Reply on RC2

Abstract. Increased rates of glacier retreat and thinning need accurate local estimates of glacier elevation change to predict future changes in glacier runoff and their contribution to sea level rise. Glacier elevation change is typically derived from digital elevation models (DEMs) tied to surface change analysis from satellite imagery. Yet, the rugged topography in mountain regions can cast shadows onto glacier surfaces, making it difficult to detect local glacier elevation changes in remote areas. A rather untapped resource comprises precise, time-stamped metadata on the solar position and angle in satellite images. These data are useful for simulating shadows from a given DEM. Accordingly, any differences in shadow length between simulated and mapped shadows in satellite images could indicate a change in glacier elevation relative to the acquisition date of the DEM. We tested this hypothesis at five selected glaciers with long-term monitoring programmes. For each glacier, we projected cast shadows onto the glacier surface from freely available DEMs and compared simulated shadows to cast shadows mapped from ∼40 years of Landsat images. We validated the relative differences with geodetic measurements of glacier elevation change where these shadows occurred. We find that shadow-derived glacier elevation changes are consistent with independent photogrammetric and geodetic surveys in shaded areas. Accordingly, a shadow cast on Baltoro Glacier (the Karakoram, Pakistan) suggests no changes in elevation between 1987 and 2020, while shadows on Great Aletsch Glacier (Switzerland) point to negative thinning rates of about 1 m yr−1 in our sample. Our estimates of glacier elevation change are tied to occurrence of mountain shadows and may help complement field campaigns in regions that are difficult to access. This information can be vital to quantify possibly varying elevation-dependent changes in the accumulation or ablation zone of a given glacier. Shadow-based retrieval of glacier elevation changes hinges on the precision of the DEM as the geometry of ridges and peaks constrains the shadow that we cast on the glacier surface. Future generations of DEMs with higher resolution and accuracy will improve our method, enriching the toolbox for tracking historical glacier mass balances from satellite and aerial images.

Ice elevation and areal changes of glaciers from the Northern Patagonia Icefield, Chile

Abstract. There is a major need for a complete and detailed global glacier inventory that is freely available in a digital format (vector outlines with attribute data) for all kinds of glaciological assessments, e.g. sea level rise, hydro-power, run-off and natural hazards. However, such an inventory is not yet available and the uncertainties due to the missing data for related calculations are large. While the existing World Glacier Inventory offers tabular data that were compiled from aerial photography and maps during the 1960s to 1970s for about 72'000 glaciers, current efforts are being exerted within the framework of the Global Land Ice Measurements from Space (GLIMS) initiative to compile vector outlines from satellite data and combine them with digital elevation models (DEMs). At this point, inventory data from about 100'000 of the estimated 160'000 glaciers are available in the GLIMS database. Due to the now free availability of satellite data from the United States Geological Survey's archive and near global DEMs with appropriate spatial resolution, a globally complete and detailed glacier inventory appears viable. This contribution provides an overview of past and ongoing activities related to the creation of glacier inventory data from satellite sensors, along with the methods employed and the challenges encountered in different parts of the world.

<strong class="journal-contentHeaderColor">Abstract.</strong> Delineation of Glaciers is a challenging task in the Himalaya due to its complex topography, cloud cover, seasonal snow cover, hillshade, debris cover. Glacio-hydrological studies including mass balance, run-off, and dynamic modelling rely on the availability of consistent and reliable glacier inventory datasets. This article on data set presents a homogenous, multidecadal inventory of glaciers in the Chandra-Bhaga Basin (CB Basin), western Himalaya, for 1993, 2000, 2010, and 2019. Landsat Thematic Mapper (TM), Enhanced Thematic mapper (ETM+), and Operational Land Imager (OLI) imageries, with minimum snow and cloud cover have been used for enhanced accuracy and consistency. Uncertainty assessment for the generated glacier inventory was performed, following various approaches such as buffer method, standard error estimation, and manual digitisation error and the maximum uncertainty has been quantified. We have identified and manually mapped a total of 251 glaciers with an area &gt; 0.5 km², and in order to minimise the uncertainty, field surveys were carried out on 6 glaciers in the basin. Out of these 251 glaciers, 217 are clean ice and 35 were debris-covered glaciers. The estimated total glacier area was 996 &plusmn; 62 km² in 1993 that decreased to 973 &plusmn; 70 km² in 2019. Apart from quantifying temporal changes in glacier area, this inventory further allows the estimation of supraglacial debris cover and glacier volume. The supraglacial debris cover area has increased by 14.1 &plusmn; 2.54 km² (15.2 %) during 1993&ndash;2019. Accuracy of the debris cover dataset estimated using ground surveys is 82 % with a kappa coefficient of 0.87. Moreover, a glacier ice volume dataset was also generated by incorporating the inventory into Glacier Bed Topography Version 2 (GlabTop2) model and shows a total of 112.5 &plusmn; 41 km³ of ice volume stored in the CB Basin glaciers. For accuracy assessment of the DEMs generated using ASTER Stereopairs images, DGPS surveys were carried out on (28 GCPs) and off (6 GCPs) the glaciers. Glacier volume uncertainty with respect to the generated DEMs and model bias is 5.3 km³ and 35.5 km³ respectively. Overestimation of glacier volume due to over deepening is estimated to be 1.2 km³. The impact of climate change on the Himalayan glaciers is a matter of serious concern and such holistic multitemporal inventory datasets can help quantify that impact with improved certainty.

