Digital early warnings make dams safer: Quang Tri’s key lessons

The presentation delivered by the Viet Nam Academy for Water Resources (VAWR) at the forum “Digital Transformation and Technology Applications in Reservoir Operation and Dam Safety” underscores how data-driven systems are reshaping disaster-risk management and reservoir operations. Drawing from the research project “Application of Advanced Technologies to Build an Early Warning System for Flood and Inundation Risks in Quang Tri Province and Surrounding Areas”, the Academy showcased an integrated, technology-enabled early-warning and decision-support model with strong potential for nationwide application.

Viet Nam frequently faces heavy rainfall, severe storms, flash floods, and widespread inundation. In this context, reservoirs serve as crucial flood-control structures but can also become sources of risk when operated with insufficient data or delayed forecasts. VAWR emphasized that digital transformation is no longer optional; it is essential for building unified data platforms, enhancing multi-scenario simulations, and delivering timely, accurate warnings to downstream communities.

According to the presentation, the Early Warning System (EWS) developed for Quang Tri integrates meteorological and hydrological data, radar rainfall estimates, tidal levels, satellite cloud imagery, downstream demographic and infrastructure information, and reservoir-operation data. The accompanying Decision Support System (DSS) enables real-time simulations of flood scenarios based on 24–72-hour rainfall forecasts, analyzes flood-routing capacity, and projects the extent and depth of inundation over time. This capacity allows authorities to assess risk days in advance and make more informed decisions on reservoir releases and water storage.

A key component is the comprehensive Flood Risk Map Database, which consolidates information on population, housing, public works, and critical infrastructure within potential inundation zones. This database supports emergency response planning, evacuation strategies, and the assessment of downstream impacts under different operational scenarios.

The system also includes an automatic alert module that transmits warnings via mobile applications and social media to local authorities and at-risk households. This real-time dissemination significantly shortens the warning chain, an urgent need in rapidly evolving flood situations.

One of the system’s most compelling features is its validation by satellite remote sensing. Using Sentinel-1 imagery after the October 2024 floods, researchers compared observed inundation with DSS simulation outputs. The difference in flooded area was only 2.5% to 5.3% across three major basins (Ben Hai, Thach Han, and O Lau). According to VAWR, this small margin of error demonstrates high model reliability and confirms that DSS outputs can serve as a robust reference for operational decisions and community alerts.

Building on these results, the Academy offered several recommendations to accelerate digital transformation in dam and reservoir safety. First, flood-risk maps and warning channels should be standardized and incorporated into land-use planning and disaster-management frameworks. VAWR also proposed deeper integration of reservoir-operation modules into the DSS to simulate dam-release scenarios under both single-reservoir and cascade-operation procedures.

The Academy stressed the importance of deploying IoT-based sensors on dam bodies and foundations to monitor internal deformation, seepage pressure, and water levels. When connected to the shared data platform, these sensors provide a critical layer of real-time structural-safety information.

Another recommendation is the establishment of a unified data-sharing mechanism among meteorological agencies, reservoir management authorities, and disaster response units, addressing one of the biggest bottlenecks in current operations. VAWR also highlighted the need for specialized training to ensure that local engineers and operators can fully utilize EWS/DSS technologies.

According to the presentation, the experience in Quang Tri demonstrates that Viet Nam is fully capable of mastering advanced technologies to build a “digital brain” for reservoir operations. If scaled and deployed systematically, integrated early-warning systems combining data management, modeling, and remote-sensing validation can become a foundational tool for strengthening dam safety, reducing downstream risks, and enhancing national resilience amid increasingly extreme weather patterns.

The DSS framework is structured to support real-time data ingestion, automatic scenario generation, and visualization on a digital map interface. Different operating modules allow users to select conditions for simulations, including whether or not to incorporate reservoir operations. The system’s design emphasizes multi-layer data handling, enabling simultaneous processing of rainfall, terrain, and hydraulic-model outputs.