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2023 Work Plan: the University of Zurich Aims to Safeguard Communities in Central Asia

In a significant effort to enhance safety and disaster preparedness in Central Asia, researchers at the University of Zurich (UZH) have planned the work for 2023 under the GLOFCA project, led by UNESCO and funded by Adaptation Fund. The work plan seeks to develop advanced tools, guidance documents, and early warning systems to protect communities from potential glacial lake outburst floods (GLOFs).

Component 1: Tool Box Development

Under this component, UZH researchers are working on the development and finalization of a prototype tool for lake mapping. The tool will enable experts to gather crucial data from glacial lakes, even in challenging conditions like cloud cover or remote locations. The final prototype will be presented through webinars and workshops, fostering knowledge transfer among stakeholders.

Moreover, a comprehensive atlas on glacial lakes will be created based on the developed tool. This updated inventory will provide valuable insights into lake conditions and serve as a regional reference. By sharing this atlas with partners through online webinars and workshops, the project aims to promote better lake monitoring and management strategies.

Component 2: Best Practice Guidance Document

A methodological framework for assessing and mapping downstream GLOF hazard based on international best practices is being prepared. By co-authoring chapters of the Best Practice Guidance Document with inputs from local partners, the researchers will ensure a collaborative and region-specific approach.

The project team will also conduct hazard and risk modeling, using the outlined approaches in the guidance document. This modeling will help identify potential GLOF risks in both first-order (all lakes) and local detailed scenarios (pilot communities). Local scientists will be engaged in joint modeling and capacity building to ensure a comprehensive understanding of the hazards.

Component 3: Institutional DRR Framework

Institutional profiles for involved authorities will be developed to establish a local to regional framework for disaster risk reduction (DRR). By creating a GLOF Early Warning System (EWS) protocol, the project aims to streamline responsibilities, procedures, and information sharing among authorities. This step will contribute to efficient decision-making during emergencies.

Component 4: Site-Specific EWS Design and Implementation

One of the most critical aspects of the project is the site-specific monitoring concepts for each EWS. Based on detailed hazard assessments and process understanding, these concepts will provide essential data for reliable monitoring and warning capabilities. The involvement of local experts ensures that the EWS is tailored to each community’s needs and risks.

Component 5: Contribution to Web-Based Knowledge Platform

The University will contribute to the web-based knowledge platform to disseminate project outputs, including maps and plans. The platform serves as a valuable resource for researchers, authorities, and communities seeking information on glacial lake management and disaster preparedness.

Importance of the Tasks

The tasks under each component hold immense importance for safeguarding communities living near glacial lakes. By developing advanced tools and guidance documents, researchers can provide authorities and stakeholders with crucial data and knowledge for decision-making. The implementation of site-specific monitoring concepts will significantly improve early warning capabilities, allowing communities to respond effectively to potential GLOFs.

Through the web-based knowledge platform, the project ensures that the gathered information and best practices are easily accessible to a wide range of users. This knowledge exchange is vital for strengthening regional collaboration and disaster resilience in Central Asia.

As glacial lakes continue to be both awe-inspiring natural wonders and potential hazards, the UZH-led project’s comprehensive efforts demonstrate a commitment to sustainable development and the safety of communities in the region. By fostering collaboration and leveraging advanced technology, this ambitious initiative sets a precedent for enhanced glacial lake management worldwide.

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New Technology Empowers Safer Management of Glacial Lakes

In the remote corners of the world, vast glacial lakes pose both natural wonders and potential hazards. For civil servants, journalists, and developmental agency workers, understanding these lakes is crucial for sustainable development and disaster preparedness. A groundbreaking scientific paper has explored and evaluated cutting-edge approaches for mapping glacial lakes, using advanced technology and innovative methodologies.

Tapping into Satellite-Based Sensors

In this study, researchers tapped into the power of satellite-based sensors, like optical imagery and Synthetic Aperture Radar (SAR) data. These technologies enable scientists to gather vital information from glacial lakes even in challenging conditions, like cloud cover or remote locations. The optical imagery offers different spectral features for analysis, helping to distinguish between water and land surfaces. On the other hand, SAR data relies on the backscattering of surface waters, providing crucial insights into lake mapping.

Classical Approaches and Limitations

Classical approaches, such as the Normalised Difference Water Index (NDWI) and Enhanced Water Index (EWI), proved to be valuable tools for distinguishing water from land. However, the study also highlighted the limitations of these methods, especially when dealing with factors like glacial lake turbidity, cloud shadows, and seasonal snow. Additional post-processing steps are often needed to ensure accurate results.

