et al. 10.1016/j.jhydrol.2023.129535
- Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model S. Xu et al. 10.3390/w13030272
Latest update: 19 Apr 2024
Short summary
Quantitative precipitation forecast produced by the WRF model has a similar pattern to that estimated by rain gauges in a southern China large watershed, hydrological model parameters should be optimized with QPF produced by WRF, and simulating floods by coupling the WRF QPF with a distributed hydrological model provides a good reference for large watershed flood warning and could benefit the flood management communities due to its longer lead time.
Quantitative precipitation forecast produced by the WRF model has a similar pattern to that...
Special issue