The GAMDAM Glacier Inventory: a quality controlled inventory of Asian glaciers

Nuimura, T.; Sakai, Akiko; Taniguchi, Keisuke; Nagai, Hiroto; Lamsal, Damodar; Tsutaki, Shun; Kozawa, Akiya; Yu, Ho‐Shing; Takenaka, S.; Omiya, Satoshi; Tsunematsu, Kae; Tshering, Phuntsho; Fujita, Koji

doi:10.5194/tcd-8-2799-2014

Cited by 28 publications

(34 citation statements)

References 31 publications

(37 reference statements)

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“…Although not calibrated with measurements from HK glaciers, the latter is the only available area-related parameterization that exists for High Asian glaciers, and it is also used for all ICIMOD glacier inventories (e.g., Mool et al, 2006;Bajracharya and Shresta, 2011). The applied scaling parameters are given in Table 2.…”

Section: Area-related Thickness Estimationsmentioning

confidence: 99%

“…Considering the older reference period of Cogley's (2011) inventory, a modification of glacier areas of ±5 % is assumed as an upper bound for uncertainty in the input glacier area, which corresponds to the findings of the mapping accuracy analysis by Paul et al (2013). Larger area differences between different glacier inventories (e.g., Nuimura et al, 2014) are normally caused by differing definitions of glaciers used for the mapping. Thus, the area of each glacier used for V-A calculations was modified by ±5 % for the sensitivity analysis.…”

Section: Uncertainty and Sensitivity Assessmentmentioning

confidence: 99%

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Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods

et al. 2014

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Abstract. Ice volume estimates are crucial for assessing water reserves stored in glaciers. Due to its large glacier coverage, such estimates are of particular interest for the Himalayan-Karakoram (HK) region. In this study, different existing methodologies are used to estimate the ice reserves: three area-volume relations, one slope-dependent volume estimation method, and two ice-thickness distribution models are applied to a recent, detailed, and complete glacier inventory of the HK region, spanning over the period [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010] and revealing an ice coverage of 40 775 km 2 . An uncertainty and sensitivity assessment is performed to investigate the influence of the observed glacier area and important model parameters on the resulting total ice volume. Results of the two ice-thickness distribution models are validated with local icethickness measurements at six glaciers. The resulting ice volumes for the entire HK region range from 2955 to 4737 km 3 , depending on the approach. This range is lower than most previous estimates. Results from the ice thickness distribution models and the slope-dependent thickness estimations agree well with measured local ice thicknesses. However, total volume estimates from area-related relations are larger than those from other approaches. The study provides evidence on the significant effect of the selected method on results and underlines the importance of a careful and critical evaluation.

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Section: Area-related Thickness Estimationsmentioning

confidence: 99%

Section: Uncertainty and Sensitivity Assessmentmentioning

confidence: 99%

Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods

et al. 2014

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“…A cloud free Landsat-7 Enhanced Thematic Mapper (ETM) scene of 15 October 2000 was selected to map the glacier surface type. Glacier outlines were taken from the recently published Randolph Glacier Inventory (RGI 5.0) [22,23]. Table 1.…”

Section: Datamentioning

confidence: 99%

“…We approximated 12% uncertainties in our glacier outlines [23]. Consequently, using error propagation, the uncertainty in volume change (V), i.e., (ε V ), was estimated as,…”

Section: Uncertainty Estimatementioning

confidence: 99%

Elevation Change Rates of Glaciers in the Lahaul-Spiti (Western Himalaya, India) during 2000–2012 and 2012–2013

