Articles | Volume 28, issue 7
https://doi.org/10.5194/hess-28-1665-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1665-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Apr 2024
Research article |
| 11 Apr 2024
| 11 Apr 2024
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
School of Civil Engineering, Southeast University, Nanjing 211189, China
Xiaodong Qin
School of Civil Engineering, Southeast University, Nanjing 211189, China
Dongyang Zhou
School of Civil Engineering, Southeast University, Nanjing 211189, China
Tiantian Yang
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA
Xinyi Song
School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China
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- An improved SWAT snowmelt model and its application in non-stationary spring flood frequency analysis in alpine regions: a case study of the upper Jinsha River basin Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133808
- Streamflow forecasting with a hybrid model integrating a distributed hydrological model and deep learning methods X. Liang et al. https://doi.org/10.1016/j.ejrh.2026.103444
- Dynamic Simulation Model to Monitor Flow Growth Rivers in Rapid-Response Catchments Using Humanitarian Logistic Strategies J. Delgado-Maciel et al. https://doi.org/10.3390/technologies13060213
- Uncertainty analysis of satellite rainfall input data in a hydrological model using the Generalized Likelihood Uncertainty Estimation (GLUE) method H. Rinduan et al. https://doi.org/10.1051/bioconf/202624201007
- Dependence of Simulated High Flows and Flood Events on Meteorological Forcing Products in the Songhua River Basin: A CLM5–CaMa-Flood Assessment M. Li et al. https://doi.org/10.3390/w18161929
- Impact of baseflow separation on improving streamflow and its extremes with a hybrid model coupling hydrological and machine learning models K. Zhu et al. https://doi.org/10.1080/02626667.2025.2513478
- Data and knowledge-driven model for flood peak runoff forecasting H. Malik et al. https://doi.org/10.1016/j.jher.2026.100695
- Diurnal variation features and dry times impact based on the latest hourly satellite-based precipitation data across China Y. Gu et al. https://doi.org/10.1016/j.ejrh.2025.102859
- Improvement of physics-based and data-driven model simulations based on multi-source soil moisture datasets X. Liang et al. https://doi.org/10.1016/j.ejrh.2025.102557
- Integrating MFDFA with hybrid deep learning for enhanced daily precipitation-runoff prediction in the Minjiang River Basin H. Zeng et al. https://doi.org/10.2166/hydro.2025.043
- Flood mitigation role of traditional tank cascades: quantifying land use change and siltation effects using integrated hydrologic-hydraulic modelling S. Natarajan et al. https://doi.org/10.2166/wcc.2026.159
- Enhancing flood-exposure assessment with high-resolution settlement data: An integrated study from flood hazard to exposure in the Nilwala River Basin, Sri Lanka J. Jayapadma et al. https://doi.org/10.1016/j.ejrh.2025.102949
- The impact of spatial resolution on hourly flood modeling in large watersheds L. Ye et al. https://doi.org/10.5194/hess-30-2995-2026
- Future flood exposure of flatland and mountainous rivers in Sri Lanka - case study of the Gin and Nilwala River Basins J. Jayapadma et al. https://doi.org/10.1080/19475705.2026.2729946
- Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez https://doi.org/10.1016/j.ejrh.2025.102719
- Avaliação hidrodinâmica de medidas de controle de cheias na bacia do Rio Jiquiá (Recife–PE) sob cenários de mudanças climáticas A. Caetano da Silva et al. https://doi.org/10.26848/rbgf.v19.02.p1157-1180
16 citations as recorded by crossref.
- An improved SWAT snowmelt model and its application in non-stationary spring flood frequency analysis in alpine regions: a case study of the upper Jinsha River basin Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133808
- Streamflow forecasting with a hybrid model integrating a distributed hydrological model and deep learning methods X. Liang et al. https://doi.org/10.1016/j.ejrh.2026.103444
- Dynamic Simulation Model to Monitor Flow Growth Rivers in Rapid-Response Catchments Using Humanitarian Logistic Strategies J. Delgado-Maciel et al. https://doi.org/10.3390/technologies13060213
- Uncertainty analysis of satellite rainfall input data in a hydrological model using the Generalized Likelihood Uncertainty Estimation (GLUE) method H. Rinduan et al. https://doi.org/10.1051/bioconf/202624201007
- Dependence of Simulated High Flows and Flood Events on Meteorological Forcing Products in the Songhua River Basin: A CLM5–CaMa-Flood Assessment M. Li et al. https://doi.org/10.3390/w18161929
- Impact of baseflow separation on improving streamflow and its extremes with a hybrid model coupling hydrological and machine learning models K. Zhu et al. https://doi.org/10.1080/02626667.2025.2513478
- Data and knowledge-driven model for flood peak runoff forecasting H. Malik et al. https://doi.org/10.1016/j.jher.2026.100695
- Diurnal variation features and dry times impact based on the latest hourly satellite-based precipitation data across China Y. Gu et al. https://doi.org/10.1016/j.ejrh.2025.102859
- Improvement of physics-based and data-driven model simulations based on multi-source soil moisture datasets X. Liang et al. https://doi.org/10.1016/j.ejrh.2025.102557
- Integrating MFDFA with hybrid deep learning for enhanced daily precipitation-runoff prediction in the Minjiang River Basin H. Zeng et al. https://doi.org/10.2166/hydro.2025.043
- Flood mitigation role of traditional tank cascades: quantifying land use change and siltation effects using integrated hydrologic-hydraulic modelling S. Natarajan et al. https://doi.org/10.2166/wcc.2026.159
- Enhancing flood-exposure assessment with high-resolution settlement data: An integrated study from flood hazard to exposure in the Nilwala River Basin, Sri Lanka J. Jayapadma et al. https://doi.org/10.1016/j.ejrh.2025.102949
- The impact of spatial resolution on hourly flood modeling in large watersheds L. Ye et al. https://doi.org/10.5194/hess-30-2995-2026
- Future flood exposure of flatland and mountainous rivers in Sri Lanka - case study of the Gin and Nilwala River Basins J. Jayapadma et al. https://doi.org/10.1080/19475705.2026.2729946
- Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez https://doi.org/10.1016/j.ejrh.2025.102719
- Avaliação hidrodinâmica de medidas de controle de cheias na bacia do Rio Jiquiá (Recife–PE) sob cenários de mudanças climáticas A. Caetano da Silva et al. https://doi.org/10.26848/rbgf.v19.02.p1157-1180
Saved (final revised paper)
Latest update: 01 Oct 2026
Short summary
Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Input data, model and calibration strategy can affect the accuracy of flood event simulation and...
