Large-watershed flood forecasting with high-resolution distributed hydrological model

Chen, Yangbo; Li, Ji; Wang, Huanyu; Qin, Jianming; Dong, Liming

doi:10.5194/hess-21-735-2017

Cited by 44 publications

(32 citation statements)

References 39 publications

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“…The Liujiang River basin (LRB) is selected as the studied area, which is the largest first-order tributary of the Pearl River with a drainage area of 58 270 km 2 (Chen et al, 2017). LRB is in the monsoon area with heavy storms that induced severe flooding in the watershed and caused huge flood damages in the past centuries.…”

Section: Study Areamentioning

confidence: 99%

“…More information about the LBR can be found in Li et al (2015) and Chen et al (2017). The model domain is centered at 23.8 • N, 109.2 • W with the Lambert conformal projection.…”

Section: Configuration Of Wrf For Lrbmentioning

confidence: 99%

“…KainFritsch cumulus parameterization (Kain, 2004) as well as the YSU boundary layer microphysics scheme (Hong and Lim, 2006) are used. Other physics schemes used include the NOAH scheme for the land surface physics (Ek et al, 2003), the Goddard scheme for the shortwave radiation physics (based on Chou and Suarez, 1994), and the Rapid Radiative Transfer Model (RRTM) scheme for the longwave radiation physics (Mlawer et al, 1997).…”

Section: Configuration Of Wrf For Lrbmentioning

confidence: 99%

“…Now the Liuxihe model is deployed on a supercomputer system with parallel computation techniques (Chen et al, 2013) that largely facilitates the model parameter optimization of the Liuxihe model. Chen et al (2017) set up the Liuxihe model in the LRB with freely downloaded terrain property data from the website at a spatial resolution of 200 m × 200 m, and optimized model parameters with observed hydrological data. The model was validated by observed flood event data, and the model performance was found to be rational and could be used for real-time flood forecasting.…”

Section: Liuxihe Modelmentioning

confidence: 99%

“…While the model parameter optimization by Chen et al (2017) is done by using the rain gauge precipitation, this study uses the WRF QPF as the precipitation input. So the parameters of the Liuxihe model that were set up in the LRB may not be appropriate for coupling the WRF QPF.…”

Section: Liuxihe Model Parameter Optimizationmentioning

confidence: 99%

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Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

Chen

Wang

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

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Section: Study Areamentioning

confidence: 99%

“…More information about the LBR can be found in Li et al (2015) and Chen et al (2017). The model domain is centered at 23.8 • N, 109.2 • W with the Lambert conformal projection.…”

Section: Configuration Of Wrf For Lrbmentioning

confidence: 99%

Section: Configuration Of Wrf For Lrbmentioning

confidence: 99%

Section: Liuxihe Modelmentioning

confidence: 99%

Section: Liuxihe Model Parameter Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

Chen

Wang

et al. 2017

Hydrol. Earth Syst. Sci.

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Downscaling climate projections over large and data sparse regions: Methodological application in the Zambezi River Basin

Peleg

Sinclair

Fatichi

et al. 2020

Intl Journal of Climatology

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Climate impact studies often require climate data at a higher space-time resolution than is available from global and regional climate models. Weather generator (WG) models, generally designed for mesoscale applications (e.g., 10 1-10 5 km 2), are popular and widely used tools to downscale climate data to finer resolution. One advantage of using WGs is their ability to generate the necessary climate variables for impact studies in data sparse regions. In this study, we evaluate the ability of a previously established state of the art WG (the AWE-GEN-2d model) to perform in data sparse regions that are beyond the mesoscale, using the Zambezi River basin (10 6 km 2) in southeast Africa as a case study. The AWE-GEN-2d model was calibrated using data from satellite retrievals and climate re-analysis products in place of the absent observational data. An 8-km climate ensemble at hourly resolution, covering the period of 1976-2099 (present climate and RCP4.5 emission scenario from 2020), was then simulated. Using the simulated 30-member ensemble, climate indices for both present and future climates were computed. The high-resolution climate indices allow detailed analysis of the effects of climate change on different areas within the basin. For example, the southwestern area of the basin is predicted to experience the greatest change due to increased temperature, while the southeastern area was found to be already so hot that is less affected (e.g., the number of 'very hot days' per year increase by 18 and 9 days, respectively). Rainfall intensities are found to increase most in the eastern areas of the basin (1 mmÁd −1) in comparison to the western region (0.3 mmÁd −1). As demonstrated in this study, AWE-GEN-2d can be calibrated successfully using data from climate reanalysis products in the absence of ground station data and can be applied at larger scales than the mesoscale.

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Forecasting of pre-monsoon flash flood events in the northeastern Bangladesh using coupled hydrometeorological NWP modelling system

Shuvo

Rashid

Panda

et al. 2021

Meteorol Atmos Phys

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Large-watershed flood forecasting with high-resolution distributed hydrological model

Cited by 44 publications

References 39 publications

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

Downscaling climate projections over large and data sparse regions: Methodological application in the Zambezi River Basin

Forecasting of pre-monsoon flash flood events in the northeastern Bangladesh using coupled hydrometeorological NWP modelling system

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