Contributors: Jacqueline Bannwart (University of Zurich), Inés Dussailan (University of Zurich), Frank Paul (University of Zurich), Michael Zemp (University of Zurich)
Issued by: UZH / Frank Paul
Date: 27/05/2024
Ref: C3S2_312a_Lot4.WP2-FDDP-GL-v2_202312_A_PQAR-v5_i1.2
Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1
History of modifications
List of datasets covered by this document
Related documents
Acronyms
General definitions
For the glacier area product we define the Randolph Glacier Inventory (RGI) as a Climate Data Record (CDR) and the regionally and temporarily constrained improvements or updates submitted to the Global Land Ice Measurements from Space (GLIMS) database as Interim Climate Data Records (ICDRs). The latter might be integrated in new releases of the CDR. This document is related to the generated ICDRs.
Debris-cover: Debris on a glacier is usually composed of unsorted rock fragments with highly variable grain size (from mm to several m). These might cover the ice in lines of variable width separating ice with origin in different accumulation regions of a glacier (so called medial moraines) up to a complete coverage of the ablation region. Automated mapping of glacier ice is only possible when the debris is not covering the ice completely in regard to the image pixel size.
Digitizing uncertainty: The standard deviation of the glacier area differences resulting from independent multiple digitizing of the same glacier outline by the same analyst. This value usually increases towards smaller glaciers.
Interpretation uncertainty: The standard deviation of the glacier area differences resulting from independent digitizing of the same glacier outlines by at least two different analysts. This value usually increases with the number of difficult glaciers (e.g. debris-covered) in the sample.
Glacier area: The area (or size) of a glacier, usually given in the unit km2. Also used by Global Climate Observing System (GCOS) to name the related Essential Climate Variable (ECV) product.
Glacier outline: A vector dataset with polygon topology marking the boundary of a glacier.
Glacier inventory: A compilation of glacier outlines with associated attribute information.
Scope of the document
This document is the Product Quality Assessment Report (PQAR) for the Copernicus glacier distribution service providing results of the quality assessment for the glacier area product generated for the Copernicus Climate Change Service (C3S). The here presented datasets is a regionally constrained (Interim Climate Data Records; ICDRs) subset of glacier outlines for the Randolph Glacier Inventory 7 (RGI 7.0), which is a global dataset of glacier outlines that is available as a Climate Data Record (CDR) from the Climate Data Store (CDS). The focus is on a quantification of the achieved quality improvements compared to the outlines available in RGI 6.0. An overall assessment of RGI quality based on Pfeffer et al. (2014) is provided in the Product Quality Assurance Document (PQAD) [RD1]. Statistics on the overall improvements of RGI 7.0 compared to RGI 6.0 are presented in Section 6 of the RGI 7.0 user guide [RD5].
Executive summary
The datasets for which we here present a quality assessment has been largely created earlier by other analysts. The methodological approach we use to determine the quality of the dataset is thus not based on multiple digitizing of glacier outlines by the analyst or the buffer method (see [RD1]), but derived from a direct comparison of the differences between the two datasets. We thus present first the difference between digitizing and interpretation uncertainty to explain why we here only analyse the latter. We then describe details of the method (addition of two gridded versions to obtain omission and commission errors) and present in Section 2 the results of the comparison. Differences between the old (RGI 6.0) and the new datasets created by us are mostly caused by missing glaciers, debris cover and rock outcrops or temporal changes, i.e. the glacier outlines were simply out-dated. In the last section we evaluate the results compared to user and Global Climate Observing System (GCOS) requirements, concluding that our adjustments provide a clear improvement in regard to the most important aspects for creating RGI 7.0 (better quality and closer to the year 2000).
