Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows
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
Permanent Research Commons link: https://hdl.handle.net/10289/13762
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.
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