Estimating snow cover decline using the RSLE in Google Earth Engine

Estimating snow cover decline using the RSLE in Google Earth Engine: A Caucasus case study

van Esch, Thijs (TU Delft Civil Engineering and Geosciences)

Hrachowitz, M. (mentor)
Lhermitte, S.L.M. (graduation committee)
Steele-Dunne, S.C. (graduation committee)

Delft University of Technology

Water Management

2019-12-18

Snow in mountainous areas is of great importance for the water supply in many catchments. To get data on snow cover, ground station data is not enough and, in many catchments, not available. Therefore, satellite data is used to measure snow cover. In this thesis the MODIS daily snow cover dataset (MOD10A1) is used. These images are obstructed by clouds. In order to create a complete dataset, the Regional Snowline Elevation Method is used which uses the elevation of the snowline to interpolate over the missing data. This method is accurate but is computationally demanding. Using Google Earth Engine, it is attempted to improve the method. The method developed combines grid cells with daily images and computes the RSLE for each cell, for each day. The results are exported to a CSV file, reducing the downloaded data from 150GB to 41.6 MB for the 0.50° resolution and 168MB for the 0.25° resolution. The computation time however was not improved with this method. The developed code was used on the Caucasus area. After the data from Google Earth Engine was downloaded, trends on yearly snow cover duration were computed using the Mann-Kendall test. It followed that 17% of the trends in both resolutions were significant and, except for one location, were all decreasing trends. The decreasing trends show a decline of snow cover duration of 1-4 days per year. Looking at regional differences it becomes clear the greatest number of trends can be found in the south-west. The Google Earth Engine code was able to compute the required data however, it took a long time doing so. Therefore, a more sophisticated code has been developed, making used of the ability to reduce the resolution of an image, and computing the value of pixels at the same time. This code runs quicker, but is at this moment unusable, due to problems with the thresholds and export. Being unable to export an image collection is one of the shortcomings of Google Earth Engine. Others include: download tasks that run out after twelve days without raising an error when starting the task, limits to the amounts of bands used, and sensitivity of the computation time to busyness on servers. Things that need to be improved and need further research are: the filters applied to smoothen the dataset, the use of other trend analyses, the effect of snow cover trends on the area, and the resolution in combination with the cloud threshold value. The analyses took six hours to run, which can be improved by using function-based programming, instead of process-based.
In the end the goal to develop a more efficient method has partly been met by decreasing the amount of downloaded data significantly, even though the running time has not been improved. Using the data from the developed method, decreasing trends were found in snow cover duration over the entire Caucasus but mainly in the South-West, which can greatly influence the water supply to a large part of the Caucasus and its surrounding areas.

Snow
Google Earth Engine
Caucasus

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Student theses

master thesis

© 2019 Thijs van Esch