Accurate measurements of glacier surface topography and their changes play an essential role in various glaciological studies related to glacier dynamics and mass balance. The focus of this study is on mapping glacier digital elevation model (DEM) and elevation changes in the western Qilian Mountains, northern Tibetan Plateau, by synergistically using the TanDEM-X (TDX) bistatic Interferometric Synthetic Aperture Radar (InSAR) data in 2013 and Shuttle Radar Topography Mission (SRTM) DEM in 2000. The first high-resolution and high-precision glacier DEM is derived in this region by a TDX InSAR procedure with a nonlocal (NL) filter. Validated against the Ice, Cloud, and land Elevation Satellite (ICESat) height references, the absolute height error of the TanDEM-X DEM derived with the NL filter and the Goldstein filter with the parameters investigated is, respectively, 1.493 ± 0.747 and 1.857 ± 1.709 m. Further, four combinations of differential phase method (DiffPha) and DEM differencing method (DiffDem) with Goldstein filter and NL filter are applied to estimate glacier elevation changes between 2000 and 2013. The synergistic use of the DiffPha method and the NL filter is superior to other three combinations in terms of uncertainty and noise reduction. Generally, a clear surface thinning can be found in most glacier tongue regions, the maximum value of elevation lowering up to approximately −40 m, whereas a slight thickening is detected in accumulation areas, which are in agreement with the height difference results between GPS measurements and SRTM DEM over Laohugou Glacier No.12. This study demonstrates the potential of the TanDEM-X bistatic InSAR in mapping surface topography and elevation changes of valley glaciers in the Tibetan Plateau.

Glaciers in the Pamir are the water tower of arid Central Asia, and more than 50 million people depend on meltwater from them. Many studies have reported glacier changes in the Pamir, but the glacier inventories employed in the previous investigations are both outdated and lack comparability. In this study, we present a new multitemporal glacier inventory for the Pamir based primarily on the Landsat ETM+/OLI scenes from the period around 2000 and 2017. Glacier outlines in 2000 and 2017 were delineated manually using Landsat images, SRTM digital elevation model (DEM), and high-resolution Google Earth images, using the same criteria and methods. We mapped 12,186 glaciers covering 10,396.20 ± 421.16 km2 in 2017 for the Pamir and demonstrated that the Pamir lost 124.28 ± 81.33 km2 of its glacier areas with a shrinkage rate of 1.17 ± 0.77 percent or 0.07 ± 0.05 percent·a−1 from 2000 to 2017. Based on the new inventories and the existing elevation change data sets of High Mountain Asia (HMA) from 2000 to 2016, we calculated a weaker glacier mass loss of −0.05 ± 0.06 m w.e. a−1. Spatial heterogeneity of glacier changes has been found in both the area and mass balance, but our results still suggest that the Pamir glaciers changed slightly since 2000.

Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field has attracted a lot of attention from the scientific community. However, relevant studies that are based on satellite data have mainly focused on a few periods from 2000–2016. Long-term and multiperiod observations remain to be conducted. To this end, we estimated the changes in the glacier area and mass balance of the Puruogangri Ice Field over five subperiods between 1975 and 2021, based on multisource remote sensing data. Specifically, we employed KH-9 and Landsat images to estimate the area change from 1975 to 2021 using the band ratio method. Subsequently, based on KH-9 DEM, SRTM DEM, Copernicus DEM and ZY-3 DEM data, we evaluated the glacier elevation changes and mass balance over five subperiods during 1975–2021. The results showed that the total glacier area decreased from 427.44 ± 12.43 km2 to 387.87 ± 11.02 km2 from 1975 to 2021, with a decrease rate of 0.86 km2 a−1. The rate of area change at a decade timescale was −0.74 km2 a−1 (2000–2012) and −1.00 km2 a−1 (2012–2021). Furthermore, the rates at a multiyear timescale were −1.23 km2 a−1, −1.83 km2 a−1 and −0.42 km2 a−1 for 2012–2015, 2015–2017 and 2017–2021, respectively. In terms of the glacier mass balance, the region-wide results at a two-decade timescale were −0.23 ± 0.02 m w.e. a−1 for 1975–2000 and −0.29 ± 0.02 m w.e. a−1 for 2000–2021, indicating a sustained and relatively stable mass loss over the past nearly five decades. After 2000, the loss rate at a decade timescale was −0.04 ± 0.01 m w.e. a−1 for 2000–2012 and −0.17 ± 0.01 m w.e. a−1 for 2012–2021, indicating an increasing loss rate over recent decades. It was further found that the mass loss rate was −0.12 ± 0.02 m w.e. a−1 for 2012–2015, −0.03 ± 0.01 m w.e. a−1 for 2015–2017 and −0.40 ± 0.03 m w.e. a−1 for 2017–2021. These results indicated that a significant portion of the glacier mass loss mainly occurred after 2017. According to our analysis of the meteorological measurements in nearby regions, the trends of precipitation and the average annual air temperature both increased. Combining these findings with the results of the glacier changes implied that the glacier changes seemed to be more sensitive to temperature increase in this region. Overall, our results improved our understanding of the status of glacier changes and their reaction to climate change in the central Tibetan Plateau.

Debris cover over glaciers greatly affects their rate of ablation and is a sensitive indicator of glacier health. This study focuses on estimation of debris cover over Samudratapu glacier, Chenab basin, Himalaya, using optical remote-sensing data. Remote-sensing image data of IRS-1C LISS-III (September 2001), IRS-P6 AWiFS (September 2004) and Terra ASTER (September 2004) along with Survey of India topographical maps (1963) were used in the study. Supervised classification of topographically corrected reflectance image data was systematically conducted to map six land-cover classes in the glacier terrain: snow, ice, mixed ice and debris, debris, valley rock, and water. An accuracy assessment of the classification was conducted using the ASTER visible/near-infrared data as the reference. The overall accuracies of the glacier-cover maps were found to range from 83.7% to 89.1%, whereas the individual class accuracy of debris-cover mapping was found to range from 82% to 95%. This shows that supervised classification of topographically corrected reflectance data is effective for the extraction of debris cover. In addition, a comparative study of glacier-cover maps generated from remote-sensing data (supervised classification) of September 2001 and September 2004 and Survey of India topographical maps (1963) has highlighted the trends of glacier depletion and recession. The glacier snout receded by about 756 m from 1963 to 2004, and the total glacier area was reduced by 13.7 km2 (from 110 km2 in 1963). Further, glacier retreat is found to be accompanied by a decrease in mixed ice and debris and a marked increase in debris-cover area. The area covered by valley rock is found to increase, confirming an overall decrease in the glacier area. The results from this study demonstrate the applicability of optical remote-sensing data in monitoring glacier terrain, and particularly mapping debris-cover area.

<p indent="0mm">This dataset provides the latest glacier inventory for the southeastern Qinghai- Tibet Plateau based on multi-temporal Landsat images acquired during 2011 – 2014, L-band PALSAR data obtained during 2009 – 2010, and the modified SRTM (Shuttle Radar Topography Mission) Digital Elevation Model (DEM). Within the spatial extent of 28°N – 31°N and 92°E – 97°E, the region covers the central and eastern Nyainqêntanglha and western Hengduan mountains, with a total area of around <sc>115,000 km2.</sc> Three sub-datasets are provided: ① extent of the study region in shapefile format, ② glacier outlines in shapefile format, and ③ parameter statistics for each individual glacier. The parameters for each glacier include the glacier area, maximum and minimum elevation, mean elevation, mean slope, mean aspect, flag indicating presence or absence of debris cover, and area of debris-covered glaciers. To mitigate influences of cloud cover on optical images, we adopted semi-automated identification of cloud cover and clean snow/ ice to combine observations from multi-temporal Landsat images. Debris-covered glaciers were separately determined with a semi-automated method that integrates information derived from Landsat images, coherence information generated from PALSAR (Phased Array type L-band Synthetic Aperture Radar) image pairs, and slope information. In the post-processing stage, we performed manual editing of the glacier outlines to improve the data quality, for example, to ensure seamless shared boundaries between clean-ice glaciers and their debris-covered parts, to adjust the individual ice divides, and to correct the effect of shadow and water bodies. Compared with manually digitized glacier outlines, this dataset has an overall error within 3%. The quality of the latest glacier inventory was improved; it has less uncertainty associated with cloud and seasonal snow cover in glacier maps and is a fundamental dataset for future glacier-related studies.

