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Advancing river flood forecasting with a collaborative integrated modeling method.

Yuanqing He1, Yongning Wen1, Ruoyu Tao1

  • 1Key Laboratory of Virtual Geographic Environment (Ministry of Education of PR China), Nanjing Normal University, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, 210023, China.

Journal of Environmental Management
|December 14, 2024
PubMed
Summary
This summary is machine-generated.

A new web-based method enhances collaborative river flood forecasting by integrating diverse hydrologic models and data. This approach improves efficiency and aids expert decision-making for better flood risk management.

Keywords:
Collaborative integrated modelingHydrological ensemble forecastingRiver flood forecastingWeb-based environmental modeling

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Area of Science:

  • Hydrology and Water Resources
  • Environmental Modeling
  • Computational Science

Background:

  • River flood forecasting is vital for risk reduction, relying on complex hydrologic cycle models.
  • Current integrated modeling approaches face challenges in collaboration due to data heterogeneity and model integration difficulties.
  • Existing methods often require local configurations, limiting real-time synchronization and expert cooperation.

Purpose of the Study:

  • To introduce a web-based collaborative integrated modeling method for river flood forecasting and assessment.
  • To address the limitations of existing centralized modeling strategies in fostering expert collaboration.
  • To enhance the efficiency and effectiveness of developing and deploying integrated flood forecasting models.

Main Methods:

  • Development of a web-based platform supporting both tightly and loosely integrated modeling modes.
  • Implementation of three core modules: data/model description, model access/integration, and scenario configuration.
  • Utilizing the Global Ensemble Forecast System (GEFS) v12 dataset with the Coupled Routing and Excess Storage (CREST) hydrologic model for validation.

Main Results:

  • Demonstrated enhanced efficiency in collaborative development of river flood forecasts.
  • Improved accessibility of models and data for hydrology experts.
  • Streamlined data processing, simulation, and evaluation workflows for integrated flood forecasting.

Conclusions:

  • The proposed web-based method significantly improves collaborative river flood forecasting and assessment.
  • The approach facilitates better integration of heterogeneous data and models, overcoming current obstacles.
  • This advancement has the potential to aid decision-making processes for flood risk management.