2016

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Abstract:Previous studies have shown contrasting glacier elevation and mass changes in the sub-regions of high-mountain Asia. However, the elevation changes on an individual catchment scale can be potentially influenced by supraglacial debris, ponds, lakes and ice cliffs besides regionally driven factors. Here, we present a detailed study on elevation changes of glaciers in the Lahaul-Spiti region derived from TanDEM-X and SRTM C-/X-band DEMs during 2000-2012 and 2012-2013. We observe three elevation change patterns during 2000-2012 among glaciers with different extent of supraglacial debris. The first pattern (<10% debris cover, type-1) indicates maximum thinning rates at the glacier terminus and is observed for glaciers with no or very low debris cover. In the second pattern (>10% debris cover, type-2), maximum thinning is observed up-glacier instead of glacier terminus. This is interpreted as the insulating effect of a thick debris cover. A third pattern, high elevation change rates near the terminus despite high debris cover (>10% debris cover, type-3) is most likely associated with either thinner debris thickness or enhanced melting at supraglacial ponds and lakes as well as ice cliffs. We empirically determined the SRTM C-and X-band penetration differences for debris-covered ice, clean ice/firn/snow and correct for this bias in our elevation change measurements. We show that this penetration bias, if uncorrected, underestimates the region-wide elevation change and geodetic mass balance by 20%. After correction, the region-wide elevation change (1712 km 2 ) was estimated to be −0.65 ± 0.43 m yr −1 during 2000-2012. Due to the short observation period, elevation change measurements from TanDEM-X for selected glaciers in the period 2012-2013 are subject to large uncertainties. However, similar spatial patterns were observed during 2000-2012 and 2012-2013, but at different magnitudes. This study reveals that the thinning patterns of debris-covered glaciers cannot be generalized and spatially detailed mapping of glacier elevation change is required to better understand the impact of different surface types under changing climatic conditions.

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“…That said, the RGI inventory is widely used in the region. Other recently developed glacier inventories for HMA in particular, such as the ICIMOD and GAMDAM inventories (Bajracharya and Shrestha, 2011;Nuimura et al, 2014) have not yet been widely used or verified, hence we use the RGI 4.0 here in order to avoid introducing further uncertainty. We define the Indus hydrologic watershed by the Global Runoff Database (World Meteorological Organization, 2014).…”

Section: Glacier Areamentioning

confidence: 99%

Sensitivity of glacier runoff projections to baseline climate data in the Indus River basin

et al. 2015

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Quantifying the contribution of glacier runoff to water resources is particularly important in regions such as High Mountain Asia, where glaciers provide a large percentage of seasonal river discharge and support large populations downstream. In remote areas, direct field measurements of glacier melt rates are difficult to acquire and rarely observed, so hydro-glaciological modeling and remote sensing approaches are needed. Here we present estimates of glacier melt contribution to the Upper Indus watershed over the last 40 years using a suite of seven reanalysis climate datasets that have previously been used in hydrological models for this region, a temperature-index melt model and >29,000 km 2 of ice cover. In particular, we address the uncertainty in estimates of meltwater flux that is introduced by the baseline climate dataset chosen, by comparing the results derived from each. Mean annual glacier melt contribution varies from 8 to 169 km 3 yr −1 , or between 4 and 78% of the total annual runoff in the Indus, depending on temperature dataset applied. Under projected scenarios of an additional 2-4 • C of regional warming by 2100 AD, we find annual meltwater fluxes vary by >200% depending on the baseline climate dataset used and, importantly, span a range of positive and negative trends. Despite significant differences between climate datasets and the resulting spread in meltwater fluxes, the spatial pattern of melt is highly correlated and statistically robust across all datasets. This allows us to conclude with confidence that fewer than 10% of the >20,000 glaciers in the watershed contribute more than 80% of the total glacier runoff to the Indus. These are primarily large, low elevation glaciers in the Karakoram and Hindu Kush. Additional field observations to ground-truth modeled climate data will go far to reduce the uncertainty highlighted here and we suggest that efforts be focused on those glaciers identified to be most significant to water resources.

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The GAMDAM Glacier Inventory: a quality controlled inventory of Asian glaciers

Cited by 28 publications

References 31 publications

Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods

Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods

Elevation Change Rates of Glaciers in the Lahaul-Spiti (Western Himalaya, India) during 2000–2012 and 2012–2013

Sensitivity of glacier runoff projections to baseline climate data in the Indus River basin

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