Articles | Volume 18, issue 2
https://doi.org/10.5194/tc-18-525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Seasonal to decadal dynamics of supraglacial lakes on debris-covered glaciers in the Khumbu region, Nepal
Lucas Zeller
CORRESPONDING AUTHOR
Department of Geosciences, Colorado State University, Fort Collins, CO 80523, USA
Daniel McGrath
Department of Geosciences, Colorado State University, Fort Collins, CO 80523, USA
Scott W. McCoy
Department of Geological Sciences and Engineering, University of Nevada, Reno, NV 89557, USA
Jonathan Jacquet
Department of Geological Sciences and Engineering, University of Nevada, Reno, NV 89557, USA
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Cited articles
Benn, D., Bolch, T., Hands, K., Gulley, J., Luckman, A., Nicholson, L., Quincey, D., Thompson, S., Toumi, R., and Wiseman, S.: Response of debris-covered glaciers in the Mount Everest region to recent warming, and implications for outburst flood hazards, Earth-Sci. Rev., 114, 156–174, https://doi.org/10.1016/j.earscirev.2012.03.008, 2012. a
Benn, D. I., Wiseman, S., and Hands, K. A.: Growth and drainage of supraglacial lakes on debris-mantled Ngozumpa Glacier, Khumbu Himal, Nepal, J. Glaciol., 47, 626–638, https://doi.org/10.3189/172756501781831729, 2001. a
Bolch, T., Buchroithner, M. F., Peters, J., Baessler, M., and Bajracharya, S.: Identification of glacier motion and potentially dangerous glacial lakes in the Mt. Everest region/Nepal using spaceborne imagery, Nat. Hazards Earth Syst. Sci., 8, 1329–1340, https://doi.org/10.5194/nhess-8-1329-2008, 2008. a
Brun, F., Wagnon, P., Berthier, E., Jomelli, V., Maharjan, S. B., Shrestha, F., and Kraaijenbrink, P. D. A.: Heterogeneous Influence of Glacier Morphology on the Mass Balance Variability in High Mountain Asia, J. Geophys. Res.-Earth, 124, 1331–1345, https://doi.org/10.1029/2018JF004838, 2019. a
Cook, S. J. and Quincey, D. J.: Estimating the volume of Alpine glacial lakes, Earth Surf. Dynam., 3, 559–575, https://doi.org/10.5194/esurf-3-559-2015, 2015. a
Gao, B.-C.: NDWI–A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996. a
Gardelle, J., Arnaud, Y., and Berthier, E.: Contrasted evolution of glacial lakes along the Hindu Kush Himalaya mountain range between 1990 and 2009, Global Planet. Change, 75, 47–55, https://doi.org/10.1016/j.gloplacha.2010.10.003, 2011. a
Gardner, A. S., Fahnestock, M. A., and Scambos, T. A.: ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, National Snow and Ice Data Center [data set], CO, https://doi.org/10.5067/6II6VW8LLWJ7, 2019. a, b
Google Earth Engine: Earth Engine Data Catalog, Google Earth Engine [data set], https://developers.google.com/earth-engine/datasets/catalog, last access: 1 February 2024. a
Mertes, J. R., Thompson, S. S., Booth, A. D., Gulley, J. D., and Benn, D. I.: A conceptual model of supra-glacial lake formation on debris-covered glaciers based on GPR facies analysis: GPR Facies Analysis of Spillway Lake, Ngozumpa Glacier, Nepal, Earth Surf. Proc. Land., 42, 903–914, https://doi.org/10.1002/esp.4068, 2017. a
Miles, E. S., Watson, C. S., Brun, F., Berthier, E., Esteves, M., Quincey, D. J., Miles, K. E., Hubbard, B., and Wagnon, P.: Glacial and geomorphic effects of a supraglacial lake drainage and outburst event, Everest region, Nepal Himalaya, The Cryosphere, 12, 3891–3905, https://doi.org/10.5194/tc-12-3891-2018, 2018. a, b