Harnessing Machine Learning for Enhanced Lake Monitoring

Excitingly, researchers delved into the world of machine learning, where integration of different data sources significantly improved the accuracy of unsupervised land/water classification in high alpine areas. By using a random forest machine learning classifier on data from Sentinel-2 MSI and Sentinel-1 SAR, along with digital elevation models (DEMs), scientists achieved better glacial lake monitoring results. These advanced methodologies open doors for more effective and precise lake mapping, which can lead to better-informed decisions and risk management.

Using Auxiliary Information Sources

Additionally, the study tapped into auxiliary information sources, such as DEMs, to mitigate errors caused by steep slopes and cast shadows. These sources provided reliable data on mountainous areas, enabling scientists to get a clearer picture of the lakes and their surroundings.

To illustrate the practical applications of these findings, let’s dive into a real-life story that could have been true:

In a remote region of the Pamir region, the tiny village is nestled between majestic peaks and glacial lakes. For years, the community has lived in awe of the lakes’ beauty, but also in fear of potential hazards. The regional development agency and research institute, together with local civil servants and journalists, decided to adopt the latest lake mapping technology to protect the villagers.

Creating Early Warning Systems for Safer Development

Using the state-of-the-art satellite-based sensors, researchers captured optical imagery and SAR data, unveiling vital insights about the glacial lakes. Machine learning techniques were employed to process the vast amount of data, creating accurate maps that distinguished between land and water. This new information allowed the research insititute to identify potential risks and develop strategies to minimize them.

Armed with Knowledge: Early Warning System Implementation

Furthermore, auxiliary information sources, like digital elevation models, were used to create a comprehensive view of the landscape, reducing errors and improving accuracy. Armed with this knowledge, the regional development agency implemented an early warning system for the village, ensuring that the community would be alerted in case of any sudden lake changes or potential glacial lake outbursts.

Inspiring Global Change: A Safer Future for Glacial Lakes

Thanks to the collaborative efforts of the agency, institute, civil servants, and journalists, the village of San Juan now stands better equipped to manage the glacial lakes. They can appreciate the beauty of these natural wonders while being prepared for any risks they might pose. The innovative mapping technology has not only protected the community but also inspired other regions worldwide to adopt similar strategies for safer and more sustainable glacial lake management.

Conclusion

In conclusion, the scientific paper has unveiled groundbreaking methodologies for mapping glacial lakes, empowering civil servants, journalists, and developmental agency workers to safeguard communities and enhance sustainable development in regions surrounded by these awe-inspiring natural formations. With technology and knowledge at their disposal, these professionals can now act with greater confidence and foresight, protecting both people and the environment for generations to come.

Reference

For detailed information, please refer to Chapter 2 Lake mapping of the comprehensive document titled: Glacial Lakes Outburst Flood: Best Practice Guidance

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Understanding Glacial Lake Hazards Made Easier with GLOFCA Project

In a bid to comprehend the potential dangers of glacial lakes, the Glacial Lake Outburst Floods in Central Asia (GLOFCA) project has emerged as a significant contributor to regional best practices. By leveraging cutting-edge technologies and international expertise, the project aims to create a safer future for Central Asian communities.

Mapping Glacial Lakes – A Crucial First Step

One of GLOFCA’s main contributions lies in creating a comprehensive and up-to-date inventory of glacial lakes across the region. This step is crucial as it allows experts to monitor changes over time and identify lakes that may pose potential hazards. Central Asia’s unique terrain has shown that new lakes can form rapidly, making regular mapping essential, even during adverse weather conditions or cloud cover.

To achieve this, the project is developing the Glacial Lakes Inventory (GLI) toolbox, utilizing the python Tkinter library. This toolbox will monitor lake dynamics and provide statistics on surface area changes, the appearance of new lakes, and the disappearance of existing ones. The toolbox relies on Sentinel-2 Normalised Difference Water Index (NDWI) to detect glacial lakes, and an exciting deep learning-based methodology that fuses information from Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite data, allowing high-resolution monitoring year-round.

Overcoming Challenges

Mapping and monitoring glacial lakes using satellite sensors come with challenges. Many of these lakes are small and frozen for a significant part of the year, making detection difficult. Furthermore, clouds, cast shadows, lake turbidity, and atmospheric conditions pose additional hurdles. However, the GLOFCA project’s state-of-the-art algorithms and techniques show promising results, even detecting lakes as small as 0.01 kmĀ² in area.

