A stochastic birth and death model to represent the evolution of ice cliff population and their total contribution to melt at the glacier scale
Abstract
Ice cliffs play a key role in the mass balance of debris-covered glaciers by dramatically enhancing melt, but have only been represented in distributed glacier melt models by adding a constant melt enhancement to the entire glacier. This representation ignores the dynamics of the ice cliff population, as well as their total area or the changes in their backwasting rates. Physically based melt models of ice cliffs have been developed at the scale of a single feature but are currently too computationally expensive to be run over long time-periods for more than a few cliffs. In this study, we mapped ice cliffs manually at a yearly interval between 2009 and 2019 on three debris-covered glaciers located in the Karakorum, Western and Central Himalaya using RapidEye satellite imagery. We tracked the individual features using an automated approach based on identifying corresponding features in the different images to understand their dynamics in terms of birth, death, area changes and backwasting rates. We use this data to quantify for each glacier the interannual ice cliff emergence and persistence, and document the size evolution and backwasting rates of cliffs individually. We compare the ice cliff dynamics for our three sites to account for different geological, glaciological and climatic settings. We find that precipitation is the main climatic driver of ice cliff birth rate variability, but has little impact on the death rates. On one hand, precipitation rich periods enhance the emergence of new cliffs on the glacier; on the other hand, the existing cliff population reduces exponentially as a function of lifetime, similar to the distribution of arrival rates in a random Poisson process. Furthermore, we find that ice cliffs have similar sizes when they are born, and grow larger with ice cliff persistence, while backwasting rates are mainly dependent on the glacier and climate characteristics. We use these observations to build a stochastic birth and death model to represent the evolution of the ice cliff population and their total melt contribution at the glacier scale. This model is computationally efficient and can be easily implemented in future distributed glacier melt models.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMC042...08K
- Keywords:
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- 0720 Glaciers;
- CRYOSPHERE;
- 0758 Remote sensing;
- CRYOSPHERE;
- 0762 Mass balance;
- CRYOSPHERE;
- 0798 Modeling;
- CRYOSPHERE