Репозиторий КазНИТУ

Advancements in Remote Sensing for Monitoring and Risk Assessment of Glacial Lake Outburst Floods

Авторы:

Nurakynov, S. (Соавтор)

Sydyk, N. (Соавтор)

Balakay, L. (Соавтор)

Издательство:

Multidisciplinary Digital Publishing Institute (MDPI)

Дата издания

05.07.2025

Область исследования

Инжиниринг и технологии

Тип исследования

Фундаментальное

Аннотация (краткое описание)

Glacial Lake Outburst Floods (GLOFs) have emerged as a critical threat to high-mountain communities and ecosystems, driven by accelerated glacier retreat and lake expansion under climate change. This review synthesizes advancements in remote sensing technologies and methodologies for GLOF monitoring, risk assessment, and mitigation. Through a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-guided systematic literature review and bibliometric analysis of studies from 2010 to 2025, we evaluate the transformative role of remote sensing in overcoming traditional field-based limitations. Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. This review underscores the importance of these technologies in improving GLOF risk assessments and supporting early warning systems, which are crucial for safeguarding vulnerable high-mountain communities. It addresses existing challenges, such as data integration and model calibration, and advocates for collaborative efforts between scientists, policymakers, and local stakeholders to translate technological advancements into effective mitigation strategies, ensuring the sustainability of these at-risk regions. © 2025 by the authors.

Соответствие целям устойчивого развития (ЦУР)

13.

Борьба с изменением климата

URI

https://www.scopus.com/record/display.url?eid=2-s2.0-105009140255&origin=resultslist

DOI

10.3390/geosciences15060211

Тип публикаций

Статья

Сведения о финансировании

This research was funded by Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant number BR21882365.

Данный документ располагается в коллекциях