The Role of Deep Learning

The GLOFCA project employs an advanced deep learning algorithm called the Glacial Lakes Monitoring (GLM) network. This network uses a combination of satellite data from both Sentinel-1 SAR and Sentinel-2 sensors to create a comprehensive and data-driven approach to glacial lake monitoring. The deep learning process involves semantic segmentation, classifying each pixel as either lake or background, ultimately providing experts with valuable data on glacial lake extents.

Advancing Susceptibility Assessment

The GLOFCA project not only focuses on lake mapping but also advances susceptibility assessment. By combining regional expertise and international best practices, the project presents a comprehensive checklist for experts to evaluate factors that contribute to GLOF susceptibility. This guidance aids in prioritizing glacial lakes for further monitoring and action.

A Safer Future for Central Asia

As GLOFCA continues its mission, Central Asian communities can rest assured that innovative technologies and international collaborations are making great strides in understanding glacial lake hazards. By harnessing the power of satellite data and deep learning, the project is laying the groundwork for safer, more resilient communities in the face of potential glacial lake outburst floods.

Conclusion

For detailed information, please refer to Chapter 4 GLOFCA contribution to regional best practices of the comprehensive document titled: Glacial Lakes Outburst Flood: Best Practice Guidance

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Protecting Communities from Glacial Lake Hazards: Central Asian Approaches and Practical Applications

Glacial lakes, though stunning, can hold hidden dangers for nearby communities. A recent scientific paper explores how Central Asian countries assess these hazards to safeguard their people. By understanding the factors that make some lakes more susceptible to catastrophic outbursts, authorities can take practical steps to protect communities.

A Journey Through Central Asian Approaches

In Central Asia, experts refer to “lake hazard assessment,” which is similar to “lake susceptibility assessment.” This first step involves selecting lakes for detailed evaluation. For instance, Uzbekistan assesses lakes above 1500 m a.s.l. that drain into the country. Kazakhstan, since 2015, examines all moraine lakes, while Kyrgyzstan focuses on lakes prone to Glacial Lake Outburst Floods (GLOFs), listed in a national lake atlas. Tajikistan conducts hazard assessments in three stages: preliminary analysis, identifying possible hazards, and analyzing consequences.

Categorizing Lakes for Safety

Central Asian countries categorize lakes based on various characteristics influenced by atmospheric, geological, and hydrological processes. Lakes fall into 2 hazard categories in Kazakhstan, 3 in Uzbekistan and Tajikistan, and 4 in Kyrgyzstan. Expert judgment and field observations, like bathymetry and aerial photographs, guide the categorization process. Parameters like lake type, dam composition, and glacier proximity are considered.

The Hidden Threat of Non-Stationary Lakes

Although non-stationary lakes are fewer, they account for over 50% of catastrophic GLOFs. These dynamic lakes fill up quickly during the ablation period, leading to potential outbursts. The blockage of ice tunnels and subsequent opening of underground runoff channels often cause these outbursts. Lakes aged 20 or more years pose minimal threats, but monitoring remains vital.

The Role of Remote Sensing

Advanced remote sensing methods, such as space imagery and daily synoptic forecasts, play a crucial role in monitoring lake changes in size and volume. Special attention is given to non-stationary lakes, ensuring timely action if needed. Regular reassessment of lake inventories and hazard potential further enhances community safety.

Conclusion

Central Asian countries take lake hazard assessment seriously to protect communities from glacial lake outbursts. By understanding the factors contributing to lake hazards and employing remote sensing technologies, authorities can stay vigilant. With ongoing monitoring and reassessment, they ensure timely preventive measures and secure the safety of those living near these awe-inspiring natural wonders.

Reference

For detailed information, please refer to Chapter 3.2 National approaches used for susceptibility assessment of the comprehensive document titled: Glacial Lakes Outburst Flood: Best Practice Guidance

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Understanding Glacial Lake Outburst Floods: Key Determinants and Future Threats

Glacial lakes are not just serene bodies of water; they can pose a significant threat to nearby areas. A recent scientific paper sheds light on the key factors that determine the susceptibility and magnitude of glacial lake outburst floods (GLOFs). Understanding these determinants is crucial for predicting future threats and implementing effective measures to protect vulnerable communities.

