Abstract
Accurate estimates of regional ice thickness, which are generally produced by ice-thickness inversion models, are crucial for assessments of available freshwater resources and sea level rise. Digital elevation model (DEM) derived surface topography of glaciers is a primary data source for such models. However, the scarce in-situ measurements of glacier surface elevation limit the evaluation of DEM uncertainty, and hence its influence on ice-thickness modelling over the glacierized area of the Tibetan Plateau (TP). Here, we examine the performance over the glacierized TP of six widely used and mainly global-scale DEMs: AW3D30 (30 m), SRTM-GL1 (30 m), NASADEM (30 m), TanDEM-X (90 m), SRTM v4.1 (90 m) and MERIT (90 m) by using ICESat-2 laser altimetry data while considering the effects of glacier dynamics, terrain, and DEM misregistration. The results reveal NASADEM as the best performer, with a small mean error (ME) of −1.0 and a root mean squared error (RMSE) of 12.6 m. A systematic vertical offset existed in AW3D30 (−35.3 ME and 34.9 m RMSE), although it had a similar relative accuracy to NASADEM (~ 13 m STD). TanDEM-X also performs well (−0.1 ME and 15.1 m RMSE), but suffers from serious errors and outliers on steep slopes. SRTM-based DEMs (SRTM-GL1, SRTM v4.1, and MERIT) (all ~ 36 m RMSE) had an inferior performance to NASADEM. However, their errors were reduced in the ablation zone when glacier variations were excluded. Errors in the six DEMs increased from the south-facing to the north-facing aspect and become larger with increasing slope. Misregistration of DEMs relative to ICESat-2 footprint in most glacier areas is small (less than one pixel). An intercomparison of four ice-thickness models: GlabTop2, Open Global Glacier Model (OGGM), Huss-Farinotti (HF), Ice Thickness Inversion Based on Velocity (ITIBOV), show that GlabTop2 is sensitive to the accuracy of both elevation and slope, while OGGM and HF are less sensitive to DEM quality, and ITIBOV is the most sensitive to slope accuracy. Considering the inconsistency of DEMs acquisition dates, NASADEM would be a best choice for ice-thickness estimates over the TP, followed by AW3D30, and TanDEM-X (if steep and high elevation terrain can be avoided). Our assessment figures out the performances of mainly global DEMs over the glacierized TP. This study not only avails the glacier thickness estimation with ice thickness inversion models, but also offered references for other cryosphere studies using DEM.
Highlights
The Tibetan Plateau (TP), which includes the Pamir, Hindu Kush, Karakoram, Himalaya, and Tibet regions, covers an area of ~3 million km2 and has a mean elevation of more than 4000 m a.s.l. (Fig. 1)
The scarce in-situ measurements of glacier surface elevation limit the evaluation of Digital elevation model (DEM) uncertainty, and its influence on ice-thickness modelling over the glacierized area of the Tibetan Plateau (TP)
The four standard deviations filter on the differences between ICESat-2 and DEMs used to filter out extreme outliers excluded less than 1% of the data
Summary
The Tibetan Plateau (TP), which includes the Pamir, Hindu Kush, Karakoram, Himalaya, and Tibet regions, covers an area of ~3 million km and has a mean elevation of more than 4000 m a.s.l. (Fig. 1). The TP has a glacierized area of ~8.3×104 km (RGI Consortium, 2017) with an ice volume of ~6.2×103 km (Farinotti et al 2019), mainly distributed in the Karakoram and Himalaya regions. In the TP, owing to the lack of in-situ ice thickness measurements (Welty et al 2020), regional glacier thickness is mainly estimated by ice-thickness inversion models (ITIMs) using open access digital elevation models (DEMs) (Farinotti et al 2009; Farinotti et al 2019; Frey et al 2014). In addition to its use in ITIMs, the DEM has been an essential model input for a wide range of TP glaciology studies, such as glacier inventory (Bhambri et al 2011; Frey et al 2012; Ke et al 2016; Mölg et al 2018), glacier mass change (Brun et al 2017; Shean et al 2020; Zhou et al 2018), glacier related disasters (Allen et al 2019; Kääb et al 2018; Zhang et al 2019) and projections of glacier or glacial lake evolution (Kaser et al 2010; Kraaijenbrink et al 2017; Zheng et al 2021). To our knowledge, the uncertainty in different open access DEMs and its influence on various ITIM outputs over the TP has not been evaluated
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