Modeling Surface Processes on Debris-Covered Glaciers: A Review with Reference to the High Mountain Asia

Huo, Da; Chi, Zhaohui; Ma, Andong

doi:10.3390/w13010101

Cited by 11 publications

(11 citation statements)

References 134 publications

(449 reference statements)

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“…2. Surface topographic conditions control supraglacial hydrology, especially the development of ponds and lakes (Huo et al, 2021). Water transport is enhanced with steeper gradients, while ponding is enhanced with lower gradients, therefore supraglacial water bodies are more abundant where the glacier surface is flatter (Reynolds, 2000;Sakai et al, 2002;Immerzeel et al, 2014;Reid and Brock, 2014).…”

Section: The Role Of Glacier Surface Topographymentioning

confidence: 99%

Understanding Complex Debris-Covered Glaciers: Concepts, Issues, and Research Directions

2021

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Understanding the climate-glacier dynamics of debris-covered glaciers is notoriously difficult given a multitude of controlling factors and feedback mechanisms involving climate forcing, debris-load properties, supraglacial water bodies, and multi-scale topographic effects. Recent studies have provided insights into controlling factors, and have reported the presence of anomalies that contradict the general consensus of the protective influence of debris loads on ablation dynamics. Nevertheless, numerous processes that regulate glacier dynamics at various spatial and temporal scales have not been adequately accounted for in statistical and numerical modeling studies. Furthermore, important feedbacks involving ablation, topography, irradiance, gravitational debris flux, and supraglacial ponding are often neglected or oversimplified in existing models, which poses a challenge to our understanding of conflicting field observations such as the accelerated mass loss of many Himalayan glaciers, and glacier-subsystem responses (ice-flow, debris flux, surface morphology, and supraglacial water bodies) to climate forcing. This paper provides insights into the complexity of debris-covered glacier systems by addressing concepts and issues associated with forcing factors and glacial processes, and highlights the importance of understanding system couplings and feedbacks. Specifically, we review recent studies on debris-covered glaciers and utilize simulation results based on the Baltoro Glacier in the central Karakoram to discuss important concepts and issues. Our results demonstrate that climate forcing, the properties and transport of debris, topography and supraglacial water bodies are the key controlling factors in a debris-covered glacier system, and that their coupled effects and positive feedbacks may increase the ice loss of a debris-covered glacier. We also recommend new research directions for future studies.

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Section: The Role Of Glacier Surface Topographymentioning

confidence: 99%

Understanding Complex Debris-Covered Glaciers: Concepts, Issues, and Research Directions

2021

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“…Multiple processes control supraglacial ponding (Benn and Lehmkuhl, 2000;Benn et al, 2012;Huo et al, 2021c). Our model characterizes two important positive feedback mechanisms that govern pond expansion on a DCG: 1) The pond size-ablation rate feedback.…”

Section: Feedback Mechanismsmentioning

confidence: 99%

Modeling Supraglacial Ponding and Drainage Dynamics: Responses to Glacier Surface Topography and Debris Flux Conditions

Huo

Bishop

2021

Preprint

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Abstract. Supraglacial ponds play a significant role in the mass loss of many debris-covered glaciers in the Himalaya. Glacier surface topography and debris flux conditions are thought to govern supraglacial ponding and drainage. Existing studies, however, have not adequately investigated the relationships and feedbacks between meltwater production, debris transport, topographic evolution and ponding, because field measurements are limited in time and space, and most existing models either neglect these processes or use oversimplified assumptions. Such limitations restrict our understanding of supraglacial hydrology and introduce uncertainties in our assessments of glacier sensitivity to climate forcing. This study develops a more comprehensive numerical model to provide insights into the couplings between topographically-controlled surface ablation, meltwater drainage, ponding, and gravitational debris transport under radiative forcing. We investigate supraglacial ponding and drainage dynamics in response to different topographic and debris flux conditions through numerical simulations based on Baltoro Glacier in the Karakoram and several hypothetical scenarios. Results suggest that: 1) Supraglacial ponds make a significant contribution to the total ice loss (more than 20 %) in the lower-mid ablation zone over one ablation season, which elevates the glacier's nonlinear response to radiative forcing. 2) Gravitational debris transport has a non-negligible control on the growth rate of supraglacial ponds by governing debris thickness and ablation rates on the ice-cliffs around ponds. 3) Glacier surface gradient and local topographic depressions control pond formation by affecting supraglacial water storage and drainage. Our simulations provide a possible explanation to the abundance of ponds in the mid ablation zone where slope is gentle and more local depressions are present. These findings may contribute to more accurate predictions of future glacier changes in response to climate change.

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“…Through the study of glacier changes, it is possible to ascertain glacier dynamics [14], which has significant research value for understanding past glacial behavior and predicting future climate, environmental, and water resource changes [15]. Mountain glaciers are located in high-altitude remote mountainous areas, which have complex terrains [16][17][18][19]. Since these glaciers are of various types, traditional ground survey methods cannot adequately track large-scale, periodic, systematic, and comprehensive glacier changes.…”

Section: Introductionmentioning

confidence: 99%

“…Since these glaciers are of various types, traditional ground survey methods cannot adequately track large-scale, periodic, systematic, and comprehensive glacier changes. A technique to effectively measure and monitor mountain glaciers is crucial at this stage [16][17][18]. However, in high mountain areas, which experience frequent cloud activity and have a large amount of debris coverage, automatic and semi-automatic glacier mapping based on remote sensing images is challenging [1,11,20], and short-term studies such as these do not necessarily show stable changes over time.…”

Section: Introductionmentioning

confidence: 99%

“…Further, objects with less spatial information are likely to be misclassified [45,46]. RF [16], which can accept high-dimensional data with different attributes, is a pixel-based classification method; pixel-by-pixel classification makes the boundaries of features clearer [47][48][49]. Zhang et al [25] used multi-temporal data based on the RF method to realize the automatic classification of the borders of non-debris-covered glaciers and debris-covered glaciers.…”

Section: Introductionmentioning

confidence: 99%

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Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

Zhang

Shangguan

et al. 2021

Remote Sensing

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Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.

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Modeling Surface Processes on Debris-Covered Glaciers: A Review with Reference to the High Mountain Asia

Cited by 11 publications

References 134 publications

Understanding Complex Debris-Covered Glaciers: Concepts, Issues, and Research Directions

Understanding Complex Debris-Covered Glaciers: Concepts, Issues, and Research Directions

Modeling Supraglacial Ponding and Drainage Dynamics: Responses to Glacier Surface Topography and Debris Flux Conditions

Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

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