Key Determinants

According to the study, the size of the glacier lake, the outburst mechanism, and the characteristics of the downstream torrent are the main determinants of GLOF susceptibility and event magnitude. Large lakes have the potential for greater flood magnitudes, but they are also more susceptible to impacts from rock and ice. However, solely focusing on absolute lake size can be misleading, as dangerous situations involving smaller or rapidly changing lakes may be overlooked.

Measuring Lake Volumes

Direct measurements of lake volumes in remote regions have been challenging and rare. To overcome this, researchers have found innovative solutions. The study highlights the use of small unmanned boats equipped with sonar instruments as a safe and cost-effective option for surveying critical lakes and obtaining detailed bathymetries. Additionally, empirical equations linking mean lake depths with lake area provide a first-order estimate of lake volume for regional to basin-scale studies.

Anticipating Future Threats

The research emphasizes the importance of anticipating future threats related to glacial lake expansion and development. By considering the geomorphological context and utilizing morphological criteria or modelled bed topography, possible locations where lakes may develop in the future can be identified. While estimating future lake volumes remains approximate, monitoring glacier dynamics through remote sensing and photogrammetry, combined with regular lake development monitoring, can provide valuable insights.

Protecting Critical Structures

Understanding permafrost conditions is crucial for protecting critical dam structures. Characterizing permafrost in the surrounding steep bedrock slopes and the dam area helps infer the presence and condition of ground ice, which is highly susceptible to further warming and melting. Geophysical techniques are employed to determine subsurface thermal conditions accurately, ensuring the stability and safety of these structures.

Conclusion

The findings of this study have significant implications for civil servants, journalists, and developmental agency workers involved in disaster risk management. By understanding the key determinants of GLOF susceptibility and event magnitude, practitioners can prioritize monitoring efforts and implement measures to protect vulnerable communities. Anticipating future threats and ensuring the stability of critical dam structures are crucial steps towards mitigating the risks associated with glacial lake outburst floods. Through continued research and collaboration, we can work towards a safer future in the face of these natural hazards.

Reference

For detailed information, please refer to Chapter 3.1.1 Cryospheric Factors of the comprehensive document titled: Glacial Lakes Outburst Flood: Best Practice Guidance

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Unravelling the Secrets of Glacial Lake Susceptibility: A Pathway to Safer Communities

Glacial lakes, with their majestic beauty, can hide hidden dangers for nearby communities. A recent scientific paper has shed light on the factors that contribute to the susceptibility of these lakes and their potential for triggering catastrophic events. By understanding these factors, we can take practical steps to protect vulnerable areas and ensure the safety of communities at risk.

Factors Influencing Lake Susceptibility

Lake susceptibility assessment involves considering two key factors: those critical to the stability of the lake dam and those that influence the potential for triggering events. These triggering events could be rock or ice avalanches or debris flows. Studies have also highlighted the importance of hydro-geomorphic characteristics of the lake catchment area, as they impact susceptibility to precipitation or melt-triggered outburst events. Identifying these factors is crucial in assessing the risks associated with glacial lakes.

The Power of High-Resolution Optical Imagery and Digital Terrain Models

Thanks to technological advancements, we now have access to high-resolution optical imagery, such as that available from platforms like Google Earth, along with digital terrain models. This breakthrough has allowed us to remotely quantify various physical characteristics of dams and catchment areas over large spatial scales. Researchers have been able to analyze these images and models to gain insights into the stability and potential risks associated with glacial lakes. However, on-site investigations are still necessary for precise measurements of geometric features and in situ characteristics.

Harnessing GIS Tools for Assessment

Geographical Information System (GIS) tools have proven invaluable in assessing glacial lakes. These tools enable us to determine the upstream catchment area of each lake and quantify essential hydrological characteristics. While empirical evidence linking catchment characteristics with glacial lake outburst flood (GLOF) susceptibility is still limited, it is generally understood that lakes fed by steep, fast-draining catchment areas are more susceptible to rapid inflow from precipitation or snowmelt. GIS tools also help in evaluating the topographic and geomorphological characteristics of downstream flood paths below the lake.

Practical Applications and Implications

The findings of this scientific paper hold practical significance for civil servants, journalists, and developmental agency workers involved in disaster risk management. By understanding the factors that contribute to lake susceptibility, we can prioritize monitoring efforts, implement early warning systems, and develop effective mitigation strategies. Armed with remote sensing technologies and GIS tools, we can assess risks from a broader perspective, enabling better decision-making to protect communities at risk.

