From fragmented data to integrated disaster early warning systems

Amid increasingly extreme and unpredictable natural disasters, improving forecasting and early warning capacity has become an urgent priority. Speaking at the workshop “New Technologies in Disaster Forecastingand Early Warning” held on March 18, Dr. Bui Du Duong, Deputy Director of the Institute of Water Resources Science, said that a “hybrid” integrated approach, combining multi-source data and multi-model systems, offers an effective pathway to improving forecast quality in Vietnam.

From standalone forecasts to multi-source integration

According to Duong, traditional forecasting systems typically rely on a single data source or a single type of model, limiting their effectiveness when dealing with extreme weather events. By contrast, an integrated approach enables the combination of diverse data sources, including ground observations, remote sensing, physical models, and AI.

“Integrated models do not replace traditional models, they complement them by leveraging the strengths of each method,” he said.

Physical models help describe the natural processes governing meteorology and hydrology, while AI can learn from data to correct biases. Remote sensing provides broad spatial coverage, and observed data serves as validation. When combined, these elements enhance both the stability and reliability of forecasts.

Beyond rainfall and streamflow forecasting, the hybrid approach is being applied across multiple domains, which can be grouped into four main areas:

- Rainfall and streamflow forecasting from two weeks to seasonal scales, forming the basis for early warnings of floods, droughts, and water shortages.

- Landslide risk warnings, integrating rainfall, remote sensing, and hydrological data to assess risks based not only on precipitation but also terrain conditions and soil moisture.

- Watershed erosion, reservoir sedimentation, and sediment transport assessment, which are critical for long-term water resource management and reservoir capacity.

- Flood forecasting, including inundation extent, depth, and duration. Unlike traditional forecasts that provide only water levels at monitoring stations, hybrid models generate more intuitive, actionable information for response planning.

Improving forecast quality through better input data

A key highlight of the presentation was the role of input data. According to Duong, many forecast errors stem from incomplete or inconsistent data.

To address this, his team has developed methods to integrate rainfall data from multiple sources, including ground stations, satellites, and reanalysis datasets, into a unified, high-resolution dataset.

At the same time, ensemble rainfall forecasting systems are being used, combining outputs from multiple global models such as GFS, ECMWF, and Google, rather than relying on a single source. This approach reduces bias and increases forecast reliability.

In reservoir inflow forecasting, critical for hydropower operations and flood control, the hybrid model has demonstrated clear advantages. By combining physical models, big data, and machine learning algorithms, the system can simulate inflows more accurately.

According to the research team, this approach can improve forecast accuracy by around 40% compared to traditional methods, although results vary depending on basin characteristics and conditions.

Hybrid models are also being applied to monitor and forecast flows in major river basins such as the Red River and the Mekong, enabling cross-regional and transboundary monitoring.

One of the most significant strengths of the hybrid approach is its ability to translate technical data into actionable information.

In flood forecasting, for example, systems can generate inundation maps within seconds, providing detailed information on extent, depth, and duration. This allows both authorities and communities to better understand risks and respond proactively.

Similarly, in landslide warning systems, integrating multiple data sources enables location-specific early warnings, rather than reactive responses after an event occurs.

According to Duong, in the context of climate change, no single model or technology can address the full complexity of disaster forecasting. Instead, flexible systems that integrate multiple data sources and methodologies are required.

The integrated approach is particularly well-suited to Vietnam, where data remains fragmented and uneven. By leveraging a combination of observed data, remote sensing, and AI, forecasting capacity can be significantly improved without relying entirely on costly infrastructure.

However, broader implementation will require continued investment in data infrastructure, information-sharing mechanisms, and operational capacity.

“The core value of integration lies not in the algorithms themselves, but in the ability to connect data and transform it into information that supports decision-making,” Duong emphasized.