- Floods are the costliest natural hazard-induced disaster in the People’s Republic of China.
- Since 2018, the Asian Development Bank has been piloting innovative flash flood disaster early warning systems in the PRC.
- Disaster impacts have been significantly reduced in the PRC with the help of high-tech early warning systems designed as part of an ADB project.
Floods are the costliest natural hazard-induced disaster in the People’s Republic of China.
To respond to this, ADB in 2018 piloted innovative flash flood disaster early warning systems in the PRC.
In part 1 of this video series, we focus on how new technology has significantly reduced the disaster impacts in two provinces.
Transcript
Flash floods are a common and serious problem in the People’s Republic of China and elsewhere.
Recognizing this, more than 2,000 counties prone to flash floods have established monitoring and early warning systems in the PRC.
However, challenges remain.
Forecasts can be inaccurate and short warning lead time would result in insufficient time for evacuation.
Since 2018, the Asian Development Bank has been piloting innovative flash flood disaster early warning systems in the PRC.
The ADB project has focused on improving monitoring and early warning, and emergency responses at the community level.
Four communities were selected for the pilot project – they are in the flashflood-prone Shewei River basin and Kongmu River basin in Henan and Jiangxi provinces.
Flash flood modelling is at the core of the forecast and early warning system.
The newly developed software can integrate weather, including rainfall forecasts and real time river flow observation, to simulate and forecast the flash flood occurrence in any section of the river.
The project also developed a cloud-based platform that can access forecast and real-time rainfall and other data in the river basin from different sources.
Rainfall and water-stage monitoring stations are installed at important locations of the river basin.
These collect rainfall and water-stage information and compare the data with the built-in thresholds.
Once predefined technical parameters such as rainfall and water flow in the river exceed their thresholds, the flood model in the cloud platform will automatically start simulating the flash flood event.
The cloud platform will send alerts to those in charge of flood prevention through the early warning app.
Drawing on the combined strengths of professional facilities, skilled personnel, and community-based approach, disaster impacts have been significantly reduced.