Conclusion

As we delve deeper into understanding glacial lake susceptibility, we unlock the secrets that lie within these serene landscapes. By considering factors impacting lake stability and the potential for triggering events, leveraging high-resolution imagery and digital terrain models, and utilizing GIS tools, we empower ourselves to make informed decisions. Together, we can create a safer future, where vulnerable communities are shielded from the devastating impacts of glacial lake outburst floods.

Reference

For detailed information, please refer to Chapter 3.1.2 Geotechnical and geomorphic factors of the comprehensive document titled: Glacial Lakes Outburst Flood: Best Practice Guidance

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Understanding the Risks: Assessing Glacial Lake Susceptibility

In the breathtaking landscapes of mountainous regions, glacial lakes shimmer with pristine beauty. However, these picturesque lakes can pose significant risks to nearby communities and infrastructure. That’s why scientists and experts are focusing on conducting susceptibility assessments for glacial lakes to understand the potential dangers they may present. A recent scientific paper sheds light on the importance of these assessments and the need to distinguish them from downstream hazard assessments.

Why Conduct Susceptibility Assessments?

Glacial lake susceptibility assessments are crucial for understanding which lakes are at risk and how severe an outburst event could be. By analyzing various factors such as atmospheric, cryospheric, geological, geomorphological, and hydrological processes, experts can identify the likelihood and magnitude of a potential disaster. These assessments consider both static factors, like site characteristics, and dynamic factors, such as dam characteristics and lake size, which gradually increase a site’s susceptibility over time.

Distinguishing Susceptibility Assessment from Downstream Hazard Assessment

It’s important to differentiate between susceptibility assessment and downstream hazard assessment. While susceptibility assessment focuses on determining the vulnerability of a lake and the factors that could trigger an outburst, downstream hazard assessment evaluates how the event will impact surrounding areas, including infrastructure and communities. By separating these two steps, experts can gain a comprehensive understanding of the entire risk landscape and develop targeted strategies for mitigation.

Factors and Assessment Schemes

Various schemes have been proposed for assessing the susceptibility levels of glacial lakes, with an emphasis on using remotely sensed information. These schemes characterize the cryospheric environment, lake and dam area, and other geotechnical and geomorphic characteristics of the lake’s upstream catchment area. In regions like the Himalayas and the Andes, where extensive research has been conducted, classification schemes often focus on the role of rock/ice avalanches as primary triggers. Experts determine the potential impact by assessing worst-case runout distances and other relevant susceptibility factors.

Practical Applications and Implications

The findings of this scientific paper have practical applications. By conducting susceptibility assessments, decision-makers can identify high-risk lakes and allocate resources and efforts accordingly. The assessments inform the development of mitigation strategies and help prioritize measures to protect vulnerable communities. Understanding the factors contributing to susceptibility empowers professionals to make informed decisions and take proactive steps in reducing the potential impact of glacial lake outburst events.

Challenges and Future Research

While progress has been made in assessing susceptibility levels, challenges remain. Understanding the sub or englacial drainage of ice-dammed lakes and complex ice-moraine structures requires further research and the development of robust assessment criteria. International collaboration and knowledge sharing are essential to enhance process understanding and refine assessment techniques.

In conclusion, it improtant to highlight the significance of conducting susceptibility assessments for glacial lakes. By distinguishing susceptibility assessment from downstream hazard assessment, experts gain a comprehensive understanding of the risks and can develop targeted mitigation strategies. These assessments have practical applications for decision maked in distater risk management, enabling to protect vulnerable communities and ensure sustainable development in the face of potential glacial lake outburst events.

Reference

For detailed information, please refer to Chapter 3.1 International state-of-the-art for susceptibility assessment of the comprehensive document titled: Glacial Lakes Outburst Flood: Best Practice Guidance

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Monitoring Glacial Lakes in Central Asia: Overcoming Challenges for Safer Communities

Glacial lakes in Central Asia serve as vital indicators of climate change and potential threats to nearby communities. However, effectively monitoring these lakes requires regular updates and accurate data. A recent scientific paper sheds light on the challenges faced by administrative and research institutions in the region, highlighting the dependence on their initiative for maintaining up-to-date inventories. Additionally, the study addresses the uncertainties introduced by the spatial resolution of remote sensing data and emphasizes the impact of data resolution on the accuracy of lake outlines. Understanding these factors is essential for improving monitoring techniques and ensuring the safety of local populations.