Glaciers in the central Andes of Chile are fundamental freshwater sources for ecosystems and communities. Overall, glaciers in this region have shown continuous recession and down-wasting, but long-term glacier mass balance studies providing precise estimates of these changes are scarce. Here, we present the first long-term (1955–2013/2015), region-specific glacier elevation and mass change estimates for the Maipo River Basin, from which the densely populated metropolitan region of Chile obtains most of its freshwater supply. We calculated glacier elevation and mass changes using historical topographic maps, Shuttle Radar Topography Mission (SRTM), TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X), and airborne Light Detection and Ranging (LiDAR) digital elevation models. The results indicated a mean regional glacier mass balance of −0.12 ± 0.06 m w.e.a−1, with a total mass loss of 2.43 ± 0.26 Gt for the Maipo River Basin between 1955–2013. The most negative glacier mass balance was the Olivares sub-basin, with a mean value of −0.29 ± 0.07 m w.e.a−1. We observed spatially heterogeneous glacier elevation and mass changes between 1955 and 2000, and more negative values between 2000 and 2013, with an acceleration in ice thinning rates starting in 2010, which coincides with the severe drought. Our results provide key information to improve glaciological and hydrological projections in a region where water resources are under pressure.

Abstract. A semi automated technique has been developed to extract the spatial extension of valleys and mountain glaciers. The method is based on morphological properties of glaciated area extracted from Digital Elevation Model (DEM). Identification of glacial boundary based on spectral information from optical remote sensing imageries produces errors due to misclassification of debris-covered ablation area with surrounding rocky terrain and perennially snow-covered slope with debris free glaciated area. Elevation information DEM of Shuttle Radar Topography Mission (SRTM), CartoDEM and ASTER DEM have been used. A part of western Himalayas was selected as the study area that contains large glaciated basins, e.g., Bhagirathi, Baspa, Chandra basin. First order derivatives, slope aspect, and second order derivatives like, profile and plan curvatures are computed from the DEM. The derivatives are used to quantify and characterise the morphological aspects of the glaciated area and used in the decision rule models to generate the glacial boundaries. The ridge lines of the study areas are also generated from the plan curvature and used in the model to delineate the catchments areas of the glaciers. The slope based boundary is checked for consistency with the boundary from profile curvature and combined manually to generate the final glacier boundary. Area and length under the derived boundary of Gangotri glacier of Bhagirathi catchments are 90.25 sq km and 30.5 km. The result has been checked with high resolution optical data. This objective approach is important to delineate glaciated area, measure the length, width and area and generate glacial hypsometry, concentration factor of the glaciers. Accuracy of the result depends up on the quality of the DEM. DEM generated by SAR interferometric technique is found superior over DEM generated from other interpolation techniques.

This chapter contains sections titled: Introduction Sea ice chemistry Biological activity: the modification of the chemical composition of sea ice by sympagic organisms Stable carbon isotopes in sea ice Arctic river inputs into sea ice Data collection and in situ measurements: some challenges Future perspectives Acknowledgements References

