Early Warning System for Glacial Lake Outburst Floods (GLOFs)
ABSTARCT :
Background: Glacial Lake Outburst Floods (GLOFs) occur when the dam containing a glacial lake fails, releasing large volumes of water suddenly and causing catastrophic downstream flooding. Climate change is increasing the number and size of glacial lakes, heightening the risk of GLOFs. Curent monitoring and prediction methods can be improved with advanced technologies like remote sensing, sensors installed near glacial lakes and machine learning.
Description: The aim is to develop a remote sensing and/or sensor-based Early Warning System (EWS) for GLOFs by utilizing remote sensing data, network of IoT sensors and advanced data analytics.
The system will continuously monitor and identify critical changes in lake size, water level, temperature, sudden water flow, dam stability and ground movement etc. around glacial lakes. The machine learning algorithms will analyse the information to detect early signs of potential outbursts.
This approach will significantly enhance the capability to predict and respond to GLOFs, improving safety, reducing economic losses, and contributing to resilient infrastructure planning in glacial regions.
Expected Solution: A Sensor which can be installed in glacial lake area, or a predictive model that significantly improves the Early Warning System ability for GLOFs, providing critical lead time for evacuation and mitigation efforts.
EXISTING SYSTEM :
A set of three sensors are required to be interfaced for the Glacier lakes monitoring system. Open air Level sensor (OAS) will measure the distance to the water from the air.
Sensor Electronics consists of a PIC24FJ64GB106 Board with three sensor circuitry. This base board communicates with the Host unit through a RS485 link. A separate Transformer board is used for powering the sensors.
The Near-real time monitoring is being done for 2 potentially vulnerable lakes. Two such sensors have already been deployed and commissioned at vulnerable lakes, Shako Cho (4987m) and Kupup Cho (3982 m) as shown in Fig 4.
Under normal condition, the sensor records water level every 10 minutes and transmits the readings every 30 minutes through INSAT Satellite in AWS format. In case of dam break/collapse, the sensor will record water level data every 2.5 minutes, once the threshold is crossed.
DISADVANTAGE :
High Cost of Implementation: Establishing an EWS can be expensive, requiring advanced technology, infrastructure, and ongoing maintenance.
Data Reliability: The effectiveness of an EWS depends on the accuracy and reliability of the data collected. In remote areas, data may be sparse or unreliable.
Technical Challenges: Monitoring systems may be hampered by harsh environmental conditions, making it difficult to maintain and operate equipment.
Limited Predictive Capability: Predicting the timing and magnitude of GLOFs can be challenging, as they can be triggered by sudden events like heavy rainfall or seismic activity.
PROPOSED SYSTEM :
At its core, the system integrates advanced remote sensing technologies and in-situ sensors to continuously monitor glacial lakes and surrounding hydrological conditions. Data collected is managed in a centralized database, where sophisticated hydrological models analyze trends and predict potential GLOF events based on real-time inputs.
A robust communication module ensures timely alerts are disseminated to at-risk communities via multiple channels, including SMS and mobile applications.
Furthermore, the system emphasizes community engagement by incorporating local knowledge and fostering collaboration among stakeholders. Training programs and emergency response drills are conducted to prepare communities for effective action during potential flood events.
By combining cutting-edge technology with community involvement, this EWS not only aims to provide timely warnings but also empowers communities to take proactive measures, ultimately minimizing the risks associated with GLOFs.
ADVANTAGE :
Risk Mitigation: EWS can significantly reduce the risks associated with GLOFs by providing timely alerts, allowing communities to prepare and evacuate if necessary.
Enhanced Data Collection: These systems improve the collection of hydrological and meteorological data, which can enhance understanding of glacial dynamics and potential flood risks.
Community Awareness: EWS raise awareness about the dangers of GLOFs, promoting proactive measures and preparedness within vulnerable communities
Improved Response Coordination: EWS facilitate better coordination among local authorities, emergency services, and community organizations, ensuring a more organized response during a crisis.
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