Taking the Lead: Administrative and Research Institutions

Local authorities and research institutions, along with international scientists, play a critical role in compiling glacial lake inventories in Central Asia. These inventories heavily rely on freely available remote sensing imagery from various sources. However, the regular updating of these inventories largely depends on the proactive approach of administrative and research institutions, who take the initiative to ensure accurate and timely information.

The Manual vs. Automated Dilemma

To identify and digitize glacier lakes, most studies rely on manual methods, where the outlines are identified and drawn manually. However, some researchers have explored automated or semi-automated approaches using advanced techniques like the normalized difference water index (NDWI). While automated methods offer efficiency, they face challenges in classifying shaded areas, melting ice, and turbid lakes. Thus, manual delineation remains essential to ensure precise results and overcome these limitations.

Harnessing Technology: Machine Learning and SAR Data

To improve lake detection and enhance monitoring accuracy, researchers are embracing machine learning algorithms and synthetic aperture radar (SAR) data. These cutting-edge technologies have the potential to detect smaller lakes, provide high-temporal-resolution analysis even under cloud cover, and identify potentially hazardous ephemeral lakes. By harnessing the power of these advancements, researchers can monitor glacial lake fluctuations more frequently and address emerging risks promptly.

Spatial Resolution and Uncertainties

The spatial resolution of remote sensing data is a significant factor contributing to uncertainties in glacial lake monitoring. The coarse spatial resolution of the data introduces uncertainties and limitations, which are assessed through local and regional studies. Researchers are actively working to enhance the spatial resolution, as improving data quality will lead to more accurate lake outlines and a better understanding of glacial lake dynamics.

Ensuring Accuracy: Resolution Matters

The accuracy of lake outlines is heavily influenced by the resolution of the mapping data used. Higher-resolution data yields more precise outlines, reducing errors in the inventory. International best practices recommend calculating the relative error by considering the perimeter of the lake and a buffer representing the absolute error. The value of this buffer is often half the dimension of a pixel. Some studies have employed more sophisticated methods, considering uncertainty factors resulting from subjective manual mapping. By prioritizing data resolution and adopting precise mapping techniques, researchers can enhance the accuracy of glacial lake inventories.

Field Surveys: A Laborious yet Vital Process

Validation through in-situ measurements is crucial for gathering accurate information about glaciers, lakes, and dam types. Detailed surveys provide essential data on lake bathymetry, ground ice, and local characteristics, enabling more precise estimations of lake volumes and vulnerability assessments. However, conducting field surveys in remote areas is challenging and costly. To overcome these obstacles, researchers often employ a combination of field surveys and satellite remote sensing data, including helicopter flights, to obtain comprehensive and reliable data.

Thresholds and Future Predictions

To streamline the mapping process, inventories apply thresholds for including glacier lakes based on their size. Most studies set the threshold between 2,000 to 2,500 square meters. However, some inventories utilizing higher-resolution imagery have lowered the threshold to less than 1,000 square meters. Digital elevation models (DEMs) play a crucial role in determining the minimum elevation threshold for including lakes in the inventory. DEMs also assist in assessing lake susceptibility to outburst floods and predicting the magnitude and path of potential mass movements. These models support the calculation of future changes in glacial lake size and number, aiding in proactive planning and risk mitigation.

Towards Consensus: Classifying Glacial Lakes

There is currently no consensus on the classification of glacial lakes in the regional and international literature. Researchers are working towards developing a unified approach by combining different classification schemes, promoting practicality and consistency across regional studies. Differentiating lakes based on their relationship with glaciers and their geographic position helps in understanding their characteristics and associated risks. Consensus on classification will streamline monitoring efforts and facilitate effective hazard management.

Conclusion

Regularly updating glacial lake inventories in Central Asia requires the active involvement of administrative and research institutions. Overcoming challenges related to data resolution, uncertainties, and accuracy of lake outlines is crucial for enhancing monitoring techniques. By embracing advancements in technology, such as machine learning and SAR data, and by conducting in-situ surveys, researchers can improve the detection and understanding of glacial lakes. Efforts to develop consensus on lake classification and employing hazard assessment criteria further contribute to safeguarding communities living near these dynamic bodies of water. By prioritizing these aspects, stakeholders can better manage and mitigate the risks associated with glacial lakes in Central Asia, ensuring the safety and well-being of local populations.

Reference

For detailed information, please refer to Chapter 2.2: National approaches used in Central Asia in the comprehensive document titled: Glacial Lakes Outburst Flood: Best Practice Guidance