Zhang et al. (2025) Integration of deterministic initialization, real-time updating and probabilistic postprocessing in hydrological forecasting for enhancing flood risk reduction

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-12-06
• Authors: Huiming Zhang, Binquan Li, Changchang Zhu, Wei Zhou, Yunyao Chen, Yibin Jiang, Zhongmin Liang
• DOI: 10.1016/j.jhydrol.2025.134725

Research Groups

• State Key Laboratory of Water Cycle and Water Security, Hohai University, Nanjing, China
• College of Water Conservancy & Hydropower Engineering, Hohai University, Nanjing, China
• Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, China
• Technical Advisory of Pearl River Water Resources Commission (Guangzhou) Co., Ltd., Guangzhou, China
• College of Geography and Remote Sensing, Hohai University, Nanjing, China
• Finance Bureau of Changdu Municipality, Xizang Autonomous Region, Changdu, China

Short Summary

This study presents an integrated hydrological forecasting framework combining deterministic modeling, real-time correction, and probabilistic post-processing. It significantly enhances forecast accuracy and provides reliable uncertainty quantification for flood risk reduction in snowmelt-supplied river basins.

Objective

• To develop and evaluate an integrated hydrological forecasting framework that progressively improves deterministic runoff forecasts while providing reliable probabilistic information to quantify forecast uncertainty, specifically for snowmelt-supplied river basins.

Study Configuration

• Spatial Scale: Upstream basin of the Houziyan Reservoir on China’s Dadu River.
• Temporal Scale: Daily runoff simulations (2009–2019), hourly flood simulations, and 48-hour forecasts for three flood events in 2020.

Methodology and Data

• Models used:
  • Xinanjiang (XAJ) model (deterministic hydrological modeling)
  • Autoregression (AR) model (real-time correction for daily runoff)
  • Dynamic System Response Curve (DSRC) method (real-time correction for hourly flood forecasts)
  • Hydrologic Uncertainty Processor (HUP) (probabilistic forecasting)
• Data sources: Observations (daily runoff, hourly flood data) and predicted rainfall.

Main Results

• The deterministic XAJ model achieved strong performance for daily runoff simulations from 2009 to 2019, with an average Nash–Sutcliffe efficiency (NSE) of 0.85.
• For hourly flood simulations, flood volume and peak discharge errors remained within ±20 %, with NSE values ranging from 0.69 to 0.72.
• Real-time correction significantly improved 48-hour flood forecasts, reducing volume error by over 50 % and increasing NSE by over 60 %.
• Probabilistic forecasts via HUP further enhanced accuracy, with the median (Q50) outperforming corrected deterministic results.
• The 90 % confidence intervals from HUP achieved approximately 90 % coverage and a dispersion less than 0.40, effectively enclosing most observations with high reliability.

Contributions

• Presents an integrated framework that combines deterministic hydrological modeling, real-time forecast correction, and probabilistic post-processing, offering a comprehensive approach to hydrological forecasting.
• Demonstrates a progressive improvement in forecast accuracy and reliable uncertainty quantification, particularly beneficial for snowmelt-supplied river basins.
• Provides a practical solution for enhancing flood risk reduction and supporting risk-aware decision-making by delivering both deterministic and robust probabilistic forecasts.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Zhang2025Integration,
  author = {Zhang, Huiming and Li, Binquan and Zhu, Changchang and Zhou, Wei and Chen, Yunyao and Jiang, Yibin and Liang, Zhongmin},
  title = {Integration of deterministic initialization, real-time updating and probabilistic postprocessing in hydrological forecasting for enhancing flood risk reduction},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134725},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134725}
}