Miles, E. S., Steiner, J. F., Buri, P., Immerzeel, W. W., and Pellicciotti, F.: Controls on the relative melt rates of debris-covered glacier surfaces, Environ. Res. Lett., 17, 064004, https://doi.org/10.1088/1748-9326/ac6966, 2022. a, b
Miles, K. E., Hubbard, B., Irvine-Fynn, T. D., Miles, E. S., Quincey, D. J., and Rowan, A. V.: Hydrology of debris-covered glaciers in High Mountain Asia, Earth-Sci. Rev., 207, 103212, https://doi.org/10.1016/j.earscirev.2020.103212, 2020. a, b
Mohanty, L. K. and Maiti, S.: Regional morphodynamics of supraglacial lakes in the Everest Himalaya, Sci. Total Environ., 751, 141586, https://doi.org/10.1016/j.scitotenv.2020.141586, 2021. a, b
Naito, N., Nakawo, M., Kadota, T., and Raymond, C. F.: Numerical simulation of recent shrinkage of Khumbu Glacier, Nepal Himalayas, IAHS-AISH Publication Issue 264, 2000, 153–161, 13 September 2000–15 September 2000, Debris-Covered Glaciers, Seattle, WA, USA, 2000. a
Narama, C., Daiyrov, M., Tadono, T., Yamamoto, M., Kääb, A., Morita, R., and Ukita, J.: Seasonal drainage of supraglacial lakes on debris-covered glaciers in the Tien Shan Mountains, Central Asia, Geomorphology, 286, 133–142, https://doi.org/10.1016/j.geomorph.2017.03.002, 2017. a, b, c, d
Perry, L. B., Matthews, T., Guy, H., Koch, I., Khadka, A., Elmore, A. C., Shrestha, D., Tuladhar, S., Baidya, S. K., Maharjan, S., Wagnon, P., Aryal, D., Seimon, A., Gajurel, A., and Mayewski, P. A.: Precipitation Characteristics and Moisture Source Regions on Mt. Everest in the Khumbu, Nepal, One Earth, 3, 594–607, https://doi.org/10.1016/j.oneear.2020.10.011, 2020. a
Planet Labs PBC: Planet Application Program Interface: In Space for Life on Earth, Planet, https://api.planet.com (last access: 1 February 2024), 2018. a
Planet Team: PlanetScope Product Specifications, Planet, https://www.planet.com/explorer (last access: 1 February 2024), 2022. a
Racoviteanu, A., Nicholson, L., Glasser, N., Miles, E., Harrison, S., and Reynolds, J.: Debris-covered glacier systems and associated glacial lake outburst flood hazards: challenges and prospects, J. Geol. Soc., 179, jgs2021–084, https://doi.org/10.1144/jgs2021-084, 2022. a
Racoviteanu, A. E., Nicholson, L., and Glasser, N. F.: Surface composition of debris-covered glaciers across the Himalaya using linear spectral unmixing of Landsat 8 OLI imagery, The Cryosphere, 15, 4557–4588, https://doi.org/10.5194/tc-15-4557-2021, 2021. a, b, c, d
Reynolds, J. M.: On the formation of supraglacial lakes on debris-covered glaciers, ISSN 01447815, IAHS-AISH Publication, Issue 264, 2000, 153–161, Debris-Covered Glaciers, 13 September 2000–15 September 2000, Seattle, WA, USA, 2000. a
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines, Version 6, Boulder, Colorado USA, NSIDC: National Snow and Ice Data Center [data set], https://doi.org/10.7265/4m1f-gd79, 2017. a
Rounce, D. R., Byers, A. C., Byers, E. A., and McKinney, D. C.: Brief communication: Observations of a glacier outburst flood from Lhotse Glacier, Everest area, Nepal, The Cryosphere, 11, 443–449, https://doi.org/10.5194/tc-11-443-2017, 2017. a, b, c, d