Abstract. Himalayan glaciers show large uncertainty regarding their present and future state due to their sensitive reaction towards change in climatic condition. Himalayan glaciers are unique as they are located in tropical, high altitude regions, predominantly valley type and many are covered with debris. The great northern plains of India sustain on the perennial melt of glaciers meeting the water requirements of agriculture, industries, domestic sector even in the months of summer when large tracts of the country go dry. Therefore, it is important to monitor and assess the state of snow and glaciers and to know the sustainability of glaciers in view of changing global scenarios of climate and water security of the nation. Any information pertaining to Himalayan glaciers is normally difficult to be obtained by conventional means due to its harsh weather and rugged terrains. Due to the ecological diversity and geographical vividness, major part of the Indian Himalaya is largely un-investigated. Considering the fact that Himalayan glaciers are situated in a harsh environment, conventional techniques of their study is challenging and difficult both in terms of logistics and finances whereas the satellite remote sensing offers a potential mode for monitoring glaciers in long term. In order to gain an updated overview of the present state of the glacier cover and its changes since the previous inventories, an attempt has been made to generate a new remotesensing- derived glacier inventory on 1:50,000 scale for Bhaga basin (N32°28'19.7'' - N33°0'9.9'' ; E76°56'16.3'' - E77°25'23.7'' ) Western Himalaya covering an area of 1695.63 km2. having 231 glaciers and occupying glacierized area of 385.17 ±3.71 km2. ranging from 0.03 km2. to 29.28 km2. Glacier inventory has been carried out using high resolution IRS P6 LISS III data of 2011, ASTER DEM and other ancillary data. Specific measurements of mapped glacier features are the inputs for generating the glacier inventory data sheet with 37 parameters as per the UNESCO/TTS format, 11 additional parameters associated with the de-glaciated valley as per the suggestions of Space Application Center Ahmadabad and 9 newly introduced parameters of present study. The data sheet provides glacier wise details for each glacier on the significant glacier parameters like morphology, dimensions, orientation, elevation, etc. for both the active glacier component as well as the associated de-glaciated valley features. Assessment of recent variation in the glacierized area between 2001 and 2011. Results indicate that 231 glaciers covering an area of 391.56 ±3.76 km2. in 2001 has been reduced to 385.17 ±3.71 km2. in 2011; a loss of 1.63 ±1.0% in glacierized area within a period of 10 years. The present paper brings out the methodology adopted and salient results of the glacier inventory carried out which will help to enrich the existing database required for water resources assessment of the country and also meet the requirements of various researches working on climate change related studies.

The monitoring of glaciers in Switzerland has a long tradition, yet glacier changes during the 20th century are only known through sparse observations. Here, we estimate a halving of Swiss glacier volumes between 1931 and 2016 by mapping historical glacier elevation changes at high resolution. Our analysis relies on a terrestrial image archive known as TerrA, which covers about 86 % of the Swiss glacierised area with 21,703 images acquired during the period 1916–1947 (1931 on average). We developed a semi-automated workflow to generate digital elevation models (DEMs) from these images, resulting in a 45 % total glacier coverage. Using the geodetic method, we estimate a Swiss-wide glacier mass balance of –0.52 ± 0.09 m w.e. a−1 between 1931 and 2016. This equates to a 51.5 ± 6.1 % loss in glacier volume. We find that low elevation, high debris cover, and gently sloping glacier termini are conductive to particularly high mass losses. In addition to these glacier-specific, quasi- centennial elevation changes, we present a new inventory of glacier outlines with known timestamps and complete attributes from around 1931. The fragmented spatial coverage and temporal heterogeneity of the TerrA archive are the largest sources of uncertainty in our glacier-specific estimates, reaching up to 0.50 m w.e. a−1. We suggest that the high-resolution mapping of historic surface elevations could unlock great potentials also for research fields other than glaciology.

The Antarctic Peninsula (AP), encompassing the ice sheet and its peripheral glaciers, is a highly dynamic component of the cryosphere, disproportionately contributing to sea level rise. However, a large spread remains between the mass changes estimated using gravimetry, altimetry and the input/output method. Among these techniques, the satellite (radar or laser) altimetry method has a resolution of, at best, 1 km, which is too coarse to resolve the complex pattern of changes in the Peninsula. Therefore, we use digital elevation models (DEMs; 30x30 m) to map elevation changes for the entire Peninsula, combining 476 DEMs derived from SPOT5-HRS satellite images (2006-2008) and 2525 strips of the Reference Elevation Model of Antarctica (2020-2022) to provide a comprehensive 14-year record. We bias-corrected each DEM using near-synchronous ICESat/-2 laser altimetry measurements.Our observations cover 70% of the AP ice sheet and 60% of its peripheral glaciers, including for regions of the Peninsula poorly studied to date and decipher a spatially complex pattern of elevation changes. After correction with different models of firn air content and solid-earth response, we find that between 2007 and 2021, the AP ice sheet lost -27 ± 9 Gt/yr while its peripheral glaciers lost -14 ± 2 Gt/yr. For the AP ice sheet, our new estimate is 4 to 5 times more negative than the one obtained in IMBIE using purely altimetry data (-6 ± 6 Gt/yr from 2006 to 2018) and in better agreement with gravimetry and the input/output method. Our study highlights the importance of resolving fine scale elevation changes of glaciers and ice sheets.