Preprints
https://doi.org/10.5194/hess-2022-301
https://doi.org/10.5194/hess-2022-301
 
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 Zhu1, Xiaodong Qin1, Dongyang Zhou1, Tiantian Yang2, and Xinyi Song3 Qian Zhu et al.
  • 1School of Civil Engineering, Southeast University, Nanjing 211189, China
  • 2School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, 73019 , USA
  • 3School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China

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: open (until 15 Nov 2022)

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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.