Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows

Melt rate models are fundamental for understanding the impacts of climate change on glaciers and the subsequent effects on habitats and sea level rise. Ice melt models have mostly been derived from energy balance or air temperature index calculations. This research demonstrates that satellite-derive...

Full description

Bibliographic Details
Published in:Environmental Monitoring and Assessment
Main Authors: Brabyn, Lars, Stichbury, Glen
Format: Article in Journal/Newspaper
Language:English
Published: Springer Nature 2020
Subjects:
Ice
Online Access:https://hdl.handle.net/10289/13762
https://doi.org/10.1007/s10661-020-08396-x
id ftunivwaikato:oai:researchcommons.waikato.ac.nz:10289/13762
record_format openpolar
spelling ftunivwaikato:oai:researchcommons.waikato.ac.nz:10289/13762 2023-12-17T10:20:31+01:00 Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows Brabyn, Lars Stichbury, Glen 2020 application/pdf https://hdl.handle.net/10289/13762 https://doi.org/10.1007/s10661-020-08396-x en eng Springer Nature Environmental monitoring and assessment Brabyn, L., & Stichbury, G. (2020). Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows. Environmental Monitoring and Assessment, 192(7). https://doi.org/10.1007/s10661-020-08396-x 0167-6369 https://hdl.handle.net/10289/13762 doi:10.1007/s10661-020-08396-x 1573-2959 This is a post-peer-review, pre-copyedit version of an article published in Environmental Monitoring and Assessment. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10661-020-08396-x © 2020 Springer Nature Science & Technology Life Sciences & Biomedicine Environmental Sciences Environmental Sciences & Ecology Antarctic glaciers Landsat Land surface temperature Melt rate MODIS Taylor Valley Energy balance Meltwater Climate Journal Article 2020 ftunivwaikato https://doi.org/10.1007/s10661-020-08396-x 2023-11-21T18:25:45Z Melt rate models are fundamental for understanding the impacts of climate change on glaciers and the subsequent effects on habitats and sea level rise. Ice melt models have mostly been derived from energy balance or air temperature index calculations. This research demonstrates that satellite-derived land surface temperature (LST) measurements provide a simpler method for estimating surface melt rate that substitutes for energy balance models. Since these satellite images are continuous (distributed) across space, they do not need calibration for topography. Antarctic glacier melt discharge data from nearby stream gauges were used to calibrate an LST-derived melt model. The model calculations are simplified by the fact that groundwater flow is assumed to be minimal due to permafrost, and the glaciers are assumed to only melt on the surface. A new method called the Temperature Area Sum model is developed, which builds on an existing Temperature Area Index model. A daily melt rate model is developed using 77 Landsat 8 images and calculates the volume of meltwater produced per hectare for any given LST between − 7 and 0 °C. A seasonal average daily melt rate model is also developed that uses 1660 MODIS images. The utility of the seasonal MODIS model is demonstrated by calculating melt rates, water flows and wetness across the entire Ross Sea Region. An unexpected large wet area to the southwest of the Ross Ice Shelf requires further investigation and demonstrates the usefulness of these models for large remote areas. Surface melt rate and wetness can now be calculated for different climate change scenarios. Article in Journal/Newspaper Antarc* Antarctic Ice Ice Shelf permafrost Ross Ice Shelf Ross Sea The University of Waikato: Research Commons Antarctic Ross Sea Ross Ice Shelf Taylor Valley ENVELOPE(163.000,163.000,-77.617,-77.617) Environmental Monitoring and Assessment 192 7
institution Open Polar
collection The University of Waikato: Research Commons
op_collection_id ftunivwaikato
language English
topic Science & Technology
Life Sciences & Biomedicine
Environmental Sciences
Environmental Sciences & Ecology
Antarctic glaciers
Landsat
Land surface temperature
Melt rate
MODIS
Taylor Valley
Energy balance
Meltwater
Climate
spellingShingle Science & Technology
Life Sciences & Biomedicine
Environmental Sciences
Environmental Sciences & Ecology
Antarctic glaciers
Landsat
Land surface temperature
Melt rate
MODIS
Taylor Valley
Energy balance
Meltwater
Climate
Brabyn, Lars
Stichbury, Glen
Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows
topic_facet Science & Technology
Life Sciences & Biomedicine
Environmental Sciences
Environmental Sciences & Ecology
Antarctic glaciers
Landsat
Land surface temperature
Melt rate
MODIS
Taylor Valley
Energy balance
Meltwater
Climate
description Melt rate models are fundamental for understanding the impacts of climate change on glaciers and the subsequent effects on habitats and sea level rise. Ice melt models have mostly been derived from energy balance or air temperature index calculations. This research demonstrates that satellite-derived land surface temperature (LST) measurements provide a simpler method for estimating surface melt rate that substitutes for energy balance models. Since these satellite images are continuous (distributed) across space, they do not need calibration for topography. Antarctic glacier melt discharge data from nearby stream gauges were used to calibrate an LST-derived melt model. The model calculations are simplified by the fact that groundwater flow is assumed to be minimal due to permafrost, and the glaciers are assumed to only melt on the surface. A new method called the Temperature Area Sum model is developed, which builds on an existing Temperature Area Index model. A daily melt rate model is developed using 77 Landsat 8 images and calculates the volume of meltwater produced per hectare for any given LST between − 7 and 0 °C. A seasonal average daily melt rate model is also developed that uses 1660 MODIS images. The utility of the seasonal MODIS model is demonstrated by calculating melt rates, water flows and wetness across the entire Ross Sea Region. An unexpected large wet area to the southwest of the Ross Ice Shelf requires further investigation and demonstrates the usefulness of these models for large remote areas. Surface melt rate and wetness can now be calculated for different climate change scenarios.
format Article in Journal/Newspaper
author Brabyn, Lars
Stichbury, Glen
author_facet Brabyn, Lars
Stichbury, Glen
author_sort Brabyn, Lars
title Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows
title_short Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows
title_full Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows
title_fullStr Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows
title_full_unstemmed Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows
title_sort calculating the surface melt rate of antarctic glaciers using satellite-derived temperatures and stream flows
publisher Springer Nature
publishDate 2020
url https://hdl.handle.net/10289/13762
https://doi.org/10.1007/s10661-020-08396-x
long_lat ENVELOPE(163.000,163.000,-77.617,-77.617)
geographic Antarctic
Ross Sea
Ross Ice Shelf
Taylor Valley
geographic_facet Antarctic
Ross Sea
Ross Ice Shelf
Taylor Valley
genre Antarc*
Antarctic
Ice
Ice Shelf
permafrost
Ross Ice Shelf
Ross Sea
genre_facet Antarc*
Antarctic
Ice
Ice Shelf
permafrost
Ross Ice Shelf
Ross Sea
op_relation Environmental monitoring and assessment
Brabyn, L., & Stichbury, G. (2020). Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows. Environmental Monitoring and Assessment, 192(7). https://doi.org/10.1007/s10661-020-08396-x
0167-6369
https://hdl.handle.net/10289/13762
doi:10.1007/s10661-020-08396-x
1573-2959
op_rights This is a post-peer-review, pre-copyedit version of an article published in Environmental Monitoring and Assessment. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10661-020-08396-x © 2020 Springer Nature
op_doi https://doi.org/10.1007/s10661-020-08396-x
container_title Environmental Monitoring and Assessment
container_volume 192
container_issue 7
_version_ 1785522980968726528