20 Sep 2022
 | 20 Sep 2022
Status: this preprint is currently under review for the journal HESS.

Impacts of spatio-temporal resolutions of precipitation on flood events simulation based on multi-model structures — A case study over Xiang River Basin in China

Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song

Abstract. Accurate flood events simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatio-temporal resolutions of precipitation on flood events simulation in a large-scale catchment of China. We use the high spatio-temporal resolutions Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcings for hydrologic simulation. Three hydrological models (HBV, SWAT, and DHSVM) and a data-driven model (Long Short-Term Memory (LSTM) network) are utilized for flood events simulation. Two calibration strategies are carried out, one of which targets at matching the flood events and the other one is the conventional strategy to match continuous streamflow. The results indicate that the event-based calibration strategy improves the performance of flood events simulation, compared with conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood events simulation at finer temporal resolution, especially in flood peaks simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, the LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood events simulation.

Qian Zhu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2022-301', Xichao Gao, 20 Jan 2023
    • AC1: 'Reply on CC1', Qian Zhu, 14 Mar 2023
  • RC1: 'Comment on hess-2022-301', Anonymous Referee #1, 12 Feb 2023
    • AC2: 'Reply on RC1', Qian Zhu, 16 Mar 2023
  • RC2: 'Comment on hess-2022-301', Anonymous Referee #2, 19 Apr 2023
    • AC3: 'Reply on RC2', Qian Zhu, 24 Apr 2023
  • EC1: 'Comment on hess-2022-301 (Editor's comment)', Dimitri Solomatine, 17 May 2023

Qian Zhu et al.

Qian Zhu et al.


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Short summary
Input data, model and calibration strategy can affect the accuracy of flood events simulation and prediction. Satellite-based precipitation with different spatio-temporal resolutions is an important input source. Data-driven models are sometimes proved to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study is targeted at the three concerns for accurate flood events simulation and prediction.