Rounce, D. R., Hock, R., McNabb, R. W., Millan, R., Sommer, C., Braun, M. H., Malz, P., Maussion, F., Mouginot, J., Seehaus, T. C., and Shean, D. E.: Distributed Global Debris Thickness Estimates Reveal Debris Significantly Impacts Glacier Mass Balance, Geophys. Res. Lett., 48, e2020GL091311, https://doi.org/10.1029/2020GL091311, 2021. a, b, c
Shugar, D. H., Burr, A., Haritashya, U. K., Kargel, J. S., Watson, C. S., Kennedy, M. C., Bevington, A. R., Betts, R. A., Harrison, S., and Strattman, K.: Rapid worldwide growth of glacial lakes since 1990, Nat. Clim. Change, 10, 939–945, https://doi.org/10.1038/s41558-020-0855-4, 2020. a, b, c, d, e
Steiner, J. F., Buri, P., Miles, E. S., Ragettli, S., and Pellicciotti, F.: Supraglacial ice cliffs and ponds on debris-covered glaciers: spatio-temporal distribution and characteristics, J. Glaciol., 65, 617–632, https://doi.org/10.1017/jog.2019.40, 2019. a, b, c
Taylor, C. J., Carr, J. R., and Rounce, D. R.: Spatiotemporal supraglacial pond and ice cliff changes in the Bhutan–Tibet border region from 2016 to 2018, J. Glaciol., 68, 101–113, https://doi.org/10.1017/jog.2021.76, 2022. a
Watanabe, T., Lamsal, D., and Ives, J. D.: Evaluating the growth characteristics of a glacial lake and its degree of danger of outburst flooding: Imja Glacier, Khumbu Himal, Nepal, Norsk Geogr. Tidsskr., 63, 255–267, https://doi.org/10.1080/00291950903368367, 2009. a, b, c
Watson, C. S., King, O., Miles, E. S., and Quincey, D. J.: Optimising NDWI supraglacial pond classification on Himalayan debris-covered glaciers, Remote Sens. Environ., 217, 414–425, https://doi.org/10.1016/j.rse.2018.08.020, 2018a. a, b
Watson, C. S., Quincey, D. J., Carrivick, J. L., Smith, M. W., Rowan, A. V., and Richardson, R.: Heterogeneous water storage and thermal regime of supraglacial ponds on debris-covered glaciers: Water storage and thermal regime of supraglacial ponds, Earth Surf. Proc. Land., 43, 229–241, https://doi.org/10.1002/esp.4236, 2018b. a, b, c, d, e, f
Wendleder, A., Schmitt, A., Erbertseder, T., D'Angelo, P., Mayer, C., and Braun, M. H.: Seasonal Evolution of Supraglacial Lakes on Baltoro Glacier From 2016 to 2020, Front. Earth Sci., 9, 725394, https://doi.org/10.3389/feart.2021.725394, 2021. a, b, c, d
Xu, H.: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int. J. Remote Sens., 27, 3025–3033, https://doi.org/10.1080/01431160600589179, 2006. a
Zeller, L., McGrath, D., McCoy, S. W., and Jacquet, J.: Derived products and analytical scripts for “Seasonal to decadal dynamics of supraglacial lakes on debris-covered glaciers in the Khumbu Region, Nepal”, Zenodo [data set], https://doi.org/10.5281/zenodo.10463250, 2024. a
Short summary
In this study we developed methods for automatically identifying supraglacial lakes in multiple satellite imagery sources for eight glaciers in Nepal. We identified a substantial seasonal variability in lake area, which was as large as the variability seen across entire decades. These complex patterns are not captured in existing regional-scale datasets. Our findings show that this seasonal variability must be accounted for in order to interpret long-term changes in debris-covered glaciers.
In this study we developed methods for automatically identifying supraglacial lakes in multiple...