Development of measured hydrodynamic information-based flood early warning system for small streams.

Area of Science:

• Environmental Science
• Hydrology
• Disaster Management

Background:

• Small stream basins are highly vulnerable to climate change-induced flooding due to narrow channels and steep slopes.
• Increasing intensity of floods and typhoons necessitates urgent mitigation measures for these vulnerable areas.
• Existing early warning systems may not adequately address the specific challenges of small stream environments.

Purpose of the Study:

• To develop and pilot a novel small stream flood early warning system (SSFEWS).
• To enhance the accuracy and reliability of flood forecasting in small stream basins.
• To improve disaster response and reduce casualties associated with small stream flooding.

Main Methods:

• Integration of IoT-based sensor networks for real-time hydrodynamic data collection.
• Utilization of statistical models, a robust constrained nonlinear optimization algorithm (RCNOA), and the four-parameter logistic method (4PL) for flood probability forecasting.
• Implementation of an automated iterative process to refine forecasting model parameters using rainfall-discharge nomographs and rating curves.

Main Results:

• Successful development and piloting of the SSFEWS tailored for small streams.
• Demonstrated enhancement in system accuracy and reliability through continuous parameter refinement.
• The system effectively integrates data collection, forecasting, and proactive warning issuance.

Conclusions:

• The developed SSFEWS offers a promising solution for mitigating flood damage in small stream basins.
• The system is expected to significantly contribute to community safety and prevent flood-related casualties.
• This novel approach provides a foundation for more effective disaster management in vulnerable small stream environments.