1. Product validation methodology
1.1. Background
The methods available for determination of product uncertainty are detailed in Section 3 of the PQAD [RD1] and are thus not repeated here. Due to rarely available validation data, we usually determine product uncertainty rather than systematic and random errors. When creating a fresh dataset (any information linked to the production of the data is presented in the Algorithm Theoretical Basis Document (ATBD) [RD2]), uncertainty of the outlines can best be determined by independent multiple digitizing of the same glaciers. Overlay of resulting outlines and calculation of the standard deviation of the area differences are the measures to visualize and calculate the digitizing uncertainty (e.g. Paul et al. 2013 and 2020). This uncertainty is usually a few per-cent of the glacier area and much smaller than the interpretation uncertainty, which occurs when different analysts are digitizing the same glaciers. Here, much larger differences in interpretation can occur, e.g. for debris-covered glaciers or misinterpreted seasonal snow.
For the dataset provided to GLIMS for RGI 7.0 during C3S2 (see Section 2), we reinterpreted already existing datasets, i.e. we have to consider the larger interpretation uncertainty. This was achieved by calculating the area covered by omission and commission errors, add them up and compare them to the new and the original extent. In other words, we determine (a) the area mapped by both analysts, (b) the area missed in RGI 6.0 (e.g. small or debris-covered glaciers) and (c) the area that was too large in RGI 6.0 (e.g. due to wrongly mapped seasonal snow or forgotten rock outcrops). The obtained (relative) change in area might be used as a measure for the improvement in a region, but we do not recommend this as the value strongly depends on the total area of the corrected dataset. Instead, we also present overlays of the previous and new datasets to discuss the reasons for the differences. These are not always related to poor mapping quality.
1.2. Approach
To determine the regions for (a), (b) and (c), we convert the outlines of both datasets (from RGI 6.0 and our improved one) to a regular grid using vector-raster conversion and assign a value of 0 to regions without glaciers in both datasets, a value of 1 to glaciers in RGI 6.0 and a value of 2 to glaciers in our improved dataset. A simple addition of both grids gives 0 for no glaciers in both datasets, 1 for glaciers only in RGI 6.0 (too large), 2 for glaciers only present in our improved dataset (i.e. missed in RGI 6.0) and 3 for glaciers in both datasets. The pixel counts for each of the values 1, 2 and 3 are given in the value attribute table of the respective raster file. These are used to determine the relative portions of each class and, by multiplication with the grid cell area, the area in km2. Comparison with the original or the new size gives relative area changes due to the corrections.
2. Validation results for glaciers on Baffin Island
The glacier outlines for Baffin Island in RGI 6.0 are merged from a range of different datasets and are basically still the same as in RGI 1.0. According to [RD4], they partly refer to outlines derived from 1958 and 1982 aerial photography, Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite images from 1999 to 2002 and Satellite pour l'Observation de la Terre (SPOT) satellite images acquired between 2006 and 2010. In many cases ice under debris cover (from medial and lateral moraines) is missing, glaciers were too large or they were missing completely or in parts. Moreover, many rock outcrops have not been included and some outlines were slightly shifted. Other studies have also demonstrated that many of the ice divides had to be corrected.
There was thus an urgent need to improve the quality of the outlines in this region, bring the timestamp closer to the year 2000 and correct the ice divides. In Figure 1 we provide an example screenshot illustrating how the corrections from RGI 6.0 to RGI 7.0 look like (common area in grey, removed in red, added in blue). In this region the red areas mostly refer to glacier shrinkage (i.e. the map used for RGI 6.0 is out-dated and has too large glaciers) and missed or now much larger rock outcrops and the blue regions is mostly missed debris cover. The related areas for the two datasets and their differences are summarized in Table 1 for the added, deleted and common pixels along with the total area in both RGIs and percentages per category. An illustration of the improved ice divides for Baffin Island is shown on the example of Penny Ice Cap in Figure 3 of the Target Requirements and Gap Analysis Document (TRGAD) [RD3].
Overall, the area of RGI 7.0 is 296 km2 or 1.05% smaller than RGI 6.0. This means that area reductions due to the temporal evolution (resulting in smaller glaciers) are slightly larger than area gain due to now included debris cover and previously missing glaciers. When analysing omission and commission errors separately, the cumulative area differences reach 5.4 % or 1515 km2. This is quite substantial for a region with many large to very large glaciers and about five times higher than the simple area difference between the two datasets. It stresses the importance to determine both differences separately and add them up for a sound quality assessment. Figure 1 also reveals that for many individual glaciers the area difference can be much larger, often in the 20 to 30% range.
Table 1: Results of the glacier map comparison for Baffin Island, excluding Barnes Ice Cap. The columns ‘Only RGI 6.0’ and ‘Only RGI 7.0’ give the glacier area that is only mapped in RGI 6.0 (red in Figure 1) and RGI 7.0 (dark blue in Figure 1), respectively. The column ’Common’ gives the area that agrees in both datasets (grey in Figure 1). The ‘Total area’ consists of all mapped parts (red, dark blue and grey in Figure 1). The columns ‘Area RGI 6.0’ and ‘Area RGI 7.0’ gives the total area as mapped for RGI 6.0 (i.e. the red and grey part of Figure 1) and RGI 7.0 (the grey and blue part in Figure 1). The ‘difference’ row refers to the total area column.
Only RGI 6.0 | Only RGI 7.0 | Common | Total area | Area RGI 6.0 | Area RGI 7.0 | |
|---|---|---|---|---|---|---|
Area (km2) | 905.36 | 609.00 | 27,273.0 | 28,783.34 | 28,178.3 | 27,882.0 |
Difference (%) | 3.21 | 2.16 | 96.79 | -1.05 | ||
Figure 1: Overlay of classified glacier grids for a sub-region of southeast Baffin Island. RGI 6.0 includes the grey and red pixels, the new dataset for RGI 7.0 (acquired by Landsat 7 on 12.8.2002) the grey and blue ones, i.e. regions in red were deleted, those in blue added. Light blue are lakes, major rivers and ocean water. Image width is 117 km, the background map is the ESRI topographic basemap.
3. Application(s) specific assessments
We did not perform any further glacier or application specific assessments.
4. Compliance with user requirements
Basic user requirements for RGI 7.0 are to have quality improved glacier outlines that are acquired closer to the year 2000. The technical requirements given in the latest GCOS Implementation Plan (GCOS 2022) still demand an area uncertainty that is smaller than 5% for individual glaciers (see [RD3] for details). In regions with debris-covered glaciers, this can only be achieved when each glacier outline is visually checked and corrected if required. The revised dataset for Baffin Island presented in Section 2 is now much closer to the year 2000 and has 905 km2 less glacier area as most glaciers shrunk from 1958 or 1982 until 2000. On the other hand, debris-covered ice was often not mapped and many (mostly small) glaciers were missing in the dataset from RGI 6.0, which increased the area by 609 km2. When considering the strong dependence of relative area changes on glacier area and the large overall glacier area, the total relative area difference of 5.4% is huge. The new dataset for RGI 7.0 is thus certainly much closer to GCOS and user requirements.
References
GCOS (2022): The 2022 GCOS ECVs requiremnts. GCOS-245, published by WMO, pp. 244.
Millan, R., Je. Mouginot, A. Rabatel and M. Morlighem (2022). Ice velocity and thickness of the world's glaciers. Nature Geoscience, 15(2), 124–129; doi.org/10.1038/s41561-021-00885-z
Paul, F., N. Barrand, E. Berthier, T. Bolch, K. Casey, H., rey, S.P. Joshi, V. Konovalov, R. Le Bris, N. Mölg, G. Nosenko, C. Nuth, A. Pope, A. Racoviteanu, P. Rastner, B. Raup, K. Scharrer, S. Steffen and S. Winsvold (2013): On the accuracy of glacier outlines derived from remote sensing data. Annals of Glaciology, 54 (63), 171-182; doi.org/10.3189/2013AoG63A296
Paul, F., Rastner, P., Azzoni, R. S., Diolaiuti, G., Fugazza, D., Le Bris, R., Nemec, J., Rabatel, A., Ramusovic, M., Schwaizer, G.,and Smiraglia, C. (2020). Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2. Earth System Science Data, 12(3), 1805-1821; doi.org/10.5194/essd-12-1805-2020
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A.S., … and Sharp, M. J. (2014). The Randolph Glacier Inventory: a globally complete inventory of glaciers. Journal of Glaciology, 60(221), 537-552; doi.org/10.3189/2014JoG13J176
